DETAILED ACTION
This office action is in response to the communication filed on March 23, 2026. Claims 1-19 and 21 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/23/25 has been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a thread monitoring module configured to monitor a main thread corresponding to a target webpage” in claim 14.
“a webpage index acquisition module configured to generate webpage index information of the target webpage according to an end time point of a first long task and a thread task monitored in a preset time window when the first long task in the main thread ends, wherein a start time point of the preset time window is the end time point of the first long task” in claim 14.
“the webpage index acquisition module is configured to: when a second long task is monitored in the preset time window, update the second long task to the first long task, and continue to generate the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window” in claim 15.
“the webpage index acquisition module is configured to: acquire a number of network resource requests monitored in the preset time window; if the number of the network resource requests is less than a preset request threshold, generate the webpage index information of the target webpage based on the end time point of the first long task” in claim 16.
“wherein the webpage index acquisition module is configured to determine the end time point of the first long task as a time to interactive of the target webpage” in claim 17.
“wherein the thread monitoring module is configured to: detect an opening operation for the target webpage; when the opening operation for the target webpage is detected, acquire a first meaningful paint of the target webpage; and start to monitor the main thread corresponding to the target webpage after the first meaningful paint” in claim 18.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-19 and 21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In independent claims 1, 14, and 19, the feature “wherein a start time point of the preset time window is the end time point of the first long task” is indefinite because it is not clear what the end time point of the preset time window is.
In dependent claims 2, 15, and 21, the feature “updating the second long task to the first long task” is indefinite because it is not clear how a second long task is being updated to a first long task.
Dependent claims 2-13, 15-18 and 21 inherit the same deficiencies of their base claims, therefore, they are also indefinite.
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 1-19 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1:
Independent claims 1, 14, and 19 respectively recite a webpage index information acquisition method, a webpage index information acquisition apparatus, and a computer device, which are directed to a statutory category such as a process, machine, or an article of manufacture.
At step 2A, prong one:
Independent claim 1 and similarly independent claims 14 and 19 recites the limitations:
“monitoring a main thread corresponding to a target webpage”;
A person can mentally or using a pen and paper monitor a main thread corresponding to a target webpage.
“if a first long task in the main thread ends, generating webpage index information of the target webpage according to an end time point of the first long task and a thread task monitored in a preset time window, wherein a start time point of the preset time window is the end time point of the first long task”;
A person can mentally or using a pen and paper observe if a first long task in a monitored main thread corresponding to a target webpage has ended, and in response to the observation the person can mentally or using a pen and paper generate webpage index information of the target webpage according to an end time point of the first long task and a thread task monitored in a preset time window, wherein a start time point of the preset time window is the end time point of the first long task.
The limitations, as recited above, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
At step 2A, prong two:
This judicial exception is not integrated into a practical application.
There are no additional elements in claim 1 that would result in integrating the judicial exception into a practical application
The additional elements “a webpage index information acquisition apparatus, wherein the apparatus comprises:”, “a thread monitoring module configured to monitor”, and “a webpage index acquisition module configured to generate” in the steps in claim 14 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “a computer device, wherein the computer device comprises: one or more processors; a memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement steps of:” in the steps in claim 19 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not
integrate the abstract idea into a practical application, even viewing the claims a whole,
because it does not impose any meaningful limits on practicing the abstract idea.
At step 2B:
Independent claims 1, 14, and 19 recite the same additional elements as identified in step 2A prong two above. These additional elements are not sufficient to amount to significantly more than the judicial exception.
Therefore, the claims are directed to an abstract idea and are not patent eligible.
