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 .
The Amendment filed on 02/02/26 has been received and entered. Application No. 18/891,477 of which claims 1-20 are pending in the application, all of which are ready for examination by the examiner.
Response to Amendment
Applicant’s amendment necessitated new grounds of rejection.
Applicant’s response, filed on 02/02/26, with respect to 101 rejections directed to an abstract idea of claims 1-20 have been fully considered but are not persuasive. The rejections are maintained.
This action is made final in view of the new grounds of rejection.
Response to Arguments
Applicant's arguments with respect to 35 USC § 101 rejections of claims 1-20 have been fully considered but they are not persuasive. Applicant made the following arguments:
Regarding claims 1-20, Applicant argues “the recited limitations describe an unconventional ordered combination of steps that improves the output provided to a user by re-ranking a subset of search results output by a search engine. For at least the foregoing reasons, independent claim 1 complies with Section 101. The other independent claims, as amended herein, recite similar limitations and also comply with Section 101 for at least the reasons indicated above with respect to claim 1”.
Examiner respectfully disagrees. Claim 1 recites generating a retrieval query for a search engine based at least in part on the search string and at least three features selected from the first set of one or more user features; selecting a subset of search results from the plurality of search results, wherein the subset of search results comprises a first number of search results, and wherein the subset of search results is selected based at least in part on the match score of each search result; ranking, using a ranking model, the subset of search results, wherein the ranking model is configured to rank the subset of search results; and determining, using at least the multiple page features and the second set of multiple user features, a set of rank-ordered results comprising search results in the subset of search results. The limitations of generating…, selecting…, ranking…, determining…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “generating…, selecting…, ranking…, determining…,” in the context of these claims encompass the user manually generating query, selecting search results, ranking search results, determining set of rank-ordered results. 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 claim recites an abstract idea. Furthermore, this judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – receiving…, retrieving…, providing…, accessing…, causing…. The “receiving”, “retrieving”, “providing”, “accessing” and “causing” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “receiving”, “retrieving”, “providing”, “accessing” and “causing” are a well-understood, routine, and conventional activity (storing or data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea. Furthermore, Examiner points that improvement cannot be part of the abstract idea itself. Therefore, Applicant’s arguments are not persuasive.
Applicant's arguments with respect to 35 USC § 103 rejections of claims 1-20 have been fully considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
Claim Rejections - 35 USC §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim 1 recites generating a retrieval query for a search engine based at least in part on the search string and at least three features selected from the first set of one or more user features; selecting a subset of search results from the plurality of search results, wherein the subset of search results comprises a first number of search results, and wherein the subset of search results is selected based at least in part on the match score of each search result; ranking, using a ranking model, the subset of search results, wherein the ranking model is configured to rank the subset of search results; and determining, using at least the multiple page features and the second set of multiple user features, a set of rank-ordered results comprising search results in the subset of search results.
The limitations of generating…, selecting…, ranking…, determining…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “generating…, selecting…, ranking…, determining…,” in the context of these claims encompass the user manually generating query, selecting search results, ranking search results, determining set of rank-ordered results. 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 claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – receiving…, retrieving…, providing…, accessing…, causing…. The “receiving”, “retrieving”, “providing”, “accessing” and “causing” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “receiving”, “retrieving”, “providing”, “accessing” and “causing” are a well-understood, routine, and conventional activity (storing or data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 2, 11, and 20 recite wherein frequently accessed page type, recently accessed page type, frequently accessed pages, and recently accessed pages are determined by: monitoring user interactions with pages; storing at least a portion of the monitored user interactions; and determining, based on the at least the portion of the monitored user interactions, at least one user feature, wherein pages are organized in a hierarchy of pages, wherein each page is a page node of the hierarchy of pages, and wherein the frequently accessed page path is determined by: traversing, for each page of the plurality of pages, the hierarchy of pages, wherein a path for each page comprises all nodes between a root node of the hierarchy of pages and the page node; and analyzing the path of each page of the plurality of pages to determine a frequently access page path. The limitations only recite additional elements at a high level of generality. These limitations are recited at a high-level of generality (i.e., monitoring, storing, determining, traversing, analyzing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 3 and 12 recite accessing a plurality of search results responsive to a search string submitted by a user, wherein each search result of the plurality of search results corresponds to a page; and selecting a subset of search results from the plurality of search results, wherein each search result of the plurality of search results comprises a match score, and wherein the subset of search results selected based at least in part on the match score of each search result; accessing one or more page features associated with each page; accessing one or more user features associated with the user; accessing the subset of search results; and determining, using at least the search string, the one or more page features and the one or more user features, a set of rank-ordered search results comprising search results in the subset of search results, wherein the one or more page features and the one or more user features are accessed from a feature store.
