DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 18, 2026 has been entered.
Response to Amendment
3. This action is responsive to the Applicant’s request for continued examination filed on March 18, 2026.4. Claims 1-20 are pending, of which claims 1, 8, and 15 are in independent form. 5. Claims 1, 4, 8, 11, 15, and 18 are amended.
Response to Arguments
6. Applicant’s arguments, see “Double Patenting Rejection of claims 1, 8, and 15” filed on March 18, 2026. The non-statutory double patenting rejection of claims 1,8, and 15 of US Patent No. 11,954,160 is withdrawn in view of the amendments to claims 1,8, and 15. Upon reconsideration, the amended claims recite additional limitations that render the presently claimed subject matter patentable distinct from the subject matter claimed in US Patent No. 11,954,160.
7. The rejection of claims 1-20 under 35 U.S.C. § 101 is withdrawn in view of the amendments to independent claims 1,8, and 15 and Applicant’s arguments. Upon reconsideration of the claims as a whole, including the recited determination of available processing power and dynamic adjustment of the threshold level based thereon in conjunction with the claimed personalization operations, the Examiner finds that the amended claims sufficiently integrate the recited judicial exception into a practical application under Step 2A, Prong Two.
8. Applicant’s argument, see “Claims 1-3, 8-10, and 15-17 are patentable over Mehanian and Li under 35 U.S.C. § 103”, filed March 18, 2026 have been carefully considered. The arguments are related to newly added limitations and we addressed in the rejection below.
Claim Rejections - 35 USC § 103
9. 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.
10. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
11. Claims 1, 8, and 15 are rejected under 35 U.S.C. 103 as being obvious over Wang et al. US 20160140622 A1 (hereinafter Wang) in view of Iannaccone et al. US 20170345026 A1 (hereinafter Iannaccone) further in view of Breese et al. “Empirical Analysis of Predictive Algorithms for Collaborative Filtering” (hereinafter Breese), further in view of Li et al. US 20170099340 A1 (hereinafter Li).
Regarding claim 1, Wang discloses a method comprising: calculating, by a computer-based system, an affinity score of a user representing a value of the user to a provider, wherein the affinity score is calculated by at least: obtaining factors from a user attribute repository (Wang [0053] e.g., “The affinity score engine 206 can be configured to determine a product affinity score on a per-user basis. As mentioned above, an affinity score between a user and a product may represent an expected level of interest by the user in the product” then [0053] e.g., “… the affinity score engine 206 can determine a set of product affinity scores for each user that visits a merchant page.”), obtaining factors from a user attribute repository (Wang [0054] e.g., “The affinity score engine 206 can determine a product affinity score based on one or more product affinity factors. … user profile information, user activity, social trends, merchant input, any other suitable factors, or a combination thereof”. Thus, Wang teaches obtaining user-related factors from stored user information and using those factors in determining a user-specific affinity score. See also [0055] e.g., “… based on user profile information. User profile information can include an age, a gender, a name, a marital status, a health status, a number of children, an employment status, an employment pattern, preferences (e.g., product preferences), likes, dislikes, and/or attitudes of a user”. Wang further identifies the user profile information as age, gender, employment, preferences, likes/dislikes, location, income/financial standing, socioeconomic status, education, and other demographic information) [wherein the factors represent relative percentiles of data within the user attribute repository] and wherein the user attribute repository utilizes a distributed computing system (Wang [0125] e.g., “Memory 1204 can be used for storing data, metadata, and programs for execution by the processor(s). … Memory 1204 can be internal or distributed memory.”. Thus, Wang teaches memory storing data and that the memory may be distributed, therefore “the data storage utilized by its affinity-score system may be implemented as distributed memory); and [determining the affinity score by at least using collaborative filtering with the factors;] continuously updating the affinity score as the factors change (Wang [0069] e.g., “… the customization manager 208 can dynamically change the display of products within the shopping portion as product affinity scores change.”. Thus, Wang teaches that the product affinity scores are subject to change and that the customized display dynamically responds to changed affinity score. Wang determines the score from those factors and dynamically changes the personalized display as the resulting affinity scores change, thereby maintaining the affinity score and resulting personalized content considered with current user information); determining, by the computer-based system, that the affinity score is greater than the threshold level (Wang [0066] e.g., “… determine to display each product that has an affinity score above a threshold value”. Wang therefore expressly teaches comparing the affinity score with a threshold and providing affinity-selected content when the score satisfies the threshold); providing, by the computer-based system and in response to the affinity score being lower than the threshold level, non-personalization services to the user, wherein the non-personalization services include providing a non-personalized version of