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 .
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 20 January 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 4, 19 are objected to because of the following informalities: last three lines “the plurality users” should be –the plurality of users--.
Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12141713. Although the claims at issue are not identical, they are not patentably distinct from each other because they are mere obvious variations/combinations of each other.
A claim mapping is shown below.
Claims of instant application
Claims of US Pat 12141713
1. A method for semantic analysis performed by a computer device in finance, comprising:
constructing, by the computer device, a knowledge graph;
constructing, by the computer device, a plurality of first semantic vectors by processing
information published by a plurality of users on a social network through a network;
mapping, by the computer device, the plurality of first semantic vectors on the knowledge graph;
dividing, by the computer device, the plurality of users into at least one category based on a plurality of mapped semantic vectors;
generating, by the computer device, a relationship between user behaviors and user ratings based on the at least one category of the plurality of users; and
constructing, by the computer device, a user behavior model based on the relationship between the user behaviors and the user ratings.
c1. A method for semantic analysis performed by a computer device in finance, comprising:
constructing, by the computer device, a knowledge graph;
acquiring, by the computer device, information published by a plurality of users on a social network through a network;
generating, by the computer device, a user behavior of each of the plurality of users by processing the information based on semantic recognition by using the knowledge graph;
determining, by the computer device, a user rating of the each of the plurality of users based on correctness of the information by processing the information through the semantic recognition;
dividing, by the computer device, the plurality of users into at least one category based on the user behavior of the each of the plurality of users by mapping the plurality of users onto a plurality of semantic vectors mapped into the knowledge graph, wherein the plurality of semantic vectors are constructed based on the processed information, and users of a same category have similar user behavior; and
generating, by the computer device, a relationship between user behaviors and user ratings based on the at least one category of the plurality of users.
c10. The method of claim 1, further comprising:
constructing
a user behavior model
based on the relationship between user behaviors and user ratings.
c2. The method of claim 1, wherein the knowledge graph includes one or more second semantic vectors and associations among the one or more second semantic vectors, and the mapping, by the computer device, the plurality of first semantic vectors on the knowledge graph includes:
mapping and corresponding the plurality of first semantic vectors with spatial positions in the knowledge graph.
C1…..
mapping the plurality of users onto a plurality of semantic vectors mapped into the knowledge graph,
c3. The method of claim 1, further comprising: determining, by the computer device, the user behaviors and the user ratings by processing the information based on semantic recognition using the knowledge graph.
c6. The method of claim 1, wherein the generating, by the computer device, a user behavior of each of the plurality of users by processing the information based on semantic recognition by using the knowledge graph
c3. The method of claim 1, further comprising: determining, by the computer device, the user behaviors and the
user ratings
by processing the information based on semantic recognition using the knowledge graph.
c2. The method of claim 1, wherein the information includes predictive content or declarative content, and the determining, by the computer device, a user rating of each of the plurality of users based on correctness of the information
by processing the information based on the semantic recognition
c4. The method of claim 1, wherein the dividing, by the computer device, the plurality of users into at least one category based on a plurality of mapped semantic vectors includes:
determining at least one cluster by clustering the plurality of mapped semantic vectors;
for each of the at least one cluster,
constructing associations among the mapped semantic vectors in the cluster;
matching the plurality of users to the plurality of mapped semantic vectors; and
constructing
associations among the plurality users of the
same category or different categories according to the mapped semantic vectors in the at least one cluster.
c9. The method of claim 8, wherein the searching, by the computer device, for another user with a similar user behavior based on the user behavior of the each of the plurality of users comprises:
clustering the one or more semantic fields mapped into the knowledge graph, the knowledge graph constructing, based on the clustered one or more semantic fields,
associations among the one or more semantic fields in one or more clusters; mapping, based on the knowledge graph, the plurality of users onto at least one of the one or more semantic fields, the at least one semantic field corresponding to the user behavior of the each of the plurality of users; determining, based on the clustered one or more semantic fields and the at least one semantic field, a cluster that the each of the plurality of users belongs to; and constructing, based on the clustered one or more semantic fields and the knowledge graph,
associations between the plurality of users and the another user, the another user being of a same type or a different type with the plurality of users, which is mapped onto the one or more semantic fields.