The limitations of accessing…, selecting…, determining…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “accessing…, selecting…, determining…,” in the context of these claims encompass the user manually accessing search results, selecting search results, accessing page features, accessing user features, accessing search results, determining page features. 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 claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – providing…, ranking…. The “providing” and “ranking” limitations are insignificant extra-solution activity (mere data gathering and outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “providing” and “ranking” are a well-understood, routine, and conventional activity (data gathering and outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 4 and 13 recite wherein the ranking model is further configured to rank the subset of search results based at least in part on vector similarity scores, wherein a vector similarity score indicates a similarity between a search string submitted by a user and a page title of a page included in the plurality of search results, wherein determining vector similarity scores comprises: determining a vector representation of the search string; determining, for each page title, a vector representation of the page title; and computing, for each page title, a vector similarity score of the search string and the page title, wherein the vector similarity score is based on at least one of: Euclidean distance, Manhattan distance, or cosine similarity. The limitations only recite additional elements at a high level of generality. These limitations are recited at a high-level of generality (i.e., rank, determining, computing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 5 and 14 recite prior to accessing the plurality of search results: receiving the search string from the user via a user interface; accessing a first set of one or more user features from a first data store, wherein the one or more user features comprise indications of at least one of: one or more pages visited by the user, one or more pages edited by the user, one or more pages created by the user, or one or more pages commented by the user; generating a retrieval query for a search engine based at least in part on the search string and at least one feature from the first set of one or more user features; and performing a search by providing the retrieval query to a search engine. The limitations only recite additional elements at a high level of generality. These limitations are recited at a high-level of generality (i.e., receiving, accessing, generating, performing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 6 and 15 recite wherein the ranking model comprises a trained machine learning model, wherein the ranking model is trained using a training dataset; wherein the training dataset comprises a plurality of page features for a plurality of pages, a plurality of user features for a plurality of users, and a plurality of search string identifiers, wherein the training dataset is stored in a data store that is different from the feature store, wherein the feature store is configured to be accessed with a lower latency than the data store, wherein the feature store stores a subset of information included in the data store. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 7 and 16 recite wherein accessing the first set of one or more user features occurs at least partially in parallel with at least one of generating the retrieval query or performing the search. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 8 and 17 recite determining a user identity of the user; and determining, based on the user identity, access permissions for the user, wherein the access permissions indicate one or more pages to which the user has access, wherein the retrieval query includes an indication of the access permissions, and wherein the search engine is configured to include only pages to which the user has access in the plurality of search results. The limitations only recite additional elements at a high level of generality. These limitations are recited at a high-level of generality (i.e., determining) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 9 and 18 recite wherein the one or more page features comprise one or more of: last edit date, last edit user, last view date, last comment date, page view count, page title, page path, or page authority score. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 10 and 19 recite wherein the one or more user features comprise one or more of: user role, user team, frequently accessed page type, frequently accessed pages, recently accessed page type, recently accessed pages, recently accessed page path, or frequently accessed page path. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
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 of this title, 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 1-3, 9-12, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (U.S. Pub 2017/0177632; hereinafter “Kumar”) in view of Mayer (U.S. PGPub 2008/0177994; hereinafter “Mayer”) and further in view of Cucerzan et al. (U.S. PGPub 2007,0214131; hereinafter “Cucerzan”).