Dependent claim 2 and similarly dependent claims 15 and 21 recite additional limitations, such as:
“wherein the thread task comprises a long task”;
This limitation is directed to the same abstract idea under the mental processes grouping as independent claims 1, 14, and 19, because a person can mentally or using a pen and paper observe if a first long task in a monitored main thread corresponding to a target webpage has ended, and in response to the observation the person can mentally or using a pen and paper generate webpage index information of the target webpage according to an end time point of the first long task and another long task monitored in a preset time window, wherein a start time point of the preset time window is the end time point of the first long task, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
the generating the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window comprises:
“if a second long task is monitored in the preset time window, updating the second long task to the first long task, and continuing to generate the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1, 14, and 19, because a person can mentally or using a pen and paper generate a webpage index information of a target webpage according to an end time point of a first long task and a thread task monitored in a preset time window by mentally or using a pen and paper updating a second long task to the first long task, if the second long task is monitored in the preset time window, and continuing to mentally or using a pen and paper generating the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 3 and similarly dependent claim 16 recite additional limitations, such as:
“wherein the thread task comprises a webpage resource request”;
This limitation is directed to the same abstract idea under the mental processes grouping as independent claims 1 and 14, because a person can mentally or using a pen and paper observe if a first long task in a monitored main thread corresponding to a target webpage has ended, and in response to the observation the person can mentally or using a pen and paper generate webpage index information of the target webpage according to an end time point of the first long task and a webpage resource request monitored in a preset time window, wherein a start time point of the preset time window is the end time point of the first long task, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
the generating the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window comprises:
“acquiring a number of network resource requests monitored in the preset time window”, which is a step of acquiring or retrieving data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
“if the number of the network resource requests is less than a preset request threshold, generating the webpage index information of the target webpage based on the end time point of the first long task”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1 and 14, because a person can mentally or using a pen and paper generate a webpage index information of a target webpage according to an end time point of a first long task and a thread task monitored in a preset time window by mentally or using a pen and paper generating the webpage index information of the target webpage based on the end time point of the first long task if a number of network resource requests is less than a preset request threshold, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 4 and similarly dependent claim 17 recite additional limitations, such as:
wherein the generating the webpage index information of the target webpage based on the end time point of the first long task comprises:
“determining the end time point of the first long task as a time to interactive of the target webpage”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1 and 14, because a person can mentally or using a pen and paper generate a webpage index information of a target webpage according to an end time point of a first long task by mentally or using a pen and paper determining the end time point of the first long task as a time to interactive of the target webpage, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 5 and similarly dependent claim 18 recite additional limitations, such as:
wherein the monitoring the main thread corresponding to the target webpage comprises:
“detecting an opening operation for the target webpage;
when the opening operation for the target webpage is detected, acquiring a first meaningful paint of the target webpage;
starting to monitor the main thread corresponding to the target webpage after the first meaningful paint”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1 and 14, because a person can mentally or using a pen and paper monitor a main thread corresponding to a target webpage by mentally or using a pen and paper detecting an opening operation for the target webpage, by mentally or using a pen and paper acquiring a first meaningful paint of the target webpage when the opening operation for the target webpage is detected, and by mentally or using a pen and paper starting to monitor the main thread corresponding to the target webpage after the first meaningful paint, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 recites additional limitations, such as:
“wherein the acquiring the first meaningful paint of the target webpage comprises:
acquiring a site type of the target webpage;
determining a target page element from page elements of the target webpage based on the site type of the target webpage;
determining the first meaningful paint of the target webpage according to a loading time point of the target page element”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper acquire a first meaningful paint of a target webpage by mentally or using a pen and paper acquiring a site type of the target webpage, by mentally or using a pen and paper determining a target page element from page elements of the target webpage based on the site type of the target webpage, and by mentally or using a pen and paper determining the first meaningful paint of the target webpage according to a loading time point of the target page element, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 7 recites additional limitations, such as:
“wherein before generating the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window, the method further comprises:
acquiring a webpage loading time between the first meaningful paint and the end time point of the first long task;
determining a window length of the preset time window according to the webpage loading time”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper acquire a webpage loading time between a first meaningful paint and an end time point of a first long task and mentally or using a pen and paper determine a window length of a preset time window according to a webpage loading time before mentally or using a pen and paper generating the webpage index information of a target webpage according to the end time point of the first long task and a thread task monitored in the preset time window, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 8 recites additional limitations, such as:
“wherein the webpage loading time between the first meaningful paint and the end time point of the first long task is inversely proportional to the window length of the preset time window”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper acquire a webpage loading time between a first meaningful paint and an end time point of a first long task that is inversely proportional to a window length of a preset time window, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 9 recites additional limitations, such as:
“wherein the acquiring the first meaningful paint of the target webpage comprises:
after detecting the opening operation for the target webpage, real-time monitoring of loading and rendering of page elements of the target webpage;
when monitoring a time point at which all page elements of the target webpage are loaded and rendered, determining the time point at which all page elements are loaded and rendered as the first meaningful paint of the target page”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper acquire a first meaningful paint of a target webpage by mentally or using a pen and paper monitoring loading and rendering of page elements of a target webpage after detecting an opening operation for the target webpage and by mentally or using a pen and paper determining a time point at which all page elements are loaded and rendered as a first meaningful paint of the target page when mentally or using a pen and paper monitoring the time point at which all page elements of the target webpage are loaded and rendered, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 10 recites additional limitations, such as:
