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 .
Response to Amendment
2. This action is responsive to the Applicant’s request for continued examination filed on September 10, 2025.3. Claims 1-20 are pending, of which claims 1, 8, and 15 are in independent form. 4. The Applicants amended the specification (see page 10, paragraph [0029], line 12: ’90.sub.th” to “90th”). This amendment is merely a formal correction and does not change the scope of the invention. The Examiner acknowledged the amendment and entered it.
Response to Arguments
5. Applicant’s arguments, see “Non-Statutory Double Patenting Rejection” filed on September 10, 2025. The applicant requests that the provisional non-statutory double patenting rejection be held in abeyance until the claims are found to be otherwise allowable.
Response: As of the Ninth Edition, Revision 07.2015 (October publication) the MPEP § 804 states that:
As filing a terminal disclaimer, or filing a showing that the claims subject to the rejection are patentably distinct from the reference application’s claims, is necessary for further consideration of the rejection of the claims, such a filing should not be held in abeyance. Only objections or requirements as to form not necessary for further consideration of the claims may be held in abeyance until allowable subject matter is indicated (Emphasis added). Therefore, an application must not be allowed unless the required compliant terminal disclaimer(s) is/are filed and/or the withdrawal of the nonstatutory double patenting rejection(s) is made of record by the examiner.
Thus, in order for a reply to an Office Action that includes a provisional non-statutory double patenting rejection to be considered responsive, “filing a terminal disclaimer, or filing a showing that the claims subject to the rejection are patentably distinct from the reference application’s claims, is necessary.” The rejection for double patenting set forth in the previous Office action is hereby maintained.
6. Applicant’s arguments, see “Rejection of Claims 1-20 under 35 U.S.C. § 101”, filed on September 10, 2025 has been carefully considered but are not persuasive. Given the current claim language and arguments, maintaining the § 101 rejection is appropriate under both the 2019 PEG.
7. Argument 1: Applicant argues that “The claims are directed to a “technical solution (i.e., technical advance) that conserves processing power … by regulating how customization or personalization services are provided.”
Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Conserving processing power by deciding who receives personalized content and when remains a business/administrative objective implemented on generic computers, which falls within the “methods of organizing human activity” and “mental process” groupings of abstract ideas under the 2019 PEG, even if it has incidental technical effects. The claims recite high-level functional steps (calculating a score, setting a threshold based on available processing power, comparing, and serving one of two webpages) but do not recite a specific resource-management mechanism or improvement to how the computer itself operates, so this asserted “technical advance” does not take the claim out of the abstract-idea category.
8. Argument 2: Applicant argues that “The specification describes that “providing customized content takes time and processing power,” and that the invention “conserves processing power” by providing more personalization to high-value users and little or none to low-value users.”
Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Statements in the specification about conserving processing power or improving efficiency are not sufficient by themselves; the 2019 PEG and subsequent update emphasize that eligibility turns on what is actually recited in the claims, not on aspirational benefits in the written description. The recited solution—choosing different levels of personalization based on an affinity score and a threshold—is essentially a rule for allocating service levels among users, which is a business decision (who gets premium treatment) rather than a recited technological improvement to how servers, networks, or databases function at a technical level.
9. Argument 3: Applicant argues that ‘The application “provides a technical solution that regulates customization services based on an affinity score,” calculated using collaborative filtering, neural networks, demographic data, clickstream data, and percentile-based scoring”.
Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Using collaborative filtering, neural networks, or percentile scores to compute an “affinity score” is, at best, the use of known mathematical or statistical techniques to generate a business metric and does not, as claimed, change the operation of the computer itself. Under the 2019 PEG, such score calculation fits within mathematical concepts and mental processes, and in these claims is used only to support the business logic of deciding which version of a webpage to show, without any recited unconventional data structure, model training scheme, or system architecture that would amount to a technological improvement.
10. Argument 4: Applicant argues that ‘Dynamically determining a threshold level “based at least in part on an available processing power,” and raising/lowering the threshold as demand changes, is a technical feature that ties the abstract idea to an improvement in computer functioning’.
Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. The feature is recited in purely result-oriented terms—“dynamically determining” and adjusting the threshold based on “available processing power”—without specifying how processing power is measured, what components perform the monitoring, or what particular control logic or algorithm is used, which under Office guidance is treated as generic computer implementation rather than an improvement to computer technology. The dynamic threshold simply governs when the business rule (who gets personalized content) applies; it does not, as claimed, change the internal operation of processors, memory management, or network protocols in a way that would integrate the abstract idea into a practical application at Step 2A Prong Two.
11. Argument 5: Applicant argues that ‘Because the method “is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks,” it should be treated like claims that improve computer functionality and therefore is not directed to an abstract idea’. Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. The fact that the method is implemented on “computer-based systems” serving webpages over networks does not alone establish that the problem is “specifically” a computer-technology problem; the core problem is how to allocate personalization resources and target content to different users, which is a business/marketing problem executed using generic networked computers. Under the 2019 PEG, merely limiting an abstract idea to a technological environment (e.g., the internet, servers, webpages) or performing it on a generic computer does not make it any less abstract or show that it improves the functioning of the computer itself.
12. Argument 6: Applicant argues that ‘The claims “establish a clear nexus between the claim language and the technical improvement via a practical application,” and therefore are either not directed to an abstract idea or, alternatively, integrate the abstract idea into a practical application’.
Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. A “practical application” requires more than using a computer as a tool to implement an abstract business rule; the additional elements must meaningfully limit the exception, for example by reciting a particular machine configuration or an improvement to a specific technology. Here, the additional elements—generic processors, memory, webpages, and functional “providing” of personalized or non-personalized versions—are conventional computer components performing routine functions, and they do not impose any meaningful limit beyond applying the abstract personalization rule in a standard web environment.