As per claim 1, Kumar discloses a computer-implemented method for search result ranking, the computer-implemented method comprising: receiving a search string from a user via a user interface; (See Fig. 10A-10B, 11, paras. 134-137, 143-144, wherein user interface, search features are disclosed; as taught by Kumar.)
retrieving a first set of one or more user features from a feature store, wherein the first set of one or more user features comprise indications of at least three
of: one or more pages visited by the user, one or more pages edited by the user, one or more pages created by the user, or one or more pages commented by the user; (See paras. paras. 134-137, 143-144, wherein user interface, search features, data retrieval process are disclosed, also See Figs. 7, 10B, 11, paras. 40, 50, 120, wherein metadata, author, ratings, remarks, number of visits are disclosed; as taught by Kumar.)
generating a retrieval query for a search engine based at least in part on the search string and at least three features selected from the first set of one or more
user features; (See Figs. 7, 10B, paras. 40, 50, 120, wherein metadata, author, ratings, remarks, number of visits are disclosed, also See Fig. 11, paras. 138-140, wherein retrieving data based on search criterion are disclosed; as taught by Kumar.)
accessing a plurality of search results generated by the search engine, the plurality of search results corresponding to a plurality of pages, each search result
corresponding to a page of the plurality of pages; (See Figs. 8, 10A-10B, 11-12, 38-40, wherein retrieval of search results in which “the web server recovers a stored browser session, session states, session data, and stored web pages, such that the client computer continues from the recovered stored browser session” [0038] are disclosed, also See paras. 115-116, wherein obtaining search results process are disclosed; as taught by Kumar.)
selecting a subset of search results from the plurality of search results,
wherein the subset of search results comprises a first number of search
results; (See Fig. 6A, paras. 40-42, 103-104, 116, wherein subsequent searching using saved web content process in which “the user-interface (602) may provide a plurality of options (603) that enable the user to select type of content for storing and subsequent offline viewing” [0103] are disclosed; as taught by Kumar.)
accessing a second set of multiple user features, wherein the second set of
multiple user features comprises at least three of: user role, user team, frequently accessed page type, frequently accessed pages, recently accessed page type, recently accessed pages, recently accessed page path, or frequently accessed page path; (See Figs. 9, 10A, paras. 36, 40, 130, 136, wherein various visitor data, such as demographic data, background data, network-access data, actions taken by visitor, etc. are disclosed; as taught by Kumar.)
accessing the subset of search results; (See paras. 40-41, 103-105, wherein subsequent searching process are disclosed, also See para. 116, wherein subsequent viewing, retrieval information are disclosed; as taught by Kumar.)
and determining, using at least the multiple page features and the second set of multiple user features, a set of rank-ordered results comprising search results in the subset of search results; (See paras. 41, wherein ranking of search results are disclosed; as taught by Kumar.)
and causing display, to the user via the user interface, the set of rank-ordered results. (See Fig. 6A, 7, paras. 38, 40-41, 103-104, wherein displaying search results are disclosed, also See paras. 120, 136, wherein providing sorting option to sort information, retrieving information process are disclosed; as taught by Kumar.)
However, Kumar fails to disclose wherein the subset of search results is selected based at least in part on the match score of each search result; ranking, using the ranking model, the subset of search results, wherein the ranking model is configured to rank the subset of search results by: accessing multiple page features from the feature store, wherein the multiple page features comprise at least three of: page title, page edit date, page comment date, page creation date, page views, page authority score, or page verification status, wherein the page authority score is based on three or more of: page view count, number of unique visitors, or page authors, and wherein the page verification status indicates if content of the page has been verified.
On the other hand, Mayer teaches wherein the subset of search results is selected based at least in part on the match score of each search result; (See paras. 111, 116, wherein ranking, information value of search results in which “results can preferably be displayed also for example by a combined sort that combines for example relevance or importance with time, so that for example the clusters and/or sub-clusters and/or items are sorted by a score which is based on a formula that is affected both by time and by relevance and/or importance”[0111] and “determining the information value of the search results, which can be used for example for improving the ranking of web pages according to their information value and/or for example indicating near each link its information value (for example as a single score and/or as a list of scores and/or sub-scores)” [0116] are disclosed; as taught by Mayer.)