c5. The method of claim 3, further comprising:
determining, by the computer device, the user ratings of the plurality of users based
on correctness of the information.
c2. The method of claim 1, wherein the information includes predictive content or declarative content, and the determining, by the computer device, a user rating of each of the plurality of users based on correctness of the information…..
c6. The method of claim 5, wherein the information includes predictive content or declarative content, and the determining, by the computer device, the user ratings of the plurality of users based on correctness of the information includes:
for each of the plurality of users,
setting an initial value of the user rating of the user;
obtaining an evaluation result by comparing the predictive content or the declarative content with determined information, wherein the evaluation result includes correctness of the predictive content or the declarative content; and updating the initial value of the user rating based on the evaluation result.
c2. The method of claim 1, wherein the information includes predictive content or declarative content, and the determining, by the computer device, a user rating of each of the plurality of users based on correctness of the information by processing the information based on the semantic recognition comprises:
setting an initial value of the user rating of the each of the plurality of users;
obtaining an evaluation result by comparing the predictive content or the declarative content with determined information, wherein the evaluation result includes correctness of the predictive content or the declarative content; and updating the initial value of the user rating based on the evaluation result.
c7. The method of claim 6, wherein the obtaining an evaluation
result by comparing the predictive content or the declarative content with determined information includes:
ranking credibility and completeness of the determined information;
matching the determined information to the predictive content or the declarative content based on the ranking; and
comparing matched determined information to the predictive content or the declarative content to derive the correctness of the predictive content or the declarative content.
c3. The method of claim 2, wherein the obtaining, by the computer device, an evaluation result by comparing the predictive content or the declarative content with determined information comprises: ranking credibility and completeness of the determined information;
matching the determined information to the predictive content or the declarative content based on the ranking; and
comparing matched determined information to the predictive content or the declarative content to derive the correctness of the predictive content or the declarative content.
c8. The method of claim 1, wherein the generating, by the computer device, a relationship between user behaviors and user ratings based on the at least one category of the plurality of users includes:
correlating associations among users of the same category to generate an association result;
analyzing user ratings of the users of the same category to generate an analyzing result; and
generating the relationship between the user behaviors and the user ratings based on the association result and the analyzing result.
c4. The method of claim 1, wherein the generating, by the computer device, a relationship between user behaviors and user ratings based on the at least one category of the plurality of users comprises:
correlating associations among the users of the same category to generate an association result;
analyzing user ratings of the users of the same category to generate an analyzing result; and
generating the relationship between the user behaviors and the user ratings based on the association result and the analyzing result.
c9. The method of claim 8, wherein the association includes a similarity of user backgrounds.
5. The method of claim 4, wherein the association includes a similarity of user backgrounds.
c10. The method of claim 3, wherein the determining, by the computer device, the
user behaviors by processing the information includes:
for each of the plurality users,
generating standard information based on the information; and
generating the user behavior of the user based on the standard information and the knowledge graph.
c6. The method of claim 1, wherein the generating, by the computer device, a user behavior of each of the plurality of users by processing the information based on semantic recognition by using the knowledge graph comprises: generating standard information based on the information; and generating the user behavior based on the standard information and the knowledge graph.
c11. The method of claim 10, wherein the generating the user behavior based on the standard information and the knowledge graph comprises:
splitting the standard information into one or more semantic fields; mapping the one or more semantic fields into the knowledge graph, the knowledge graph recognizing the one or more semantic fields based on an association among the one or more semantic fields; and
generating the user behavior based on a recognized result that is generated by the knowledge graph matched with the one or more semantic fields.
c7. The method of claim 6, wherein the generating, by the computer device, the user behavior based on the standard information and the knowledge graph comprises:
splitting the standard information into one or more semantic fields; mapping the one or more semantic fields into the knowledge graph, the knowledge graph recognizing the one or more semantic fields based on
an association among the one or more semantic fields; and generating the user behavior based on a recognized result that is generated by the knowledge graph matched with the one or more semantic fields.
c12. The method of claim 11, further comprising: searching for another user with a similar user behavior based on the user behavior of the user.