13. Argument 7: Applicant argues that ‘Even if the claims were directed to an abstract idea, they “allow for improvements to the functioning of computer systems and describe a specific, discrete implementation that provides a technical improvement over the prior art,” providing an inventive concept’.
Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. At Step 2B, the 2019 PEG explain that adding generic computer implementation, stored instructions, and conventional client-server interactions typically does not amount to “significantly more” than the abstract idea itself. The claims do not recite any non-conventional hardware, any particular data structure beyond generic scores and thresholds, or any specific algorithm that changes how the computer manages resources; as such, the alleged improvement over prior art personalization schemes is in the realm of business logic, not in the computer’s technical operation, and does not supply the required inventive concept.
14. Argument 8: Applicant argues that ‘The USPTO “Subject Matter Eligibility Examples: Abstract Ideas,” Example 37 (GUI with usage-based automatic icon display), was found eligible because it recites “a specific manner of automatically displaying icons … resulting in an improved user interface,” and the present claim 1 “similarly recites the providing of a webpage and related services … which provides a specific improvement over prior systems.”
Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Example 37 is eligible because its additional elements recite a very specific graphical user interface behavior and arrangement that improves the user interface itself, such as particular rules for relocating icons near a start position based on usage, which the Office characterizes as an improvement to computer functionality. In contrast, the present claims do not recite any specific GUI arrangement, control technique, or change in the way the webpage is rendered; they only specify that either a personalized or non-personalized version of a webpage is provided based on business metrics and processing-load conditions, which is materially different from the UI-focused technological improvement in Example 37 and therefore does not justify withdrawal of the rejection.
15. Argument 9: Applicant argues that ‘Because claim 1 is patent-eligible, the independent system and article claims (8 and 15) and their dependents are likewise patent-eligible under § 101’.
Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Under Office guidance, the same § 101 analysis applies to method, system, and computer-readable medium claims that are substantively similar; merely casting the abstract idea in different statutory categories (process, machine, manufacture) does not change whether the claims are “directed to” a judicial exception or add significantly more. Claims 8 and 15 recite the same abstract functionality—calculating an affinity score, dynamically determining and comparing a threshold, and providing different webpage versions—implemented on generic processors and memory, so for the same reasons as claim 1 they remain ineligible, and the § 101 rejection should be maintained for all claims.
16. Applicant’s argument regarding 35 U.S.C. 103, see “Claims 1-3, 8-10, and 15-17 Are Patentable over Mehanian and Li”, filed on September 10, 2025 has been carefully considered but are not persuasive.
17. Argument 1: Applicant argues that Mehanian does not disclose “an affinity score of a user representing a value of the user to a provider.” Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. See [0113] e.g., “calculated probability (e.g., click-through rate(s), buy rate(s), etc.) and then provide …the selected promotion(s), e.g., promotion(s) with certain calculated probability(ies), for display to a user. The method …may also store …user response data (e.g., number of impressions or page views served for each promotion, click-through counts, conversion counts, etc.) used during the prior distribution calculation in a data store (e.g., the response database …) for later access and/or retrieval.” Mehanian teaches computing user-response metrics for promotions, including posterior distributions and mean user-action probabilities (e.g.,
click-through and buy probabilities), which are “values representing a mean user-action probability” and directly reflect the user’s expected value/affinity to the provider for content selection; under broadest reasonable interpretation, these are affinity scores.
18. Argument 2: Applicant argues that ‘Posterior distributions’ are for advertisements A/B and thus are not an “affinity score of a user.” Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive The posterior distributions and derived expected values are conditioned on user interaction data and are used by the ads management module to choose which content to show to that user, thereby quantifying the user’s propensity to respond favorably; a score derived from these distributions is a user-affinity/value metric under a reasonable interpretation. Office guidance allows relying on what the reference teaches to a person of ordinary skill, not just labels; here, the distributions/means function as the claimed score.
19. Argument 3: Applicant argues that Mean user-action probabilities and posterior distributions are distinct concepts from “affinity score,” and the rejection is unclear which term maps to the claim. Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Either metric independently satisfies the claimed “score” because both quantify the user’s expected value to the provider for content selection; identifying multiple, alternative teachings is permissible, and mapping both shows redundancy, not ambiguity. The BRI principle does not require the prior art to use the exact claim phrase “affinity score” so long as the art teaches an equivalent quantification used for the same purpose.
20. Argument 4: Applicant argues that “Determining … that the affinity score is greater than the threshold level” is not shown; display rate thresholds are unrelated. Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Mehanian teaches using thresholds and selection criteria based on response probabilities and rates to pick content, including selecting items with probabilities “higher than a threshold probability” and managing over-display with predetermined thresholds; comparing a user-response metric against a threshold to decide content delivery meets the claimed determination. The fact that Mehanian discusses both probability thresholds and display-rate thresholds shows multiple embodiments of thresholding; either teaches the claimed comparison (Mehanian [0148]).
21. Argument 5: Applicant argues that Non-personalization services in response to the score being below the threshold are not disclosed; “fallback logic” is speculative. Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Mehanian’s architecture (see [0080]-[0081]) includes a personalization server that instructs the website server on what to show and contemplates serving recommendations based on available data and selection results; in the absence of a qualifying score/selection, the system would serve default content, which is non-personalized, as is routine in such client-server personalization systems. Office guidance permits relying on what is reasonably implicit to a person having ordinary skill in the art (POSITA) given the disclosed control flow and server roles; pairing this with Li’s resource-based gating further supports explicit threshold-based fallback.
