Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 23, 2025 has been entered.
REMARKS
On page 13, Applicant summary of the interview, November 24, 2025, is acknowledged.
On pages 14-15, Applicant argues the prior art of record does not disclose “wherein the contacting of the candidate to solicit an expert search thread and the generation of the expert search thread by the candidate occur before the receiving of the query by the search engine, such that the displayed expert search threads are previously-generated.” Applicant is not persuasive because Applicant implies the prior art, Jain et al. in view of Lin et al., disclose the claimed invention but not in the same order as claimed. It is noted that selection of any order of performing process steps is prima facie obvious in the absence of new or unexpected results. See In re Burhans, 154 F.2d 690, 69 USPQ 330 (CCPA 1946) Therefore, it would have been prima facie obvious to perform the method of Jain et al. in view of Lin et al. in the same order as claimed to achieve the same expected results.
PENDING MATTERS
Claims 18, 20, and 22-24 are cancelled.
Claims 28-33 are new.
Claims 1-17, 19, 21, and 25-33, filed January 02, 2026, are examined on the merits.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 32 and 33 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. NEW MATTER.
Claim 32 recites “receiving from the candidate, via a user interface, edits to the plurality of links” wherein in the instant specification especially the pointed paragraphs [0019]-[0020] and [0067] the new limitation has not been found.
Claim 33 recites “wherein the edits comprise reordering of, additions to, or subtractions from the plurality of links or annotation text associated with the plurality of links” wherein in the instant specification especially the pointed paragraphs [0019]-[0020] and [0067] the new limitation has not been found.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 19, 25-28, and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (Jain hereafter, US 2009/0055384 A1) in view of Lin et al. (SmallBlue: People Mining for Expertise Search).
Claim 1, Jain discloses receiving, by a search engine (Jain, page 2, [0017], e.g. search engine), a query (Jain, page 2, [0018], e.g. a search query is received from a user);
determining, by the search engine, which of a plurality of previously-generated expert search threads to provide in response to the query (Jain, page 2, [0018], e.g. search results based at least in part upon previous actions taken by the expert relevant to the search query are identified); and
displaying, by the search engine, the determined previously-generated expert search threads in a graphical user interface (Jain, page 1, [0012], e.g. the search engine may user prior searches and/or actions by the selected expert (or experts) in determining which results to display to the user, in addition to or in lieu of the results that the search engine would typically provide without input from experts);
scoring each expert search thread based on observations of interactions of other users with the expert search thread within search results for the one or more search terms (Jain, page 1, [0016], e.g. higher weightings may be applied to search results of an identified expert than those that would normally be produced by the search engine. The results of both, however, may be presented together in a seamless manner. Alternatively, results produced due to the expert's influence on the search may be presented separately or marked as being "expert picks." The latter embodiments then allow for the possibility that users can subsequently rank the expert's picks (e.g., the value of the link to the user, on a scale of 1 to 10), resulting in a dynamic process wherein experts are not only selected based on past performance but are also being constantly reevaluated based on current performance);
wherein the link to the expert search thread is positioned in the search results according to the score (Jain, page 1, [0016], e.g. results produced due to the expert's influence on the search may be presented separately or marked as being "expert picks." The latter embodiments then allow for the possibility that users can subsequently rank the expert's picks (e.g., the value of the link to the user, on a scale of 1 to 10)).
However, Jain does not disclose wherein at least a portion of the plurality of previously-generated expert search threads are generated by: analyzing one or more search terms in social media posts across multiple social media platforms to identify a candidate for authoring an expert search thread, wherein the one or more search terms are extracted from one or more social media posts of the candidate;
Contacting, by an expert thread invitation engine by way of private message on one or more of the social media platform, the candidate to solicit an expert search thread, wherein the expert search thread generated by the candidate based on the solicitation includes a plurality of links entered via a user interface that the expert identifies as being helpful to learning about a topic associated with the one or more search terms.
Lin discloses wherein at least a portion of the plurality of previously-generated expert search threads are generated by: analyzing one or more search terms in social media posts across multiple social media platforms (page 78, column 1, e.g. analyzing data from communication sources (emails, instant messages, and calendars) and Web 2.0 sources (blogs, wikis, social bookmarking systems, online profiles, and so on), the system answers questions about who knows what and who knows whom) to identify a candidate for authoring an expert search thread, wherein the one or more search terms are extracted (page 78, column 2, e.g. periodically updates new social activities and extracts features from the captured data) from one or more social media posts of the candidate (page 81, column 1, e.g. a search string and maps it onto related keywords. The search engine aggregates the results for all the keywords and ranks them according to relevance weighting and aggregated social-network structure. SmallBlue then generates a list of the top 1,000 people who best match the search terms. The list is displayed with each person’s picture along with his or her job title, role, and online status. By default, SmallBlue shows the top matched experts in the entire company. Users can search experts within a business division);
Contacting, by an expert thread invitation engine by way of private message on one or more of the social media platform, the candidate to solicit an expert search thread, wherein the expert search thread generated by the candidate based on the solicitation includes a plurality of links entered via a user interface that the expert identifies as being helpful to learning about a topic associated with the one or more search terms (page 82, Figure 2, and column 2, e.g. To increase the likelihood of the searcher contacting someone new and receiving a response from that person, we made the social paths to each person visible as a form of six degrees of separation. That is, we display the minimal number of intermediate people to contact to reach the person. Paths of three or fewer are shown in the initial relevance ranked display with the line, ‘‘Ask ,person.’’ or ‘‘Ask ,person1.5.,person2..’’ The example of the Find page in Figure 2 shows the social distance (degrees of separation) from the seeker to each of the people on the page, up to three degrees. Of the 10 people displayed in Figure 2, this searcher is directly connected to one of them, shown as ‘‘my collaborator or contact,’’ and indirectly connected to the other nine people, either by two degrees, for example ‘‘ask Vicky’’ or three degrees. Users can also apply a filter to the results so that they only see those people who are within one, two, or three degrees of separation. The default is set to view all people).