a webpage to the user (Wang [0091] e.g., “… the social networking system 202 can determine that a product does not have a product affinity score because the product is not relevant to the user 406, or that a product affinity score for the product is below a minimum threshold”. See also [0048] e.g., “ For example, a non-product post can be directed towards general updates, advertisements, sales, promotions, pictures, contest, notices, and/or comments associated with the merchant. A product post may include product content (e.g., content or information that is related to a particular product or products”. See also [0079] e.g., “Non-product posts 412 can include a variety of general information and a plurality of options associated with the merchant 404, but that is not specific to any particular product. … include information regarding events, sales, promotions, merchant updates, etc.”. Thus, Wang teaches (1) affinity-based product content that is displayed or omitted based upon an affinity threshold and (2) general merchant-page content that is not specific to the affinity-selected products) and providing, by the computer-based system and in response to the affinity score being greater than the threshold level, the personalization services to the user, wherein the personalization services include providing a personalized version of the webpage to the user (Wang [0063] e.g., “The customization manager 208 can customize a merchant page on a per user basis. … an create and populate a shopping portion on the merchant page based on information specific to a visiting user…. can customize a user-specific storefront page that displays products offered by the merchant.”. Accordingly, Wang teaches providing a personalized version of a merchant webpage based upon a user's affinity score). Wang does not expressly disclose that wherein the factors represent relative percentiles of data within the user attribute repository; and determining the affinity score by at least using collaborative filtering with the factors.
However, Iannaccone teaches: wherein the factors represent relative percentiles of data within the user attribute repository (Iannaccone [0051] e.g., “…each 2D tier representing 1% of the total population of users for which the additional users were identified from. the 2D tier presentation module 250 divides the intersections of users into equal (or roughly equal) divisions”. Thus, Iannaccone teaches representing user-related value information according to the user's relative position within divisions of an overall user population).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang’s user-related affinity factors according to Iannaccone’s population-relative representation such that the factors represent relative percentile positions within corresponding user data. Wang already uses user characteristics and activities as factors for determining user affinity, while Iannaccone teaches representing user-related information according to relative divisions of an overall user population. The modification would provide a common population-relative representation for comparing user characteristics and activities used in determining user affinity, thereby predictably facilitating comparison and ranking of users having different underlying user attributes. The combined teachings of Wang and Iannaccone do not expressly disclose: determining the affinity score by at least using collaborative filtering with the factors. Breese discloses determining the affinity score by at least using collaborative filtering with the factors (Breese [page 44, 2 Collaborative Filtering Algorithms] e.g., “The task in collaborative filtering is to predict the utility of items to a particular user (the active user) based on a database of user votes from a sample or population of other users (the user database). See also [page 44 2 Collaborative Filtering Algorithms 2nd paragraph] e.g., “Collaborative filtering systems are often distinguished by whether they operate over implicit versus explicit votes. Explicit voting refers to a user consciously ex pressing his or her preference for a title, usually on a discrete numerical scale. … Implicit votes can based on browsing data (for example in Web ap plications), purchase history (for example in online or traditional stores), or other types of information access patterns.” Breese further teaches that collaborative filtering may operate on implicit votes, wherein user behavior or selections are interpreted as user preferences, including browsing data in Web applications, purchase history, and other information-access patterns. This user information corresponds to the types of factors employed by Wang, which include browsing history, shopping activity, purchases, product views, likes, ratings, recommendations, and other user activities. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ the collaborative-filtering technique taught by Breese when determining Wang's user-specific affinity scores using Wang's affinity factors. Wang already obtains user activity and preference information for determining product affinity, while Breese teaches using such browsing, purchasing, and preference information in collaborative filtering to predict a user's utility or preference for an item based additionally upon information associated with other users. The modification would have yielded the predictable result of determining Wang's product affinity score using the user's affinity factors together with the preferences and activities of similar users, thereby improving user-specific prediction and selection of products for presentation on Wang's customized merchant webpage. Wang, Iannaccone, and Breese do not expressly disclose