22. Argument 6: Applicant argues that Samples drawn from posteriors are not “affinity scores,” are different from probabilities, and are not compared to thresholds. Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Drawing samples from a posterior and selecting the highest sample is a standard bandit approach that operationalizes the same underlying user-response estimate; a sample value is a numeric instantiation of the user’s response likelihood for comparison/selection. Mehanian also teaches explicit thresholding on probabilities ([0098]). Thus, the art discloses both selection by comparative scoring and selection by score-vs-threshold, either satisfying the claimed determination and response.
23. Argument 7: Applicant argues that Li cannot cure Mahanian’s alleged deficiencies. Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Li teaches adapting personalization preprocessing based on resource availability and verifying adequate system resources, i.e., dynamically gating personalization using resource-based criteria—this directly provides the claimed “dynamically determining … a threshold … based at least in part on available processing power,” and it would have been obvious to incorporate Li’s resource gating into Mahanian’s personalization/selection pipeline to avoid overload while serving users. Combining known personalization selection with known resource-aware gating is a predictable improvement to web serving acknowledged in USPTO guidance.
24. Argument 8: Applicant argues that (claims 2/9/16): Affinity score calculated after authenticating user credentials is not taught. Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Mahanian’s system attributes page visits and actions to specific users and maintains user-response histories; a person having ordinary skill in the art (POSITA) would understand that reliable per-user attribution in a website context routinely involves user identification/authentication before computing user-specific metrics, rendering the limitation obvious. Office guidance recognizes that well-understood, routine steps for user identification can be relied upon under BRI and common knowledge in the field.
25. Argument 9: Applicant argues that (claims 3/10/17): Personalized welcome page with links likely to be selected, and unavailability below threshold, not taught. Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Mehanian teaches providing personalized webpages predicted to be requested next and instructing servers to present recommended offers/links to the user; this meets the “personalized version” with links likely to be selected. When the score/selection does not qualify (below threshold or insufficient data), default, non-personalized content is served, making the personalized page “unavailable” for those users, which is a routine website behavior.
26. Argument 10: Applicant argues that (claim 4 and corresponding system/CRM claims): Determining available display area and sizing links not shown; Lavonen is insufficient. Response: Examiner has carefully considered the argument but respectfully
disagrees. This argument is not persuasive. Lavonen teaches detecting available display area and adapting which links/containers are displayed, including resizing/adjusting UI elements when space changes; combining Lavonne’s responsive layout with Mehanian/Li’s personalization would have been obvious to yield predictable improvements in presenting personalized links at sizes based on available space, as is standard responsive UI design. This addresses the remaining UI sizing steps.
Double Patenting
27. 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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
28. Claims 1, 8, and 15 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, 8, and 15 of U.S. Patent 11,954,160 B2. The patented claim teaches the limitations of the instant claim as shown by comparison below:
Instant Application 18/600,215
US Patent 11,954,160
1. A method comprising: calculating, by a computer-based system, an affinity score of a user representing a value of the user to a provider;
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; determining, by the computer-based system, that the affinity score is greater than the threshold level; 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; 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.
1. (Currently Amended) A method comprising: calculating, by a computer-based system, an affinity score of a user representing a value of the user to a provider, the affinity score being calculated based at least in part on at least one of: a monetary spending capability of the user or an average monetary spending amount of the user over a time period; 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; determining, by the computer-based system, that the affinity score is greater than the threshold level; and 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, 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, 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 first link corresponding to a first function; determining, by the computer-based system, an available display area on a device of the user; calculating, by the computer-based system, a first size of the 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.
8. A computer-based system, comprising: a processor; and a tangible, non-transitory memory configured to communicate with the processor, the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations comprising: calculating, by the processor, an affinity score of a user representing a value of the user to a provider; dynamically determining, by the processor, 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; determining, by the processor, that the affinity score is greater than the threshold level; providing, by the processor 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; and providing, by the processor 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.
8. (Currently Amended) A computer-based system, comprising: a processor; and a tangible, non-transitory memory configured to communicate with the processor, the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations comprising: calculating, by the processor, an affinity score of a user representing a value of the user to a provider, the affinity score being calculated based at least in part on at least one of: a monetary spending capability of the user or an average monetary spending amount of the user over a time period; dynamically determining, by the processor, a threshold level for providing personalization services based at least in part on an available processing power in the processor, wherein the threshold level is lowered or raised based on the available processing power; determining, by the processor, that the affinity score is greater than the threshold level; and providing, by the processor 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 and providing, by the processor 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, wherein providing the personalization services to the user further comprises: selecting, by the processor and based at least in part on clickstream data of the user, a first link corresponding to a first function; determining, by the processor, an available display area on a device of the user; calculating, by the processor, a first size of the first link based at least in part on the available display area; and causing, by the processor, the first link to be presented to the user.
15. An article of manufacture including a non-transitory, tangible computer readable storage medium having instructions stored thereon that, in response to execution by a computer-based system, cause the computer-based system to perform operations comprising: calculating an affinity score of a user representing a value of the user to a provider; dynamically determining 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; determining that the affinity score is greater than the threshold level; providing, 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; and providing, 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.
15. (Currently Amended) An article of manufacture including a non-transitory, tangible computer readable storage medium having instructions stored thereon that, in response to execution by a computer-based system, cause the computer-based system to perform operations comprising: calculating, by the computer-based system, an affinity score of a user representing a value of the user to a provider, the affinity score being calculated based at least in part on at least one of: a monetary spending capability of the user or an average monetary spending amount of the user over a time period; 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; determining, by the computer-based system, that the affinity score is greater than the threshold level; and 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; 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, 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 first link corresponding to a first function; determining, by the computer-based system, an available display area on a device of the user; calculating, by the computer-based system, a first size of the 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.