ranking, using the ranking model, the subset of search results, wherein the ranking model is configured to rank the subset of search results by: accessing multiple page features from the feature store, wherein the multiple page features comprise at least three of: page title, page edit date, page comment date, page creation date, page views, page authority score, or page verification status, wherein the page authority score is based on three or more of: page view count, number of unique visitors, or page authors, and wherein the page verification status indicates if content of the page has been verified. (See paras. 25-27, 37, 111, 115-116, wherein ranking, information value of search results in which “determining the information value of the search results, which can be used for example for improving the ranking of web pages according to their information value and/or for example indicating near each link its information value (for example as a single score and/or as a list of scores and/or sub-scores)…searching for academic results and/or for example in normal web searches, the user can for example request the search engine to show only results from articles (and/or for example normal web pages which were last updated) for example from a certain date onward or for example before a certain date or for example within one or more range of dates. However, some pages include for example an automatic visitors counter and so the counter is automatically updated even if the page does not change for years, so preferably the search engine uses its historical data to estimate if the amount of change and/or the type of change justifies regarding the page as recently updated…” [0116] are disclosed; as taught by Mayer.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Mayer teachings in the Kumar system. Skilled artisan would have been motivated to incorporate the system for improving efficiency and reliability in operating systems taught by Mayer in the Kumar system for efficient searching, saving web content. In addition, both of the references (Kumar and Mayer) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as search customization. This close relation between both of the references highly suggests an expectation of success.
However, the combination of Kumar and Mayer fails to disclose a retrieval process of a two-stage process comprising the retrieval process and a ranking process; wherein the feature store comprises a subset of information stored in a log data store that stores one or more of: user information, query information, page title information, page edit information, page comment information, page view information, or dwell time information, and a subset of information stored in a data store that is used for training a ranking model; providing the retrieval query to the search engine, wherein search engine is configured to generate a plurality of search results, wherein each search result comprises a match score; in the ranking process, after the retrieval process.
On the other hand, Cucerzan teaches a retrieval process of a two-stage process comprising the retrieval process and a ranking process; (See Fig. 1, paras. 33, 40, wherein ranking component, search engine features on retrieving search results for re-ranking process are disclosed; as taught by Cucerzan.)
wherein the feature store comprises a subset of information stored in a log data store that stores one or more of: user information, query information, page title information, page edit information, page comment information, page view information, or dwell time information, and a subset of information stored in a data store that is used for training a ranking model; (See Figs. 5, 7, paras. 50, 62, wherein query log component analyzing various properties, such as, URL, page title, page content are disclosed; as taught by Cucerzan.)
providing the retrieval query to the search engine, wherein search engine is configured to generate a plurality of search results, wherein each search result comprises a match score; in the ranking process, after the retrieval process. (See paras. 36-37, wherein matching component functions in determining match score of search results process are disclosed; as taught by Cucerzan.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Cucerzan teachings in the combination of Kumar and Cucerzan system. Skilled artisan would have been motivated to incorporate the system for re-ranking search results based on query log taught by Cucerzan in the combination of Kumar and Mayer system for efficient searching, saving web content. In addition, both of the references (Kumar, Mayer, and Cucerzan) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as search customization. This close relation between both of the references highly suggests an expectation of success.
As per claims 2, 11 and 20, the combination of Kumar and Cucerzan discloses wherein the frequently accessed page path is determined by: traversing, for each page of the plurality of pages, the hierarchy of pages, wherein a path for each page comprises all nodes between a root node of the hierarchy of pages and the page node; (See Fig. 9A, 9B, paras. 36, 110, 124, wherein hierarchy webpage elements in form of nodes, navigating folder tree process are disclosed; as taught by Kumar.)
and analyzing the path of each page of the plurality of pages to determine a frequently access page path. (See paras. 40-42, 103, wherein frequency of user browsed documents, frequently viewed data are disclosed; as taught by Kumar.)
However, the combination of Kumar and Cucerzan fails to disclose wherein frequently accessed page type, recently accessed page type, frequently accessed pages, and recently accessed pages are determined by: monitoring user interactions with pages; storing at least a portion of the monitored user interactions; and determining, based on the at least the portion of the monitored user interactions, at least one user feature, wherein pages are organized in a hierarchy of pages, wherein each page is a page node of the hierarchy of pages.
