DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the application filed March 30, 2024.
Claims 1-20 are pending and are presenting for examination.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 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.
Specification
The disclosure is objected to because of the following informalities:
“processer” in paragraph [005], [006], and [047] should be --processor--.
Appropriate correction is required.
Claim Objections
Claims 13-20 are objected to because of the following informalities:
“processer” in claims 13, 14, 19, and 20 should be --processor--.
Claims 15-18 depend on the objected claims and inherit the same issue.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims 1, 13, and 20 recite:
A method for managing recaching of pages, the method comprising:
[a] extracting, by a processor, a set of attributes associated with a page;
[b] generating, by the processor, a set of first scores and a second score, wherein each of the set of first scores is generated based on an associated subset of the set of attributes and the second score is generated based on a set of network parameters; and
[c] determining, by the processor using a Machine Learning (ML) model, a recaching action for the page, based on the set of first scores and the second score.
Step 2A – prong 1:
The claims recite the limitation of:
A method for managing recaching of pages, the method comprising:
[a] extracting a set of attributes associated with a page (which can be done with human mind by observing… with the help of pen and paper);
[b] generating a set of first scores and a second score, wherein each of the set of first scores is generated based on an associated subset of the set of attributes and the second score is generated based on a set of network parameters (which can be done with human mind by observing… with the help of pen and paper); and
[c] determining a recaching action for the page, based on the set of first scores and the second score (which can be done with human mind by observing).
These limitations of steps [a] to [c] as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the function through observation, evaluation judgment and /or opinion, or even with the aid of pen and paper. Thus, this limitation recites and falls within the “Mental Processes” grouping of abstract ideas under Prong 1.
Step 2A – Prong 2:
Under Prong 2, this judicial exception is not integrated into a practical application. The claims recite the following additional elements “A system for managing recaching of pages, the system comprising: a processer; and a memory communicatively coupled to the processer, wherein the memory stores processor-executable instructions, which, on execution, causes the processer”, “A non-transitory computer-readable medium storing computer-executable instructions for managing recaching of pages, the stored computer-executable instructions, when executed by a processer, cause the processer to perform operation”, “by a processor”, and “by the processor using a Machine Learning (ML) model” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or mere computer components. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “A system for managing recaching of pages, the system comprising: a processer; and a memory communicatively coupled to the processer, wherein the memory stores processor-executable instructions, which, on execution, causes the processer”, “A non-transitory computer-readable medium storing computer-executable instructions for managing recaching of pages, the stored computer-executable instructions, when executed by a processer, cause the processer to perform operation”, “by a processor”, and “by the processor using a Machine Learning (ML) model” amount to no more than mere instructions, or generic computer/computer components to carry out the exception. The recitation of generic computer instruction and computer components to apply the judicial exception do not amount to significantly more, thus, cannot provide an inventive concept. Accordingly, the claims 1, 13, and 20 are not patent eligible under 35 USC 101.
Regarding to per claims 2 and 14, the limitation “further comprising detecting occurrence of a trigger event, wherein the set of attributes are extracted in response to the detection of occurrence of the trigger event” recites further mental processes. The additional elements of “the processor-executable instructions further cause the processer” are merely the use of a computer/instructions running on the computer to carry out the judicial exception, which is neither a practical application under prong 2, nor an inventive concept under step 2B.
Regarding to per claims 3 and 15, recite the limitation, “wherein the trigger event comprises a modification in the page, and wherein the modification comprises content alteration in the page, structure modification in the page, or updating of metadata associated with the page” merely define the plurality of event being analyzed. Since the human mind can reasonably analyze data as such as “a modification in the page,” any one of the limitations recited herein recite a mental process. Accordingly, claims 3 and 15 recite further mental processes
Regarding to per claims 4 and 16, recite the limitation, “wherein the set of attributes comprises at least one of: a periodically recorded frequency of modification for the page over a time period; a volume of traffic associated with the page over a plurality of time periods; a plurality of traffic sources associated with the page; a plurality of access patterns associated with the page; and data associated with users and devices accessing the page” merely define the plurality of attributes being analyzed. Since the human mind can reasonably analyze data as such as “a periodically recorded frequency of modification for the page” any one of the limitations recited herein recite a mental process. Accordingly, claims 4 and 16 recite further mental processes.
Regarding to per claims 5 and 17, the limitation of “wherein the set of first scores comprises a page volatility score generated based on a first subset selected of the set of attributes, and wherein the page volatility score is representative of frequency of modifications associated with the page over a time period” recites further mental process.
Regarding to per claims 6 and 18, the limitation of “wherein the set of first scores comprises a page priority score generated based on a second subset of the set of attributes, and wherein the page priority score is representative of weighted average of attributes in the second subset” recites further mental process.
