DETAILED ACTION
This communication is in response to the application filed on 10/20/2023 (domestic benefit). Claims 1-20 have been examined.
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 .
Specification
The use of the term COKE™, PEPSI™, PEPSICO™, TWITTER™, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore, the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM, or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Drawings
New corrected drawings in compliance with 37 CFR 1.121(d) are required in this application because the drawings as filed are hand drawn pencil sketches that lack the required clarity and reproducibility. Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
Claim Objections
Claim 4 objected to because it mentions the piece of content being dependent on claim 1; however, the piece of content is first introduced in claim 2 and is not explicitly mentioned nor defined in claim 1:
“A method according to claim 1 wherein the piece of content comprises a social media post.” should read:
A method according to claim 2 wherein the piece of content comprises a social media post.
Claim 20 objected to because it contains a minor informality:
“determining a second other query related to the stance, a measurement of the a response to the second query statistically indicative of at least some measure of the stance;” should read:
determining a second other query related to the stance, a measurement of a response to the second query statistically indicative of at least some measure of the stance;
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 8 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “simple negation” in claim 8 is a relative term which renders the claim indefinite. The term “simple negation” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention or how it differs from “negation”.
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.
Claim(s) 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claim(s) 1, the limitation(s) “providing a stance to be determined; determining a first query indicative of the stance,” “determining a second other query indicative of a converse of the stance,” “processing semantic analysis of stance for the first query to produce first results,” “processing semantic analysis of stance for the second other query to produce second results,” and “statistically combining the first results and the second results to determine the stance,” as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation that can be practically be performed in the human mind. More specifically, the mental processes of a human deciding on a topic to evaluate, formulating a question to ask, formulating an opposing question to the first question, reading a text and determining whether it supports the first question, reading the same text and determining whether it supports the second question, and mentally weighing the evidence from the two analyses and concluding which stance is more strongly supported. If a limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, 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. The claim does not recite any particular machine, manufacture, or technological environment that imposes a meaningful limit on the judicial exception. Nor does the claim improve the functioning of a computer or any other technology, effect a transformation of an article to a different state or thing, or otherwise apply the judicial exception in a manner that imposes a meaningful limit on the exception. Instead, the claim merely recites the abstract idea itself without any additional technological implementation. The claimed steps amount to no more than using the abstract idea as drafted and therefore do not integrate the exception into a practical application.
Because the claim(s) do(es) not include additional elements beyond the abstract idea itself, there are no elements that could provide an inventive concept. Consequently, the claim does not amount to significantly more than the judicial exception. The claim(s) is/are not patent eligible.
With respect to claim(s) 2, the claim(s) recite(s) “wherein the stance is determined for a piece of content,” which reads on a human analyzing a specific written document, which can be a letter, an email, or a social media post. No additional limitations are present.
With respect to claim(s) 3, and 14, the claim(s) recite(s) “wherein the piece of content is short text message content,” which reads on a human reading a short message, such as a note or a brief text. No additional limitations are present.
With respect to claim(s) 4, and 15, the claim(s) recite(s) “wherein the piece of content comprises a social media post,” which reads on a human reading a social media post. No additional limitations are present.
With respect to claim(s) 5, 17, and 18, the claim(s) recite(s) “wherein the stance is determined for a user,” which reads on a human reading and analyzing multiple statements made by a particular individual in regards to an identified issue, similar to a person reviewing someone’s letters or documents and concluding whether that person supports or opposes a particular topic. No additional limitations are present.
With respect to claim(s) 6, the claim(s) recite(s) “wherein the stance is determined for a set of users, the set of users selected based on a user-selection criteria and all sharing some common selection criteria,” which reads on a human selecting a group of people who share common characteristics and analyzing the group’s statements to determine their collective stance. No additional limitations are present.
With respect to claim(s) 7, the claim(s) recite(s) “wherein the second query is not affirmed when the first query is affirmed, the second query being a simple negation of the first query,” which reads on a human recognizing that if a statement supports “likes PEPSI,” then it does not support “dislikes PEPSI.” No additional limitations are present.
With respect to claim(s) 8, the claim(s) recite(s) “wherein the second query comprises a plurality of queries that as a group statistically would not be true when the first query is true,” which reads on a human recognizing that if a statement supports “likes PEPSI,” then it is unlikely to support “likes COKE,” when it is statistically known that those who prefer PEPSI tend to choose it over COKE. No additional limitations are present.
With respect to claim(s) 9, the claim(s) recite(s) “wherein some queries of the plurality of queries relate to known related issues to the first query,” which reads on a human recognizing that if a statement supports “likes PEPSI,” then it is unlikely to support “likes COKE,” “likes DR. PEPPER,” and “likes SPRITE,” when it is statistically known that those who prefer PEPSI tend to choose it over other brands. No additional limitations are present.