On the other hand, Mayer teaches wherein frequently accessed page type, recently accessed page type, frequently accessed pages, and recently accessed pages are determined by: monitoring user interactions with pages; (See paras. 7, 46, 66, 86, wherein monitoring user behavior process, user interactions are disclosed; as taught by Mayer.)
storing at least a portion of the monitored user interactions; (See paras. 7, 66, 86, wherein user interactions are disclosed, also See paras. 112-113, wherein updating data based on user interactions are disclosed; as taught by Mayer.)
and determining, based on the at least the portion of the monitored user interactions, at least one user feature, wherein pages are organized in a hierarchy of pages, wherein each page is a page node of the hierarchy of pages. (See paras. 56-57, wherein hierarchy of data in which “word processor remembers this automatically and keeps the moved items at the correct level in the hierarchy the next time the table of contents is refreshed (and preferably of course automatically corrects the headline to the appropriate automatically generated fonts for the new level in the hierarchy, preferably both at the table of contents and at the corresponding headline in the document's text itself), unless the user changes it again explicitly or for example changes the letters or digits at the beginning of the relevant line in the actual text” [0056] are disclosed; as taught by Mayer.)
See claim 1 for motivation above.
As per claims 3 and 12, Kumar discloses a computer-implemented method for search result ranking comprising: accessing a plurality of search results responsive to a search string submitted by a user, wherein each search result of the plurality of search results corresponds to a page; (See Fig. 10A-10B, 11, paras. 134-137, 143-144, wherein user interface, search features are disclosed; as taught by Kumar.)
ranking, using a ranking model, the subset of search results, wherein the ranking model is configured to rank the subset of search results by: accessing one or more page features associated with each page; (See paras. 40-42, wherein features of web page based on visitor context, ranking of search results are disclosed; as taught by Kumar.)
accessing one or more user features associated with the user; (See paras. 40-42, wherein features of web page based on visitor context, ranking of search results are disclosed; as taught by Kumar.)
accessing the subset of search results; (See Fig. 6A, paras. 40-42, 103-104, 116, wherein subsequent searching using saved web content process in which “the user-interface (602) may provide a plurality of options (603) that enable the user to select type of content for storing and subsequent offline viewing” [0103] are disclosed; as taught by Kumar.)
and determining, using at least the search string, the one or more page features and the one or more user features, a set of rank-ordered search results comprising search results in the subset of search results, wherein the one or more page features and the one or more user features are accessed from a feature store. (See Fig. 6A, paras. 40-42, wherein features of web page based on visitor context, ranking of search results are disclosed, also See paras. 103-104, 116, wherein subsequent searching using saved web content process in which “the user-interface (602) may provide a plurality of options (603) that enable the user to select type of content for storing and subsequent offline viewing” [0103] are disclosed; as taught by Kumar.)
However, Kumar fails to disclose selecting a subset of search results from the plurality of search results, wherein each search result of the plurality of search results comprises a match score, and wherein the subset of search results selected based at least in part on the match score of each search result.
On the other hand, Mayer selecting a subset of search results from the plurality of search results, wherein each search result of the plurality of search results comprises a match score, and wherein the subset of search results selected based at least in part on the match score of each search result; (See paras. 111, 116, wherein ranking, information value of search results in which “results can preferably be displayed also for example by a combined sort that combines for example relevance or importance with time, so that for example the clusters and/or sub-clusters and/or items are sorted by a score which is based on a formula that is affected both by time and by relevance and/or importance”[0111] and “determining the information value of the search results, which can be used for example for improving the ranking of web pages according to their information value and/or for example indicating near each link its information value (for example as a single score and/or as a list of scores and/or sub-scores)” [0116] are disclosed; as taught by Mayer.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Mayer teachings in the Kumar system. Skilled artisan would have been motivated to incorporate the system for improving efficiency and reliability in operating systems taught by Mayer in the Kumar system for efficient searching, saving web content. In addition, both of the references (Kumar and Mayer) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as search customization. This close relation between both of the references highly suggests an expectation of success.