“when the number of the network resource requests monitored in the preset time window is greater than or equal to the preset request threshold, continuing to monitor the main thread corresponding to the target webpage”;
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper continue to monitor a main thread corresponding to a target webpage when a number of network resource requests monitored in a preset time window is greater than or equal to a preset request threshold, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
“when a next long task is monitored and the next long task ends, taking an end time point of the next long task as a start time point, monitoring the thread task again in the preset time window”;
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper take an end time point of a next long task as a start time point when the next long task is monitored and the next long task ends and the person can mentally or using a pen and paper monitor a thread task again in a preset time window, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
generating the webpage index information of the target webpage according to the end time point of the next long task and a task status of the thread task monitored in the preset time window.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper generate a webpage index information of a target webpage according to an end time point of a next long task and a task status of a thread task monitored in a preset time window, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 11 recites additional limitations, such as:
“wherein the site type comprises at least a search webpage type, a shopping webpage type, and an advisory information webpage type; the target page element in the target webpage of the search webpage type is a search box; the target page element in the target webpage of the shopping webpage type is a product image on a first screen; the target page element in the target webpage of the advisory information webpage type is a banner image on the first screen”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper determining a target page element from page elements of a target webpage based on a site type of the target webpage, wherein the site type comprises at least a search webpage type, a shopping webpage type, and an advisory information webpage type, the target page element in the target webpage of the search webpage type is a search box, the target page element in the target webpage of the shopping webpage type is a product image on a first screen, and the target page element in the target webpage of the advisory information webpage type is a banner image on the first screen, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 12 recites additional limitations, such as:
wherein the generating the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window if the first long task in the main thread ends comprises:
“recording the end time point of the first long task when the first long task ends;
taking the end time point of the first long task as the start time point, starting to monitor the thread task in the preset time window, and determining whether the preset time window is an idle time window according to a task status of the thread task in the preset time window;
when it is determined that the preset time window is the idle time window, generating the webpage index information of the target webpage based on the end time point of the first long task”
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper generating a webpage index information of a target webpage according to an end time point of a first long task and a thread task monitored in a preset time window if the first long task in a main thread ends by mentally or using a pen and paper recording the end time point of the first long task when the first long task ends, by mentally or using a pen and paper taking the end time point of the first long task as a start time point, by mentally or using a pen and paper starting to monitor the thread task in the preset time window, by mentally or using a pen and paper determining whether the preset time window is an idle time window according to a task status of the thread task in the preset time window, and by mentally or using a pern and paper generating the webpage index information of the target webpage based on the end time point of the first long task when it is determined that the preset time window is the idle time window, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 13 recites additional limitations, such as:
“when the number of the network resource requests monitored in the preset time window is greater than or equal to the preset request threshold, continuing to monitor the main thread corresponding to the target webpage”;
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper continue to monitor a main thread corresponding to a target webpage when a number of network resource requests monitored in a preset time window is greater than or equal to a preset request threshold, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
“when a next long task is monitored and the next long task ends, taking an end time point of the next long task as the start time point, monitoring the thread task again in the preset time window, and generating the webpage index information of the target webpage according to the end time point of the next long task and a task status of the thread task monitored in the preset time window”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper take an end time point of a next long task as a start time point, the person can mentally or using a pen and paper monitor a thread task again in a preset time window, and the person can mentally or using a pen and paper generate a webpage index information of a target webpage according to the end time point of the next long task and a task status of the thread task monitored in the preset time window when the next long task is monitored and the next long task ends, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, dependent claims 2-13, 15-18, and 21 are also directed to abstract idea without significantly more and are not patent eligible.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 10, 12-16, 19, and 21 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jain (US Pub 2020/0213211).
With respect to claim 1, Jain discloses a webpage index information acquisition method, wherein the method comprises:
monitoring a main thread corresponding to a target webpage (Jain in [0035] and [0045] discloses monitoring to generate object-level stat data for webpage objects, object-level stat data indicates stats for each of a plurality of webpage objects that are accessed or downloaded, feeding the stat data to estimation models to estimate starts and ends of webpage downloads, webpage made up of one or more web objects, requesting resource from a webpage, transmitting portions of the requested resource; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; and
if a first long task in the main thread ends, generating webpage index information of the target webpage according to an end time point of the first long task and a thread task monitored in a preset time window, wherein a start time point of the preset time window is the end time point of the first long task (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0008] discloses predicting quality of experience performance of an application downloading a webpage over a network, estimating a quality of experience value based on estimated performance metric; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated).
With respect to claim 2, Jain discloses the method according to claim 1, wherein the thread task comprises a long task (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated);
the generating the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window comprises: if a second long task is monitored in the preset time window, updating the second long task to the first long task, and continuing to generate the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated).
With respect to claim 3, Jain discloses the method according to claim 1, wherein the thread task comprises a webpage resource request (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated);
the generating the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window comprises: acquiring a number of network resource requests monitored in the preset time window (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated);
if the number of the network resource requests is less than a preset request threshold, generating the webpage index information of the target webpage based on the end time point of the first long task (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated).