With respect to claim(s) 10, the claim(s) recite(s) “wherein some queries of the plurality of queries relate to known related issues to the first query,” which reads on a human asking questions about topics that are related to the main topic, such as asking someone whether they like “PEPSI commercials,” “PEPSI events,” “other PEPSI products,” to determine PEPSI preference. No additional limitations are present.
With respect to claim(s) 11, the claim(s) recite(s) “wherein some queries of the plurality of queries relate to known related positions of people 'for' the first query,” which reads on a human recognizing that people who support a particular position often hold other related positions. For example, a human might infer that someone who “likes PEPSI” may also criticize COKE’s campaigns or support celebrities who endorse PEPSI. No additional limitations are present.
With respect to claim(s) 12, the claim(s) recite(s) “providing a first stance to be determined; determining a first query indicative of the first stance,” “determining a second other query indicative of a second different stance having a known statistical relation to the first stance,” “processing semantic analysis to determine stance for the first query to provide first results”, “processing semantic analysis to determine stance for the second other query to provide second results,” and “statistically combining the first results and the second results to determine stance within a piece of content,” as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation that can be practically be performed in the human mind. More specifically, the mental processes of a human selecting a topic to evaluate, formulating a question to ask, formulating a related question based on the statistical relationship between the first question and the second question, reading a text and determining whether it supports the first question, reading the same text and determining whether it supports the second question, and mentally weighing the evidence from the two analyses and concluding which stance is more strongly supported. If a limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, 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. The claim does not recite any particular machine, manufacture, or technological environment that imposes a meaningful limit on the judicial exception. Nor does the claim improve the functioning of a computer or any other technology, effect a transformation of an article to a different state or thing, or otherwise apply the judicial exception in a manner that imposes a meaningful limit on the exception. Instead, the claim merely recites the abstract idea itself without any additional technological implementation. The claimed steps amount to no more than using the abstract idea as drafted and therefore do not integrate the exception into a practical application.
Because the claim(s) do(es) not include additional elements beyond the abstract idea itself, there are no elements that could provide an inventive concept. Consequently, the claim does not amount to significantly more than the judicial exception. The claim(s) is/are not patent eligible.
With respect to claim(s) 13, the claim(s) recite(s) “determining a third other query indicative of a third different stance having a known statistical relation to the first stance; and processing semantic analysis to determine stance for the third other query to provide third results, wherein statistically combining is performed to statistically combine the first results, the second results and the third results to determine stance within a piece of content,” which reads on a human adding a third question to the analysis, reading the text to analyze and determining whether it supports the third question, and mentally weighing the evidence from the three analyses and concluding which stance is more strongly supported. No additional limitations are present.
With respect to claim(s) 16, the claim(s) recite(s) “wherein the social media post is a tweet within the Twitter® ecosystem,” which reads on a human reading a tweet from TWITTER/X. No additional limitations are present.
With respect to claim(s) 19, the claim(s) recite(s) “wherein the stance is determined for one of a user and a group of users and wherein the determined stance is then displayed as a time varying graph of the stance of the one of a user and group of users,” which reads on a human analyzing statements from an individual or group, and drawing a simple chart or graph showing how opinions change over time. The mere display of abstract information in a graphical format does not transform the underlying mental process into a patent-eligible invention. The claim(s) is/are directed to an abstract idea. No additional limitations are present.
With respect to claim(s) 20, the claim(s) recite(s) “providing a first query indicative of a stance to be determined,” “determining a second other query related to the stance, a measurement of the a response to the second query statistically indicative of at least some measure of the stance,” “processing semantic analysis of stance for the first query to produce first results,” “processing semantic analysis of stance for the second other query to produce second results,” and “statistically combining the first results and the second results to determine the stance,” as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation that can be practically be performed in the human mind. More specifically, the mental processes of a human formulating a question about a topic, identifying a related question based on the relationship between the first question and the second question, reading a text and determining whether it supports the first question, reading the same text and determining whether it supports the second question, and mentally weighing the evidence from the two analyses and concluding if the stance is strongly supported. If a limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, 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. The claim does not recite any particular machine, manufacture, or technological environment that imposes a meaningful limit on the judicial exception. Nor does the claim improve the functioning of a computer or any other technology, effect a transformation of an article to a different state or thing, or otherwise apply the judicial exception in a manner that imposes a meaningful limit on the exception. Instead, the claim merely recites the abstract idea itself without any additional technological implementation. The claimed steps amount to no more than using the abstract idea as drafted and therefore do not integrate the exception into a practical application.
