DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
2. The action is responsive to the Applicant’s Amendment filed on 8/21/2025. Claims 1-9 and 11-21 are pending in the application. Claims 1, 5, 11-14, 16 and 19-21 are amended.
Applicant’s amendments to the claims integrate the processes into a practical application. The 101 rejection of claims 1-9 and 11-21 previously set forth in the Non-Final Office Action mailed 5/21/2025 is hereby withdrawn.
Response to Arguments
3. Applicant’s arguments with respect to the rejections of claims 1-9 and 11-21 have been fully considered. In view of the claim amendment filed, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
Further, regarding the new limitations recited in claims 1, 5, 11-14, 16 and 19-21, it is submitted that they are properly addressed by the new ground of rejection.
Furthermore, it is also submitted that all limitations in pending claims, including those not specifically argued, are properly addressed. The reason is set forth in the rejections. See claim analysis below for detail.
Specification
4. Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it recites the term "thereby” which suggests legal limitations or conditions. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 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.
Claims 1-9 and 11-21 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20180189614 A1) in view of Yu et al. (US 20220129509 A1).
7. Regarding Claim 1, Chen discloses a web page classification method ([Abstract]: A method and device for classifying webpages are provided), wherein the method comprises:
obtaining feature information of a web page to be classified (Fig. 1; [0030]: In step 101, multiple webpage elements are parsed from a webpage to be predicted),
the feature information comprising at least two of search engine optimization information, web page sharing information shared from the web page to be classified to a third-party website, web page advertisement information related to the web page to be classified and released on a platform by a website corresponding to the web page to be classified, and web page rendering information extracted from a rendering image result of the rendering of the web page to be classified ([0031]: The webpage element may be a part of the webpage to be predicted, and for example, may include any of a root domain name of the webpage, a webpage title, a webpage text and a webpage URL);
respectively predicting a candidate web page category of the web page to be classified according to each feature information (Fig. 1; [0031]: In an embodiment of the disclosure, the webpage classification is predicted according to the webpage elements; [0032]: In step 102, a candidate webpage classification to which the webpage to be predicted belongs is predicted separately according to respective webpage elements);
determining a target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified (Fig. 1; [0035]: In step 103, a final webpage classification of the webpage to be predicted is determined by comparing the candidate webpage classifications predicted based on the respective webpage elements).
However, Chen does not explicitly teach “transmit, based on the target web page category, the web page to a client for displaying, wherein the feature information comprises the web page sharing information, and the computer program causing the computer to determine the target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified further causes the computer to: determine a sharing rate at which users share the web page to a third-party website; and determine the target web page category based on the sharing rate.”
On the other hand, in the same field of endeavor, Yu teaches
transmitting, based on the target web page category, the web page to a client for displaying (Fig. 1; [0046]: Once website publishing system 110 receives the rule library, the requested web page can be modified based on the received set of links, and then served to user terminal 130; [0115]: Once the webpage is reconfigured using the steps above, it may be rendered, served, saved, or displayed),
wherein the feature information comprises the web page sharing information ([0036] In another embodiment, a method comprises determining, by a processor, what an end-user electronic device should do next to get or access webpage components. Such instructions could, optionally (a) direct the browser to a specific uniform resource locator “URL”), and
determining the target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified comprises ([0118] An optimization, including one or more modules that, in an embodiment, determine and recommend optimizations for one or more URLs for various types of optimizations and categories of optimizations):
determining a sharing rate at which users share the web page to a third-party website ([0120]-[0130]: The sources of marketing data are configured to provide data, or a range of types of data, to one or more optimization engines. The data can include, but is not limited to… end-user and audience identification and personalization data… Furthermore, such data may be available from website operators or from third-party systems or the operators of search engines [Sharing rate corresponds to marketing data]): and
determining the target web page category based on the sharing rate ([0118]-[0121]: An optimization, including one or more modules that, in an embodiment, determine and recommend optimizations for one or more URLs for various types of optimizations and categories of optimizations, receives a variety of marketing data from one or more sources).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Chen to incorporate the teachings of Yu to transmit, based on the target web page category, the web page to a client for displaying, determine a sharing rate, and determine the target web page category based on the sharing rate.