With respect to claim 10, Jain in view of Webber discloses the method according to claim 3, wherein the method further comprises:
when the number of the network resource requests monitored in the preset time window is greater than or equal to the preset request threshold, continuing to monitor the main thread corresponding to the target webpage (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated);
when a next long task is monitored and the next long task ends, taking an end time point of the next long task as a start time point, monitoring the thread task again in the preset time window (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated);
generating the webpage index information of the target webpage according to the end time point of the next long task and a task status of the thread task monitored in the preset time window (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated).
With respect to claim 12, Jain discloses the method according to claim 1, wherein the generating the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window if the first long task in the main thread ends (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated) comprises:
recording the end time point of the first long task when the first long task ends (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated);
taking the end time point of the first long task as the start time point, starting to monitor the thread task in the preset time window, and determining whether the preset time window is an idle time window according to a task status of the thread task in the preset time window (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] and [0054] discloses time window of a fixed number of seconds, indicating starts and ends of webpage loads and generating output for each input of a fixed time window, indicate estimation of an on-going webpage load, indicating estimation of not being in a webpage load or periods of relative inactivity or non-browsing traffic; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated);
when it is determined that the preset time window is the idle time window, generating the webpage index information of the target webpage based on the end time point of the first long task (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] and [0054] discloses time window of a fixed number of seconds, indicating starts and ends of webpage loads and generating output for each input of a fixed time window, indicate estimation of an on-going webpage load, indicating estimation of not being in a webpage load or periods of relative inactivity or non-browsing traffic; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated);.
With respect to claim 13, Jain discloses the method according to claim 3, wherein the method further comprises:
when the number of the network resource requests monitored in the preset time window is greater than or equal to the preset request threshold, continuing to monitor the main thread corresponding to the target webpage (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated);
when a next long task is monitored and the next long task ends, taking an end time point of the next long task as the start time point, monitoring the thread task again in the preset time window, and generating the webpage index information of the target webpage according to the end time point of the next long task and a task status of the thread task monitored in the preset time window (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated).
With respect to claim 14, Jain discloses a webpage index information acquisition apparatus (Jain in [0120] and [0127] and in Figure 15 discloses an apparatus comprising modules), wherein the apparatus comprises:
a thread monitoring module configured to monitor a main thread corresponding to a target webpage (Jain in [0035] and [0045] discloses monitoring to generate object-level stat data for webpage objects, object-level stat data indicates stats for each of a plurality of webpage objects that are accessed or downloaded, feeding the stat data to estimation models to estimate starts and ends of webpage downloads, webpage made up of one or more web objects, requesting resource from a webpage, transmitting portions of the requested resource; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0120] and [0127] and in Figure 15 discloses an apparatus comprising modules); and
a webpage index acquisition module configured to generate webpage index information of the target webpage according to an end time point of a first long task and a thread task monitored in a preset time window when the first long task in the main thread ends, wherein a start time point of the preset time window is the end time point of the first long task (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0008] discloses predicting quality of experience performance of an application downloading a webpage over a network, estimating a quality of experience value based on estimated performance metric; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; Jain in [0120] and [0127] and in Figure 15 discloses an apparatus comprising modules).
With respect to claim 15, Jain discloses the apparatus according to claim 14, wherein the thread task comprises a long task; and the webpage index acquisition module is configured to: when a second long task is monitored in the preset time window, update the second long task to the first long task, and continue to generate the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated).
With respect to claim 16, Jain discloses the apparatus according to claim 14, wherein the thread task comprises a webpage resource request; and the webpage index acquisition module is configured to: acquire a number of network resource requests monitored in the preset time window; if the number of the network resource requests is less than a preset request threshold, generate the webpage index information of the target webpage based on the end time point of the first long task (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated).
With respect to claim 19, Jain discloses a computer device, wherein the computer device (Jain in [0120] and [0121] and in Figure 15 discloses a computer device) comprises:
one or more processors (Jain in [0120] and [0121] and in Figure 15 discloses a computer device comprising one or more processors);
a memory (Jain in [0120] and [0121] and in Figure 15 discloses a computer device comprising a memory); and
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement steps (Jain in [0108], [0117] and [0120] and in Figure 15 discloses one or more applications stored in memory and executed by the processor to perform functions) of:
monitoring a main thread corresponding to a target webpage (Jain in [0035] and [0045] discloses monitoring to generate object-level stat data for webpage objects, object-level stat data indicates stats for each of a plurality of webpage objects that are accessed or downloaded, feeding the stat data to estimation models to estimate starts and ends of webpage downloads, webpage made up of one or more web objects, requesting resource from a webpage, transmitting portions of the requested resource; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.); and
if a first long task in the main thread ends, generating webpage index information of the target webpage according to an end time point of the first long task and a thread task monitored in a preset time window, wherein a start time point of the preset time window is the end time point of the first long task (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0008] discloses predicting quality of experience performance of an application downloading a webpage over a network, estimating a quality of experience value based on estimated performance metric; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated).