Because the claim(s) do(es) not include additional elements beyond the abstract idea itself, there are no elements that could provide an inventive concept. Consequently, the claim does not amount to significantly more than the judicial exception. The claim(s) is/are not patent eligible.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
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, 7, 8, 9, 10, 11, 12, 13, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iwama (US 12626063 B2) in view of Chen (US 20260017332 A1).
Regarding claim 1, Iwama teaches:
providing a stance to be determined (see column 5, lines 46-50, where FIG. 3 illustrates an embodiment of operations performed by the hypothesis extractor 106 and the document stance generator 132 to determine a hypothesis set 130 to use to determine the stances of the documents with respect to the hypothesis set of sentences extracted from the documents);
processing semantic analysis of stance for the first query to produce first results; processing semantic analysis of stance for the second other query to produce second results (see column 5, lines 54-58, where the stance detector 114 determines (at block 304) stances of sentences in each of the documents with sentences in other documents. In one embodiment, the stance detector 114 may determine stances between each pair of sentences in the same and different documents); and
statistically combining the first results and the second results to determine the stance (see column 5, lines 54-56, where the stance detector 114 determines (at block 304) stances of sentences in each of the documents with sentences in other documents. Also see column 5, lines 60-67, and column 6, line 1, where after determining stances, a stance cluster generator 118 forms (at block 306) stance clusters 120 to cluster sentences having strong pro or con stance likelihoods that satisfy a stance criteria, such as exceed a stance threshold, such as a high likelihood, e.g., 80%, of a pro or con stance between two sentences. A cluster may be formed such that each sentence in the cluster has a strong pro or con stance score with another sentence in the cluster.
Iwama fails to teach determining a first query indicative of the stance; and determining a second other query indicative of a converse of the stance. However, Chen does teach:
determining a first query indicative of the stance (see [0043], where first search information set: in this application, it refers to a set including intelligent search information (which may be briefly referred to as an intelligent search information set). Also see [0160], where FIG. 16 is a schematic diagram of third search information according to some embodiments. An interface 161 in FIG. 16 indicates that the object marks an interested content segment in a content detail interface. For example, after a content segment 8 is marked, corresponding recommended search information “Movie C of director B is not as good as Movie M” is presented in a search box of a search sub-interface).
determining a second other query indicative of a converse of the stance (see [0045], where third search information: in this application, it refers to reverse search information corresponding to the first search information. Specifically, the third search information and the first search information, which are reverse search information to each other, have search results whose contents are semantically opposite. Also see [0160], where based on an NLP model and search result calculation, it is determined that the recommended search information has a search result “Movie C of director B receives rave reviews” semantically opposite to its corresponding search result).
Iwama and Chen are both considered to be analogous to the claimed invention because they are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to have modified Iwama to incorporate the teachings of Chen to converse the stance of the first query in order to expand the context of the subject and obtain more relevant information to determine a stance (see [0164], where by generating the third search information, this application may better satisfy a reverse search requirement of the object, which may not only improve the search efficiency, but also help the object to discover and acquire more related information, thereby improving the search experience of the object, and obtaining other information opposite to a viewpoint of the marked content segment).
Regarding claim 7, which depends on claim 1, Iwama in view of Chen teaches all of the limitations in claim 1, but Iwama fails to teach wherein the second query is not affirmed when the first query is affirmed, the second query being a simple negation of the first query.
Furthermore, Chen does teach wherein the second query is not affirmed when the first query is affirmed, the second query being a simple negation of the first query (see [0043], where first search information set: in this application, it refers to a set including intelligent search information (which may be briefly referred to as an intelligent search information set). Also see [0160], where FIG. 16 is a schematic diagram of third search information according to some embodiments. An interface 161 in FIG. 16 indicates that the object marks an interested content segment in a content detail interface. For example, after a content segment 8 is marked, corresponding recommended search information “Movie C of director B is not as good as Movie M” is presented in a search box of a search sub-interface. See [0045], where third search information: in this application, it refers to reverse search information corresponding to the first search information. Specifically, the third search information and the first search information, which are reverse search information to each other, have search results whose contents are semantically opposite. Also see [0160], where based on an NLP model and search result calculation, it is determined that the recommended search information has a search result “Movie C of director B receives rave reviews” semantically opposite to its corresponding search result).
Regarding claim 8, which depends on claim 1, Iwama in view of Chen teaches all of the limitations in claim 1, but Iwama fails to teach wherein the second query is a query that statistically would not be true when the first query is true but is not a simple negation of the first query.
