Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
In amendments dated 6/11/26, Applicant amended claims 1, 5-9, 12-13, 15, and 19-20, canceled no claims, and added no new claims. Claims 1-20 are presented for examination.
Claim Construction
Examiner notes that the last limitation in each of claims 1, 15, and 19 recites “for subsequent execution by the web crawler system to discover additional URLs for the identified URL class instead of generating an additional URL discovery action prompt for the identified URL class using the generative Al model or performing a re-crawl cycle according to the previously executed set of URL discovery actions.” This language describes an intended use of the stored updated set of URL discovery actions and does not carry patentable weight.
Objections
Claims 1, 15, and 19 are objected to because of the following informality: the second limitation recites “generating a URL discovery action prompt for the identified URL class selected for URL discovery action optimization, the URL discovery action prompt including URL discovery instructions, class statistics for the identified URL class, and a previously executed set of URL discovery actions for the identified URL class,” and the first limitation recites “one or more previously executed sets of URL discovery actions for the identified URL class,” so the antecedent basis of “a previously executed set of URL discovery actions for the identified URL class” in the second limitation is unclear.
Claims 5, 6, and 20 are objected to because of the following informality: each claim recites “the previously executed set of URL discovery actions for the identified URL class” which has unclear antecedent basis due to the objection to this language in claims 1 and 19 for this reason above.
Rejections under 35 U.S.C. 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes without significantly more. Independent claims 1 and 19 each recites identifying an identified URL class from a set of URL classes based on the identified URL class having a discovery loss score that is above an optimization-selection threshold, wherein the optimization-selection threshold indicates that the identified URL class is to be selected for URL discovery action optimization, wherein the identified URL class includes a collection of URLs, and wherein the discovery loss score indicates a difference between expected discovery action scores and actual discovery action scores for one or more previously executed sets of URL discovery actions for the identified URL class; generating a URL discovery action prompt for the identified URL class selected for URL discovery action optimization, the URL discovery action prompt including URL discovery instructions, class statistics for the identified URL class, and a previously executed set of URL discovery actions for the identified URL class; generating an updated discovery loss score for the identified URL class by computing a difference between an expected discovery action score and an actual discovery action score for the updated set of URL discovery actions, wherein the expected discovery action score is received from the generative Al model for the updated set of URL discovery actions, and wherein the actual discovery action score is generated based on a count of matching URLs between the set of discovered URLs indicated in the URL discovery report and a set of previously identified URLs for the identified URL class stored in a statistical URL information log; and determining that the updated set of URL discovery actions is optimized for the identified URL class based on the updated discovery loss score satisfying an action-optimization threshold, wherein the action-optimization threshold indicates convergence between an expected discovery action score and an actual discovery action score for a corresponding set of URL discovery actions. Identifying a URL class from a set of URL classes is an evaluation and a mental process; and generating a URL discovery action prompt and generating an updated discovery loss score are each recited broadly and are mental processes accomplishable in the human mind or on paper. Generating an updated discovery loss score by computing a difference between an expected discovery action score and an actual discovery action score is a mathematical calculation, and determining the updated set of URL discovery actions is optimized is evaluating and a mental process. Each claim recites additional elements of receiving an updated set of URL discovery actions for the identified URL class from a generative Al model in response to providing the URL discovery action prompt for the identified URL class to the generative Al model; in response to providing the updated set of URL discovery actions to a web crawler system for execution, receiving a URL discovery report from the web crawler system, that includes a set of discovered URLs for the identified URL class discovered based on the web crawler system executing one or more actions from the updated set of URL discovery actions for the identified URL class; and based on the updated set of URL discovery actions being optimized, storing the updated set of URL discovery actions as an optimized URL discovery action set for the identified URL class for subsequent execution by the web crawler system to discover additional URLs for the identified URL class instead of generating an additional URL discovery action prompt for the identified URL class using the generative Al model or performing a re-crawl cycle according to the previously executed set of URL discovery actions. Receiving an updated set of URL discovery actions and receiving a URL discovery report are both data gathering or input steps and insignificant extra-solution activity. Storing updated set of URL discovery actions is also insignificant extra-solution activity. Claim 19 recites a processing system and a computer memory which are generic components of a computer. Examiner notes specification paragraphs 0001 and 0015 discuss drawbacks when analyzing outgoing URL links during an index recrawl cycle which can be resource-intensive, time-consuming, and unreliable, and which can lead to delays in discovering new URLs. Specification paragraphs 0016-0018 discuss how the invention addresses said drawbacks, improving efficiency by minimizing the number of recrawls for a website or domain and improving accuracy by determining the best URL discovery actions for a URL class from multiple possible actions. These paragraphs recite specific techniques which are not claimed, and the claimed steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, the receiving steps are recited broadly and amount to receiving data across a network per specification paragraph 0035-0036, 0142-0143, and figure 2 network 260, which is routine and conventional activity per the list of routine and conventional activities in MPEP 2106.05(d) part II. The processing system and computer memory are still generic components of a computer. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Independent claim 15 recites identifying an identified URL class from a set of URL classes based on the identified URL class having a discovery loss score that is above an optimization-selection threshold, wherein the optimization-selection threshold indicates that the identified URL class is to be selected for URL discovery action optimization, wherein the identified URL class includes a collection of URLs, and wherein the discovery loss score indicates a difference between expected discovery action scores and actual discovery action scores for one or more previously executed sets of URL discovery actions for the identified URL class; generating a URL discovery action prompt for the identified URL class selected for URL discovery action optimization, the URL discovery action prompt including URL discovery instructions, class statistics for the identified URL class, and the a previously executed set of URL discovery actions for the identified URL class; generating an actual discovery action score for the action based on comparing newly discovered URLs in the set of discovered URLs indicated in the URL discovery report with previously visited URLs from statistical URL information of the identified URL class; generating an updated discovery loss score for the identified URL class by computing a difference between the actual discovery action score and an expected discovery action score received from the generative AI model for the updated set of URL discovery actions; and determining that the updated set of URL discovery actions is optimized for the identified URL class based on the updated discovery loss score satisfying an action-optimization threshold, wherein the action-optimization threshold indicates convergence between an expected discovery action score and an actual discovery action score for a corresponding set of URL discovery actions. Identifying a URL class from a set of URL classes is an evaluation and a mental process; and generating a URL discovery action prompt and generating an actual discovery action score are each recited broadly and are mental processes accomplishable in the human mind or on paper. Generating an updated discovery loss score by computing a difference between the actual discovery action score and an expected discovery action score is a mathematical calculation, and determining the updated set of URL discovery actions is optimized is evaluating and a mental process. Each claim recites additional elements of receiving an updated set of URL discovery actions for the identified URL class from a generative Al model in response to providing the URL discovery action prompt for the identified URL class to the generative Al model; in response to providing the updated set of URL discovery actions to a web crawler system for execution, receiving a URL discovery report from the web crawler system, wherein the URL discovery report includes a set of discovered URLs for the identified URL class, and wherein the set of discovered URLs is discovered based on the web crawler system executing an action from the updated set of URL discovery actions for the identified URL class; and based on the updated set of URL discovery actions being optimized, storing the updated set of URL discovery actions as an optimized URL discovery action set for the identified URL class for subsequent execution by the web crawler system to discover additional URLs for the identified URL class instead of generating an additional URL discovery action prompt for the identified URL class using the generative AI model or performing a re-crawl cycle according to the previously executed set of URL discovery actions. Receiving an updated set of URL discovery actions and receiving a URL discovery report are both data gathering or input steps and insignificant extra-solution activity. Storing updated set of URL discovery actions is also insignificant extra-solution activity. Claim 19 recites a processing system and a computer memory which are generic components of a computer. Examiner notes specification paragraphs 0001 and 0015 discuss drawbacks when analyzing outgoing URL links during an index recrawl cycle which can be resource-intensive, time-consuming, and unreliable, and which can lead to delays in discovering new URLs. Specification paragraphs 0016-0018 discuss how the invention addresses said drawbacks, improving efficiency by minimizing the number of recrawls for a website or domain and improving accuracy by determining the best URL discovery actions for a URL class from multiple possible actions. These paragraphs recite specific techniques which are not claimed, and the claimed steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, the receiving steps are recited broadly and amount to receiving data across a network per specification paragraph 0035-0036 and figure 2 network 260, which is routine and conventional activity per the list of routine and conventional activities in MPEP 2106.05(d) part II. The processing system and computer memory are still generic components of a computer. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claim 2 recites wherein the URL discovery instructions direct the generative AI model to generate the updated set of URL discovery actions that follow an action syntax that includes a discovery condition, an action time, a URL count, an action frequency, and an expected discovery action score (URL discovery instructions are merely data, and generating updated URL discovery actions from the instructions is recited broadly and a mental process accomplishable in the human mind or on paper). Claim 3 recites wherein the class statistics for the identified URL class include a number of URLs in the identified URL class, a number of clicks, and URL examples (class statistics are data and a mental process accomplishable in the human mind or on paper). Claim 4 recites wherein the URL examples include positive URL examples and random URL examples of the identified URL class (URL examples are data and a mental process accomplishable in the human mind or on paper). Claim 5 recites wherein the previously executed URL discovery actions for the identified URL class include: previous URL discovery actions executed by the web crawler system based on previous sets of URL discovery actions provided by the generative AI model for the identified URL class (discovery actions are recited broadly and a mental process accomplishable in the human mind or on paper); and actual discovery action scores corresponding to the previous URL discovery actions determined based on results of the previous URL discovery actions being executed (discovery action scores are data and a mental process accomplishable in the human mind or on paper).