Lin discloses groups in large companies take on informal roles that can improve innovation effectiveness. A global study recently found that CEOs are most interested in establishing a supportive culture and climate to help their companies innovate, including developing new products, services, and markets; creating new business models; or improving existing operations (page 78, column 1). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Lin to improve the method of Jain. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Jain as modified with the search expert of Lin. The benefit would be to improve innovation effectiveness, creating new business models; or improving existing operations.
However, Jain as modified does not disclose “wherein the contacting of the candidate to solicit an expert search thread and the generation of the expert search thread by the candidate occur before the receiving of the query by the search engine, such that the displayed expert search threads are previously-generated.” Jain as modified discloses the claimed invention but not in the same order as claimed. It is noted that selection of any order of performing process steps is prima facie obvious in the absence of new or unexpected results. See In re Burhans, 154 F.2d 690, 69 USPQ 330 (CCPA 1946) Therefore, it would have been prima facie obvious to perform the method of Jain et al. in view of Lin et al. in the same order as claimed to achieve the same expected results.
Claim 19, Jain as modified discloses the expert search thread further includes an order associated with the plurality of links that the expert identifies as being optimal for viewing the plurality of links to learn about the topic (Lin, page 82, e.g. Figure 2).
Claim 25, Jain as modified discloses a computer-implemented method for providing search results, comprising:
receiving, by a search engine, a query comprising input (Jain, page 2, [0017], e.g. search engine and [0018], e.g. a search query is received from a user);
determining, by the search engine, which of a plurality of previously-generated expert search threads to provide in response to the query (Jain, page 2, [0018], e.g. search results based at least in part upon previous actions taken by the expert relevant to the search query are identified); and
providing, by the search engine, links to the determined previously-generated expert search threads in a graphical user interface (Jain, page 2, [0018], e.g. These results may then be returned to the user);
scoring each expert search thread based on observations of interactions of other users with the expert search thread within search results for the one or more search terms; a link to the expert search thread is positioned in the search results according to the scoring (Jain, page 1, [0016], e.g. higher weightings may be applied to search results of an identified expert than those that would normally be produced by the search engine. The results of both, however, may be presented together in a seamless manner. Alternatively, results produced due to the expert's influence on the search may be presented separately or marked as being "expert picks." The latter embodiments then allow for the possibility that users can subsequently rank the expert's picks (e.g., the value of the link to the user, on a scale of 1 to 10), resulting in a dynamic process wherein experts are not only selected based on past performance but are also being constantly reevaluated based on current performance).
However, Jain does not disclose wherein at least a portion of the plurality of previously-generated expert search threads are generated by:
analyzing social media posts to identify a candidate for authoring an expert search thread, wherein one or more search terms are extracted from one or more social media posts of the candidate; contacting the candidate to solicit an expert search thread, wherein the expert search thread generated by the candidate based on the solicitation includes a plurality of links entered via a user interface that the expert identifies as being helpful to learning about a topic associated with the one or more search terms, wherein the expert search thread generated by the candidate further comprises annotations generated by the candidate for navigating the expert search thread.
Lin discloses analyzing social media posts to identify a candidate for authoring an expert search thread (page 81, column 1, e.g. a search string and maps it onto related keywords. The search engine aggregates the results for all the keywords and ranks them according to relevance weighting and aggregated social-network structure. SmallBlue then generates a list of the top 1,000 people who best match the search terms. The list is displayed with each person’s picture along with his or her job title, role, and online status. By default, SmallBlue shows the top matched experts in the entire company. Users can search experts within a business division), wherein one or more search terms are extracted from one or more social media posts of the candidate (page 78, column 2, e.g. periodically updates new social activities and extracts features from the captured data, and page 79, column 1, e.g. a person belongs as well as a person’s public activities in blogs, forums, social bookmarks, profiles, and so on);
Contacting, by an expert thread invitation engine, the candidate to solicit an expert search thread, wherein the expert search thread generated by the candidate based on the solicitation includes a plurality of links entered via a user interface that the candidate identifies as being helpful to learning about a topic associated with the one or more search terms, wherein the expert search thread generated by the candidate further comprises annotations generated by the candidate for navigating the expert search thread (page 82, Figure 2, and column 2, e.g. To increase the likelihood of the searcher contacting someone new and receiving a response from that person, we made the social paths to each person visible as a form of six degrees of separation. That is, we display the minimal number of intermediate people to contact to reach the person. Paths of three or fewer are shown in the initial relevance ranked display with the line, ‘‘Ask ,person.’’ or ‘‘Ask ,person1.5.,person2..’’ The example of the Find page in Figure 2 shows the social distance (degrees of separation) from the seeker to each of the people on the page, up to three degrees. Of the 10 people displayed in Figure 2, this searcher is directly connected to one of them, shown as ‘‘my collaborator or contact,’’ and indirectly connected to the other nine people, either by two degrees, for example ‘‘ask Vicky’’ or three degrees. Users can also apply a filter to the results so that they only see those people who are within one, two, or three degrees of separation. The default is set to view all people).