Regarding to per claims 7 and 19, the limitation of “determining a time required to recache the page, based on a plurality of pre-recorded values of the set of network parameters” recites further mental process.
Regarding to claim 8, the limitation of “wherein the second score comprises a recaching execution score representative of the time required to recache the page” recites further mental process.
Regarding to claim 9, the limitation of “further comprising training the ML model, wherein the training comprises: selecting a training dataset of pages, wherein a first recaching pattern for each of the training data set of pages is predetermined by a user based on the associated set of first scores and the second score; determining a second recaching pattern for each of the training dataset of pages, based on the associated set of first scores and the second score; comparing, for each of the training dataset of pages, the second recaching pattern with the first recaching pattern; and determining a degree of accuracy of the ML model based on the comparing” recites further mental process. The additional element of “by the ML model” and “performing reinforcement learning on the ML model, based on the degree of accuracy determined for the ML model” is merely the use of a computer/instructions running on the computer to carry out the judicial exception, which is neither a practical application under prong 2, nor an inventive concept under step 2B.
Regarding to claim 10, the limitation of “wherein determining the recaching action comprises determining a cumulative score for the page, and wherein the recaching action comprises one of: recaching the page when the cumulative score is above a first predefined threshold; scheduling recaching of the page at a predetermined time, when the cumulative score is less than equal to the first predefined threshold and above a second predefined threshold; and invalidating of an existing cache page when the cumulative score is less than equal to the second predefined threshold” recites further mental process. The additional element of “by the ML model” is merely the use of a computer/instructions running on the computer to carry out the judicial exception, which is neither a practical application under prong 2, nor an inventive concept under step 2B.
Regarding to claim 11 does not include any additional abstract idea, however, recites the additional element of “wherein the ML model determines a cumulative score for each of a plurality of pages, wherein the plurality of pages comprises the page” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or mere computer components, which is neither a practical application under prong 2, nor an inventive concept under step 2B.
Regarding to claim 12, the limitation of “further comprising: determining a sequence of transmitting the recaching action determined for each of the plurality of pages, based on the cumulative score associated with each of the plurality of pages” recites further mental process. Furthermore, the additional element of “transmitting the recaching action determined for each of the plurality of pages in accordance with the determined sequence” which is nothing more than insignificant extra solution activity which is not a practical application under prong 2. Under step 2B, the courts have identified functions such as gathering, displaying, updating, transmitting and storing data as well-understood, routine, conventional activity, thus do not amount to significantly more than the judicial exception. See MPEP 2106.05(d).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8 and 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over Malcolm et al. (US 6128701 A, hereinafter Malcolm) in view of WHITE et al. (US 20250217420 A1, hereinafter White).
As per claims 1, 13, and 20, Malcolm discloses a method for managing recaching of pages – (E.g., One aspect of the invention is to automatically refresh the information maintained in the cache—see at least col. 1: 47-49), the method comprising:
extracting, by a processor, a set of attributes associated with a page – (e.g., web object—set of attributes-- “EUI is the estimated mean interval between updates to the web object 114…the cache 110 estimates EUI in response to the times of actual updates of the web object 114” – see at least col. 5: 30-38, “where EAI is the estimated mean interval between requests for the web object 114 i at the cache 110 ... the cache 110 estimates EAI in response to the request-times of actual requests for the web object 114” – see at least col. 5: 49-55, and “the cache 110 estimates a value for EUI in response to the type determined for the web object 114” – see at least col. 6: 31-33);
generating, by the processor, a set of first scores and a second score, wherein each of the set of first scores is generated based on an associated subset of the set of attributes and the second score is generated based on a set of network parameters – (E.g., “determine Psi (t) – first score--in response to EUI” and “to determine Pri (h) – second score--in response to EAI” – and “determining the load duration for the web object 114…actual connection time to the server device 130 for this load or reload, and …actual transmission time to the server device 130 for this load or reload…estimated connection time to the server device 130, and…estimated transfer time of the web object 114 from the server device --see at least col. 5: 39-40, 54-55, and col. 8: 3-31); and
determining a recaching action for the page, based on the set of first scores and the second score—(E.g., “At a step 212, the cache 110 determines which of the web objects 114 to refresh. The cache 110 performs the step 212 by determining the probabilities Psi (t) and Pri (h) for each web object 114 i and selecting for refresh the web object 114 i with the largest product Pi (current time, current request-time)” – see at least col. 6: 60-65, Fig. 2, and associated text).