Claim 20 recites limitations from claims 2, 3, and 5, which are each mental processes as shown above. Claim 6 recites wherein the previously executed set of URL discovery actions for the identified URL class include the discovery loss score for the identified URL class determined based on the actual discovery action scores of the previous URL discovery actions (URL discovery actions including a discovery loss score is data and a mental process accomplishable in the human mind or on paper). Claim 7 recites determining that a number of URL discovery actions in the updated set of URL discovery actions exceeds a URL discovery action threshold (determining is recited broadly and a mental process); selecting a subset of URL discovery actions for the identified URL class from the set of URL discovery actions for the identified URL class based on the URL discovery action threshold being exceeded (selecting is evaluating and a mental process); providing the subset of URL discovery actions for the identified URL class to the web crawler system (providing discovery actions is providing data and recited broadly, amounts to sending data across a network and is routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II); and receiving the set of discovered URLs discovered by the web crawler system executing the subset of URL discovery actions (receiving discovered URLs is receiving data and recited broadly, amounts to receiving data across a network, which is routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II).
Claim 8 recites determining that a number of URLs in the collection of URLs for the identified URL class exceeds a URL count included in an action from the subset of URL discovery actions (determining that a number exceeds a count is evaluating and a mental process); selecting a random subset of URLs for the identified URL class from the collection of URLs for the identified URL class based on the URL count for the action being exceeded (selecting a subset of URLs is evaluating and a mental process); and providing the action and the random subset of URLs to the web crawler system for discovering new URLs within the random subset of URLs by following the action (providing the action and a subset of URLS is an output step and recited broadly, amounts to sending data across a network, which is routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II). Claim 9 recites receiving the URL discovery report from the web crawler system that includes a set of newly discovered URLs identified from executing an action from the subset of URL discovery actions (receiving a report is receiving data and recited broadly, amounts to receiving data across a network, which is routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II); identifying a set of previously identified URLs for the identified URL class from a statistical URL information log (identifying URLs is evaluating and a mental process); and determining an actual discovery action score for the action based on the URL discovery report and the set of previously identified URLs for the identified URL class (determining a score is evaluating and a mental process).
Claim 10 recites wherein determining the actual discovery action score for the action includes: determining a number of clicks for the set of newly discovered URLs based on click counts included in the set of previously identified URLs for the identified URL class (determining a number of clicks is evaluating and a mental process); determining a number of unfound URLs based on comparing the set of newly discovered URLs to the set of previously identified URLs for the identified URL class (determining unfound URLs is evaluating and a mental process); and generating the actual discovery action score for the action based on the number of clicks for the set of newly discovered URLs, the number of unfound URLs, and a number of crawls of the action (generating the actual discovery action score is recited broadly and a mental process accomplishable in the human mind or on paper). Claim 11 recites storing the action and the actual discovery action score in a URL discovery action datastore fir future use by the web crawler system (storing data is routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II). Claim 12 recites receiving, from the generative AI model, an expected discovery action score for the identified URL class based on the updated set of URL discovery actions determined for the identified URL class (receiving an expected discovery action score is receiving data and recited broadly, amounts to receiving data across a network, which is routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II).