Furthermore, Chen does teach wherein the second query is a query that statistically would not be true when the first query is true but is not a simple negation of the first query (see [0043], where first search information set: in this application, it refers to a set including intelligent search information (which may be briefly referred to as an intelligent search information set). Also see [0160], where FIG. 16 is a schematic diagram of third search information according to some embodiments. An interface 161 in FIG. 16 indicates that the object marks an interested content segment in a content detail interface. For example, after a content segment 8 is marked, corresponding recommended search information “Movie C of director B is not as good as Movie M” is presented in a search box of a search sub-interface. See [0045], where third search information: in this application, it refers to reverse search information corresponding to the first search information. Specifically, the third search information and the first search information, which are reverse search information to each other, have search results whose contents are semantically opposite. Also see [0160], where based on an NLP model and search result calculation, it is determined that the recommended search information has a search result “Movie C of director B receives rave reviews” semantically opposite to its corresponding search result).
Regarding claim 9, which depends on claim 1, Iwama in view of Chen teaches all of the limitations in claim 1, but Iwama fails to teach wherein the second query comprises a plurality of queries that as a group statistically would not be true when the first query is true.
Furthermore, Chen does teach wherein the second query comprises a plurality of queries that as a group statistically would not be true when the first query is true (see [0163], where recommended search information in 8-1 to 1-1 contained in an intelligent search module in the interface 182, i.e., recommended search information corresponding to all content segments currently marked by the object, not only contains original first search information, but also contains third search information associated with the first search information, as partially shown in S181: third search information “Review of Movie C of director B” associated with first search information “Review of director B's plagiarized work, Movie C” in 6-1, third search information “Clarification on plagiarism of Movie C of director B” associated with the first search information “Movie C of director B plagiarized movie of director F” in 7-1, and third search information “Movie C of director B receives rave reviews” associated with the first search information “Movie C of director B is not as good as Movie M” in 8-1, and the like. A search result corresponding to the third search information is semantically opposite to a search result corresponding to the first search information). The interface 182 shows multiple opposite queries (8-1, 7-1, 6-1) that as a group are semantically opposite to the first search query information.
Regarding claim 10, which depends on claim 9, Iwama in view of Chen teaches all of the limitations in claim 9, but Iwama fails to teach wherein some queries of the plurality of queries relate to known related issues to the first query.
Furthermore, Chen does teach wherein some queries of the plurality of queries relate to known related issues to the first query (see [0163], where recommended search information in 8-1 to 1-1 contained in an intelligent search module in the interface 182, i.e., recommended search information corresponding to all content segments currently marked by the object, not only contains original first search information, but also contains third search information associated with the first search information, as partially shown in S181: third search information “Review of Movie C of director B” associated with first search information “Review of director B's plagiarized work, Movie C” in 6-1, third search information “Clarification on plagiarism of Movie C of director B” associated with the first search information “Movie C of director B plagiarized movie of director F” in 7-1, and third search information “Movie C of director B receives rave reviews” associated with the first search information “Movie C of director B is not as good as Movie M” in 8-1, and the like. A search result corresponding to the third search information is semantically opposite to a search result corresponding to the first search information). The third search information is generated based on the first search information. For example, “clarification on plagiarism,” relates to the known related issue of a “review of director B's plagiarized work, Movie C” from the first query.
Regarding claim 11, which depends on claim 9, Iwama in view of Chen teaches all of the limitations in claim 9, but Iwama fails to teach wherein some queries of the plurality of queries relate to known related positions of people 'for' the first query.
Furthermore, Chen does teach wherein some queries of the plurality of queries relate to known related positions of people 'for' the first query (see [0160], where recommended search information in 8-1 contained in an intelligent search module in the interface 162 not only contains original first search information, but also contains third search information associated with the first search information, for example, the third search information “Movie C of director B receives rave reviews” associated with the first search information “Movie C of director B is not as good as Movie M.” Also see [0161], where recommended search information in 7-1 contained in an intelligent search module in the interface 172 not only contains original first search information, but also contains third search information associated with the first search information, for example, the third search information “Clarification on plagiarism of Movie C of director B” associated with the first search information “Review of director B's plagiarized work, Movie C.” Also see [0177], where different opinions may be recognized using the text classification technology. If it may be recognized that a search result of a piece of recommended search information (query) has a plurality of viewpoints or positions, and proportions of the plurality of viewpoints or positions are similar, it may be determined that the recommended search information is controversial).
Regarding claim 12, Iwama teaches:
providing a stance to be determined (see column 5, lines 46-50, where FIG. 3 illustrates an embodiment of operations performed by the hypothesis extractor 106 and the document stance generator 132 to determine a hypothesis set 130 to use to determine the stances of the documents with respect to the hypothesis set of sentences extracted from the documents);
processing semantic analysis of stance for the first query to produce first results; processing semantic analysis of stance for the second other query to produce second results (see column 5, lines 54-58, where the stance detector 114 determines (at block 304) stances of sentences in each of the documents with sentences in other documents. In one embodiment, the stance detector 114 may determine stances between each pair of sentences in the same and different documents); and
statistically combining the first results and the second results to determine the stance within a piece of content (see column 5, lines 54-56, where the stance detector 114 determines (at block 304) stances of sentences in each of the documents with sentences in other documents. Also see column 5, lines 60-67, and column 6, line 1, where after determining stances, a stance cluster generator 118 forms (at block 306) stance clusters 120 to cluster sentences having strong pro or con stance likelihoods that satisfy a stance criteria, such as exceed a stance threshold, such as a high likelihood, e.g., 80%, of a pro or con stance between two sentences. A cluster may be formed such that each sentence in the cluster has a strong pro or con stance score with another sentence in the cluster.