It is to note that while Malcolm discloses determining a recaching action for the page based on the set of first scores and the second score – see Malcolm, at least col. 6: 60-65, Fig. 2, and associated text, but does not explicitly disclose; however, White, in an analogous art, discloses that the determining is by the processor using a Machine Learning (ML) model—
(e.g., using machine learning algorithm to recaching the webpages as such “the execution may continue with S230 based on a refresh policy of the cache. The refresh policy includes a plurality of rules to trigger crawling of the webpage data to refresh the cache with respect to the webpage. The plurality of rules determines frequency and/or timing, identify duplicate webpages, and the like, and more, based on at least one algorithm such as a machine learning algorithm to tailor for the webpage (e.g., domain, URL, etc.). In an embodiment, the refresh policy is updated through a learning phase and/or concurrent learning with usage to enable adaptive caching. It should be noted that execution of S230, to crawl webpage data from the requested webpage, may be performed even when cached crawled data exists in the cache based on its refresh policy. The refresh policy may also trigger a request for webpage data (S210), which leads to crawling of webpage data. The refresh policy enables the CCS (e.g., the CCS 120, FIG. 1) to store “fresh” webpage data that is current in the rapidly changing digital environment. It should be further noted that the adaptive refresh policy of the cache enables efficient refreshing and crawling of webpage data that reduces cache
memory and processing.” – See White, at least 0027, 0058, Fig. 2, and associated text).
Thus, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to have incorporated White’s teaching into Malcolm’s teaching for further optimize and enable efficient extracting of webpage data that reduces cache memory and processing as seen in White (e.g., 0027 and 0058).
Further regarding to claim 13, Malcolm discloses a system – (e.g., device 120, Fig. 1, -- see at least col. 3: 45-57, Fig. 1, and associated text) for managing recaching of pages, the system comprising: a processer; and a memory communicatively coupled to the processer, wherein the memory stores processor-executable instructions, which, on execution, causes the processer to implement method steps as of claim 1 above.
Further regarding to claim 20, Malcolm discloses a non-transitory computer-readable medium – (see at least col. 3: 45-57, col. 4: 9-10, Fig. 1, and associated text) storing computer-executable instructions for managing recaching of pages, the stored computer-executable instructions, when executed by a processer, cause the processer to perform method steps as of claim 1 above.
As per claims 2 and 14, modified Malcolm with White discloses further comprising detecting occurrence of a trigger event, wherein the set of attributes are extracted in response to the detection of occurrence of the trigger event – (e.g., “EUI is the estimated mean interval between updates to the web object 114…the cache 110 estimates EUI in response to the times of actual updates of the web object 114” – see Malcolm, at least col. 5: 30-38, “where EAI is the estimated mean interval between requests for the web object 114 i at the cache 110 ... the cache 110 estimates EAI in response to the request-times of actual requests for the web object 114” – see Malcolm, at least col. 5: 49-55, and “the cache 110 estimates a value for EUI in response to the type determined for the web object 114” – see Malcolm, at least col. 6: 31-33).
As per claims 3 and 15, modified Malcolm with White discloses wherein the trigger event comprises a modification in the page, and wherein the modification comprises content alteration in the page, structure modification in the page, or updating of metadata associated with the page--(e.g., EUI may be updated responsive to (1) the last time at which the web object 114 was actually updated, (2) the amount of time actually passed since that update, and (3) any earlier estimate for EUI. – see Malcolm, at least col. 5: 30-38, 49-55, col. 6: 31-33 and col. 7: 27-30).
As per claims 4 and 16, modified Malcolm with White discloses wherein the set of attributes comprises at least one of: a periodically recorded frequency of modification for the page over a time period; a volume of traffic associated with the page over a plurality of time periods; a plurality of traffic sources associated with the page; a plurality of access patterns associated with the page; and data associated with users and devices accessing the page – (e.g., web object—set of attributes-- “EUI is the estimated mean interval between updates to the web object 114…the cache 110 estimates EUI in response to the times of actual updates of the web object 114” – see Malcolm, at least col. 5: 30-38, “where EAI is the estimated mean interval between requests for the web object 114 i at the cache 110 ... the cache 110 estimates EAI in response to the request-times of actual requests for the web object 114” – see Malcolm, at least col. 5: 49-55, and “the cache 110 estimates a value for EUI in response to the type determined for the web object 114” – see Malcolm, at least col. 6: 31-33).
As per claims 5 and 17, modified Malcolm with White discloses wherein the set of first scores comprises a page volatility score generated based on a first subset selected of the set of attributes, and wherein the page volatility score is representative of frequency of modifications associated with the page over a time period – (E.g., determines a probability Psi(t) – page volatility score --that the web object …at time t – see Malcolm, at least col. 4:34-38, 55-67 and col. 5: 1-7, and 39-40)
As per claims 6 and 18, modified Malcolm with White discloses wherein the set of first scores comprises a page priority score generated based on a second subset of the set of attributes, and wherein the page priority score is representative of weighted average of attributes in the second subset – (e.g., the web object 114 with the highest – priority--such product Pi (current time, current request-time) - see Malcolm, at least col. 4:34-38, 55-67 and col. 5: 1-7, and 39-40).