The motivation for doing so would be to recommend optimizations for one or more URLs for various types of categories, as recognized by Yu ([0118] of Yu: [0118] An optimization, including one or more modules that, in an embodiment, determine and recommend optimizations for one or more URLs for various types of optimizations and categories of optimizations, receives a variety of marketing data from one or more sources, such sources including data from databases or from real-time and ad-hoc digital signal data or from other digital systems).
8. Regarding Claim 2, the combined teachings of Chen and Yu disclose the method according to Claim 1, wherein determining a target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified comprises:
determining a confidence of each feature information (Fig. 1; [0036]: Specifically, the final webpage classification may be selected according to the text similarity between each of the candidate webpage classifications and the webpage to be predicted);
normalizing all the confidences (Fig. 2; [0042]: In step S203, the webpage elements are normalized);
determining a candidate web page category corresponding to the feature information corresponding to the largest confidence as the target web page category to which the web page to be classified belongs in case that a largest confidence among all the normalized confidences is greater than or equal to a first preset threshold ([0036]: the candidate webpage classification for which the text similarity compared with the webpage to be predicted is ranked high or exceeds a threshold may be taken as the final webpage classification).
Regarding Claim 3, the combined teachings of Chen and Yu disclose the method according to Claim 2, wherein the method further comprises:
determining a preset category as the target web page category to which the web page to be classified belongs, in case that the largest confidence among all the normalized confidences is less than the first preset threshold, wherein the preset category comprises a low-quality web page category ([0036]: Optionally, the characterization weights of the preset webpage elements to the webpage to be predicted are compared, and the candidate webpage classification regarding which the characterization weight for the webpage to be predicted is ranked high may be taken as the final webpage classification).
Regarding Claim 4, the combined teachings of Chen and Yu disclose the method according to Claim 2, wherein the feature information comprises the search engine optimization information ([0018]: Further, the webpage classification of the disclosure can be generated by mining the historical search logs, which makes full use of the historical search data on one hand), the confidence of the search engine optimization information being determined by:
determining a first rank value of the web page to be classified in a first search engine according to the search engine optimization information ([0036]: the candidate webpage classification for which the text similarity compared with the webpage to be predicted is ranked high or exceeds a threshold may be taken as the final webpage classification);
determining the confidence of the search engine optimization information as a preset confidence when the first rank value is within a preset number of top rank ([0036]: Optionally, the characterization weights of the preset webpage elements to the webpage to be predicted are compared);
determining an auxiliary web page of the web page to be classified when the first rank value is out of the preset number of top rank, wherein the auxiliary web page is a web page that belongs to the same category as the web page category corresponding to the search engine optimization information ([0036]: the candidate webpage classification regarding which the characterization weight for the webpage to be predicted is ranked high may be taken as the final webpage classification);
determining a second rank value of the web page to be classified and the auxiliary page in a second search engine ([0036]: Moreover, the number of taking the candidate webpage classification as the final webpage classification may be counted, and the candidate webpage classification for which the number is ranked high may be taken as the final webpage classification);
determining an average rank value of the web page to be classified and the auxiliary web page according to the second rank value of the web page to be classified and the auxiliary web page in the second search engine; calculating a confidence of the search engine optimization information by following equation: Conl=sigmoid((M+T)/R+(K-R)/M); where Con1 is the confidence of the search engine optimization information, M is a lowest rank value of the web page to be classified and the auxiliary web page in the second search engine, T is the preset number, K is the average rank value, and R is the first rank value of the web page to be classified ([0036]: Any suitable way may also be used for determining the final webpage classification from the candidate webpage classifications, and the number of the final webpage classification may be one or multiple, which is not limited in the disclosure).