29. Claims 1, 8, and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, and 15 of U.S. Patent No. US 11,954,160 B2. The claims of U.S. Patent No. US 11,954,160 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claims include all the limitation of the instant claims and would thus anticipate those claims. The patented claims recite more specificity, and would still render the instant claims obvious or anticipated.
Claim Rejections - 35 USC § 101
30. 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.
31. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-7 are recited as being directed to a “method”. Claims 8-14 are recited as being directed to a “computer-based system”, and Claims 15-20 are recited an “article of manufacture”. Yes, the claims fall into a statutory category (method, system, and article of manufacture). Below is further analysis related to step 2.
Regarding claim 1,
Step 2A: Prong One:
Claim 1 recites limitations:
[a] calculating, by a computer-based system, an affinity score of a user representing a value of the user to a provider;
[b] dynamically determining, by the computer-based system, a threshold level for providing personalization services based at least in part on an available processing power
[c] determining, by the computer-based system, that the affinity score is greater than the threshold level;
[d] 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,
[e] 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,
Explanation: Limitation [a] is an example of data evaluation and user classification, which is a mental process akin to evaluating a customer’s value in a traditional business setting.
Limitation [b] recites adjusting a threshold based on system conditions-this is a business logic rule reflecting resource management, not a technical improvement to the computer itself.
Limitation [c] involves a comparison of numerical values-i.e., determining whether one score exceeds another-which courts have repeatedly held to be basic mathematical concepts.
Limitation [d] and [e] involves choosing between a personalized or non-personalized user experience, which is a business decision tied to user segmentation and not a technological advance.
Accordingly, the claim as a whole is directed to an abstract idea comprising user classification and content delivery based on business criteria and system capacity-activities that have long been performed by human and business systems.
Step 2A, Prong 2 – Integration into a Practical Application
The following elements are examined for integration into a practical application:
[a] calculating affinity score;
[b] determining system-based threshold;
[c] score comparison;
[d] and [e] conditional content delivery.
These elements do not integrate the abstract idea into a practical application:
There is improvement to computer functionality (e.g., memory, processor performance, network efficiency).
There is no improvement to another technology or technical field.
The method does not use any technological tool in a novel or improved manner.
Instead, the claims merely automate a business practice of targeting user experience based scoring logic and resources constraints using a generic computing environments.
The system simply implements the decision-making logic on a computer, which does not amount to integration of the abstract idea into a practical application.
Step 2B – Significantly MoreThe claim elements-individually and in combination-do not amount to significantly more than the judicial exception:
The computer-based implementation is generic and lacks any inventive concept
The affinity score calculation, threshold adjustment, and conditional content delivery are all routing operations performed by known computing infrastructure.
No specific technical solution or technological innovation is presented.
The ordered combination of steps merely automates a known business decision model (i.e., delivering different content to users base on value and system status), which is not sufficient to confer eligibility.
Dependent claims (2-7):
Claim 2: Affinity score calculated after login-conventional user management.
Claim 3: Personalized welcome page with links based on user likelihood-well-known in the art of website personalization.
Claims 4-6: Adjusting link size and order based on user likelihood or device display-routine UI customization practices using basic data (clickstream, screen size).
Claim 7: Selecting a link based on clickstream-standard behavioral analytics application.
This limitations merely provide additional non-technical personalization rules or presentation logic, which are considered insignificant extra-solution activity under MPEP 2106.05(g) and do not add significantly more to the abstract idea.
Conclusion - §101 Abstract Idea Analysis
Based on the analysis above, claims 1-7 are directed to ineligible subject matter under 35 U.S.C. §101. The claims recite mental processes, mathematical comparisons, and user interface adjustment that amount to abstract ides implemented on generic computer infrastructure, without reciting any technical improvement or inventive concept.
Claim Rejections - 35 USC § 103
32. 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.
33. 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.
34. 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.
35. Claims 1-3, 8-10, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Mehanian et al. US20160350802A1 (hereinafter Mehanian) in view of Li et al. U.S. 2017/0099340 A1 (hereinafter Li).
Regarding claim 1, Mehanian discloses a method comprising:
calculating, by a computer-based system, an affinity score1 of a user representing a value of the user to a provider (Mehanian [0006] e.g., “calculating for the promotion a posterior distribution of a user-action probability reflecting estimates for a user response to a display of the promotion for the product on a computing device of the user;”. This clearly discloses calculating an affinity score representing a value of the user to the provider), see also [0126] e.g., “the method 900 may calculate 908 a value representing a mean user-action probability (e.g., click-through probability Pclick, buy probability Pbuy, etc.) for each promotion using previously calculated shape parameter values”. This describes computing user-specific posterior distributions, expected values, or probabilities like P_click and P_buy. These effectively represent user affinity or value metrics. See also [0128] & [0131]);
determining, by the computer-based system, that the affinity score is greater than the threshold level (Mehanian [0106] e.g., “If the ads diversity module 750 determines the display rate of the same advertisement is higher than a certain threshold (e.g., constantly being displayed, such as 95% of the time relative to competing advertisements), then it may update the parameters used by the distribution calculator 742”, see also [0139] e.g., “the method 1100 may determine if the same promotion is consistently being determined as having the highest click-through rate or buy rate and provided for display more than an allowable, predetermined threshold. If the result of the determination is positive, then the method 1100 may proceed to determine varying promotions to display over a moving time window”, see also [0148] e.g., “For example, the method 1200 may select top 10 promotions from a set of 100 promotions that have click-through/buy probabilities higher than a certain threshold probability”); 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 (Mehanian [0080] and [0082] paragraph [0082] contemplates flexible system architecture -which would reasonable include fallback logic. [0080] establishes that the personalization server “computer offers” and instructs the website server on what to show the user. If the personalization server lacks sufficient data or cannot generate a recommendation that the website server would still return content -likely default (non-personalized) offers); and
providing, by the computer-based system and in response to the affinity score being greater than the threshold level (Mehanian [0057] e.g., “ In block 612, the ads management module 740 may compare the samples, for example using either via EQU. 15, EQU. 16, EQU. 18, or EQU. 19, and then selecting the winning advertisement for display to a user. The click-through rate from the user is measured in block 614 and then stored 616 in the response database 618.”), the personalization services to the user, wherein the personalization services include providing a personalized version of the webpage to the user (Mehanian [0013] e.g., “provide personalized webpages to user devices in a more expeditious manner by identifying the webpage that the user will likely request next and obtaining the information that will go into the webpage prior to the user requesting the webpage”, see also [0080] e.g., “The personalization server 730 instructs the website server 720 to provide the recommended offers or advertisements to the user device 170 of the user”, see also [0148] e.g., “For example, the method 1200 may select top 10 promotions from a set of 100 promotions that have click-through/buy probabilities higher than a certain threshold probability. The method 1200 may then determine 1208, from the top set of promotions, similarity values between the promotions, such as between the two promotions i and j”, see also [0080] e.g., “These page visits and user actions are in turn communicated to a personalization server 730. The personalization server 730 maintains a database of user responses to the offers that are displayed. Using the techniques taught by the present subject matter, the personalization server 730 computes offers or advertisements to be shown to the user. The personalization server 730 instructs the website server 720 to provide the recommended offers or advertisements to the user device 170 of the user”).