Lin discloses groups in large companies take on informal roles that can improve innovation effectiveness. A global study recently found that CEOs are most interested in establishing a supportive culture and climate to help their companies innovate, including developing new products, services, and markets; creating new business models; or improving existing operations (page 78, column 1). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Lin to improve the method of Jain. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Jain as modified with the search expert of Lin. The benefit would be to improve innovation effectiveness, creating new business models; or improving existing operations.
However, Jain as modified does not disclose “wherein the contacting the candidate to solicit an expert search thread and the generation of the expert search thread by the candidate occur before the receiving of the query by the search engine, such that the displayed expert search threads are previously-generated.” Jain as modified discloses the claimed invention but not in the same order as claimed. It is noted that selection of any order of performing process steps is prima facie obvious in the absence of new or unexpected results. See In re Burhans, 154 F.2d 690, 69 USPQ 330 (CCPA 1946) Therefore, it would have been prima facie obvious to perform the method of Jain et al. in view of Lin et al. in the same order as claimed to achieve the same expected results.
Claim 26, Jain as modified discloses a computer-implemented method for providing search results, comprising: receiving, by a search engine, a query (Jain, page 2, [0017], e.g. search engine and [0018], e.g. a search query is received from a user);
determining, by the search engine, which of a plurality of previously-generated expert search threads to provide in response to the query (Jain, page 2, [0018], e.g. search results based at least in part upon previous actions taken by the expert relevant to the search query are identified);
displaying, by the search engine, the determined previously-generated expert search threads in a graphical user interface (Jain, page 1, [0012], e.g. the search engine may user prior searches and/or actions by the selected expert (or experts) in determining which results to display to the user, in addition to or in lieu of the results that the search engine would typically provide without input from experts);
scoring the new expert search thread based on observations of interactions of other users with the new expert search thread within search results for the one or more keywords; and wherein a link to the new expert search thread is positioned in the search results according to the scoring (Jain, page 1, [0016], e.g. higher weightings may be applied to search results of an identified expert than those that would normally be produced by the search engine. The results of both, however, may be presented together in a seamless manner. Alternatively, results produced due to the expert's influence on the search may be presented separately or marked as being "expert picks." The latter embodiments then allow for the possibility that users can subsequently rank the expert's picks (e.g., the value of the link to the user, on a scale of 1 to 10), resulting in a dynamic process wherein experts are not only selected based on past performance but are also being constantly reevaluated based on current performance).
However, Jain does not disclose wherein at least a portion of the plurality of previously-generated expert search threads are generated by:
identifying a candidate for authoring a new expert search thread by extracting by a keyword extractor, one or more keywords in at least one social media post of the candidate;
contacting the candidate to solicit the new expert search thread;
pre-populating a user interface with the extracted keywords to streamline creation of the new expert search thread;
Lin discloses wherein at least a portion of the plurality of previously-generated expert search threads are generated by:
identifying a candidate for authoring a new expert search thread by extracting by a keyword extractor, one or more keywords in at least one social media post of the candidate (Lin, page 78, column 1, e.g. analyzing data from communication sources (emails, instant messages, and calendars) and Web 2.0 sources (blogs, wikis, social bookmarking systems, online profiles, and so on), the system answers questions about who knows what and who knows whom);
contacting the candidate to solicit the new expert search thread (Lin, page 82, Figure 2, and column 2, e.g. Paths of three or fewer are shown in the initial relevance ranked display with the line, ‘‘Ask, person.’’ or ‘‘Ask, person1.5., person2..’’ The example of the Find page in Figure 2 shows the social distance (degrees of separation) from the seeker to each of the people on the page, up to three degrees. Of the 10 people displayed in Figure 2, this searcher is directly connected to one of them, shown as ‘‘my collaborator or contact,’’ and indirectly connected to the other nine people, either by two degrees, for example ‘‘ask Vicky’’ or three degrees. Users can also apply a filter to the results so that they only see those people who are within one, two, or three degrees of separation. The default is set to view all people);
pre-populating a user interface with the extracted keywords to streamline creation of the new expert search thread (Lin, page 82, Figure 2).
Lin discloses groups in large companies take on informal roles that can improve innovation effectiveness. A global study recently found that CEOs are most interested in establishing a supportive culture and climate to help their companies innovate, including developing new products, services, and markets; creating new business models; or improving existing operations (page 78, column 1). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Lin to improve the method of Jain. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Jain as modified with the search expert of Lin. The benefit would be to improve innovation effectiveness, creating new business models; or improving existing operations.
However, Jain as modified does not disclose “wherein the contacting the candidate to solicit an expert search thread and the generation of the expert search thread by the candidate occur before the receiving of the query by the search engine, such that the displayed expert search threads are previously-generated.” Jain as modified discloses the claimed invention but not in the same order as claimed. It is noted that selection of any order of performing process steps is prima facie obvious in the absence of new or unexpected results. See In re Burhans, 154 F.2d 690, 69 USPQ 330 (CCPA 1946) Therefore, it would have been prima facie obvious to perform the method of Jain et al. in view of Lin et al. in the same order as claimed to achieve the same expected results.