Regarding Claim 5, the combined teachings of Chen and Yu disclose the method according to Claim 2, wherein the confidence of the web page sharing information being determined by:
obtaining a first user number shared from the third-party website to the web page to be classified ([0053]: Specifically, the queries inputted by the user may be obtained by parsing the search log) and a second user number accessing the web page to be classified ([0053]: other queries associated with the queries satisfying the requirement may be regarded as queries belonging to the webpage classification);
determining the confidence of the web page sharing information according to the first user number and the second user number ([0053]: the queries satisfying the requirement may be extracted as a webpage classification according to the preset rules; [0036]: Any suitable way may also be used for determining the final webpage classification from the candidate webpage classifications, and the number of the final webpage classification may be one or multiple, which is not limited in the disclosure).
Regarding Claim 6, the combined teachings of Chen and Yu disclose the method according to Claim 2, wherein the feature information comprises the web page advertisement information, the confidence of the web page advertisement information being determined by:
obtaining a click-through rate, a bounce rate and an exit rate of an advertisement corresponding to the web page advertisement information ([0052]: The historical search behaviors between the terminal and the server are recorded in the search log, which may include various search behaviors, such as inputting search keywords, feeding back search results based on the query keywords, clicking on search results, turning pages or re-entering search results);
calculating the confidence of the web page advertisement information by following equation:Con2 =CTRl (bouncete + A * exiterate);where Con2 is the confidence of the web page advertisement information, CTR is the click-through rate, bouncerate is the bounce rate, exiterate is the exit rate, and A is a preset website parameter ([0038]-[0046]: Referring to FIG. 2, FIG. 2 shows a flowchart of a method for classifying webpages… the webpage to be predicted is calculated, and the final webpage classification is selected based on whether the text similarity meeting the selecting condition… The specific selection method may be set according to the actual application and needs; [0066]: Here, the method for calculating the probability is just as an example, and any other applicable method may be used in the specific implementation).
Regarding Claim 7, the combined teachings of Chen and Yu disclose the method according to Claim 2, wherein the feature information comprises the web page rendering information, the confidence of the web page rendering information being determined by:
extracting a preset number of rendering local information at different positions in the rendering image result ([0053]: Specifically, the queries inputted by the user may be obtained by parsing the search log, the queries may be recorded statistically, the queries satisfying the requirement may be extracted as a webpage classification according to the preset rules);
determining whether each rendering local information is related to the candidate web page category corresponding to the web page rendering information according to each rendering local information ([0053]: other queries associated with the queries satisfying the requirement may be regarded as queries belonging to the webpage classification);
determining the confidence of the web page rendering information according to the number of rendering local information related to the candidate web page category corresponding to the web page rendering information and the preset number ([0053]: the corresponding queries may be taken as the webpage classification of the target webpage, the target webpage is further parsed, and then a predicting model for predicting the webpage classification based on the webpage elements is created according to the obtained correspondence between the webpage elements and the webpage classifications; [0036]: Any suitable way may also be used for determining the final webpage classification from the candidate webpage classifications, and the number of the final webpage classification may be one or multiple, which is not limited in the disclosure).
Regarding Claim 8, the combined teachings of Chen and Yu disclose the method according to any of Claim 2, wherein determining the confidences of the each feature information comprises:
for each two candidate web page categories among all the candidate web page categories, determining a similarity between the two candidate web page categories ([0036]: Specifically, the final webpage classification may be selected according to the text similarity between each of the candidate webpage classifications and the webpage to be predicted);
determining the confidence of the each feature information in case that at least one similarity among all the similarities is less than a second preset threshold ([0036]: the candidate webpage classification for which the text similarity compared with the webpage to be predicted is ranked high or exceeds a threshold may be taken as the final webpage classification).
Regarding Claim 9, the combined teachings of Chen and Yu disclose the method according to Claim 8, wherein the method further comprises:
determining any one of all the candidate web page categories as the target web page category to which the web page to be classified belongs in case that all the similarities are greater than or equal to the second preset threshold ([0360]: the candidate webpage classification for which the text similarity compared with the webpage to be predicted is ranked high or exceeds a threshold may be taken as the final webpage classification).