Mehanian does not explicitly disclose dynamically determining, by the computer-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; and 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.
However, Li discloses dynamically determining, by the processor, 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”. The system dynamically adapts personalization behavior based on current resource availability, which implies threshold adjustment behavior. See also [0056]-[0057] & [0061]); and It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the server side preprocessing of web content taught ty Li, in the promotion selection for online customers using Bayesian Bandits taught by Mehanian, to yield the predictable results of allowing the method enables providing a solution to expeditiously providing personalized webpages to the user device (Li [0013]).
Claims 8 and 15 incorporates substantively all the imitations of claim 1 in a computer-based system comprising a processor (Mehanian [0079] e.g., “a processor”) and a tangible, non-transitory memory (Mehanian [0087] e.g., “the memory(ies)..) and an article of manufacture (Mahanian [0087]) and rejected under the same rationale.
Regarding claim 2, the proposed combination of Mehanian and Li teaches the method of claim 1, wherein the affinity score is calculated in response to the computer-based system authenticating credentials for the user (Mehanian [0080] e.g., “The user's page visits and actions, using the user device 710, on the website are communicated to the website server 720. These page visits and user actions are in turn communicated to a personalization server 730. The personalization server 730 maintains a database of user responses to the offers that are displayed.”. A person of ordinary skill in the art would understand that attributing page visits and user action to a specific user necessary requires the user to identified-commonly through authentication of credential).
Regarding claim 3, the proposed combination of Mehanian and Li teaches the method of claim 1, wherein the personalized version of the webpage comprises a personalized welcome page to a website of the provider, wherein the personalized welcome page provides selectable links to functions of the website that the user is likely to select (Mehanian [0013] e.g., “provide personalized webpages to user devices in a more expeditious manner by identifying the webpage that the user will likely request next and obtaining the information that will go into the webpage prior to the user requesting the webpage”, see also [0080] e.g., “The personalization server 730 instructs the website server 720 to provide the recommended offers or advertisements to the user device 170 of the user”), wherein the personalized welcome page for the website is unavailable for users having an affinity score lower than the threshold level (Mehanian [0080] and [0082] paragraph [0082] contemplates flexible system architecture -which would reasonable include fallback logic. [0080] establishes that the personalization server “computer offers” and instructs the website server on what to show the user. If the personalization server lacks sufficient data or cannot generate a recommendation that the website server would still return content -likely default (non-personalized) offers).
Regarding claim 9, the proposed combination of Mehanian and Li teaches the computer-based system of claim 8, wherein the affinity score is calculated in response to the computer-based system authenticating credentials for the user (Mehanian [0080] e.g., “The user's page visits and actions, using the user device 710, on the website are communicated to the website server 720. These page visits and user actions are in turn communicated to a personalization server 730. The personalization server 730 maintains a database of user responses to the offers that are displayed.”. A person of ordinary skill in the art would understand that attributing page visits and user action to a specific user necessary requires the user to identified-commonly through authentication of credential).
Regarding claim 10, the proposed combination of Mehanian and Li teaches the computer-based system of claim 8, wherein the personalized version of the webpage comprises a personalized welcome page to a website of the provider, wherein the personalized welcome page provides selectable links to functions of the website that the user is likely to select (Mehanian [0013] e.g., “provide personalized webpages to user devices in a more expeditious manner by identifying the webpage that the user will likely request next and obtaining the information that will go into the webpage prior to the user requesting the webpage”, see also [0080] e.g., “The personalization server 730 instructs the website server 720 to provide the recommended offers or advertisements to the user device 170 of the user”), wherein the personalized welcome page for the website is unavailable for users having an affinity score lower than the threshold level (Mehanian [0013] e.g., “provide personalized webpages to user devices in a more expeditious manner by identifying the webpage that the user will likely request next and obtaining the information that will go into the webpage prior to the user requesting the webpage”, see also [0080] e.g., “The personalization server 730 instructs the website server 720 to provide the recommended offers or advertisements to the user device 170 of the user”).