With respect to claim 21, Jain discloses the computer device according to claim 19, wherein the thread task comprises a long task; if a second long task is monitored in the preset time window, the one or more applications are stored in the memory and configured to be executed by the processor to implement updating the second long task to the first long task, and continuing to generate the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated).
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain (US Pub 2020/0213211) in view of Idema (US Pub 2024/0054174).
With respect to claim 4, Jain discloses the method according to claim 3, wherein the generating the webpage index information of the target webpage based on the end time point of the first long task comprises:
determining the end time point of the first long task as a time…of the target webpage (Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; here Jain does not explicitly disclose time to interactive, but the Idema reference discloses the feature, as discussed below).
Jain discloses determining end time point of a first long task as a load time data of a target webpage, however, Jain does not explicitly disclose:
determining…a time to interactive of the…webpage;
The Idema reference discloses determining a time to interactive of a webpage (Idema in [0001] and [0017] discloses obtaining performance indicators for rendered web pages, performance indicators include an accessibility metric; Idema in [0018] and [0099] discloses an accessibility metric and a representation of a webpage giving rise to the metric, such as a particular score of the metric, are stored, a performance score is obtained as a weighted average of a plurality of individual component scores, individual component score includes time to interactive, which is the amount of time it takes for a page to become fully interactive).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Jain and Idema, to have combined Jain and Idema. The motivation to combine Jain and Idema would be to analyze performance of rendered web page by obtaining performance indicators for rendered web pages (Idema: [0001]).
With respect to claim 17, Jain discloses the apparatus according to claim 16, wherein the webpage index acquisition module is configured to determine the end time point of the first long task as a time…of the target webpage (Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; here Jain does not explicitly disclose time to interactive, but the Idema reference discloses the feature, as discussed below).
Jain discloses determining end time point of a first long task as a load time data of a target webpage, however, Jain does not explicitly disclose:
determining…a time to interactive of the…webpage;
The Idema reference discloses determining a time to interactive of a webpage (Idema in [0001] and [0017] discloses obtaining performance indicators for rendered web pages, performance indicators include an accessibility metric; Idema in [0018] and [0099] discloses an accessibility metric and a representation of a webpage giving rise to the metric, such as a particular score of the metric, are stored, a performance score is obtained as a weighted average of a plurality of individual component scores, individual component score includes time to interactive, which is the amount of time it takes for a page to become fully interactive).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Jain and Idema, to have combined Jain and Idema. The motivation to combine Jain and Idema would be to analyze performance of rendered web page by obtaining performance indicators for rendered web pages (Idema: [0001]).
Claim(s) 5, 7-9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain (US Pub 2020/0213211) in view of Webber (US Pub 2020/0396303).
With respect to claim 5, Jain discloses the method according to claim 1, wherein the monitoring the main thread corresponding to the target webpage comprises:
detecting an opening operation for the target webpage (Jain in [0039] and [0045] discloses webpage access/download application implemented as a web browser, requests are issued by a browser to a web server to request web objects, response from the server includes the web objects; Jain in [0048] and [0087] discloses stat monitor generating data for website objects detected, generating webpage load time and other quality metrics, in some instances return an estimated webpage load time or other performance metric for only a first webpage of a website and then ignore subsequent objects; Jain in [0107] discloses connections for one webpage load can carry over into the next page load, long open connections from previous website visit can be re-used for a current visit);
when the opening operation for the target webpage is detected, acquiring a first…of the target webpage (Jain in [0039] and [0045] discloses webpage access/download application implemented as a web browser, requests are issued by a browser to a web server to request web objects, response from the server includes the web objects; Jain in [0048] and [0087] discloses stat monitor generating data for website objects detected, generating webpage load time and other quality metrics, in some instances return an estimated webpage load time or other performance metric for only a first webpage of a website and then ignore subsequent objects; Jain in [0107] discloses connections for one webpage load can carry over into the next page load, long open connections from previous website visit can be re-used for a current visit; here Jain does not explicitly disclose a first meaningful paint, but the Webber reference discloses the feature, as discussed below);
starting to monitor the main thread corresponding to the target webpage after the first…(Jain in [0039] and [0045] discloses webpage access/download application implemented as a web browser, requests are issued by a browser to a web server to request web objects, response from the server includes the web objects; Jain in [0048] and [0087] discloses stat monitor generating data for website objects detected, generating webpage load time and other quality metrics, in some instances return an estimated webpage load time or other performance metric for only a first webpage of a website and then ignore subsequent objects; Jain in [0107] discloses connections for one webpage load can carry over into the next page load, long open connections from previous website visit can be re-used for a current visit).