Regarding Claim 11, Chen discloses a non-transitory computer readable medium, on which a computer program is stored, wherein the computer program, when executed by a processor, causes a computer to: (Fig. 6; [0187]: The calculating device traditionally includes a processor 610 and a computer program product or a computer readable medium embodying as a storage 620):
obtain feature information of a web page to be classified (Fig. 1; [0030]: In step 101, multiple webpage elements are parsed from a webpage to be predicted),
the feature information comprising at least two of search engine optimization information, web page sharing information shared from the web page to be classified to a third-party website, web page advertisement information related to the web page to be classified and released on a platform by a website corresponding to the web page to be classified, and web page rendering information extracted from a rendering image result of the rendering of the web page to be classified ([0031]: The webpage element may be a part of the webpage to be predicted, and for example, may include any of a root domain name of the webpage, a webpage title, a webpage text and a webpage URL);
respectively predict a candidate web page category of the web page to be classified according to each feature information (Fig. 1; [0031]: In an embodiment of the disclosure, the webpage classification is predicted according to the webpage elements; [0032]: In step 102, a candidate webpage classification to which the webpage to be predicted belongs is predicted separately according to respective webpage elements);
determine a target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified (Fig. 1; [0035]: In step 103, a final webpage classification of the webpage to be predicted is determined by comparing the candidate webpage classifications predicted based on the respective webpage elements).
However, Chen does not explicitly teach “transmit, based on the target web page category, the web page to a client for displaying, wherein the feature information comprises the web page sharing information, and the computer program causing the computer to determine the target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified further causes the computer to: determine a sharing rate at which users share the web page to a third-party website; and determine the target web page category based on the sharing rate.”
On the other hand, in the same field of endeavor, Yu teaches
transmit, based on the target web page category, the web page to a client for displaying (Fig. 1; [0046]: Once website publishing system 110 receives the rule library, the requested web page can be modified based on the received set of links, and then served to user terminal 130; [0115]: Once the webpage is reconfigured using the steps above, it may be rendered, served, saved, or displayed);
wherein the feature information comprises the web page sharing information ([0036] In another embodiment, a method comprises determining, by a processor, what an end-user electronic device should do next to get or access webpage components. Such instructions could, optionally (a) direct the browser to a specific uniform resource locator “URL”), and
the computer program causing the computer to determine the target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified further causes the computer to: ([0118] An optimization, including one or more modules that, in an embodiment, determine and recommend optimizations for one or more URLs for various types of optimizations and categories of optimizations):
determine a sharing rate at which users share the web page to a third-party website ([0120]-[0130]: The sources of marketing data are configured to provide data, or a range of types of data, to one or more optimization engines. The data can include, but is not limited to… end-user and audience identification and personalization data… Furthermore, such data may be available from website operators or from third-party systems or the operators of search engines [Sharing rate corresponds to marketing data]): and
determine the target web page category based on the sharing rate ([0118]-[0121]: An optimization, including one or more modules that, in an embodiment, determine and recommend optimizations for one or more URLs for various types of optimizations and categories of optimizations, receives a variety of marketing data from one or more sources).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Chen to incorporate the teachings of Yu to transmit, based on the target web page category, the web page to a client for displaying, determine a sharing rate, and determine the target web page category based on the sharing rate.
The motivation for doing so would be to recommend optimizations for one or more URLs for various types of categories, as recognized by Yu ([0118] of Yu: [0118] An optimization, including one or more modules that, in an embodiment, determine and recommend optimizations for one or more URLs for various types of optimizations and categories of optimizations, receives a variety of marketing data from one or more sources, such sources including data from databases or from real-time and ad-hoc digital signal data or from other digital systems).