Iwama fails to teach determining a first query indicative of the stance; determining a second other query indicative of a second different stance having a known statistical relation to the first stance. However, Chen does teach:
determining a first query indicative of the stance (see [0043], where first search information set: in this application, it refers to a set including intelligent search information (which may be briefly referred to as an intelligent search information set). Also see [0160], where FIG. 16 is a schematic diagram of third search information according to some embodiments. An interface 161 in FIG. 16 indicates that the object marks an interested content segment in a content detail interface. For example, after a content segment 8 is marked, corresponding recommended search information “Movie C of director B is not as good as Movie M” is presented in a search box of a search sub-interface).
determining a second other query indicative of a second different stance having a known statistical relation to the first stance (see [0045], where third search information: in this application, it refers to reverse search information corresponding to the first search information. Specifically, the third search information and the first search information, which are reverse search information to each other, have search results whose contents are semantically opposite. Also see [0160], where based on an NLP model and search result calculation, it is determined that the recommended search information has a search result “Movie C of director B receives rave reviews” semantically opposite to its corresponding search result. Also see [0163], where recommended search information in 8-1 to 1-1 contained in an intelligent search module in the interface 182, i.e., recommended search information corresponding to all content segments currently marked by the object, not only contains original first search information, but also contains third search information associated with the first search information, as partially shown in S181: third search information “Review of Movie C of director B” associated with first search information “Review of director B's plagiarized work, Movie C” in 6-1, third search information “Clarification on plagiarism of Movie C of director B” associated with the first search information “Movie C of director B plagiarized movie of director F” in 7-1, and third search information “Movie C of director B receives rave reviews” associated with the first search information “Movie C of director B is not as good as Movie M” in 8-1, and the like. A search result corresponding to the third search information is semantically opposite to a search result corresponding to the first search information).
Iwama and Chen are both considered to be analogous to the claimed invention because they are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to have modified Iwama to incorporate the teachings of Chen to include a statistically related the stance to the first query in order to expand the context of the subject and obtain more relevant information to determine a stance (see [0164], where by generating the third search information, this application may better satisfy a reverse search requirement of the object, which may not only improve the search efficiency, but also help the object to discover and acquire more related information, thereby improving the search experience of the object, and obtaining other information opposite to a viewpoint of the marked content segment).
Regarding claim 13, which depends on claim 12, Iwama in view of Chen teaches all of the limitations as in claim 12. Furthermore, Iwama teaches:
processing semantic analysis to determine stance for the third other query to provide third results, wherein statistically combining is performed to statistically combine the first results, the second results and the third results to determine stance within a piece of content. (see column 5, lines 54-58, where the stance detector 114 determines (at block 304) stances of sentences in each of the documents with sentences in other documents. In one embodiment, the stance detector 114 may determine stances between each pair of sentences in the same and different documents);
Furthermore, Chen teaches:
determining a third other query indicative of a third different stance having a known statistical relation to the first stance (see [0045], where third search information refers to reverse search information corresponding to the first search information. Specifically, the third search information and the first search information, which are reverse search information to each other, have search results whose contents are semantically opposite. Also see [0163], where recommended search information in 8-1 to 1-1 contained in an intelligent search module in the interface 182, i.e., recommended search information corresponding to all content segments currently marked by the object, not only contains original first search information, but also contains third search information associated with the first search information, as partially shown in S181: third search information “Review of Movie C of director B” associated with first search information “Review of director B's plagiarized work, Movie C” in 6-1, third search information “Clarification on plagiarism of Movie C of director B” associated with the first search information “Movie C of director B plagiarized movie of director F” in 7-1, and third search information “Movie C of director B receives rave reviews” associated with the first search information “Movie C of director B is not as good as Movie M” in 8-1, and the like. A search result corresponding to the third search information is semantically opposite to a search result corresponding to the first search information).