dynamically determining the claimed threshold based upon available processing power. In particular, the combination does not expressly disclose: dynamically determining, by the computer-based system, a threshold level for providing personalization services based at least in part on an available processing power in the computer-based system, wherein the threshold level is lowered or raised based on the available processing power. However, Li teaches dynamically determining, by the computer-based system, a threshold level for providing personalization services based at least in part on an available processing power in the computer-based system, wherein the threshold level is lowered or raised based on the available processing power (Li [0017] e.g., “… the web server may first verify that there are adequate system resources available to do so. … prior to preprocessing webpages … the web server may implement various techniques for determining whether a subsequent request for a particular webpage is sufficiently likely to justify the web server preprocessing the webpage.”. Li teaches checking whether adequate resources exist and determining whether a request is sufficiently likely to justify preprocessing. See also [0056]-[0057] and [0061]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang's affinity-threshold-based webpage customization in view of Li such that the threshold governing whether resource-consuming personalization is performed is dynamically adjusted based upon the available processing resources. Wang already teaches comparing affinity scores with a threshold to determine affinity-based content to display, while Li teaches determining whether adequate system resources are available before performing additional webpage preprocessing. Adjusting Wang's threshold upward when fewer processing resources are available and downward when greater processing resources are available would have been a predictable application of Li's resource-dependent webpage-processing technique to Wang's threshold-based personalization technique, thereby conserving processing resources during constrained conditions while permitting additional personalization when processing resources are available.
Claims 8 and 15 incorporate substantively all the limitations of claim 1 in a computer-based system and an article of manufacture and rejected under the same rationale.
12. Claims 2, 3, 9, 10, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. US 20160140622 A1 (hereinafter Wang) in view of Iannaccone et al. US 20170345026 A1 (hereinafter Iannaccone) further in view of Breese et al. “Empirical Analysis of Predictive Algorithms for Collaborative Filtering” (hereinafter Breese), further in view of Li et al. US 20170099340 A1 (hereinafter Li). as applied to claims 1, 8, and 15 above, and further in view of Cashman et al. US 2013/0124229 A1 (hereinafter Cashman).
Regarding claim 2, the rejection of claim 1 is hereby incorporated by reference,
Wang, Iannaccone, Breese and Li does not expressly disclose a method, wherein the affinity score is calculated in response to the computer-based system authenticating credentials for the user. However, Cashman discloses a method, wherein the affinity score is calculated in response to the computer-based system authenticating credentials for the user (Cashman [0141] e.g., “After receiving valid login credentials, the security module sets a dynamic authentication score for the user identity and role…” This gives the required temporal/causal relationship valid credentials /authentication and calculation/setting of a user-specific score). It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to modify Wang to calculate the user-specific affinity score in response to successful authentication of the user’s credentials, as taught by Cashman, so that the affinity score and resulting personalization are associated with the authenticated user.
Regarding claim 3, , the rejection of claim 1 is hereby incorporated by reference, Wang, Iannaccone, Breese , Li, and Cashman disclose a method, wherein the personalized version of the webpage comprises a personalized welcome page to a website of the provider (Wang [0063] e.g., “The customization manager 208 can customize a merchant page on a per user basis. … can create and populate a shopping portion on the merchant page … “. See also [0064]-[0066]. Wang further teaches using user-specific product affinity scores to determine the products presented to the user, including displaying products having affinity scores above a threshold. Thus, Wang teaches a personalized webpage containing user-selectable content selected according to the user’s predicted interest.), wherein the personalized welcome page provides selectable links to functions of the website that the user is likely to select (Cashman [0057] e.g., “A personalized version of the graphical user interface 300 is displayed after the user provides a user identify and correctly supplies one or more security credentials”), wherein the personalized welcome page for the website is unavailable for users having an affinity score lower than the threshold level (Wang [0091] e.g., “… the social networking system 202 can determine that a product does not have a product affinity score because the product is not relevant to the user 406, or that a product affinity score for the product is below a minimum threshold”. See also [0048] e.g., “ For example, a non-product post can be directed towards general updates, advertisements, sales, promotions, pictures, contest, notices, and/or comments associated with the merchant. A product post may include product content (e.g., content or information that is related to a particular product or products”).
13. Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Iannaccone, Breese, and Li, as applied to claim 1 above, and further in view of Lavonen et al. US 2017/0192632 A1 (“Lavonen”).
Regarding claim 4, the rejection of claim 1 is hereby incorporated by reference, Wang, Iannaccone, Breese and Li does not expressly disclose a method, wherein providing the personalization services to the user comprises: determining, by the computer-based system, an available display area on a device of the user; calculating, by the computer-based system, a calculated size of a first link based at least in part on the available display area; and causing, by the computer-based system, the first link to be presented to the user at the calculated size on the personalized version of the webpage.
However, Lavonen determining, by the computer-based system, an available display area on a device of the user (Lavonen [0040] e.g., “… the ribbon display manager unit determines in step 401 the display width (d-w) of the application. … [wherein] … the display width of the application is also the maximum usable width of the container area”);
calculating, by the computer-based system, a calculated size of a first link based at least in part on the available display area (Lavonen [0023] e.g., “The link list 214-1 contains entries associating an internal link identifier with its title (i.e. the displayed information, like the name and/or icon), its location in the link area, it's output size, and possible other information…. The output size may be given as an exact size (height and width or mere width if the link area definitions define the height)”. See also [0042] e.g., “… the displayable information (controls/items) of the container and the container width (c-w) required for the container are determined … the container width (c-w) is compared in step 410 with the usable width (u-w)”. Lavonen teaches determining an available display area and determining output dimension of links/displayable elements within that available area); and
causing, by the computer-based system, the first link to be presented to the user at the calculated size on the personalized version of the webpage (Lavonen [0041] e.g., “The link names are outputted in step 403 in the link area according to their order defined in the link list.”. see also [0049] e.g., “… the link portions and corresponding containers are generated in step 422. … displaying as many of them that will fit into the display area is caused in step 423”). It would have been obvious to one of ordinary skill in the art to determine the output size of Lavonne’s link based upon the available display area already determined by Lavonen, so that the link is appropriately sized for presentation within the available display area, thereby accommodating different display dimensions while maintaining presentation of the selectable interface elements.
Regarding claim 11, the rejection of claim 8 is hereby incorporated by reference, Wang, Iannaccone, Breese, Li and Lavonen disclose a system, wherein providing the personalization services to the user comprises: determining, by the computer-based system, an available display area on a device of the user (Lavonen [0040] e.g., “… the ribbon display manager unit determines in step 401 the display width (d-w) of the application. … [wherein] … the display width of the application is also the maximum usable width of the container area”);
calculating, by the computer-based system, a calculated size of a first link based at least in part on the available display area (Lavonen [0023] e.g., “The link list 214-1 contains entries associating an internal link identifier with its title (i.e. the displayed information, like the name and/or icon), its location in the link area, it's output size, and possible other information…. The output size may be given as an exact size (height and width or mere width if the link area definitions define the height)”. See also [0042] e.g., “… the displayable information (controls/items) of the container and the container width (c-w) required for the container are determined … the container width (c-w) is compared in step 410 with the usable width (u-w)”. Lavonen teaches determining an available display area and determining output dimension of links/displayable elements within that available area); and
causing, by the computer-based system, the first link to be presented to the user at the calculated size on the personalized version of the webpage (Lavonen [0041] e.g., “The link names are outputted in step 403 in the link area according to their order defined in the link list.”. see also [0049] e.g., “… the link portions and corresponding containers are generated in step 422. … displaying as many of them that will fit into the display area is caused in step 423”). Regarding claim 18, the rejection of claim 15 is hereby incorporated by reference, Wang, Iannaccone, Breese, Li and Lavonen disclose an article of manufacture, wherein providing the personalization services to the user comprises: determining, by the computer-based system, an available display area on a device of the user (Lavonen [0040] e.g., “… the ribbon display manager unit determines in step 401 the display width (d-w) of the application. … [wherein] … the display width of the application is also the maximum usable width of the container area”);