Regarding Claim 12, Chen discloses an electronic device ([0187]: For example, FIG. 6 shows a calculating device for achieving the method for classifying webpages according to the disclosure) comprising:
A memory, on which a computer program is stored ([0187]: The calculating device traditionally includes a processor 610 and a computer program product or a computer readable medium embodying as a storage 620); and
a processor, wherein the computer program, when executed by the processor, causes the device to (Fig. 6; [0187]: the code can be read by processors such as 610 and the like)
obtain feature information of a web page to be classified (Fig. 1; [0030]: In step 101, multiple webpage elements are parsed from a webpage to be predicted),
the feature information comprising at least two of search engine optimization information, web page sharing information shared from the web page to be classified to a third-party website, web page advertisement information related to the web page to be classified and released on a platform by a website corresponding to the web page to be classified, and web page rendering information extracted from a rendering image result of the rendering of the web page to be classified ([0031]: The webpage element may be a part of the webpage to be predicted, and for example, may include any of a root domain name of the webpage, a webpage title, a webpage text and a webpage URL);
respectively predict a candidate web page category of the web page to be classified according to each feature information (Fig. 1; [0031]: In an embodiment of the disclosure, the webpage classification is predicted according to the webpage elements; [0032]: In step 102, a candidate webpage classification to which the webpage to be predicted belongs is predicted separately according to respective webpage elements);
determine a target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified (Fig. 1; [0035]: In step 103, a final webpage classification of the webpage to be predicted is determined by comparing the candidate webpage classifications predicted based on the respective webpage elements).
However, Chen does not explicitly teach “transmit, based on the target web page category, the web page to a client for displaying, wherein the feature information comprises the web page sharing information, and the computer program causing the computer to determine the target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified further causes the computer to: determine a sharing rate at which users share the web page to a third-party website; and determine the target web page category based on the sharing rate.”
On the other hand, in the same field of endeavor, Yu teaches
transmit, based on the target web page category, the web page to a client for displaying (Fig. 1; [0046]: Once website publishing system 110 receives the rule library, the requested web page can be modified based on the received set of links, and then served to user terminal 130; [0115]: Once the webpage is reconfigured using the steps above, it may be rendered, served, saved, or displayed);
wherein the feature information comprises the web page sharing information ([0036] In another embodiment, a method comprises determining, by a processor, what an end-user electronic device should do next to get or access webpage components. Such instructions could, optionally (a) direct the browser to a specific uniform resource locator “URL”), and
the computer program causing the computer to determine the target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified further causes the computer to: ([0118] An optimization, including one or more modules that, in an embodiment, determine and recommend optimizations for one or more URLs for various types of optimizations and categories of optimizations):
determine a sharing rate at which users share the web page to a third-party website ([0120]-[0130]: The sources of marketing data are configured to provide data, or a range of types of data, to one or more optimization engines. The data can include, but is not limited to… end-user and audience identification and personalization data… Furthermore, such data may be available from website operators or from third-party systems or the operators of search engines [Sharing rate corresponds to marketing data]): and
determine the target web page category based on the sharing rate ([0118]-[0121]: An optimization, including one or more modules that, in an embodiment, determine and recommend optimizations for one or more URLs for various types of optimizations and categories of optimizations, receives a variety of marketing data from one or more sources).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Chen to incorporate the teachings of Yu to transmit, based on the target web page category, the web page to a client for displaying, determine a sharing rate, and determine the target web page category based on the sharing rate.
The motivation for doing so would be to recommend optimizations for one or more URLs for various types of categories, as recognized by Yu ([0118] of Yu: [0118] An optimization, including one or more modules that, in an embodiment, determine and recommend optimizations for one or more URLs for various types of optimizations and categories of optimizations, receives a variety of marketing data from one or more sources, such sources including data from databases or from real-time and ad-hoc digital signal data or from other digital systems).