Claim 27, Jain as modified discloses a computer-implemented method for providing search results, comprising:
receiving, by a search engine, a query (Jain, page 2, [0018], e.g. a search query is received from a user);
determining, by the search engine, which of a plurality of previously-generated expert search threads to provide in response to the query (Jain, page 1, [0012], e.g. the search engine may user prior searches and/or actions by the selected expert (or experts) in determining which results to display to the user, in addition to or in lieu of the results that the search engine would typically provide without input from experts); and
displaying, by the search engine, the determined previously-generated expert search threads in a graphical user interface (Jain, page 1, [0012], e.g. the search engine may user prior searches and/or actions by the selected expert (or experts) in determining which results to display to the user, in addition to or in lieu of the results that the search engine would typically provide without input from experts); and
scoring each expert search thread based on observations of interactions of other users with the expert search thread within search results for the one or more search terms; and wherein a link to the expert search thread is positioned in the search results according to the scoring (Jain, page 1, [0016], e.g. higher weightings may be applied to search results of an identified expert than those that would normally be produced by the search engine. The results of both, however, may be presented together in a seamless manner. Alternatively, results produced due to the expert's influence on the search may be presented separately or marked as being "expert picks." The latter embodiments then allow for the possibility that users can subsequently rank the expert's picks (e.g., the value of the link to the user, on a scale of 1 to 10), resulting in a dynamic process wherein experts are not only selected based on past performance but are also being constantly reevaluated based on current performance).
However, Jain does not disclose wherein at least a portion of the plurality of previously-generated expert search threads are generated by:
analyzing one or more search terms in communications to identify a candidate for authoring an expert search thread, wherein the one or more search terms are extracted from one or more communications of the candidate;
contacting, by an expert thread invitation engine by way of a message, the candidate to solicit an expert search thread, wherein the expert search thread generated by the candidate based on the solicitation includes a plurality of links entered via a user interface that the candidate identifies as being helpful to learning about a topic associated with the one or more search terms.
Lin discloses wherein at least a portion of the plurality of previously-generated expert search threads are generated by:
analyzing one or more search terms in communications to identify a candidate for authoring an expert search thread (page 78, column 1, e.g. analyzing data from communication sources (emails, instant messages, and calendars) and Web 2.0 sources (blogs, wikis, social bookmarking systems, online profiles, and so on), wherein the one or more search terms are extracted from one or more communications of the candidate (Lin, page 79, e.g. by extracting information from existing sources. Those sources could be public, such as coauthored documents; patents; or user-generated blogs, wikis, and social tagging systems);
contacting, by an expert thread invitation engine by way of a message, the candidate to solicit an expert search thread, wherein the expert search thread generated by the candidate based on the solicitation includes a plurality of links entered via a user interface that the candidate identifies as being helpful to learning about a topic associated with the one or more search terms (Lin, page 82, Figure 2, and column 2, e.g. Paths of three or fewer are shown in the initial relevance ranked display with the line, ‘‘Ask ,person.’’ or ‘‘Ask ,person1.5.,person2..’’ The example of the Find page in Figure 2 shows the social distance (degrees of separation) from the seeker to each of the people on the page, up to three degrees. Of the 10 people displayed in Figure 2, this searcher is directly connected to one of them, shown as ‘‘my collaborator or contact,’’ and indirectly connected to the other nine people, either by two degrees, for example ‘‘ask Vicky’’ or three degrees. Users can also apply a filter to the results so that they only see those people who are within one, two, or three degrees of separation. The default is set to view all people).
Lin discloses groups in large companies take on informal roles that can improve innovation effectiveness. A global study recently found that CEOs are most interested in establishing a supportive culture and climate to help their companies innovate, including developing new products, services, and markets; creating new business models; or improving existing operations (page 78, column 1). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Lin to improve the method of Jain. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Jain as modified with the search expert of Lin. The benefit would be to improve innovation effectiveness, creating new business models; or improving existing operations.
However, Jain as modified does not disclose “wherein the contacting the candidate to solicit an expert search thread and the generation of the expert search thread by the candidate occur before the receiving of the query by the search engine, such that the displayed expert search threads are previously-generated.” Jain as modified discloses the claimed invention but not in the same order as claimed. It is noted that selection of any order of performing process steps is prima facie obvious in the absence of new or unexpected results. See In re Burhans, 154 F.2d 690, 69 USPQ 330 (CCPA 1946) Therefore, it would have been prima facie obvious to perform the method of Jain et al. in view of Lin et al. in the same order as claimed to achieve the same expected results.
Claim 28, Jain as modified discloses a computer-implemented method for providing search results, comprising:
receiving, by a search engine, a query (Jain, page 2, [0017], e.g. search engine), a query (Jain, page 2, [0018], e.g. a search query is received from a user);
determining, by the search engine, which of a plurality of generated expert search threads to provide in response to the query (Jain, page 2, [0018], e.g. search results based at least in part upon previous actions taken by the expert relevant to the search query are identified); and displaying, by the search engine (Jain, page 1, [0012], e.g. the search engine may user prior searches and/or actions by the selected expert (or experts) in determining which results to display to the user, in addition to or in lieu of the results that the search engine would typically provide without input from experts), the determined generated expert search threads in a graphical user interface positioned according to the scoring (Jain, page 1, [0016], e.g. higher weightings may be applied to search results of an identified expert than those that would normally be produced by the search engine. The results of both, however, may be presented together in a seamless manner. Alternatively, results produced due to the expert's influence on the search may be presented separately or marked as being "expert picks." The latter embodiments then allow for the possibility that users can subsequently rank the expert's picks (e.g., the value of the link to the user, on a scale of 1 to 10), resulting in a dynamic process wherein experts are not only selected based on past performance but are also being constantly reevaluated based on current performance).