Regarding claim 20, Iwama teaches:
processing semantic analysis of stance for the first query to produce first results; processing semantic analysis of stance for the second other query to produce second results (see column 5, lines 54-58, where the stance detector 114 determines (at block 304) stances of sentences in each of the documents with sentences in other documents. In one embodiment, the stance detector 114 may determine stances between each pair of sentences in the same and different documents); and
statistically combining the first results and the second results to determine the stance (see column 5, lines 54-56, where the stance detector 114 determines (at block 304) stances of sentences in each of the documents with sentences in other documents. Also see column 5, lines 60-67, and column 6, line 1, where after determining stances, a stance cluster generator 118 forms (at block 306) stance clusters 120 to cluster sentences having strong pro or con stance likelihoods that satisfy a stance criteria, such as exceed a stance threshold, such as a high likelihood, e.g., 80%, of a pro or con stance between two sentences. A cluster may be formed such that each sentence in the cluster has a strong pro or con stance score with another sentence in the cluster.
Iwama fails to teach providing a first query indicative of a stance to be determined; and determining a second other query related to the stance, a measurement of a response to the second query statistically indicative of at least some measure of the stance. However, Chen does teach:
providing a first query indicative of a stance to be determined (see [0043], where first search information set: in this application, it refers to a set including intelligent search information (which may be briefly referred to as an intelligent search information set). Also see [0160], where FIG. 16 is a schematic diagram of third search information according to some embodiments. An interface 161 in FIG. 16 indicates that the object marks an interested content segment in a content detail interface. For example, after a content segment 8 is marked, corresponding recommended search information “Movie C of director B is not as good as Movie M” is presented in a search box of a search sub-interface).
determining a second other query related to the stance, a measurement of a response to the second query statistically indicative of at least some measure of the stance (see [0045], where third search information refers to reverse search information corresponding to the first search information. Specifically, the third search information and the first search information, which are reverse search information to each other, have search results whose contents are semantically opposite. Also see [0163], where recommended search information in 8-1 to 1-1 contained in an intelligent search module in the interface 182, i.e., recommended search information corresponding to all content segments currently marked by the object, not only contains original first search information, but also contains third search information associated with the first search information, as partially shown in S181: third search information “Review of Movie C of director B” associated with first search information “Review of director B's plagiarized work, Movie C” in 6-1, third search information “Clarification on plagiarism of Movie C of director B” associated with the first search information “Movie C of director B plagiarized movie of director F” in 7-1, and third search information “Movie C of director B receives rave reviews” associated with the first search information “Movie C of director B is not as good as Movie M” in 8-1, and the like. A search result corresponding to the third search information is semantically opposite to a search result corresponding to the first search information).
Iwama and Chen are both considered to be analogous to the claimed invention because they are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to have modified Iwama to incorporate the teachings of Chen to include a statistically related to a measure of the stance in the first query in order to expand the context of the subject and obtain more relevant information to determine the overall stance (see [0164], where by generating the third search information, this application may better satisfy a reverse search requirement of the object, which may not only improve the search efficiency, but also help the object to discover and acquire more related information, thereby improving the search experience of the object, and obtaining other information opposite to a viewpoint of the marked content segment).
Claim(s) 2, 3, 4, 5, 6, 14, 15, 16, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iwama (US 12626063 B2) in view of Chen (US 20260017332 A1) and further in view of Aldayel et al. (2019) “Your Stance Is Exposed! Analysing Possible Factors for Stance Detection on Social Media.” Proceedings of the ACM on Human-Computer Interaction [New York, NY, USA], vol. 3, no. CSCW, no. 205.
Regarding claim 2 which depends on claim 1, Iwama in view of Chen teaches all of the limitations in claim 1, but Iwama and Chen fail to teach wherein the stance is determined for a piece of content.
However, Aldayel does teach wherein the stance is determined for a piece of content (see Abstract, Page 1, where this paper examines various online features of users to detect their stance towards different topics. Also see Section 1, Page 2, where we examine four groups of signals that might indicate the stance, namely: 1) on-topic posts by the user, which models users who explicitly express their stance on a topic; 2) user’s interactions on social media with other users or websites, which models users interactions online regardless having them expressing their stance or not (IN); 3) user’s preferences the posts they like, which enable modeling silent users who do not post or share content only (PN); and finally 4) the network of users they are connected, which enable modeling passive users who might have no content or interaction on social media, but just follow other accounts online (CN)).
Iwama, Chen, and Aldayel are considered to be analogous to the claimed invention because they are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to have modified Iwama and Chen to further incorporate the teachings of Aldayel to determine the stance of a piece of content, which can be a short text or social media post from TWITTER, because it can provide concise, publicly available expressions of user opinion that facilitate stance analysis (see Section 2.1, Page 4, where over the last decade, Twitter has become the most commonly used platform to study the expressed stance towards various events/topic. This platform featured to be open and capable to reach a significant proportion of audience. As a social media platform, the network structure in Twitter has a profound existence through various features provided within this platform. These features have made Twittersphere an attractive source of data to study stance and detect opinions toward a broad range of topics in real-time).
