DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Final Office Action is in response to the application 18/419,544 filed on 02/06/2026.
Status of Claims:
Claims 1, 3, 5, and 7 are amended in this Office Action.
Claims 1-16 are pending in this Office Action.
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 1-16 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.
Regarding claims 1, 3, 5, and 7, the disclosure does not provide adequate description in the specification of the claimed function “shifting the focus of retrieval of search results to the search query to a particular ontology of the plurality of new ontologies”. In paragraph [0032] of the specification, the applicant discloses “The ontology shifter would shift the focus of the search to the appropriate ontology, and provide the results to the search router 115, which would provide search results 116 to the user most closely matching the user's predicted intent.”. At best, one of ordinary skills in the art can understand that the function of shifting the focus is to an appropriate ontology that provides search results to the user most closely matching the user's predicted intent, rather than to an ontology of new ontologies. The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the specification does not provide any description that shifting the focus is to a particular ontology of the plurality of new ontologies. The examiner struggles to find sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Claims 2, 4, 6, 8-16 are also rejected because they are dependent on the rejected claims, claims 1, 3,5, and 7, as set forth above.
Response to Arguments
CLAIM REJECTIONS UNDER 35 U.S.C. § 103
Applicant’s arguments filed on 02/06/2026 (page 11-13) have been fully considered. However, after further examination, new grounds of rejection are presented necessitated by applicant’s amendments.
Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Somnez et al. (USPGPUB 20150095303) "Somnez" in view of Gardner et al. (PGPUB 20060053098) "Gardner", Cooper et al. (PGPUB 20140074826) "Cooper", Vainas et al. (US PGPUB 20180246877) “Vainas”, and Fang (US PGPUB 20150199425) “Fang”.
Regarding claim 1, Somnez teaches a computing system for automatically creating and merging ontological databases of knowledge employing an automated ontology engine, the computing system comprising: one or more hardware processors configured for: receiving data from a plurality of relational structures, wherein at least two of the relational structures are in a ontological domain ([0041]: “FIG. 7 is a flowchart of an embodiment of a method 700 for knowledge transformation, which illustrates how to collect and transform data (data) to knowledge using a RICE platform… Internal enterprise data or external web data may be collected based on a pre-defined data acquisition configuration. The data acquisition configuration may include data wrappers for extracting information from entity-related data sources (plurality of relational structures) in target domains. The collected data may be cleaned and filtered to enhance its quality… semantic data analysis may be performed in which the filtered data may be mapped to a corresponding domain ontology”); receiving additional information from a plurality of sources relevant to the data ([0040]: “An entity may refer to any piece of information, such as a person, an event, a location, etc. The entity-centric knowledge base comprises information about the entities and relationships between the entities”… [0041]: “Data from various sources (plurality of sources) for each entity may be unified to yield a single result”….[0043]: “Data related to a plurality of entities may be acquired from a plurality of heterogeneous data sources based on a customized configuration. The entity-centric knowledge base may be used by an enterprise or company that accesses both internal data sources and external data sources”… Examiner’s note: Thus, data related to entities can be collected from plurality of sources and data that have similar relationship to each other can be grouped in a same graph database so an entity and data related to entity can be equivalent to the data and additional information); analyzing the data and the additional information using one or more machine learning models to identify previously unknown semantic relationships, similarities and differences across the different ontological domains (Fig. 6 & [0022]: “Disclosed data system embodiments may acquire, extract, and analyze knowledge, and may further link distributed knowledge bases together by using natural language processing, semantic web, and machine learning technologies, and the support of Big Data Infrastructure. The data system may integrate both structured and unstructured data sources, and convert the integrated data to semantic knowledge by connecting small graph databases or knowledge graphs together”…[0027]: “The semantic analysis unit 233 may discover relationships between entities, and annotate the acquired data with existing entities and entity relationships defined in the knowledge base”…[0040]: “A graph database (or knowledge graph) may have any size or contain any information in one or more graph structures where nodes represent entities and edges define the relation across entities... The relationships may be discovered by performing analysis on collected text data using a semantic analysis unit (e.g., the unit 233 in FIG. 2)”.. Examiner’s note: Thus, the system uses machine learning technologies to acquire, extract, and analyze knowledge, and further link distributed knowledge bases together. The collected data and relationships are mapped into a corresponding domain ontology or knowledge base wherein the knowledge of ontologies are further linked together so semantic ontological relationships are identified across ontological domains. The system further discover relationships between entities of different graph databases and further link the relationships to build an enriched knowledge base. Thus, the discovering of relationships between entities can be equivalent to identify previously unknown semantic relationships, similarities and differences across the different ontological domains) and to determine one or more new ontological data models from said previously unknown semantic relationships, similarities and differences ([0040]: “The graph databases 610 and 620 may be integrated by specifying relationships linking the graph databases, e.g., the fact that Brad Pitt has also casted in Ocean's Twelve. The relationships may be discovered by performing analysis on collected text data using a semantic analysis unit (e.g., the unit 233 in FIG. 2). Thus, mapping relationships between the entities of different graph databases may lead to the entity-centric knowledge base 600 with enriched information related to its entities”… Examiner’s note: The system creates new ontological data model such as an entity-centric knowledge base from the results of discovered relationships between graph databases wherein the discovered relationships are equivalent to previously unknown relationships); automatically generating, based on the determined one or more new ontological data models, a plurality of new ontologies representing the identified relationships (Fig. 6 & [0030]: “The knowledge base, as the output of the data reconciliation module, may store integrated and unified entity-centric knowledge base in a graph structure with a common upper ontology. An upper ontology may describe general concepts that are the same or similar across most, if not all, knowledge domains. The upper ontology may support very broad semantic interoperability between a large number of domain ontologies that are accessible under the upper ontology”… [0040]: “An entity-centric knowledge base, in which domain entities, such as actors, movies, cities, and other information, are linked to each other to provide enriched information. The knowledge base may be implemented as the knowledge base in FIG. 2. An entity may refer to any piece of information, such as a person, an event, a location, etc. The entity-centric knowledge base comprises information about the entities and relationships between the entities. In an embodiment, the knowledge base may be generated by integrating a number of graph databases including graph databases 610, 620, 630, and 640”…Examiner’s note: Paragraph [0036] of the specification states “The term "ontology" refers to a formal naming and definition of the types, properties, and interrelationships of the entities that exist in a particular domain of discourse. Ontologies are a method of classification of things and their relationships with other things”. One of ordinary skills in the art can understand that ontology is a classification that describes plurality of entities and the relationships between the entities. In figure 6 of Sonmez, each graph database consists of entities and edges in between that represents relationships between the entities. The graph databases are combined together to form a knowledge base and this is equivalent to creating a new ontology from identified entities and their relationships); generating a searchable index of the plurality of new ontologies ([0035]: “A vertical search engine may index contents specialized by location, by topic, or by industry, and may be geared to businesses or enterprises. Instead of returning thousands of links from a query, which may be common on a general purpose search engine, a vertical search engine query may deliver more relevant results to the user”); receiving a search query from a user ([0038]: “The system has an API server that connects to a search engine which allows sending a query by the API user, and returning structured results back to an API user”).
Sonmez does not explicitly teach wherein the least two of the relational structures are in different ontological domains; obtaining contextual information about the search query and the user; predicting a semantic intent of the user based on the contextual information and the search query using a trained machine learning model; and retrieving search results from the searchable index based on the predicted semantic intent.