However, Jain does not disclose analyzing one or more search terms in social media posts across one or more social media platforms to identify a candidate for authoring an expert search thread, wherein the one or more search terms are extracted from social media content associated with the candidate; contacting, by an expert thread invitation engine, the candidate to solicit an expert search thread, wherein the expert search thread generated by the candidate based on the solicitation includes a plurality of links entered via a user interface that the candidate identifies as being helpful to learning about a topic associated with the one or more search terms; scoring each expert search thread based on observations of interactions of other users with the expert search thread within search results for the one or more search terms or similar search terms.
analyzing one or more search terms in social media posts across one or more social media platforms (Lin, page 78, column 1, e.g. analyzing data from communication sources (emails, instant messages, and calendars) and Web 2.0 sources (blogs, wikis, social bookmarking systems, online profiles, and so on), the system answers questions about who knows what and who knows whom) to identify a candidate for authoring an expert search thread (Lin, page 81, column 1, e.g. SmallBlue shows the top matched experts in the entire company. Users can search experts within a business division), wherein the one or more search terms are extracted from social media content associated with the candidate (Lin, page 78, column 2, e.g. periodically updates new social activities and extracts features from the captured data);
contacting, by an expert thread invitation engine, the candidate to solicit an expert search thread, wherein the expert search thread generated by the candidate based on the solicitation includes a plurality of links entered via a user interface that the candidate identifies as being helpful to learning about a topic associated with the one or more search terms (Lin, page 82, Figure 2, and column 2, e.g. the searcher contacting someone new and receiving a response from that person, we made the social paths to each person visible as a form of six degrees of separation. That is, we display the minimal number of intermediate people to contact to reach the person. Paths of three or fewer are shown in the initial relevance ranked display with the line, ‘‘Ask, person.’’ or ‘‘Ask, person1.5., person2..’’ The example of the Find page in Figure 2 shows the social distance (degrees of separation) from the seeker to each of the people on the page, up to three degrees. Of the 10 people displayed in Figure 2, this searcher is directly connected to one of them, shown as ‘‘my collaborator or contact,’’ and indirectly connected to the other nine people, either by two degrees, for example ‘‘ask Vicky’’ or three degrees. Users can also apply a filter to the results so that they only see those people who are within one, two, or three degrees of separation. The default is set to view all people);
scoring each expert search thread based on observations of interactions of other users with the expert search thread within search results for the one or more search terms or similar search terms (Jain, page 1, [0016], e.g. higher weightings may be applied to search results of an identified expert than those that would normally be produced by the search engine. The results of both, however, may be presented together in a seamless manner. Alternatively, results produced due to the expert's influence on the search may be presented separately or marked as being "expert picks." The latter embodiments then allow for the possibility that users can subsequently rank the expert's picks (e.g., the value of the link to the user, on a scale of 1 to 10), resulting in a dynamic process wherein experts are not only selected based on past performance but are also being constantly reevaluated based on current performance);
Claim 31, Jain as modified discloses the social media content associated with the candidate comprises a social media post, a profile page, or a website of the candidate (Lin, page 78, column 1, e.g. analyzing data from communication sources (emails, instant messages, and calendars) and Web 2.0 sources (blogs, wikis, social bookmarking systems, online profiles, and so on).
Claim(s) 2, 3, 16, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (Jain hereafter, US 2009/0055384 A1) in view of Lin et al. (SmallBlue: People Mining for Expertise Search), as applied to claims 1, 19, 25-28, and 31 above, in further view of Archambault (US 8,478,735 B1).
Claim 2, Jain as modified discloses the claimed invention except for determining a quality metric associated with the one or more social media posts of the candidate; wherein the candidate is identified based on the quality metric. Archambault discloses determining a quality metric associated with the one or more social media posts of the candidate; wherein the candidate is identified based on the quality metric (Archambault, column 6, lines 10-25, e.g. expert score represents a level of expertise of a member on a given subject. The expert score is based, among other things, on the volume of contribution and use of a social network as reflected by: sharing links, articles, music, videos, etc.; viewing and liking other people's links and comments; posting new materials; commenting on other people's content; a members's content being re-shared by others (thereby confirming that the user is good for the subject); recency of the activities by the member; and uniqueness of content shared by a member; i.e., if a member only re-shares everything from his friends, he's probably not an expert, but if he posts unique/quality content which is then re-shared by his friends, he is most likely seen as an influencer).
Archambault discloses an invention to address the need to change how people collect, find and share content on the Web by providing a method for obtaining ranked search results including names of ranked experts who are members of a social network and presenting the ranked search results to a user performing a search from a user interface of a user device (column 2). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Archambault to improve the method of Jain as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Jain with the improvements describes by Archambault. The benefit would be to address the need to change how people collect, find and share content on the Web by providing a method for obtaining ranked search results including names of ranked experts who are members of a social network and presenting the ranked search results to a user performing a search from a user interface of a user device.
Claim 3, Jain as modified discloses the quality metric is based on a topic associated with the one or more social media posts of the candidate (Archambault, column 6, lines 10-25, e.g. expert score represents a level of expertise of a member on a given subject. The expert score is based, among other things, on the volume of contribution and use of a social network as reflected by: sharing links, articles, music, videos, etc.; viewing and liking other people's links and comments; posting new materials; commenting on other people's content; a members's content being re-shared by others (thereby confirming that the user is good for the subject); recency of the activities by the member; and uniqueness of content shared by a member; i.e., if a member only re-shares everything from his friends, he's probably not an expert, but if he posts unique/quality content which is then re-shared by his friends, he is most likely seen as an influencer).