Jain discloses monitoring access to webpages of a website, monitoring page loads of objects in the webpages of the website, and generating object level stat data including acquiring page load data of the webpage, however, Jain does not explicitly disclose:
acquiring a first meaningful paint…;
The Webber reference discloses acquiring a first meaningful paint (Webber in [0007] and [0028] discloses page speed metrics include a first meaningful paint metric that indicates a duration of time between a time at which a client device requests a page and a time at which the client device renders at least one of text, an image, or a canvas of the page at a display of the client device, network request data, page speed metrics, and data used to generate playback of user sessions can be indexed and stored, indexed data searched for particular attributes of user sessions, such as pages that took more than a threshold duration of time to load or pages that took more than a threshold duration for a first meaningful paint to complete; Webber in [0035] and [0045] discloses collecting event data during a user session, event data includes page speed metrics that show page timing milestones for each page that load during a user session, page speed metrics include a first meaningful paint metric indicating a duration of time between a time at which a page is requested and a time at which first meaningful paint occurs for the page, which can be an event that occurs at the moment when the biggest “above-the-fold” layout change has happened and when web fonts have loaded for the page, such as when the browser has rendered any text, image (including background images), non-white canvas, or SVG, first meaningful paint marks the moment at which users can start consuming content on a page).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Jain and Webber, to have combined Jain and Webber. The motivation to combine Jain and Webber would be to search for user sessions that include particular attributes by indexing and storing data related to user sessions (Webber: [0028]).
With respect to claim 7, Jain in view of Webber discloses the method according to claim 5, wherein before generating the webpage index information of the target webpage according to the end time point of the first long task and the thread task monitored in the preset time window (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; Webber in [0035] and [0045] discloses collecting event data during a user session, event data includes page speed metrics that show page timing milestones for each page that load during a user session, page speed metrics include a first meaningful paint metric indicating a duration of time between a time at which a page is requested and a time at which first meaningful paint occurs for the page), the method further comprises:
acquiring a webpage loading time between the first meaningful paint and the end time point of the first long task (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; Webber in [0035] and [0045] discloses collecting event data during a user session, event data includes page speed metrics that show page timing milestones for each page that load during a user session, page speed metrics include a first meaningful paint metric indicating a duration of time between a time at which a page is requested and a time at which first meaningful paint occurs for the page);
determining a window length of the preset time window according to the webpage loading time (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; Webber in [0035] and [0045] discloses collecting event data during a user session, event data includes page speed metrics that show page timing milestones for each page that load during a user session, page speed metrics include a first meaningful paint metric indicating a duration of time between a time at which a page is requested and a time at which first meaningful paint occurs for the page).
With respect to claim 8, Jain in view of Webber discloses the method according to claim 7, wherein the webpage loading time between the first meaningful paint and the end time point of the first long task is inversely proportional to the window length of the preset time window (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; Webber in [0035] and [0045] discloses collecting event data during a user session, event data includes page speed metrics that show page timing milestones for each page that load during a user session, page speed metrics include a first meaningful paint metric indicating a duration of time between a time at which a page is requested and a time at which first meaningful paint occurs for the page; Webber in [0067] discloses presenting real-time replay of user session based on events that occurred during the user session).
With respect to claim 9, Jain in view of Webber discloses the method according to claim 5, wherein the acquiring the first meaningful paint of the target webpage comprises:
after detecting the opening operation for the target webpage, real-time monitoring of loading and rendering of page elements of the target webpage (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; Webber in [0035] and [0045] discloses collecting event data during a user session, event data includes page speed metrics that show page timing milestones for each page that load during a user session, page speed metrics include a first meaningful paint metric indicating a duration of time between a time at which a page is requested and a time at which first meaningful paint occurs for the page; Webber in [0067] discloses presenting real-time replay of user session based on events that occurred during the user session);
when monitoring a time point at which all page elements of the target webpage are loaded and rendered, determining the time point at which all page elements are loaded and rendered as the first meaningful paint of the target page (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; Webber in [0035] and [0045] discloses collecting event data during a user session, event data includes page speed metrics that show page timing milestones for each page that load during a user session, page speed metrics include a first meaningful paint metric indicating a duration of time between a time at which a page is requested and a time at which first meaningful paint occurs for the page; Webber in [0067] discloses presenting real-time replay of user session based on events that occurred during the user session).