Claim 13 recites re-identifying, after generating an initial updated discovery loss score for the identified URL class, the identified URL class from the set of URL classes based on the initial updated discovery loss score of the identified URL class satisfying the optimization-selection threshold (identifying a URL class is evaluating and a mental process); generating an additional URL discovery action prompt for the identified URL class, the additional URL discovery action prompt including the URL discovery instructions, class statistics for the identified URL class updated based on the updated set of URL discovery actions, and the previously executed set of URL discovery actions for the identified URL class, wherein the previously executed set of URL discovery actions includes one or more previously executed URL discovery actions and actual discovery action scores corresponding to the one or more previously executed URL discovery actions (generating an action prompt is generating data and a mental process accomplishable in the human mind or on paper); and providing the additional URL discovery action prompt to the generative AI model to generate an additional updated set of URL discovery actions for the identified URL class (providing an action prompt is recited broadly and amounts to sending data across a network, which is routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II). Claim 14 recites determining that the identified URL class has converged based on the updated discovery loss score being within a convergence threshold of the discovery loss score (determining is recited broadly and evaluating and a mental process); updating the identified URL class with an updated collection of URLs that differs from the collection of URLs (updating a class with URLs is storing the URLs which is routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II); identifying an additional action for the identified URL class stored in a URL discovery action datastore without providing a URL discovery action prompt to the generative AI model (identifying is evaluating and a mental process); and providing the additional action to the web crawler system for discovering an additional set of discovered URLs (providing the action is recited broadly and amounts to sending data across a network, which is routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II).
Claim 16 recites partitioning the collection of URLs into the identified URL class based on URLs in the collection of URLs sharing a common website, domain, or country (partitioning URLs is recited broadly and is a mental process accomplishable in the human mind or on paper). Claim 17 recites wherein identifying the identified URL class from the set of URL classes includes: identifying the discovery loss score of the identified URL class and discovery loss scores of additional URL classes in a URL discovery action datastore (identifying scores is evaluating and a mental process); comparing the discovery loss score of the identified URL class with the discovery loss scores of the additional URL classes to determine that the discovery loss score of the identified URL class is equal to or greater than the discovery loss scores of the additional URL classes (comparing scores is evaluating and a mental process); and selecting the identified URL class based on the discovery loss score of the identified URL class (selecting a URL class is evaluating and a mental process). Claim 18 recites providing the URL discovery action prompt to the generative AI model for the identified URL class (providing an action prompt is recited broadly and amounts to sending data across a network, which is routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II); and receiving a URL discovery action response that includes the updated set of URL discovery actions for the identified URL class (receiving an action response is recited broadly and amounts to receiving data across a network, which is routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II).
Relevant Prior Art
During his search for prior art, Examiner found the following references to be relevant to Applicant's claimed invention. Each reference is listed on the Notice of References form included in this office action:
Pham, Kien et al, “Learning to Discover Domain-Specific Web Content,” teaches domain-specific re-crawling to discover new content in a timely manner by learning page change patters to drive efficient re-crawling, does not teach identifying a URL class from a discovery loss score or an optimization-selection threshold, using an AI model for URL discovery actions, expected and actual discovery action scores (Abstract, Introduction pages 432-434); and
Wolf, J.L. “Optimal Crawling Strategies for Web Search Engines,” teaches optimizing the crawling/re-crawling process by determining optimal crawling frequencies and creating an optimal schedule for crawlers, does not teach identifying a URL class from a discovery loss score or an optimization-selection threshold, using an AI model for URL discovery actions, expected and actual discovery action scores (Abstract, Introduction pages 136-137).