Claim 16, Jain as modified discloses the quality metric is based on a number of interactions of others with the one or more social media posts of the candidate (Archambault, column 6, lines 1-9, e.g. Other pieces of information include "social signals" such as how much interaction there is with a user on a social network wherein the greater the interaction, the higher the user's results will be ranked).
Claim 17, Jain as modified discloses the interactions include likes, shares, and replies (Archambault, column 6, lines 10-25, e.g. expert score represents a level of expertise of a member on a given subject. The expert score is based, among other things, on the volume of contribution and use of a social network as reflected by: sharing links, articles, music, videos, etc.; viewing and liking other people's links and comments).
Claim(s) 4 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (Jain hereafter, US 2009/0055384 A1) in view of Lin et al. (SmallBlue: People Mining for Expertise Search) and Archambault (US 8,478,735 B1), as applied to claims 1-3, 16, 17, 19, 25-28, and 31 above, in further view of Ghosh et al. (Ghosh hereafter, Pub No. US 20130297581 A1).
Claims 4, Jain as modified discloses the claimed invention except for the limitation of the one or more social media posts are associated with a trending topic. Ghosh discloses the one or more social media posts are assigned a high-quality metric when the one or more social media posts are associated with a trending topic (Ghosh, page 7, [0115], e.g. social media content analysis engine 104 presents the trending top posts for the keywords and parameters specified, where the view displays the actual post, along with the author of the post, a timestamp of when the post was originally communicated, and the corresponding mention, influential (number of influential mentions), momentum, velocity, and peak metrics).
Ghosh discloses social media content collection engine 102 utilizes a user's historical comments/posts/citations to improve accuracy for language detection for search results or analytics (Ghosh, page 5, [0084]). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Ghosh to improve the method of Jain as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Jain as modified with the social media content collection engine of Ghosh. The benefit would be to improve accuracy for language detection for search results or analytics.
Claim 5, Jain as modified discloses the one or more social media posts are assigned a high-quality quality metric when the one or more social media posts are associated with a trending topic and the one or more social media posts were made well before the trending topic began trending (Ghosh, page 7, [0115], e.g. social media content analysis engine 104 presents the trending top posts for the keywords and parameters specified, where the view displays the actual post, along with the author of the post, a timestamp of when the post was originally communicated, and the corresponding mention, influential (number of influential mentions), momentum, velocity, and peak metrics).
Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (Jain hereafter, US 2009/0055384 A1) in view of Lin et al. (SmallBlue: People Mining for Expertise Search) and Archambault (US 8,478,735 B1), as applied to claims 1-3, 16, 17, 19, 25-28, and 31 above, in further view of Ray et al. (Ray hereafter, Pub No. US 20120310928 A1).
Claim 6, Jain as modified discloses the claimed invention except for the limitation of identifying publications of the candidate that are associated with the topic, wherein the quality metric is increased when the candidate has published one or more publications associated with the topic. Ray discloses identifying publications of the candidate that are associated with the topic, wherein the quality metric is increased when the candidate has published one or more publications associated with the topic (Ray, page 2, [0013], e.g. extract author information from only certain types of search items included as part of a corpus implicitly considered well-structured, such as those used for specifications, design plans, estimates, white papers, curriculum vitae, published paper lists, citation lists, patent applications, patents, etc. and [0014], e.g. a search engine that uses an expertise mining algorithm and author-ranking heuristics to mine for authors having a certain level of expertise in an enterprise-type profile-based setting. In one embodiment, a search engine uses an expertise mining query and an expertise mining algorithm that includes the use of a number of author-ranking post-processing stages as part of providing expanded queries and/or providing expertise information).
Ray discloses an improvement to a conventional search service that returns a set of results with Hit-Highlighting over terms in a profile matching the user query. Such limited search capabilities provide overly constrained search results and lack promotion of user confidence in the search system or service (Ray, page 1, [0002]). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Ray to improve the conventional system of Jain as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use search engine of Jain as modified with the author extraction of Ray. The benefit would be to promote user confidence in the search system or service.
Claim 7, Jain as modified discloses the publications are identified by reviewing one or more web pages associated with the candidate (Ray, page 5, [0071], e.g. search engine 104 can use any number of relevancy algorithms as part of returning search results including links associated with files, documents, web pages, file content, virtual content, web-based content, etc).
Claim 8, Jain as modified discloses the one or more web pages include a social media profile page (Ray, pages 2-3, [0021], e.g. corpus 118 includes a profile store 120 including user profile information, well-structured information or search items 122, and other structured and/or unstructured information 124 (e.g., index information, blog data, collaboration data, social networking and other social data, metadata, meta-metadata, etc.).
Claim(s) 9 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (Jain hereafter, US 2009/0055384 A1) in view of Lin et al. (SmallBlue: People Mining for Expertise Search) and Archambault (US 8,478,735 B1), as applied to claims 1-3, 16, 17, 19, 25-28, and 31 above, in further view of Bates et al. (Bates hereafter, US 9253138 B2).