With respect to claim 18, Jain discloses the apparatus according to claim 14, wherein the thread monitoring module is configured to:
detect an opening operation for the target webpage (Jain in [0039] and [0045] discloses webpage access/download application implemented as a web browser, requests are issued by a browser to a web server to request web objects, response from the server includes the web objects; Jain in [0048] and [0087] discloses stat monitor generating data for website objects detected, generating webpage load time and other quality metrics, in some instances return an estimated webpage load time or other performance metric for only a first webpage of a website and then ignore subsequent objects; Jain in [0107] discloses connections for one webpage load can carry over into the next page load, long open connections from previous website visit can be re-used for a current visit);
when the opening operation for the target webpage is detected, acquire a first…of the target webpage (Jain in [0039] and [0045] discloses webpage access/download application implemented as a web browser, requests are issued by a browser to a web server to request web objects, response from the server includes the web objects; Jain in [0048] and [0087] discloses stat monitor generating data for website objects detected, generating webpage load time and other quality metrics, in some instances return an estimated webpage load time or other performance metric for only a first webpage of a website and then ignore subsequent objects; Jain in [0107] discloses connections for one webpage load can carry over into the next page load, long open connections from previous website visit can be re-used for a current visit; here Jain does not explicitly disclose a first meaningful paint, but the Webber reference discloses the feature, as discussed below); and
start to monitor the main thread corresponding to the target webpage after the first…(Jain in [0039] and [0045] discloses webpage access/download application implemented as a web browser, requests are issued by a browser to a web server to request web objects, response from the server includes the web objects; Jain in [0048] and [0087] discloses stat monitor generating data for website objects detected, generating webpage load time and other quality metrics, in some instances return an estimated webpage load time or other performance metric for only a first webpage of a website and then ignore subsequent objects; Jain in [0107] discloses connections for one webpage load can carry over into the next page load, long open connections from previous website visit can be re-used for a current visit).
Jain discloses monitoring access to webpages of a website, monitoring page loads of objects in the webpages of the website, and generating object level stat data including acquiring page load data of the webpage, however, Jain does not explicitly disclose:
acquiring a first meaningful paint…;
The Webber reference discloses acquiring a first meaningful paint (Webber in [0007] and [0028] discloses page speed metrics include a first meaningful paint metric that indicates a duration of time between a time at which a client device requests a page and a time at which the client device renders at least one of text, an image, or a canvas of the page at a display of the client device, network request data, page speed metrics, and data used to generate playback of user sessions can be indexed and stored, indexed data searched for particular attributes of user sessions, such as pages that took more than a threshold duration of time to load or pages that took more than a threshold duration for a first meaningful paint to complete; Webber in [0035] and [0045] discloses collecting event data during a user session, event data includes page speed metrics that show page timing milestones for each page that load during a user session, page speed metrics include a first meaningful paint metric indicating a duration of time between a time at which a page is requested and a time at which first meaningful paint occurs for the page, which can be an event that occurs at the moment when the biggest “above-the-fold” layout change has happened and when web fonts have loaded for the page, such as when the browser has rendered any text, image (including background images), non-white canvas, or SVG, first meaningful paint marks the moment at which users can start consuming content on a page).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Jain and Webber, to have combined Jain and Webber. The motivation to combine Jain and Webber would be to search for user sessions that include particular attributes by indexing and storing data related to user sessions (Webber: [0028]).
Claim(s) 6 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain (US Pub 2020/0213211) in view of Webber (US Pub 2020/0396303) and in further view of Bloem (US Pub 2015/0339754).