Regarding claim 3, which depends on claim 2, Iwama and Chen in view of Aldayel teach all of the limitations in claim 2, but Iwama and Chen fail to teach wherein the piece of content is short text message content.
Furthermore, Aldayel does teach wherein the piece of content is short text message content (see Section 3.2, Page 6, where On-Topic Content (TXT), models the text of the tweet, including features combining both word and character n-grams as presented in the best performing system in SemEVal 2016 [40]. This set of features models stance of users who explicitly express it in text).
Regarding claim 4, which depends on claim 2, Iwama and Chen in view of Aldayel teach all of the limitations in claim 2, but Iwama and Chen fail to teach wherein the piece of content comprises a social media post.
Furthermore, Aldayel does teach wherein the piece of content comprises a social media post (see Section 3.2, Page 6, where we define four features sets to model the stance in social media. These sets are: on-topic content, user’s network interactions, preferences and connections. Those are defined as follow: • On-Topic Content (TXT), models the text of the tweet, including features combining both word and character n-grams as presented in the best performing system in SemEVal 2016 [40]. This set of features models stance of users who explicitly express it in text. • Interaction Network (IN), models the network the user interacts with in their posts. It includes the mentioned accounts (IN@) and website domains (INDM) the user interacts with directly either by retweeting, replying, mentioning, or linking. • Preference Network (PN), models the network the user prefers from the tweets they like. It includes the mentioned accounts (PN@) and linked website domains (PNDM) in the tweets the user likes. • Connection Network (CN), models the online social ties between the users, which includes the accounts who follow the users (followers CNFL), and those the user follows (friends CNFR).
Regarding claim 14, which depends on claim 13, Iwama in view of Chen teaches all of the limitations in claim 13, but Iwama and Chen fail to teach wherein the piece of content is short text message content. Claim 14 is unpatentable over the same prior art and reasons applied against claim 3.
Regarding claim 15, which depends on claim 14, Iwama and Chen in view of Aldayel teach all of the limitations in claim 14, but Iwama and Chen fail to teach wherein the piece of content comprises a social media post. Claim 15 is unpatentable over the same prior art and reasons applied against claim 4.
Regarding claim 16, which depends on claim 15, Iwama and Chen in view of Aldayel teach all of the limitations in claim 15, but Iwama and Chen fail to teach wherein the social media post is a tweet within the Twitter® ecosystem.
Furthermore, Aldayel does teach wherein the social media post is a tweet within the Twitter® ecosystem (see Section 2.2, Page 4, where one of the well-known stance datasets derived from Twitter is the SemEval stance dataset. This dataset is designed for supervised stance detection (task A) [40]. The dataset contains a (topic, tweet) pair for five topics covering political, social, and religious domains. Over 4000 tweets are released in this dataset, each labeled with stance as favor, against, or none to one of the five topics).
Regarding claim 5, which depends on claim 1, Iwama in view of Chen teaches all of the limitations in claim 1, but Iwama and Chen fail to teach wherein the stance is determined for a user.
However, Aldayel does teach wherein the stance is determined for a user (see Abstract, Page 1, where results show that stance of a user can be detected with multiple signals of user’s online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. Also see Section 1, Page 2, where our hypothesis is that user’s embedded viewpoint in a post is related to the user’s identity which could be better modeled by their interactions and connections in the social network).
Iwama, Chen, and Aldayel are considered to be analogous to the claimed invention because they are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to have modified Iwama and Chen to further incorporate the teachings of Aldayel to determine the stance of selected user(s) in regards to a specific topic or issue in order to capture a more accurate representation of a social group’s viewpoint than relying on an individual document or post (see Abstract, Page 1, where our objective is to understand the online signals that can reveal the users’ stance. Also see Section 1, Page 2, where our hypothesis is that user’s embedded viewpoint in a post is related to the user’s identity which could be better modeled by their interactions and connections in the social network. This idea is related to the concept of homophily in which users with same believes tend to have common interests and group together).
Regarding claim 6, which depends on claim 5, Iwama and Chen in view of Aldayel teach all of the limitations in claim 5, but Iwama and Chen fail to teach wherein the stance is determined for a set of users, the set of users selected based on a user-selection criteria and all sharing some common selection criteria.
Furthermore, Aldayel does teach wherein the stance is determined for a set of users, the set of users selected based on a user-selection criteria and all sharing some common selection criteria (see Section 4.1, Page 7, where we further used the Twitter REST API to collect the network information of the users in SemEval stance dataset. Basically, we collected two timelines for each of the users posted the tweets in our dataset, namely Home timeline, which we use to construct the user’s IN; and the Likes timeline, which we use to construct the user’s PN. In addition, we collected the user’s list of followers and friends to construct the user’s CN. Unfortunately, we found that around 25% of these users have been deleted or suspended. Therefore, we end up with smaller number of tweets in the collection that we can apply our approach to them. Also see Section 72, Page 16, where the accounts are not linked to the physical identity of the users. In the process of collecting the additional tweets to extend the SemEval stance dataset we are using authorized developers accounts approved by Twitter application developer portal).