Claim 9, Jain as modified discloses the claimed invention except for the limitation of the quality metric is based on a comparison of words used in the one or more social media posts and words used within a past particular period of time in articles across a plurality of circulated publications. Bates discloses the quality metric is based on a comparison of words used in the one or more social media posts and words used within a past particular period of time in articles across a plurality of circulated publications (Bates, column 7, lines 18-20, e.g. scores a message by comparing the words and phrases in a message to the filtered word/phrase list 610. The filtering rules 620 provide rules that govern when filtering of recipients is recommended for a message. Filtering rules 620 may include one or more rules that apply to all messages for all users. In the alternative, filtering ruled 620 may include different rules for different users. For example, if a user posts a message and receives feedback from an authorized user that the authorized user was greatly offended by the message, the user could define a filtering rule for the offended user to filter (exclude) the user from future messages that have certain words or phrases. In similar fashion, filtering rules 620 could include rules for different user groups).
Bates disclose an improvement to over the prior art for improving a manual process by filtering social media messages (Bates, column 1, lines 19-22). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Bates to improve the method of Jain as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Jain as modified with the comparison of words used in the one or more social media posts and words used within a past particular period of time in articles across a plurality of circulated publications. The benefit would be to improve a manual process by filtering social media messages.
Claim 11, Jain as modified discloses a set of words are filtered from the comparison (Bates, column 7, lines 18-20, e.g. scores a message by comparing the words and phrases in a message to the filtered word/phrase list 610. The filtering rules 620 provide rules that govern when filtering of recipients is recommended for a message).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (Jain hereafter, US 2009/0055384 A1) in view of Lin et al. (SmallBlue: People Mining for Expertise Search), Archambault (US 8,478,735 B1) and Bates et al. (Bates hereafter, US 9253138 B2), as applied to claims 1-3, 9, 11, 16, 17, 19, 25-28, and 31 above, in further view of Taboriskiy et al. (Taboriskiy hereafter, US 20160241605 A1).
Claim 10, Jain as modified discloses the claimed invention except for the limitation of the circulated publications include newspaper articles, magazines, online articles, and blogs, and wherein the past particular period of time is one week. Taboriskiy discloses the circulated publications include newspaper articles, magazines, online articles, and blogs, and wherein the past particular period of time is one week (page 3, [0030], e.g. Examples of media items 134A and 134B can include, and are not limited to, digital video, digital movies, digital photos, digital music, website content, social media updates, electronic books (ebooks), electronic magazines, digital newspapers, digital audio books, electronic journals, blogs, real simple syndication (RSS) feeds, electronic comic books, software applications, text-based messages, social posts, etc., and page 4, [0033], e.g. the pairing process may only be initiated a certain number of times within a certain period, such as three times in a month or five times in a year). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Taboriskiy to improve the method of Jain as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Jain as modified with the publications of Taboriskiy. The benefit would be for improved the resolution of such “big-screen” devices and their refresh rates as well, offering a richer experience (page 1, [0002]).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (Jain hereafter, US 2009/0055384 A1) in view of Lin et al. (SmallBlue: People Mining for Expertise Search), Archambault (US 8,478,735 B1) and Bates et al. (Bates hereafter, US 9253138 B2), as applied to claims 1-3, 9-11, 16, 17, 19, 25-28, and 31 above in further view of Egendorf et al. (Egendorf hereafter, Patent No. US 9348911 B2).
Claim 12, Jain as modified discloses the filter removes articles and conjunctions, wherein the filter further removes or limits the search to a search key word, wherein the search key word filter supports wildcard and stem operators. Egendor discloses the filter removes articles and conjunctions, wherein the filter further removes or limits the search to a search key word, wherein the search key word filter supports wildcard and stem operators (Egendor, column 3, line 60, column 4, line 2, e.g. A user query can then be analyzed for the words it contains, and a list of documents containing those words can be retrieved from the indexes. Refinements include the use of common word filtering (removes “a”, “the”, “and”, etc.), Boolean combinations (“and”, “not”, etc.) of the words sought, phrases (search for the phrase “poisoned by food”), proximity (the word “food” within 20 words of the word “poison”), word stems (e.g., converting plurals to singular form), wildcards, word-level synonyms, and so on).
Egendor discloses an improved method of searching for information on a computer information network (column 12, lines 62-64). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Egendor to improve the method of Jain as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Jain as modified with filter of Egendor. The benefit would be for an improved method of searching for information on a computer information network.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (Jain hereafter, US 2009/0055384 A1) in view of Lin et al. (SmallBlue: People Mining for Expertise Search) and Archambault (US 8,478,735 B1), as applied to claims 1-3, 16, 17, 19, 25-28, and 31 above, in further view of Adel et al. (Adel hereafter, US 20180247189 A1).
Claim 13, Jain as modified discloses the claimed inventio except for the limitation of the quality metric is based on a length of the one or more social media posts of the candidate. Adel discloses the quality metric is based on a length of the one or more social media posts of the candidate (page 4, [0041], e.g. may be applied to extract the most relevant scores and obtain a fixed-length sentence representation of each social media post). Adel discloses an invention that improves performance (page 10, [0113]). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Adel to improve the method of Jain as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Jain as modified with the quality metric of Adel. The benefit would be for improved performance.
Claim(s) 14 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (Jain hereafter, US 2009/0055384 A1) in view of Lin et al. (SmallBlue: People Mining for Expertise Search) and Archambault (US 8,478,735 B1), as applied to claims 1-3, 16, 17, 19, 25-28, and 31 above, in further view of Moore, D., (Moore hereafter, US 20160188597 A1).