Regarding Claim 13, the combined teachings of Chen and Yu disclose the device according to Claim 12, wherein the computer program causing the device to determine a target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified further causes the device to:
determine a confidence of each feature information (Fig. 1; [0036]: Specifically, the final webpage classification may be selected according to the text similarity between each of the candidate webpage classifications and the webpage to be predicted);
normalize all the confidences (Fig. 2; [0042]: In step S203, the webpage elements are normalized);
determine a candidate web page category corresponding to the feature information corresponding to the largest confidence as the target web page category to which the web page to be classified belongs in case that a largest confidence among all the normalized confidences is greater than or equal to a first preset threshold ([0036]: the candidate webpage classification for which the text similarity compared with the webpage to be predicted is ranked high or exceeds a threshold may be taken as the final webpage classification).
Regarding Claim 14, the combined teachings of Chen and Yu disclose the device according to Claim 13, wherein the computer program further causes the device to: determine a preset category as the target web page category to which the web page to be classified belongs, in case that the largest confidence among all the normalized confidences is less than the first preset threshold, wherein the preset category comprises a low-quality web page category ([0036]: Optionally, the characterization weights of the preset webpage elements to the webpage to be predicted are compared, and the candidate webpage classification regarding which the characterization weight for the webpage to be predicted is ranked high may be taken as the final webpage classification).
Regarding Claim 15, the combined teachings of Chen and Yu disclose the device according to Claim 13, wherein the feature information comprises the search engine optimization ([0018]: Further, the webpage classification of the disclosure can be generated by mining the historical search logs, which makes full use of the historical search data on one hand), the confidence of the search engine optimization information being determined by:
determining a first rank value of the web page to be classified in a first search engine according to the search engine optimization information ([0036]: the candidate webpage classification for which the text similarity compared with the webpage to be predicted is ranked high or exceeds a threshold may be taken as the final webpage classification);
determining the confidence of the search engine optimization information as a preset confidence when the first rank value is within a preset number of top rank ([0036]: Optionally, the characterization weights of the preset webpage elements to the webpage to be predicted are compared);
determining an auxiliary web page of the web page to be classified when the first rank value is out of the preset number of top rank, wherein the auxiliary web page is a web page that belongs to the same category as the web page category corresponding to the search engine optimization information ([0036]: the candidate webpage classification regarding which the characterization weight for the webpage to be predicted is ranked high may be taken as the final webpage classification);
determining a second rank value of the web page to be classified and the auxiliary page in a second search engine ([0036]: Moreover, the number of taking the candidate webpage classification as the final webpage classification may be counted, and the candidate webpage classification for which the number is ranked high may be taken as the final webpage classification);
determining an average rank value of the web page to be classified and the auxiliary web page according to the second rank value of the web page to be classified and the auxiliary web page in the second search engine; calculating a confidence of the search engine optimization information by following equation: Conl=sigmoid((M+T)/R+(K-R)/M); where Con1 is the confidence of the search engine optimization information, M is a lowest rank value of the web page to be classified and the auxiliary web page in the second search engine, T is the preset number, K is the average rank value, and R is the first rank value of the web page to be classified ([0036]: Any suitable way may also be used for determining the final webpage classification from the candidate webpage classifications, and the number of the final webpage classification may be one or multiple, which is not limited in the disclosure).
Regarding Claim 16, the combined teachings of Chen and Yu disclose the device according to Claim 13, wherein the confidence of the web page sharing information being determined by:
obtaining a first user number shared from the third-party website to the web page to be classified ([0053]: Specifically, the queries inputted by the user may be obtained by parsing the search log) and a second user number accessing the web page to be classified ([0053]: other queries associated with the queries satisfying the requirement may be regarded as queries belonging to the webpage classification);
determining the confidence of the web page sharing information according to the first user number and the second user number ([0053]: the queries satisfying the requirement may be extracted as a webpage classification according to the preset rules; [0036]: Any suitable way may also be used for determining the final webpage classification from the candidate webpage classifications, and the number of the final webpage classification may be one or multiple, which is not limited in the disclosure).