calculating, by the computer-based system, a calculated size of a first link based at least in part on the available display area (Lavonen [0023] e.g., “The link list 214-1 contains entries associating an internal link identifier with its title (i.e. the displayed information, like the name and/or icon), its location in the link area, it's output size, and possible other information…. The output size may be given as an exact size (height and width or mere width if the link area definitions define the height)”. See also [0042] e.g., “… the displayable information (controls/items) of the container and the container width (c-w) required for the container are determined … the container width (c-w) is compared in step 410 with the usable width (u-w)”. Lavonen teaches determining an available display area and determining output dimension of links/displayable elements within that available area); and
causing, by the computer-based system, the first link to be presented to the user at the calculated size on the personalized version of the webpage (Lavonen [0041] e.g., “The link names are outputted in step 403 in the link area according to their order defined in the link list.”. see also [0049] e.g., “… the link portions and corresponding containers are generated in step 422. … displaying as many of them that will fit into the display area is caused in step 423”).
14. Claims 5, 6, 7, 12, 13, 14, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Iannaccone, Breese, and Li, as applied to claim 1 above, and further in view of Schwartz US 20180121944 A1 (hereinafter Schwartz).
Regarding claim 5, the rejection of claim 4 is hereby incorporated by reference, Wang, Iannaccone, Breese and Li does not expressly disclose a system, wherein providing the personalization services to the user further comprises selecting, by the computer-based system and based at least in part on clickstream data of the user, a second link corresponding to a first function; and causing, by the computer-based system, the second link to be presented to the user on the personalized version of the webpage.
However Schwartz discloses wherein providing the personalization services to the user further comprises selecting, by the computer-based system and based at least in part on clickstream data of the user, a second link corresponding to a first function (Schwartz [0022] e.g., “… the clickstream behavioral pattern of the user derived from aggregate CDR information…”. Schwartz also identifies topics in which the user is interested based on websites visited, personal taste preferences, payment activity, and other behavioral information. See also [0045] e.g., “(1) data the user intentionally provides (also known as (a.k.a.) intentional data) (e.g., clickstream, transactions, authored input, etc.)” as well as “(3) behavior pattern of the user) and “(5) clickstream preferences”. Schwartz uses behavioral information in determining what should be offered to the user. See also [0027] e.g., “… a probability of the user being engaged based on the purchase history of the product and/or similar products…”. Schwartz adjusts a base purchase probability using current promotions options. See also [0031] e.g., “… select an option from the base set according to pre-selected strategy…”); and
causing, by the computer-based system, the second link to be presented to the user on the personalized version of the webpage (Schwartz [0035] e.g., “… dynamically initiates a display of the reward(s)… where the display is presented on the computing device of the user.”). It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to utilize Schwartz’s clickstream-derived user information to select which of Lavonne’s functional links is presented in the personalized webpage, thereby presenting a selectable function corresponding to the user’s demonstrated interaction behavior.
Regarding claim 6, the rejection of claim 5 is hereby incorporated by reference, Wang, Iannaccone, Breese, Li, and Schwartz disclose a method, wherein providing the personalization services to the user further comprises determining, by the computer-based system, that the user is more likely to select the first link than the second link (Schwartz [0022], [0027], [0031], and [0045]. Schwartz teaches determining user behavioral information, including clickstream behavioral patterns and clickstream preferences, and determining a probability that the user will be engaged based on the user’s prior behavior, and selecting an option from a set of available options); and presenting, by the computer-based system, the first link in a greater size than the second link (Lavonen [0023] e.g., “The link list 214-1 contains entries associating an internal link identifier with the title … its location in the link area, its output size, and possible other information … The output size may be given as an exact size (height and width or mere width”. See also [0038] e.g., “… the user has selected the container that comprises controls the user is most repeatedly using to be the active container…”). The motivation for the proposed combination is maintained.