The combination of Kumar and Mayer fails to disclose wherein the plurality of search results is obtained by providing, to a search engine in a retrieval process, a retrieval query comprising one or more user features from a feature store, wherein the feature store comprises a subset of information stored in a log data store that stores one or more of: user information, query information, page title information, page edit information, page comment information, page view information, or dwell time information, and a subset of information stored in a data store that is used for training a ranking model; wherein a match score generated by the search engine in the retrieval process.
On the other hand, Cucerzan teaches wherein the plurality of search results is obtained by providing, to a search engine in a retrieval process, a retrieval query comprising one or more user features from a feature store, wherein the feature store comprises a subset of information stored in a log data store that stores one or more of: user information, query information, page title information, page edit information, page comment information, page view information, or dwell time information, and a subset of information stored in a data store that is used for training a ranking model; (See Figs. 5, 7, paras. 50, 62, wherein query log component analyzing various properties, such as, URL, page title, page content are disclosed; as taught by Cucerzan.)
wherein a match score generated by the search engine in the retrieval process. (See paras. 36-37, wherein matching component functions in determining match score of search results process are disclosed; as taught by Cucerzan.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Cucerzan teachings in the combination of Kumar and Cucerzan system. Skilled artisan would have been motivated to incorporate the system for re-ranking search results based on query log taught by Cucerzan in the combination of Kumar and Mayer system for efficient searching, saving web content. In addition, both of the references (Kumar, Mayer, and Cucerzan) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as search customization. This close relation between both of the references highly suggests an expectation of success.
As per claims 9 and 18, the combination of Kumar, Mayer, and Cucerzan discloses wherein the one or more page features comprise one or more of: last edit date, last edit user, last view date, last comment date, page view count, page title, page path, or page authority score. (See Figs. 7, 10B, paras. 40, 50, 120, wherein metadata, author, ratings, remarks, number of visits are disclosed, also See Fig. 9A, 11, paras. 130, 138-140, wherein latest folder being access upon saving process, retrieving data based on search criterion are disclosed; as taught by Kumar.)
As per claims 10 and 19, the combination of Kumar, Mayer, and Cucerzan discloses wherein the one or more user features comprise one or more of: user role, user team, frequently accessed page type, frequently accessed pages, recently accessed page type, recently accessed pages, recently accessed page path, or frequently accessed page path. (See Figs. 9, 10A, paras. 36, 40, 130, 136, wherein various visitor data, such as demographic data, background data, network-access data, actions taken by visitor, etc. are disclosed, also See Fig. 9A, 11, paras. 130, 138-140, wherein latest folder being access upon saving process, retrieving data based on search criterion are disclosed; as taught by Kumar.)
Claims 4, 5, 7, 8, 13, 14, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (U.S. Pub 2017/0177632; hereinafter “Kumar”) in view of Mayer (U.S. PGPub 2008/0177994; hereinafter “Mayer”) and further in view of Cucerzan et al. (U.S. PGPub 2007/0214131; hereinafter “Cucerzan”) and further in view of Hashimoto (U.S. PGPub 2024/0143701).
As per claims 4 and 13, the combination of Kumar, Mayer, and Cucerzan fails to disclose wherein the ranking model is further configured to rank the subset of search results based at least in part on vector similarity scores, wherein a vector similarity score indicates a similarity between a search string submitted by a user and a page title of a page included in the plurality of search results, wherein determining vector similarity scores comprises: determining a vector representation of the search string; determining, for each page title, a vector representation of the page title; and computing, for each page title, a vector similarity score of the search string and the page title, wherein the vector similarity score is based on at least one of: Euclidean distance, Manhattan distance, or cosine similarity.
On the other hand, Hashimoto teaches wherein the ranking model is further configured to rank the subset of search results based at least in part on vector similarity scores, (See Fig. 2, paras. 27, 89, wherein ranking, score indicating level of validity are disclosed; as taught by Hashimoto.)
wherein a vector similarity score indicates a similarity between a search string submitted by a user and a page title of a page included in the plurality of search results, (See paras. 129-131, wherein utilizing cosine similarity based on search query, comparison between vectors, such as Euclidean distance are disclosed; as taught by Hashimoto.)
wherein determining vector similarity scores comprises: determining a vector representation of the search string; (See paras. 30, 57, wherein search service, character strings are disclosed; as taught by Hashimoto.)