Regarding Claim 17, the combined teachings of Chen and Yu disclose the device according to Claim 13, wherein the feature information comprises the web page advertisement information, the confidence of the web page advertisement information being determined by:
obtaining a click-through rate, a bounce rate and an exit rate of an advertisement corresponding to the web page advertisement information ([0052]: The historical search behaviors between the terminal and the server are recorded in the search log, which may include various search behaviors, such as inputting search keywords, feeding back search results based on the query keywords, clicking on search results, turning pages or re-entering search results);
calculating the confidence of the web page advertisement information by following equation:Con2 =CTRl (bouncete + A * exiterate);where Con2 is the confidence of the web page advertisement information, CTR is the click-through rate, bouncerate is the bounce rate, exiterate is the exit rate, and A is a preset website parameter ([0038]-[0046]: Referring to FIG. 2, FIG. 2 shows a flowchart of a method for classifying webpages… the webpage to be predicted is calculated, and the final webpage classification is selected based on whether the text similarity meeting the selecting condition… The specific selection method may be set according to the actual application and needs; [0066]: Here, the method for calculating the probability is just as an example, and any other applicable method may be used in the specific implementation).
Regarding Claim 18, the combined teachings of Chen and Yu disclose the device according to Claim 13, wherein the feature information comprises the web page rendering information, the confidence of the web page rendering information being determined by:
extracting a preset number of rendering local information at different positions in the rendering image result ([0053]: Specifically, the queries inputted by the user may be obtained by parsing the search log, the queries may be recorded statistically, the queries satisfying the requirement may be extracted as a webpage classification according to the preset rules);
determining whether each rendering local information is related to the candidate web page category corresponding to the web page rendering information according to each rendering local information ([0053]: other queries associated with the queries satisfying the requirement may be regarded as queries belonging to the webpage classification);
determining the confidence of the web page rendering information according to the number of rendering local information related to the candidate web page category corresponding to the web page rendering information and the preset number ([0053]: the corresponding queries may be taken as the webpage classification of the target webpage, the target webpage is further parsed, and then a predicting model for predicting the webpage classification based on the webpage elements is created according to the obtained correspondence between the webpage elements and the webpage classifications; [0036]: Any suitable way may also be used for determining the final webpage classification from the candidate webpage classifications, and the number of the final webpage classification may be one or multiple, which is not limited in the disclosure).
Regarding Claim 19, the combined teachings of Chen and Yu disclose the device according to Claim 13, wherein the computer program causing the device to determine the confidences of each feature information further causes the device to:
for each two candidate web page categories among all the candidate web page categories, determine a similarity between the two candidate web page categories ([0036]: Specifically, the final webpage classification may be selected according to the text similarity between each of the candidate webpage classifications and the webpage to be predicted);
determine the confidence of the each feature information in case that at least one similarity among all the similarities is less than a second preset threshold ([0036]: the candidate webpage classification for which the text similarity compared with the webpage to be predicted is ranked high or exceeds a threshold may be taken as the final webpage classification).
Regarding Claim 20, the combined teachings of Chen and Yu disclose the device according to Claim 18, wherein the computer program causing the computer to determine a target web page category to which the web page to be classified belongs in case that all the similarities are greater than or equal to the second preset threshold ([0360]: the candidate webpage classification for which the text similarity compared with the webpage to be predicted is ranked high or exceeds a threshold may be taken as the final webpage classification).
Regarding Claim 21, the combined teachings of Chen and Yu disclose the medium according to Claim 11, wherein the computer program causing the computer to determine a target web page category to which the web page to be classified belongs from all the candidate web page categories of the web page to be classified further causes the computer to:
determine a confidence of each feature information (Fig. 1; [0036]: Specifically, the final webpage classification may be selected according to the text similarity between each of the candidate webpage classifications and the webpage to be predicted);
normalize all the confidences (Fig. 2; [0042]: In step S203, the webpage elements are normalized);
determine a candidate web page category corresponding to the feature information corresponding to the largest confidence as the target web page category to which the web page to be classified belongs in case that a largest confidence among all the normalized confidences is greater than or equal to a first preset threshold ([0036]: the candidate webpage classification for which the text similarity compared with the webpage to be predicted is ranked high or exceeds a threshold may be taken as the final webpage classification).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S D H/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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