Regarding claim 16, the proposed combination of Mehanian and Li teaches an article of manufacture of claim 15, wherein the affinity score is calculated in response to the computer-based system authenticating credentials for the user (Mehanian [0080] e.g., “The user's page visits and actions, using the user device 710, on the website are communicated to the website server 720. These page visits and user actions are in turn communicated to a personalization server 730. The personalization server 730 maintains a database of user responses to the offers that are displayed.”. A person of ordinary skill in the art would understand that attributing page visits and user action to a specific user necessary requires the user to identified-commonly through authentication of credential).
Regarding claim 17, the proposed combination of Mehanian and Li teaches an article of manufacture of claim 15, wherein the personalized version of the webpage comprises a personalized welcome page to a website of the provider, wherein the personalized welcome page provides selectable links to functions of the website that the user is likely to select, wherein the personalized welcome page for the website is unavailable for users having an affinity score lower than the threshold level (Mehanian [0013] e.g., “provide personalized webpages to user devices in a more expeditious manner by identifying the webpage that the user will likely request next and obtaining the information that will go into the webpage prior to the user requesting the webpage”, see also [0080] e.g., “The personalization server 730 instructs the website server 720 to provide the recommended offers or advertisements to the user device 170 of the user”), wherein the personalized welcome page for the website is unavailable for users having an affinity score lower than the threshold level (Mehanian [0080] and [0082] paragraph [0082] contemplates flexible system architecture-which would reasonable include fallback logic. [0080] establishes that the personalization server “computer offers” and instructs the website server on what to show the user. If the personalization server lacks sufficient data or cannot generate a recommendation that the website server would still return content -likely default (non-personalized) offers).
36. Claims 4-7, 11-14, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mehanian et al. US20160350802A1 (hereinafter Mehanian) in view of Li et al. US 2017/0099340 A1 (hereinafter Li) as applied to claims 1-3, 8-10, and 15-17 above, and further in view of Lavonen et al. US 2017/0192632 A1 (hereinafter Lavonen).
Regarding claim 4, the proposed combination of Mehanian and Li teaches the method of claim 1, wherein providing the personalization services to the user comprises: [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 (Mehanian [0013] e.g., “provide personalized webpages to user devices in a more expeditious manner by identifying the webpage that the user will likely request next and obtaining the information that will go into the webpage prior to the user requesting the webpage”, see also [0080] e.g., “The personalization server 730 instructs the website server 720 to provide the recommended offers or advertisements to the user device 170 of the user”)
The combination of Mehanian and Li does not explicitly disclose determining, by the computer-based system, an available display area on a device of the user;
calculating, by the computer-based system, a 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. Lavonen discloses determining, by the computer-based system, an available display area on a device of the user (Lavonen [0036] e.g., “FIG. 3D illustrates what is displayed when the display width of the application, and hence the ribbon, is smaller than in FIGS. 3B and 3C … when it is detected that not all link titles can be displayed, a scrolling aid for link titles, formed by two arrows, 341, 341′ has been outputted in the link area ... in response to making the display area smaller, less containers will be outputted”. The system detects available display area. Adapts what content is shown based on the space (i.e., determines visibility and positioning of links/containers));
calculating, by the computer-based system, a size of a first link based at least in part on the available display area (Lavonen [0036] e.g., “…the size of outputted controls, and the size of the link titles remains the same,… However, it should be appreciated that also other rules may be used”. This indicates a size calculation occurs, even if in the illustrated embodiment the control size remains fixed. The section explicitly contemplates alternative implementation); 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 [0036] e.g., “…in response to making the display area smaller, less containers will be outputted… the ribbon area remains recognizable since it will be displayed with a similar (although less) content”. The system render and presents UI elements (including links) at calculated positions/sized depending on screen space). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the display area wide control area taught by Lavonen, in the proposed combination of Mehanian and Li, to yield the predictable results of adjusting the size of the link or control based on display area-especially in the system already aware of screen constraints and outputting visual components accordingly (Lavonen [0076]).
Regarding claim 5, the proposed combination of Mehanian, Li, and Lavonen teaches the method of claim 4, 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 (Mehanian [0134] e.g., “ promotions along with associated clickstream data describing user action attributes (e.g., click-through rate, buy rate, etc.) may be stored in the response database 618, as discussed elsewhere herein, and the revenue estimator 748 may access the response database 618 to retrieve the promotion with the corresponding user-action attribute(s), such as the highest click-through rate or buy rate stored therein”. This teaches using clickstream data to infer user preference), 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 (Lavonen [0044] e.g., “Then the ribbon display manager unit indicates in step 416 the first non-fixed container and a corresponding link as an active ones and starts in step 417 to monitor input information on user input (selections) to detect selections that affect to what is displayed in the container area”. Allow dynamic rendering of links and containers on the UI depending on active link selection, user input or system controlled configuration, see[0035]. The selection of a second link based on clickstream data (from Mehanian) and causing that link to be displayed (via dynamic ribbon rendering in Lavonen). The motivation for the proposed combination is maintained.
Regarding claim 6, the proposed combination of Mehanian, Li, and Lavonen teaches the method of claim 5, 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 (Mehanian [0107] e.g., “the ads diversity module 750 may cooperate with the distribution calculator 742 to determine the user action probability associated with each advertisement. The ads management module 740 may then determine the set of diverse advertisements based on the similarity between the two or more advertisements and/or the user-action”. This is equivalent to determining likelihood); and presenting, by the computer-based system, the first link in a greater size than the second link (Lavonen [0036] e.g., “FIG. 3D illustrates what is displayed when the display width of the application, and hence the ribbon, is smaller than in FIGS. 3B and 3C … when it is detected that not all link titles can be displayed, a scrolling aid for link titles, formed by two arrows, 341, 341′ has been outputted in the link area ... in response to making the display area smaller, less containers will be outputted”. The system detects available display area. Adapts what content is shown based on the space (i.e., determines visibility and positioning of links/containers). The motivation for the proposed combination is maintained.