Regarding claim 7, the rejection of claim 1 is hereby incorporated by reference, Wang, Iannaccone, Breese, Li, and Schwartz disclose a method, wherein providing the personalization services to the user comprises selecting, by the computer-based system and based at least in part on clickstream data of the user (Schwartz [0022] e.g., “… the clickstream behavioral pattern of the user derived from aggregated CDR information..” Schwartz expressly teaches user interest information based on websites visited personal taste preferences, payment activity, etc. see [0045] “(1) data the user intentionally provides (also known as (a.k.a.) intentional data) (e.g., clickstream, transactions, authored input, etc.) … clickstream preferences,…”. Schwartz also teaches using-specific behavioral information in selecting what is provided to the user, For example, see [0027] determines: “… a probability of the user being engaged based on the purchase history of the product and/or similar product…” see also [0031] e.g., “… select an option from the base set…”) and a link corresponding to a first function (Lavonen [0019] e.g., “The name, or other information displayed on the link portion may be called also a title of the container, a container identifier, or a group identifier.”); and causing, by the computer-based system, the link to be presented to the user (Lavonen [0041] “The link names are outputted in step 403 in the link area according to their order defined in the link list”. See also [0049] e.g., “… displaying as many of them that will fit into the display area”) on the personalized version of the webpage (Lavonen [0041] “The link names are outputted in step 403 in the link area according to their order defined in the link list”. See also [0049] e.g., “… displaying as many of them that will fit into the display area”). It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to utilize Schwartz’s clickstream-derived user information to select which of Lavonne’s functional links is presented in the personalized webpage, thereby presenting a selectable function corresponding to the user’s demonstrated interaction behavior. Regarding claim 12, the rejection of claim 11 is hereby incorporated by reference, Wang, Iannaccone, Breese, Li and Lavonen disclose a system, wherein providing the personalization services to the user comprises: wherein providing the personalization services to the user further comprises selecting, by the computer-based system and based at least in part on clickstream data of the user, a second link corresponding to a first function (Schwartz [0022] e.g., “… the clickstream behavioral pattern of the user derived from aggregate CDR information…”. Schwartz also identifies topics in which the user is interested based on websites visited, personal taste preferences, payment activity, and other behavioral information. See also [0045] e.g., “(1) data the user intentionally provides (also known as (a.k.a.) intentional data) (e.g., clickstream, transactions, authored input, etc.)” as well as “(3) behavior pattern of the user) and “(5) clickstream preferences”. Schwartz uses behavioral information in determining what should be offered to the user. See also [0027] e.g., “… a probability of the user being engaged based on the purchase history of the product and/or similar products…”. Schwartz adjusts a base purchase probability using current promotions options. See also [0031] e.g., “… select an option from the base set according to pre-selected strategy…”); and
causing, by the computer-based system, the second link to be presented to the user on the personalized version of the webpage (Schwartz [0035] e.g., “… dynamically initiates a display of the reward(s)… where the display is presented on the computing device of the user.”). Regarding claim 13, the rejection of claim 5 is hereby incorporated by reference, Wang, Iannaccone, Breese, Li, and Schwartz disclose a computer-based system, wherein providing the personalization services to the user further comprises determining, by the computer-based system, that the user is more likely to select the first link than the second link (Schwartz [0022], [0027], [0031], and [0045]. Schwartz teaches determining user behavioral information, including clickstream behavioral patterns and clickstream preferences, and determining a probability that the user will be engaged based on the user’s prior behavior, and selecting an option from a set of available options); and presenting, by the computer-based system, the first link in a greater size than the second link (Lavonen [0023] e.g., “The link list 214-1 contains entries associating an internal link identifier with the title … its location in the link area, its output size, and possible other information … The output size may be given as an exact size (height and width or mere width”. See also [0038] e.g., “… the user has selected the container that comprises controls the user is most repeatedly using to be the active container…”). The motivation for the proposed combination is maintained.