determining, for each page title, a vector representation of the page title; (See paras. 30, 57, wherein search service, character strings, title of web page are disclosed, also See paras. 129-131, wherein utilizing cosine similarity based on search query, comparison between vectors, such as Euclidean distance are disclosed; as taught by Hashimoto.)
and computing, for each page title, a vector similarity score of the search string and the page title, wherein the vector similarity score is based on at least one of: Euclidean distance, Manhattan distance, or cosine similarity. (See paras. 129-131, wherein utilizing cosine similarity based on search query, comparison between vectors, such as Euclidean distance are disclosed; as taught by Hashimoto.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Hashimoto teachings in the combination of Kumar, Mayer, and Cucerzan system. Skilled artisan would have been motivated to incorporate processing execution system on results for validity taught by Hashimoto in the combination of Kumar, Mayer, and Cucerzan system for efficient searching, saving web content. In addition, both of the references (Kumar, Mayer, Cucerzan, and Hashimoto) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as search customization. This close relation between both of the references highly suggests an expectation of success.
As per claims 5 and 14, the combination of Kumar, Mayer, Cucerzan, and Hashimoto discloses prior to accessing the plurality of search results: receiving the search string from the user via a user interface; (See Fig. 10A-10B, 11, paras. 134-137, 143-144, wherein user interface, search features are disclosed; as taught by Kumar.)
accessing a first set of one or more user features from a first data store, wherein the one or more user features comprise indications of at least one of: one or more pages visited by the user, one or more pages edited by the user, one or more pages created by the user, or one or more pages commented by the user; (See paras. 134-137, 143-144, wherein user interface, search features, data retrieval process are disclosed, also See Figs. 7, 10B, 11, paras. 40, 50, 120, wherein metadata, author, ratings, remarks, number of visits are disclosed; as taught by Kumar.)
generating a retrieval query for a search engine based at least in part on the search string and at least one feature from the first set of one or more user features; (See Figs. 9, 10A, paras. 36, 40, 130, 136, wherein various visitor data, such as demographic data, background data, network-access data, actions taken by visitor, etc. are disclosed; as taught by Kumar.)
and performing a search by providing the retrieval query to a search engine. (See Figs. 2A, 2B, 10A, paras. 61, 70, 79, wherein retrieval information are disclosed, also See para. 116, wherein subsequent viewing, retrieval information are disclosed; as taught by Kumar.)
As per claims 7 and 16, the combination of Kumar, Mayer, Cucerzan, and Hashimoto discloses wherein accessing the first set of one or more user features occurs at least partially in parallel with at least one of generating the retrieval query or performing the search. (See paras. 134-137, 143-144, wherein user interface, search features, data retrieval process are disclosed, also See Figs. 2A, 2B, 10A, paras. 10, 61, 70, 79, wherein performing search, retrieval information are disclosed; as taught by Kumar.)
As per claims 8 and 17, the combination of Kumar, Cucerzan and Hashimoto fails to disclose determining a user identity of the user; and determining, based on the user identity, access permissions for the user, wherein the access permissions indicate one or more pages to which the user has access, wherein the retrieval query includes an indication of the access permissions, and wherein the search engine is configured to include only pages to which the user has access in the plurality of search results.
On the other hand, Mayer teaches determining a user identity of the user; (See paras. 63, 106, wherein user identify are disclosed; as taught by Mayer.)
and determining, based on the user identity, access permissions for the user, wherein the access permissions indicate one or more pages to which the user has access, (See paras. 47, 54, 70, wherein user’s permission are disclosed; as taught by Mayer.)
wherein the retrieval query includes an indication of the access permissions, (See paras. 47, 54, 70, wherein user’s permission are disclosed, also See paras. 73-74, 90, 115, wherein user’s access permission in which “it should take much less than 2 second to determine if the user has a right to view that channel, so even if the system checks the permissions only after the user jumps to the channel, the data stream can preferably be blocked automatically even sooner than the for example 2 seconds and/or for example a message can be displayed on the screen for a certain minimal time period” [0090] are disclosed; as taught by Mayer.)
and wherein the search engine is configured to include only pages to which the user has access in the plurality of search results. (See paras. 47, 54, 70, wherein user’s permission are disclosed, also See paras. 73-74, 90, 115, wherein user’s access permission in which “it should take much less than 2 second to determine if the user has a right to view that channel, so even if the system checks the permissions only after the user jumps to the channel, the data stream can preferably be blocked automatically even sooner than the for example 2 seconds and/or for example a message can be displayed on the screen for a certain minimal time period” [0090] are disclosed; as taught by Mayer.)