With respect to claim 6, Jain in view of Webber discloses the method according to claim 5, wherein the acquiring the first meaningful paint of the target webpage comprises:
acquiring a site…of the target webpage (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0072] discloses window width of a particular time; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; Webber in [0030] discloses a website is one or more resources associated with a domain name, website is a collection of web pages that contain text, imagers, multimedia content, and programming elements; Webber in [0035] and [0045] discloses collecting event data during a user session, event data includes page speed metrics that show page timing milestones for each page that load during a user session, page speed metrics include a first meaningful paint metric indicating a duration of time between a time at which a page is requested and a time at which first meaningful paint occurs for the page; Webber in [0067] discloses presenting real-time replay of user session based on events that occurred during the user session);
determining a target page element from page elements of the target webpage based on the site…of the target webpage (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; Webber in [0030] discloses a website is one or more resources associated with a domain name, website is a collection of web pages that contain text, imagers, multimedia content, and programming elements; Webber in [0035] and [0045] discloses collecting event data during a user session, event data includes page speed metrics that show page timing milestones for each page that load during a user session, page speed metrics include a first meaningful paint metric indicating a duration of time between a time at which a page is requested and a time at which first meaningful paint occurs for the page; Webber in [0067] discloses presenting real-time replay of user session based on events that occurred during the user session);
determining the first meaningful paint of the target webpage according to a loading time point of the target page element (Jain in [0005] and [0035] discloses monitoring webpage requests and subsequent requests, determining webpage load time once all objects on the webpage have finished loading, generating stat data for each of a plurality of objects accessed for download; Jain in [0036] and [0037] discloses estimating webpage load activity within a time spanned by a time window, incrementing the time window along a portion of a time horizon, providing stat data within the time window to an estimator model, estimating when a webpage load starts and when the load ends, estimating load duration, estimating whether the time window spans part of webpage download; Jain in [0046]-[0048] discloses monitoring each requested webpage object and generating object level stat data including request-response size and timing information, index of the object, time at which the object was requested, time when response started, time when response ended etc.; Jain in [0053] discloses time window of a fixed number of seconds; Jain in [0087] and [0108] discloses during monitoring a certain time window for a page load discarding long opens using filters based on a time threshold, filter can group stat data into frames having a fixed number of objects, as well as grouping by time window, increased gram size will encompass all objects within a webpage load; Jain in [0109] discloses quality of experience estimated using metrics additional to webpage load time, such as content load time and speed index; Jain in [0112] and [0113] discloses once a start and end of a webpage load are known or estimated, the total objects and bytes for that webpage load are determined, object index and byte index determined based on packet and object arrival timing within a webpage load duration, object index and byte index metrics defined upon knowing the object and byte arrival times, and the webpage load start and end times, values for object index and byte index metrics imply completion of objects/bytes of a webpage, index capturing visual aspects of page completion, after obtaining or estimating webpage load starts and ends and the object and byte arrival timestamps the object and byte index are estimated; Webber in [0030] discloses a website is one or more resources associated with a domain name, website is a collection of web pages that contain text, imagers, multimedia content, and programming elements; Webber in [0035] and [0045] discloses collecting event data during a user session, event data includes page speed metrics that show page timing milestones for each page that load during a user session, page speed metrics include a first meaningful paint metric indicating a duration of time between a time at which a page is requested and a time at which first meaningful paint occurs for the page; Webber in [0067] discloses presenting real-time replay of user session based on events that occurred during the user session).
Jain and Webber discloses acquiring a site of a target webpage and indexing data, however, Jain and Webber do not explicitly disclose:
acquiring a site type…;
The Bloem reference discloses acquiring a site type (Bloem in [0008] and [0059] discloses index any user selection of images, input text, product template, and/or specific products to build user morels and/or score content, information collected includes clicking on certain objects, adding objects to a shopping cart, dwell time associated with a page, object, and/or site, a product, a webpage, or spending extra time on a particular page or in front of an object; Bloem in [0060], [0135], and [0137] discloses user’s activity can be indexed, information can be classified, indexed, and/or stored according to a respective classification, classifications include a type associated with information about searchable content/items, information about products presented on an ecommerce site are indexed, information provided in conjunction with products are indexed, information about search terms are indexed and saved; Bloem in [0065], [0108], and [0152] discloses websites accessed by users include advertising banners, user can type text in a text input box, user can enter search terms in input box; Bloem in [0079] discloses shopping cart information fed as advertising banner with images as recommendations to users during a search session).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Jain, Webber, and Bloem, to have combined Jain, Webber, and Bloem. The motivation to combine Jain, Webber, and Bloem would be to deliver customized content using observed actions and/or behaviors (Bloem: [0004]).
With respect to claim 11, Jain in view of Webber and in further view of Bloem discloses the method according to claim 6, wherein the site type comprises at least a search webpage type, a shopping webpage type, and an advisory information webpage type; the target page element in the target webpage of the search webpage type is a search box; the target page element in the target webpage of the shopping webpage type is a product image on a first screen; the target page element in the target webpage of the advisory information webpage type is a banner image on the first screen (Bloem in [0008] and [0059] discloses index any user selection of images, input text, product template, and/or specific products to build user morels and/or score content, information collected includes clicking on certain objects, adding objects to a shopping cart, dwell time associated with a page, object, and/or site, a product, a webpage, or spending extra time on a particular page or in front of an object; Bloem in [0060], [0135], and [0137] discloses user’s activity can be indexed, information can be classified, indexed, and/or stored according to a respective classification, classifications include a type associated with information about searchable content/items, information about products presented on an ecommerce site are indexed, information provided in conjunction with products are indexed, information about search terms are indexed and saved; Bloem in [0065], [0108], and [0152] discloses websites accessed by users include advertising banners, user can type text in a text input box, user can enter search terms in input box; Bloem in [0079] discloses shopping cart information fed as advertising banner with images as recommendations to users during a search session).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REZWANUL MAHMOOD whose telephone number is (571)272-5625. The examiner can normally be reached M-F 9-5:30.
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/R.M/Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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