Responses to Applicant’s Remarks
Regarding objections to claims 1, 2, and 6 for antecedent basis of “the previously executed URL discovery actions,” in view of Applicant’s amendments these objections are withdrawn. Examiner notes the objections to claims 1, 5-6, 15, 19-20 above for antecedent basis of “a previously executed set of URL discovery actions for the identified URL class” due to the present amendments. Regarding rejections of claims 1-20 under 35 U.S.C. 101 for reciting mental processes without significantly more, Applicant’s arguments have been considered but are not persuasive. On pages 15-16 of his Remarks Applicant discusses the problems described in specification paragraphs 0001 and 0015 ("paragraph [0001] explains that analyzing out-links during a recrawl cycle can be 'resource-intensive, time-consuming, and unreliable,' resulting in delays in discovering new URLs. Paragraph [0015] further explains that existing systems may randomly sample from lists of millions or billions of URLs, which is inefficient and wastes computing resources, and that many websites limit crawler traffic"). Applicant asserts the claims address these problems through a specific technical workflow (“amended claim 1 recites ‘identifying an identified URL class’ based on a discovery loss score ‘above an optimization-selection threshold,’ where the threshold indicates that the class is selected for ‘URL discovery action optimization.’ Claim 1 then recites generating a prompt including ‘class statistics’ and ‘a previously executed set of URL discovery actions,’ receiving ‘an updated set of URL discovery actions’ from the generative AI model, providing that updated set to ‘a web crawler system for execution,’ and receiving a URL discovery report including discovered URLs. Claim 1 further recites generating an updated discovery loss score by ‘computing a difference between an expected discovery action score and an actual discovery action score’ for the updated set, where the actual score is generated based on ‘a count of matching URLs’ between the discovered URLs and previously identified URLs stored in ‘a statistical URL information log.’”). Examiner disagrees and notes the amended claims still do not recite specific details regarding how the invention addresses the described problems. For example, while the claims recite identifying an identified URL class based on a discover loss score, the claims does not recite how the invention identifies the URL class or how the invention calculates the discovery loss score. The claims recite that the discovery loss score is above an optimization-selection threshold but do not recite how the invention calculates said threshold. The claims recite generating a prompt including class statistics and a previously executed set of URL discovery actions but do not recite how the invention generates said prompt. The claims do not recite how the invention generates the URL discovery report or calculates the updated loss score, in particular calculating an expected discovery action score and actual discovery action score. The claims further do not recite how the invention determines an updated set of URL discovery actions is optimized, how the invention calculates the action-optimization threshold. Most importantly, in the last limitation, the claims do not recite how/why the invention does not generate an additional prompt or perform a re-crawl cycle, which is described in specification paragraphs 0001 and 0015 as a problem in the technology of web discovery systems. Examiner believes the claim limitations identified as mental processes in the rejections above are conclusive statements and do not recite an improvement in a technology and thus the claims do not recite a practical application per MPEP 2106.04(d).
On pages 16-17 Applicant asserts “the amended claims are not reasonable characterized as mental processes” and “The claims require, among other things, providing an updated set of URL discovery actions to a web crawler system for execution, receiving a URL discovery report from the web crawler system, generating an actual discovery action score based on matching URLs between discovered URLs and previously identified URLs stored in a statistical URL information log, and storing an optimized URL discovery action set for subsequent execution by the web crawler system.” Examiner notes the claims do not actually recite “providing an updated set of URL discovery actions to a web crawler system for execution” but only recite receiving a URL discovery report in response to said providing. Furthermore, “receiving a URL discovery report from the web crawler system” and storing an optimized URL discovery action set for subsequent execution by the web crawler system” are each identified in the rejections above an input or data gathering step, and “generating an actual discovery action score based on matching URLs between discovered URLs and previously identified URLs stored in a statistical URL information log” is not claimed. The mantal process steps are the “identifying an identified URL class,” “generating a URL discovery action prompt,” “generating an updated discovery loss score,” and “determining that the updated set of URL discovery actions is optimized” limitations. The identifying and determining steps involve evaluating and are mental processes and the generating steps are recited broadly and a BRI for each includes use of a physical aid such as pen and paper. Thus Examiner believes these steps are mental processes.
On page 17 Applicant asserts “when considered as an ordered combination, the amended independent claims integrate any alleged abstract idea into a practical application.” Examiner disagrees as the claims as a whole still recites identifying a URL class, generating a prompt, receiving an updated set of URL discovery actions and a report of discovered URLs, generating an updated discovery loss score, somehow determining that the updated set of discovery actions is optimized, and storing the updated set of actions. The additional elements receiving the updated discovery actions as output from a generative AI model and receiving a report that includes a set of discovered URLs and the store the updated discovery actions. These are routine and conventional activities on a computer and, while each claim is implemented on a computer, the mental process steps are also conventional activities on a computer recite no further detail than that showing how the step is so implemented and thus applies the step on the computer or each step uses the computer as a tool. Thus Examiner does not believe the additional elements recite significantly more than the recited mental processes.
Conclusion
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.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 8/18/26
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