Regarding claim 7, the proposed combination of Mehanian, Li, and Lavonen teaches the method of claim 1, 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, a link corresponding to a first function (Mehanian [0134] e.g., “ promotions along with associated clickstream data describing user action attributes (e.g., click-through rate, buy rate, etc.) may be stored in the response database 618, as discussed elsewhere herein, and the revenue estimator 748 may access the response database 618 to retrieve the promotion with the corresponding user-action attribute(s), such as the highest click-through rate or buy rate stored therein”. This teaches using clickstream data to infer user preference); and
causing, by the computer-based system, the link to be presented to the user on the personalized version of the webpage (Lavonen [0044] e.g., “Then the ribbon display manager unit indicates in step 416 the first non-fixed container and a corresponding link as an active ones and starts in step 417 to monitor input information on user input (selections) to detect selections that affect to what is displayed in the container area”. Allow dynamic rendering of links and containers on the UI depending on active link selection, user input or system controlled configuration, see[0035]. The selection of a second link based on clickstream data (from Mehanian) and causing that link to be displayed (via dynamic ribbon rendering in Lavonen). The motivation for the proposed combination is maintained. Regarding claim 11, the proposed combination of Mehanian and Li teaches the computer-based system of claim 8, wherein providing the personalization services to the user comprises:
[causing, by the processor, the first link to be presented to the user at the calculated size] on the personalized version of the webpage (Mehanian [0013] e.g., “provide personalized webpages to user devices in a more expeditious manner by identifying the webpage that the user will likely request next and obtaining the information that will go into the webpage prior to the user requesting the webpage”, see also [0080] e.g., “The personalization server 730 instructs the website server 720 to provide the recommended offers or advertisements to the user device 170 of the user”) The combination of Mehanian and Li does not explicitly disclose determining, by the computer-based system, an available display area on a device of the user;
calculating, by the computer-based system, a 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].
Lavonen discloses determining, by the computer-based system, an available display area on a device of the user (Lavonen [0036] e.g., “FIG. 3D illustrates what is displayed when the display width of the application, and hence the ribbon, is smaller than in FIGS. 3B and 3C … when it is detected that not all link titles can be displayed, a scrolling aid for link titles, formed by two arrows, 341, 341′ has been outputted in the link area ... in response to making the display area smaller, less containers will be outputted”. The system detects available display area. Adapts what content is shown based on the space (i.e., determines visibility and positioning of links/containers));
calculating, by the computer-based system, a size of a first link based at least in part on the available display area (Lavonen [0036] e.g., “…the size of outputted controls, and the size of the link titles remains the same,… However, it should be appreciated that also other rules may be used”. This indicates a size calculation occurs, even if in the illustrated embodiment the control size remains fixed. The section explicitly contemplates alternative implementation); 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 [0036] e.g., “…in response to making the display area smaller, less containers will be outputted… the ribbon area remains recognizable since it will be displayed with a similar (although less) content”. The system render and presents UI elements (including links) at calculated positions/sized depending on screen space). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the display area wide control area taught by Lavonen, in the proposed combination of Mehanian and Li, to yield the predictable results of adjusting the size of the link or control based on display area-especially in the system already aware of screen constraints and outputting visual components accordingly (Lavonen [0076]).
Regarding claim 12, the proposed combination of Mehanian, Li, and Lavonen teaches the computer-based system of claim 11, wherein providing the personalization services to the user further comprises selecting, by the processor and based at least in part on clickstream data of the user (Mehanian [0134] e.g., “ promotions along with associated clickstream data describing user action attributes (e.g., click-through rate, buy rate, etc.) may be stored in the response database 618, as discussed elsewhere herein, and the revenue estimator 748 may access the response database 618 to retrieve the promotion with the corresponding user-action attribute(s), such as the highest click-through rate or buy rate stored therein”. This teaches using clickstream data to infer user preference), a second link corresponding to a first function; and causing, by the processor, the second link to be presented to the user [on the personalized version of the webpage] (Lavonen [0044] e.g., “Then the ribbon display manager unit indicates in step 416 the first non-fixed container and a corresponding link as an active ones and starts in step 417 to monitor input information on user input (selections) to detect selections that affect to what is displayed in the container area”. Allow dynamic rendering of links and containers on the UI depending on active link selection, user input or system controlled configuration, see[0035]. The selection of a second link based on clickstream data (from Mehanian) and causing that link to be displayed (via dynamic ribbon rendering in Lavonen). The motivation for the proposed combination is maintained.
Regarding claim 13, the proposed combination of Mehanian, Li, and Lavonen teaches the computer-based system of claim 12, wherein providing the personalization services to the user further comprises determining, by the processor, that the user is more likely to select the first link than the second link (Mehanian [0107] e.g., “the ads diversity module 750 may cooperate with the distribution calculator 742 to determine the user action probability associated with each advertisement. The ads management module 740 may then determine the set of diverse advertisements based on the similarity between the two or more advertisements and/or the user-action”. This is equivalent to determining likelihood); and presenting, by the processor, the first link in a greater size than the second link (Lavonen [0036] e.g., “FIG. 3D illustrates what is displayed when the display width of the application, and hence the ribbon, is smaller than in FIGS. 3B and 3C … when it is detected that not all link titles can be displayed, a scrolling aid for link titles, formed by two arrows, 341, 341′ has been outputted in the link area ... in response to making the display area smaller, less containers will be outputted”. The system detects available display area. Adapts what content is shown based on the space (i.e., determines visibility and positioning of links/containers). The motivation for the proposed combination is maintained.