c8. The method of claim 7, further comprising: searching for another user with a similar user behavior based on the user behavior of the each of the plurality of users.
c13. The method of claim 12, wherein the searching, by the computer device, for another user with a similar user behavior based on the user behavior of the user includes:
clustering the one or more semantic fields mapped into the knowledge graph, the knowledge graph constructing, based on the clustered one or more semantic fields, associations among the one or more semantic fields in one or more clusters;
mapping, based on the knowledge graph, the user onto at least one of the one or more semantic fields,
the at least one semantic field corresponding to the user behavior of the user;
determining, based on the clustered one or more semantic fields and the at least one semantic field, a cluster that the user belongs to; and
constructing, based on the clustered one or more semantic fields and the knowledge graph, an association between the user and the another user,
the another user being of the same category or a different category with the user, which is mapped onto the one or more semantic fields.
c9. The method of claim 8, wherein the searching, by the computer device, for another user with a similar user behavior based on the user behavior of the each of the plurality of users comprises:
clustering the one or more semantic fields mapped into the knowledge graph, the knowledge graph constructing, based on the clustered one or more semantic fields, associations among the one or more semantic fields in one or more clusters;
mapping, based on the knowledge graph, the plurality of users onto at least one of the one or more semantic fields, the at least one semantic field corresponding to the user behavior of the each of the plurality of users;
determining, based on the clustered one or more semantic fields and the at least one semantic field, a cluster that the each of the plurality of users belongs to; and
constructing, based on the clustered one or more semantic fields and the knowledge graph, associations between the plurality of users and the another user, the another user being of a same type or a different type with the plurality of users, which is mapped onto the one or more semantic fields.
c15. The method of claim 1, wherein the generating, by the computer device, a relationship between user behaviors and user ratings based on the at least one category of the plurality of users includes:
ranking the plurality of users based on the user ratings; and
associating users of higher ratings with users of lower ratings based on the at least one category of the plurality of users.
c11. The method of claim 1, wherein the generating, by the computer device, a relationship between user behaviors and user ratings based on the at least one category of the plurality of users comprises:
ranking the plurality of users based on the user ratings; and associating users of higher ratings with users of lower ratings based on the at least one category of the plurality of users.
As shown in the claims mapping above, claims 1-13, 15 of the instant application are mere combinations/variations of claims 1-9, 11 of the U.S. Patent.
Claims 1, 2 of the instant application recite part of the limitations of claims 1, 10 of the U.S. Patent thus are anticipated by claims 1, 10 of the U.S. Patent.
Claims 3, 4 of the instant application recite limitations similar to claims 6, 9 of the U.S. Patent with a slight difference in wording thus are anticipated by claims 6, 9 of the U.S. Patent. .
Claims 5, 6 of the instant application recite part of the limitations of claim 2 of the U.S. Patent thus anticipated by claim 2 of the U.S. Patent.
Claims 7, 8, 15 of the instant application are mere duplicates of claims 3, 4, 11 of the U.S. Patent except for the language of “includes” vs. “comprises” thus are anticipated by the claims of the U.S. Patent.
Claim 9 of the instant application is an exact duplicate of claim 5 of the U.S. Patent.
Claim 11 of the instant application recites all the limitations of claim 7 of the U.S. Patent except for the language “by the computer device”.
Claims 10, 12, 13 of the instant application recite limitations similar to claims 8, 9 of the U.S. Patent with a slight difference in wording.
Claims 16, 20 merely correspond to system and non-transitory computer program product for claim 1 thus similarly map to claim 12, 20 of the U.S. Patent.
Claim 14 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12141713 in view of Dotan-Cohen et al (US 20180005194 A1).