See claims 3 and 12 for motivation above.
Claims 6, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (U.S. Pub 2017/0177632; hereinafter “Kumar”) in view of Mayer (U.S. PGPub 2008/0177994; hereinafter “Mayer”) and further in view of Cucerzan et al. (U.S. PGPub 2007/0214131; hereinafter “Cucerzan”) and further in view of Kim (U.S. PGPub 2022/0301079).
As per claims 6 and 15, the combination of Kumar, Mayer and Cucerzan fails to disclose wherein the ranking model comprises a trained machine learning model, wherein the ranking model is trained using a training dataset; wherein the training dataset comprises a plurality of page features for a plurality of pages, a plurality of user features for a plurality of users, and a plurality of search string identifiers, wherein the training dataset is stored in a data store that is different from the feature store, wherein the feature store is configured to be accessed with a lower latency than the data store, wherein the feature store stores a subset of information included in the data store.
On the other hand, Kim teaches wherein the ranking model comprises a trained machine learning model, wherein the ranking model is trained using a training dataset; (See paras. 33-35, wherein ranking webpages process in which “platform and associated features can enable a form of competition between spaces/places by providing viewers with the number of “likes” or average star rating of each webpage and ranking the webpages according to a metric or category—the most popular sites (representing places, spaces, activities, etc.) can be identified from the ranking/metric and presented in a prioritized list based on segmenting individual users or user-expressed preferences” [0033] are disclosed, also See paras. 357-359, wherein training data are disclosed; as taught by Kim.)
wherein the training dataset comprises a plurality of page features for a plurality of pages, a plurality of user features for a plurality of users, and a plurality of search string identifiers, (See paras. 8, 19-37, wherein various features, training model functions are disclosed, also See paras. 357-359, wherein training data are disclosed, also See Fig. 5, paras. 95, 207, wherein various server/platform managing information with a tag/identifier are disclosed; as taught by Kim.)
wherein the training dataset is stored in a data store that is different from the feature store, (See paras. 8, 19-37, wherein various features, training model functions are disclosed, also See paras. 357-359, wherein training data are disclosed, also See Fig. 5, paras. 95, 207, wherein various server/platform managing information with a tag/identifier are disclosed; as taught by Kim.)
wherein the feature store is configured to be accessed with a lower latency than the data store, (See paras. 20-22, wherein user’s access to map/data in which “a user may access a map and use that to identify a location or region of interest, with the server/platform proving a list of events, cultural categories, activities, etc. that are available within the identified area or region” [0020] and “A user may enter their preferred type of cultural event in a profile maintained as part of their account so that places, spaces, events, activities, etc. that are expected to be of interest to the user can be listed or prioritized and presented to the user” [0021] are disclosed, also See paras. 33-38, 47, wherein features of server/platform are disclosed; as taught by Kim.)
wherein the feature store stores a subset of information included in the data store. (See paras. 237, 261, wherein data stores in which “different data stores may include different sets of data objects. Such sets may be disjoint or overlapping” [0261] are disclosed; as taught by Kim.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Kim teachings in the combination of Kumar, Mayer, and Cucerzan system. Skilled artisan would have been motivated to incorporate system for generating and using place-based social networks taught by Kim in the combination of Kumar, Mayer, and Cucerzan system for efficient searching, saving web content. In addition, both of the references (Kumar, Mayer, Cucerzan and Kim) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as search customization. This close relation between both of the references highly suggests an expectation of success.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIN LIN M HTAY whose telephone number is (571)272-7293. The examiner can normally be reached on M-F, 7am-3pm, PST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571)272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L. L. H./
Examiner, Art Unit 2153
/KAVITA STANLEY/ Supervisory Patent Examiner, Art Unit 2153
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