As per claims 7 and 19, modified Malcolm with White discloses further comprising determining a time required to recache the page, based on a plurality of pre-recorded values of the set of network parameters — (e.g., estimate load during for the web object – see Malcolm, at least col. 8: 3-32).
As to claim 8, modified Malcolm with White discloses wherein the second score comprises a recaching execution score representative of the time required to recache the page (e.g., load during for the web object – see Malcolm, at least col. 8: 3-32).
As to claim 10, modified Malcolm with White disclose wherein determining the recaching action comprises determining, by the ML model, a cumulative score for the page, and wherein the recaching action comprises one of: recaching the page when the cumulative score is above a first predefined threshold; scheduling recaching of the page at a predetermined time, when the cumulative score is less than equal to the first predefined threshold and above a second predefined threshold; and invalidating of an existing cache page when the cumulative score is less than equal to the second predefined threshold – (E.g., The cache 110 chooses to attempt to refresh the web object 114 with the highest such product Pi (current time, current request-time). The cache 110 automatically attempts to refresh web objects 114 until the cumulative probability of all products Pi (current time, current request-time) is less than a selected threshold value. In a preferred embodiment, the selected threshold value is between about 1% and about 5%. -- see Malcolm, at least col. 4:34-38, 55-67 and col. 5: 1-10, and 39-40).
As to claim 11, modified Malcolm with White disclose wherein the ML model determines a cumulative score for each of a plurality of pages, wherein the plurality of pages comprises the page – (E.g., The sum of such products Pi (current time, current request-time) for all web objects 114 i in the cache 110 is the cumulative probability that the next web object 114 requested by one of the client devices 120 will be served stale by the cache 110 – see Malcolm, at least col. 4:34-38, 55-67 and col. 5: 1-7, and 39-40).
As to claim 12 modified Malcolm with White disclose further comprising: determining a sequence of transmitting the recaching action determined for each of the plurality of pages, based on the cumulative score associated with each of the plurality of pages; and transmitting the recaching action determined for each of the plurality of pages in accordance with the determined sequence – (E.g., The cache 110 chooses to attempt to refresh the web object 114 with the highest such product Pi (current time, current request-time). The cache 110 automatically attempts to refresh web objects 114 until the cumulative probability of all products Pi (current time, current request-time) is less than a selected threshold value. In a preferred embodiment, the selected threshold value is between about 1% and about 5%. -- see Malcolm, at least col. 4:34-38, 55-67 and col. 5: 1-10, and 39-40).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Malcolm in view of White, and in further view of Ain et al. (US 20140075299 A1, hereinafter Ain).
As to claim 9, it is to note that while modified Malcolm with White discloses using machine learning algorithm to recaching the webpages – see White, at least 0027, 0058, Fig. 2, and associated text, but the modified does not explicitly disclose; however, Ain, in an analogous art, discloses further comprising training the ML model, wherein the training comprises: selecting a training dataset of pages, wherein a first recaching pattern for each of the training data set of pages is predetermined by a user based on the associated set of first scores and the second score—(E.g., selected document receiving from user – see at least 0050-0051, 0054, Fig. 2, and associated text) ; determining, by the ML model, a second recaching pattern for each of the training dataset of pages, based on the associated set of first scores and the second score --(E.g. extraction model (machine learning model) using annotation –training data --– see at least 0050-0051, 0054, Fig. 2, and associated text); comparing, for each of the training dataset of pages, the second recaching pattern with the first recaching pattern – (e.g., evaluate the selected document against the extract model using confidence score for each page as well as the matching pattern within page see at least 0050-0051, 0054, Fig. 2, and associated text) ; determining a degree of accuracy of the ML model based on the comparing; and performing reinforcement learning on the ML model, based on the degree of accuracy determined for the ML model --(e.g., evaluate the selected document against the extract model for how well the matching using confidence score for each page as well as the matching pattern within page and re-evaluate within the exact model –machine learning model-- see at least 0050-0052, 0054, Fig. 2, and associated text).
Thus, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to have incorporated Ain’ s teaching into modified teaching of Malcolm with White for further optimize and enable validated the webpage; accordingly, promote user’s friendly and speeding the training process as seen in Ain (e.g., 0002-0004).
Conclusion
The prior art made of record and not relied upon (cited on 892 form) is considered pertinent to application disclosure.
Guy et al. (US-20190347359-A1) disclose processing web crawling queries involves determining web crawling priority score.
Smola et al. (US-20120158740-A1) discloses caching web documents.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARINA LEE whose telephone number is (571)270-1648. The examiner can normally be reached Monday to Friday (8 am to 4: 30 pm ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung S. Sough can be reached on (571)-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARINA LEE/Primary Examiner, Art Unit 2192