Claim 14, Jain as modified discloses the claimed invention except for the limitation of the quality metric is based on a number of factual statements identified in the one or more social media posts of the candidate. Moor discloses the quality metric is based on a number of factual statements identified in the one or more social media posts of the candidate (page 4, [0033], e.g. the NLP engine 202 may automatically translate text from one human language to another. This task requires knowledge of grammar, semantics, and facts in order to solve properly, [0036], e.g. metadata extraction engine 210 may thus include search tools for locating relevant metadata about the poster such as location, gender, age, ethnicity, language, home ownership status, income, and other facts, and [0041], e.g. the context filter 320 may determine if the poster talking about a trending topic and whether there are specific relevant facts surrounding the post). Moore discloses an improvement to overcome the prior art challenge of judging the factual value or customer engagement value of the posts (page 1, [0003]). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Moore to improve the method of Jain as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use Jain as modified with the quality metric of Moore. The benefit would be to overcome the prior art challenge of judging the factual value or customer engagement value of the posts.
Claim 15, Jain as modified discloses a statement is identified as a factual statement based on a part-of-speech pattern associated with the statement (Moore, page 4, [0033], e.g. Another possible function of the NLP engine 202 is part-of-speech tagging. Given a sentence, the NLP engine 202 may determine the part of speech for each word. Many words, especially common ones, can serve as multiple parts of speech. For example, “plan” can be a noun (“the plan under consideration”) or verb (“to plan a trip”); “set” can be a noun, verb or adjective; and “out” can be any of at least five different parts of speech. ).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (Jain hereafter, US 2009/0055384 A1) in view of Lin et al. (SmallBlue: People Mining for Expertise Search), as applied to claims 1, 19, 25-28, and 31 above, in further view of Li et al. (Li hereafter, Patent No. US 10942939 B2).
Claim 21, Jain as modified discloses the claimed invention except for the limitation of extracting characteristics from the one or more social media posts of the candidate; and inputting the one or more characteristics into a regression model having weights that are based on correlations between post characteristics of the one or more social media posts of the candidate and post characteristics of a predetermined subset of past posts; wherein the scoring of each expert search is further based on an output of the regression model. Li discloses extracting characteristics from the one or more social media posts of the candidate; and inputting the one or more characteristics into a regression model having weights that are based on correlations between post characteristics of the one or more social media posts of the candidate and post characteristics of a predetermined subset of past posts (column 3, lines 12-16, e.g. framework 100 extracts social latent factors for each of the plurality of data instances from the link information and selecting one or more relevant features in the social media stream from the one or more features through a regression model using the social latent factors as a constraint during unsupervised streaming feature selection).
Li discloses it is desirable and of great importance to reduce the dimensionality of social media data for many learning tasks due to the curse of dimensionality. Li resolves this problem by disclosing feature selection, which aims to select a subset of relevant features for a compact and accurate representation (Li, column 1, lines 33-38). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Li to improve the method of Jain as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Jain as modified with the regression model of Li. The benefit would be to reduce the dimensionality of social media data for many learning tasks due to the curse of dimensionality.
Claim(s) 29, 32 and 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (Jain hereafter, US 2009/0055384 A1) in view of Lin et al. (SmallBlue: People Mining for Expertise Search), as applied to claims 1, 19, 25-28, and 31 above, in further view of Brandstetter (US 9,002,837 B2).
Claim 29, Jain discloses the claimed invention except for expanding a particular expert search thread based on a user interaction to show details of the particular expert search thread, wherein the expanded thread includes indications of the plurality of network links and annotation text generated using an annotation engine based on content associated with the plurality of network links. Brandstetter discloses expanding a particular expert search thread based on a user interaction to show details of the particular expert search thread, wherein the expanded thread includes indications of the plurality of network links and annotation text generated using an annotation engine based on content associated with the plurality of network links (column 7, line 33 to column 8, line 32, e.g. When, for example, a user clicks on Expert 1's search thread link, the following search thread might be displayed (which could well include additional entries beyond the initial five included below)).
Claim 32, Jain as modified receiving from the candidate, via a user interface, edits to the plurality of links (Brandstetter, column 2, line 59, to column 3, line 4, an expert annotation engine may further allow the expert to notate, highlight, crop or create clips of search results (e.g., of search results containing image, audio or video files) within a suggested expert thread, and to provide comments, suggestions, guidelines, and the like for navigating the expert thread or any search result within the expert thread).
Claim 33, Jain as modified disclose the edits comprise reordering of, additions to, or subtractions from the plurality of links or annotation text associated with the plurality of links (Branstetter, column 3, line 14-20, expert may order the marked search results based on a variety of criteria, such as macro to micro, deductive, inductive, chronological in terms of which site a user should look at first, second, third, etc.).
STATUS OF PRIOR ART
Claim 30 is free of any prior art.
PERTINENT PRIOR ART
Li et al. (US 20080215541 A1) discloses search system may also identify an expert relating to the subject of a query. The search system may identify experts from among persons who have given answers in a discussion thread. The search system then creates an expert profile for each expert. An expert profile is a collection of keywords (e.g., from questions that the expert answers) that relate to the discussion threads in which the expert participated. When a user wants to identify an expert, the user submits a query to the search system. The search system may use conventional search techniques to identify expert profiles the best match the query. The search system then presents the experts associated with the best matching expert profiles to the user ([0007]).
CONCLUSION
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.
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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/Cheyne D Ly/
Primary Examiner, Art Unit 2152
1/9/2026