Regarding claim 17, which depends on claim 12, Iwama in view of Chen teaches all of the limitations in claim 12, but Iwama and Chen fail to teach wherein the stance is determined for a user. Claim 17 is unpatentable over the same prior art and reasons applied against claim 5.
Regarding claim 18, which depends on claim 13, Iwama in view of Chen teaches all of the limitations in claim 13, but Iwama and Chen fail to teach wherein the stance is determined for a user. Claim 18 is unpatentable over the same prior art and reasons applied against claim 5.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iwama (US 12626063 B2) in view of Chen (US 20260017332 A1), further in view of Aldayel et al. (2019) “Your Stance Is Exposed! Analysing Possible Factors for Stance Detection on Social Media.” Proceedings of the ACM on Human-Computer Interaction [New York, NY, USA], vol. 3, no. CSCW, no. 205, and further in view of Sun (US 20110078157 A1).
Regarding claim 19, which depends on claim 12, Iwama in view of Chen teaches all of the limitations of claim 12, but Iwama and Chen fail to teach wherein the stance is determined for one of a user and a group of users.
However, Aldayel teaches wherein the stance is determined for one of a user and a group of users (see Section 4.1, Page 7, where we further used the Twitter REST API to collect the network information of the users in SemEval stance dataset. Basically, we collected two timelines for each of the users posted the tweets in our dataset, namely Home timeline, which we use to construct the user’s IN; and the Likes timeline, which we use to construct the user’s PN. In addition, we collected the user’s list of followers and friends to construct the user’s CN. Unfortunately, we found that around 25% of these users have been deleted or suspended. Therefore, we end up with smaller number of tweets in the collection that we can apply our approach to them. Also see Section 72, Page 16, where the accounts are not linked to the physical identity of the users. In the process of collecting the additional tweets to extend the SemEval stance dataset we are using authorized developers accounts approved by Twitter application developer portal).
Iwama, Chen, and Aldayel are considered to be analogous to the claimed invention because they are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to have modified Iwama and Chen to further incorporate the teachings of Aldayel to determine the stance of a user and a group of users in regards to a specific topic or issue in order to capture a more accurate representation of a social group’s viewpoint than relying on an individual document or post (see Abstract, Page 1, where our objective is to understand the online signals that can reveal the users’ stance. Also see Section 1, Page 2, where our hypothesis is that user’s embedded viewpoint in a post is related to the user’s identity which could be better modeled by their interactions and connections in the social network. This idea is related to the concept of homophily in which users with same believes tend to have common interests and group together).
Even as modified by Aldayel, the combination of Iwama, Chen, and Aldayel fails to teach wherein the determined stance is then displayed as a time varying graph of the stance of the one of a user and group of users.
However, Sun does teach wherein the determined stance is then displayed as a time varying graph of the stance of the one of a user and group of users (see [0055], where the opinion search engine 60 may also display the trend of the opinion data for a single product over a period of time on a graph as shown in FIG. 3. FIG. 3 illustrates the trend of the opinion data over a time period of one year. As such, the total number of opinions for each month during one year is illustrated on the graph 300. The trend of the total number of opinions represented by the line 310. In addition to displaying the trend of the opinion data for a single product, the opinion search engine 60 may compare the trend of the opinion data over a period of time for multiple products. In this manner, the opinion data trend for each of the multiple products may be represented by different lines on a graph similar to that as illustrated in FIG. 3).
Iwama, Chen, Aldayel, and Sun are considered to be analogous to the claimed invention because they are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to have modified Iwama, Chen, and Aldayel to further incorporate the teachings of Sun to determine the stance of a user and a group of users in regards to a specific topic or issue and display their opinion over time in a time-varying graph in order to capture a more accurate representation of a social group’s viewpoint over time that can capture trends, changes, and identity shifts that may be useful for companies and organizations (see [0001], where opinion data is typically used to help consumers make an informed purchase decision about an item that they may wish to purchase based on the opinions of other consumers. Opinion data is also used to help companies learn more about how their customers rate their products, customer sentiment for their products and customer satisfaction for their products. Since anyone can add an opinion about anything on the Internet, opinion data is voluminous and includes a plethora of diverse topics, which makes it difficult for users to locate relevant opinions related to their topic of interest).
Conclusion
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/JOHN HONG FANG-WU/ Examiner, Art Unit 2653
/Paras D Shah/ Supervisory Patent Examiner, Art Unit 2653
07/25/2026
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