Regarding claim 14, the proposed combination of Mehanian, Li, and Lavonen teaches the computer-based system of claim 8, wherein providing the personalization services to the user comprises selecting, by the processor and based at least in part on clickstream data of the user, a link corresponding to a first function (Mehanian [0134] e.g., “ promotions along with associated clickstream data describing user action attributes (e.g., click-through rate, buy rate, etc.) may be stored in the response database 618, as discussed elsewhere herein, and the revenue estimator 748 may access the response database 618 to retrieve the promotion with the corresponding user-action attribute(s), such as the highest click-through rate or buy rate stored therein”. This teaches using clickstream data to infer user preference); and causing, by the processor, the link to be presented to the user on the personalized version of the webpage (Lavonen [0044] e.g., “Then the ribbon display manager unit indicates in step 416 the first non-fixed container and a corresponding link as an active ones and starts in step 417 to monitor input information on user input (selections) to detect selections that affect to what is displayed in the container area”. Allow dynamic rendering of links and containers on the UI depending on active link selection, user input or system controlled configuration, see[0035]. The selection of a second link based on clickstream data (from Mehanian) and causing that link to be displayed (via dynamic ribbon rendering in Lavonen). The motivation for the proposed combination is maintained.
Regarding claim 18, the proposed combination of Mehanian, Li, and Lavonen teaches an article of manufacture of claim 15, wherein providing the personalization services to the user comprises: [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 (Mehanian [0013] e.g., “provide personalized webpages to user devices in a more expeditious manner by identifying the webpage that the user will likely request next and obtaining the information that will go into the webpage prior to the user requesting the webpage”, see also [0080] e.g., “The personalization server 730 instructs the website server 720 to provide the recommended offers or advertisements to the user device 170 of the user”).
The combination of Mehanian and Li does not explicitly disclose determining, by the computer-based system, an available display area on a device of the user;
calculating, by the computer-based system, a 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. Lavonen discloses determining, by the computer-based system, an available display area on a device of the user (Lavonen [0036] e.g., “FIG. 3D illustrates what is displayed when the display width of the application, and hence the ribbon, is smaller than in FIGS. 3B and 3C … when it is detected that not all link titles can be displayed, a scrolling aid for link titles, formed by two arrows, 341, 341′ has been outputted in the link area ... in response to making the display area smaller, less containers will be outputted”. The system detects available display area. Adapts what content is shown based on the space (i.e., determines visibility and positioning of links/containers));
calculating, by the computer-based system, a size of a first link based at least in part on the available display area (Lavonen [0036] e.g., “…the size of outputted controls, and the size of the link titles remains the same,… However, it should be appreciated that also other rules may be used”. This indicates a size calculation occurs, even if in the illustrated embodiment the control size remains fixed. The section explicitly contemplates alternative implementation); 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 [0036] e.g., “…in response to making the display area smaller, less containers will be outputted… the ribbon area remains recognizable since it will be displayed with a similar (although less) content”. The system render and presents UI elements (including links) at calculated positions/sized depending on screen space). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the display area wide control area taught by Lavonen, in the proposed combination of Mehanian and Li, to yield the predictable results of adjusting the size of the link or control based on display area-especially in the system already aware of screen constraints and outputting visual components accordingly (Lavonen [0076]).
Regarding claim 19, the proposed combination of Mehanian, Li, and Lavonen teaches an article of manufacture of claim 18, 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 (Mehanian [0134] e.g., “ promotions along with associated clickstream data describing user action attributes (e.g., click-through rate, buy rate, etc.) may be stored in the response database 618, as discussed elsewhere herein, and the revenue estimator 748 may access the response database 618 to retrieve the promotion with the corresponding user-action attribute(s), such as the highest click-through rate or buy rate stored therein”. This teaches using clickstream data to infer user preference), 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 (Lavonen [0044] e.g., “Then the ribbon display manager unit indicates in step 416 the first non-fixed container and a corresponding link as an active ones and starts in step 417 to monitor input information on user input (selections) to detect selections that affect to what is displayed in the container area”. Allow dynamic rendering of links and containers on the UI depending on active link selection, user input or system controlled configuration, see[0035]. The selection of a second link based on clickstream data (from Mehanian) and causing that link to be displayed (via dynamic ribbon rendering in Lavonen). The motivation for the proposed combination is maintained.
Regarding claim 20, the proposed combination of Mehanian, Li, and Lavonen teaches an article of manufacture of claim 19, 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 (Mehanian [0107] e.g., “the ads diversity module 750 may cooperate with the distribution calculator 742 to determine the user action probability associated with each advertisement. The ads management module 740 may then determine the set of diverse advertisements based on the similarity between the two or more advertisements and/or the user-action”. This is equivalent to determining likelihood); and presenting, by the computer-based system, the first link in a greater size than the second link (Lavonen [0036] e.g., “FIG. 3D illustrates what is displayed when the display width of the application, and hence the ribbon, is smaller than in FIGS. 3B and 3C … when it is detected that not all link titles can be displayed, a scrolling aid for link titles, formed by two arrows, 341, 341′ has been outputted in the link area ... in response to making the display area smaller, less containers will be outputted”. The system detects available display area. Adapts what content is shown based on the space (i.e., determines visibility and positioning of links/containers). The motivation for the proposed combination is maintained.
Conclusion
37. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
38. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERHANU MITIKU whose telephone number is (571)270-1983. The examiner can normally be reached Monday – Friday 8:30AM – 4:00PM. 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, Ajay Bhatia can be reached at 571-272-3906. 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.
/BERHANU MITIKU/Examiner, Art Unit 2156
/AJAY M BHATIA/Supervisory Patent Examiner, Art Unit 2156
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