Regarding claim 14, the claims of the U.S. Patent do not specifically show, however Dotan-Cohen teach the method of claim 1, further comprising:
in response to determining that a preset condition is not met (see at least [0048] … determine a user location pattern based on repetitions of similar location features associated with a plurality of observed location events. In some embodiments, location events or patterns may be determined using location inference logic 230, such as rules, associations, conditions, prediction models, or pattern inference algorithms. The location inference logic 230 can comprise the logic (rules, associations, statistical classifiers, etc.) used for identifying and classifying user locations or visits, and also for determining location patterns. In some embodiments, the user pattern(s) determined by location pattern determiner 269 may be stored as inferred user location patterns data 245a of user features 244. ),
collecting the user behavior model constructed by the relationship between the user behaviors and the user ratings (see at least [0059] In some embodiments, patterns of user location events or visits may be determined by monitoring one or more location features, as described herein. These monitored location features may be determined from the user data described herein as tracked variables or as described in connection to user-data collection component 210). and
updating an optimized user behavior model based on the user behavior model (see at least [0020] Operating environment 100 can be utilized to implement one or more of the components of calendar item enrichment system 200, described in FIG. 2, including components for collecting calendar event data, generating calendar event behavior pattern models, generating user location patterns, generating user profile details, and/or generating and/or presenting enriched calendar event information to consumers or users.).
it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include such features while implementing the method of claim 1 of the U.S. Patent in order to enhance the users’ profiles with event data from a calendar item enrichment system.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sathish et al (US 20150286709 A1) teach a method and system for retrieving information in a knowledge-based assistive network including a plurality of information sources. The method includes receiving at least one localized query at each of the plurality of information sources, sending one or more localized queries to one or more information sources sent in response to determining intent associated with a user, determining a semantic similarity between the intent and information of respective knowledge graphs each associated with one of the plurality of information sources. The knowledge graphs each include information corresponding to the associated one of the plurality of information sources having knowledge about at least one subject. Further, the method comprises retrieving information from at least one information source in the knowledge-based assistive network in accordance with the determined semantic similarity.
Sathish et al (WO 2015122691 A1) teach collating usage information of a data source in a first electronic device (102). The collated usage information is categorized into a knowledge cluster by extracting semantic content from the usage information of the data source in the first electronic device and mapping the semantic content. A knowledge graph is formed using the knowledge cluster. An element of a user interface (UI) is dynamically modified based on the knowledge graph. The knowledge graph is stored locally in the first electronic device.
Oberle, Daniel, et al. "Conceptual user tracking." International Atlantic Web Intelligence Conference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.
ABSTRACT- Web usage mining applies data mining techniques to records of Web site visits. To better understand patterns of usage, analysis should take the semantics of visited URLs into account. This paper presents a framework for enhancing Web usage records with formal semantics based on an ontology underlying the site. Besides, it elicits automated methods of mapping URLs to application events. Using the ontology’s taxonomy, we describe user actions at different levels of abstractions. Using the ontology’s concepts and relations, we capture the multitude of user interests expressed by a visit to one page. We employ our ideas in an application of SEAL, a framework for semantic portals that uses Semantic Web technologies to support communities of interest. Different realizations of semantically enriched user tracking are discussed and related to other approaches. We describe first results from a prototypical system, and discuss benefits of Conceptual User Tracking for Web usage mining.
Liu, Xiaozhong, et al. "Automatic semantic mapping between query terms and controlled vocabulary through using WordNet and Wikipedia." Proceedings of the American Society for Information Science and Technology 45.1 (2008): 1-10.
ABSTRACT- Query log analysis can provide valuable information for improving information retrieval performance. This paper reports findings from a query log mining project, in which query terms falling in the very long tail of low to zero similarity (with the controlled vocabulary) scores were analyzed by using similarity algorithms. The query log data was collected from the Gateway to Educational Materials (GEM). The limited number of terms in the GEM controlled vocabulary was a major source for the long tail of low or zero similarity scores for the query terms. To mitigate this limitation, we employed a strategy that involved using the general-purpose (domain-independent) ontology WordNet and community-created Wikipedia as the bridge to establish semantic relatedness between GEM controlled vocabulary (as well as new concept classes identified by human experts) and user query terms. The two sources, WordNet and Wikipedia, were complementary in mapping different types of query terms. A combination of both sources achieved a modest rate of mapping accuracy. The paper discussed the implications of the findings for automatic semantic analysis and vocabulary development and validation.
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/UYEN T LE/Primary Examiner, Art Unit 2156 27 March 2026