Regarding claim 14, the rejection of claim 1 is hereby incorporated by reference, Wang, Iannaccone, Breese, Li, and Schwartz disclose a computer-based system, wherein providing the personalization services to the user comprises selecting, by the computer-based system and based at least in part on clickstream data of the user (Schwartz [0022] e.g., “… the clickstream behavioral pattern of the user derived from aggregated CDR information..” Schwartz expressly teaches user interest information based on websites visited personal taste preferences, payment activity, etc. see [0045] “(1) data the user intentionally provides (also known as (a.k.a.) intentional data) (e.g., clickstream, transactions, authored input, etc.) … clickstream preferences,…”. Schwartz also teaches using-specific behavioral information in selecting what is provided to the user, For example, see [0027] determines: “… a probability of the user being engaged based on the purchase history of the product and/or similar product…” see also [0031] e.g., “… select an option from the base set…”) and a link corresponding to a first function (Lavonen [0019] e.g., “The name, or other information displayed on the link portion may be called also a title of the container, a container identifier, or a group identifier.”); and causing, by the computer-based system, the link to be presented to the user (Lavonen [0041] “The link names are outputted in step 403 in the link area according to their order defined in the link list”. See also [0049] e.g., “… displaying as many of them that will fit into the display area”) on the personalized version of the webpage (Lavonen [0041] “The link names are outputted in step 403 in the link area according to their order defined in the link list”. See also [0049] e.g., “… displaying as many of them that will fit into the display area”). It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to utilize Schwartz’s clickstream-derived user information to select which of Lavonne’s functional links is presented in the personalized webpage, thereby presenting a selectable function corresponding to the user’s demonstrated interaction behavior. Regarding claim 19, the rejection of claim 18 is hereby incorporated by reference, Wang, Iannaccone, Breese, Li, and Schwartz wherein providing the personalization services to the user further comprises selecting, by the computer-based system and based at least in part on clickstream data of the user, a second link corresponding to a first function (Schwartz [0022] e.g., “… the clickstream behavioral pattern of the user derived from aggregate CDR information…”. Schwartz also identifies topics in which the user is interested based on websites visited, personal taste preferences, payment activity, and other behavioral information. See also [0045] e.g., “(1) data the user intentionally provides (also known as (a.k.a.) intentional data) (e.g., clickstream, transactions, authored input, etc.)” as well as “(3) behavior pattern of the user) and “(5) clickstream preferences”. Schwartz uses behavioral information in determining what should be offered to the user. See also [0027] e.g., “… a probability of the user being engaged based on the purchase history of the product and/or similar products…”. Schwartz adjusts a base purchase probability using current promotions options. See also [0031] e.g., “… select an option from the base set according to pre-selected strategy…”); and
causing, by the computer-based system, the second link to be presented to the user on the personalized version of the webpage (Schwartz [0035] e.g., “… dynamically initiates a display of the reward(s)… where the display is presented on the computing device of the user.”). Regarding claim 20, the rejection of claim 19 is hereby incorporated by reference, Wang, Iannaccone, Breese, Li, and Schwartz disclose an article of manufacture, wherein providing the personalization services to the user further comprises determining, by the computer-based system, that the user is more likely to select the first link than the second link (Schwartz [0022], [0027], [0031], and [0045]. Schwartz teaches determining user behavioral information, including clickstream behavioral patterns and clickstream preferences, and determining a probability that the user will be engaged based on the user’s prior behavior, and selecting an option from a set of available options); and presenting, by the computer-based system, the first link in a greater size than the second link (Lavonen [0023] e.g., “The link list 214-1 contains entries associating an internal link identifier with the title … its location in the link area, its output size, and possible other information … The output size may be given as an exact size (height and width or mere width”. See also [0038] e.g., “… the user has selected the container that comprises controls the user is most repeatedly using to be the active container…”). The motivation for the proposed combination is maintained.
Conclusion
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/BERHANU MITIKU/Examiner, Art Unit 2156
/AJAY M BHATIA/Supervisory Patent Examiner, Art Unit 2156