Gardner teaches wherein the least two of the relational structures are in different ontological domains ([0228]: “In one embodiment, two or more ontologies or portions of ontologies may be merged and exported. For this merger, two or more sets of ontological data may be mapped against one another. Each of the concepts and relationships from the individual sets of data may be compared to one another for corresponding concepts and relationships. A single merged ontology representing the total knowledge of the individual sets of data structure may result. An example of when two or more ontologies (or portions thereof) may be merged and/or exported may include a federated ontology environment (e.g., when more than one group contributes to the development of ontological knowledge in an area). For example, "Group A" may assemble a "kinase" ontology, while "Group B" assembles a muscle toxicity ontology, in which a number of kinases are referenced. These two ontologies may be merged and then exported as a single ontology. This single ontology may contain knowledge that was not present in the two separate ontologies by themselves”… Examiner’s note: Thus, data from different ontological domains can be collected for the subsequent mapping and merging) . It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Gardner teachings in the Somnez system. Skilled artisan would have been motivated to incorporate collecting data from sources that are in different ontologies taught by Gardner in the Somnez system to enhance the creation of a new ontology that leads to increase in relevance of results to submitted queries by the users. This close relation between both of the references highly suggests an expectation of success.
Somnez in view of Gardner does not explicitly teach obtaining contextual information about the search query and the user; predicting a semantic intent of the user based on the contextual information and the search query using a trained machine learning model; and retrieving search results from the searchable index based on the predicted semantic intent.
Cooper teaches obtaining contextual information about the search query and the user ([0012]: “When a querier, such as a customer, asks a question, the system analyzes the language patterns and concepts via a dictionary, such as a set of either rules or concepts, or both. The system also adds to query processing a contextual awareness of the question by using, for example, user profile and session information”... [0031]: “Along with these elements and/or annotations, contextual information from peripheral information repository is provided to rules engine. Contextual information includes peripheral information (other than the query itself) that is used to prepare a response by satisfying business conditions of a rule”…[0058]: “Further to the language condition is the business condition. According to one embodiment a business condition module of rules engine is configured to match peripheral information against one or more business conditions. Business condition module acquires peripheral information for use by system to provide a context in which a query has been initiated. This peripheral information can include a profile of the querier, including past questions and answers, time at which query was initiated (thus providing context to retrieve information that may be time-sensitive), session information, a web page and/or content with which the query coincides, etc”… Examiner’s note: Thus, the system adds contextual information along with the query based on the user profile and session information of the query); predicting a semantic intent of the user based on the contextual information and the search query ([0051]: “The language conditions are derived from a query submitted to system in a natural language and business conditions arise from data that describes the context of the query, including information about the querier. In processing a query, rules engine determines whether a rule in rule dictionary specifies any of these conditions”… [0053]: “IML is a regulars expression language designed to match elements against questions, sentences, documents, or any other body of textual content, and can be used to match certain user intents (when matched against the user's question), or to match documents containing specific terms (when matched against answer content)”); and retrieving search results from the searchable index based on the predicted semantic intent and the particular ontology in focus ([0058]: “According to one embodiment a business condition module of rules engine is configured to match peripheral information against one or more business conditions. Business condition module acquires peripheral information for use by system to provide a context in which a query has been initiated…For example, if the querier is a financial account holder inquiring how to close his or her account, a rule can specify "if account holder requests information about closing account, then take action," where the levels of action can depend on the amount in the account (e.g., premium account holders will receive a personal call, whereas a standard account holder will just be sent requested information to close account). When rules engine determines that a specific unit of peripheral information satisfies a business condition of a rule, then a corresponding action can commence”… [0071]: “Based on the list of actions, response engine selects one or more information retrieval technologies that best services the query. When selecting the retrieval technologies, response engine may choose to utilize an ontology to find specific answers to questions, or it may choose to find documents with the highest general relevance to the user query”… Examiner’s note: Thus, results can be returned based on the query and the predicted semantic intent with the identified ontology). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Cooper teachings in the Somnez and Gardner system. Skilled artisan would have been motivated to incorporate identifying user’s intent within a query for subsequent query processing taught by Cooper in the Somnez and Gardner system to increase the relevance and accuracy of the results that could improves user’s engagements . This close relation between both of the references highly suggests an expectation of success.
Somnez in view of Gardner and Cooper does not explicitly teach wherein predicting a semantic intent of the user based on the contextual information and the search query using a trained machine learning model.
Vainas teaches predicting a semantic intent of the user based on the contextual information and the search query using a trained machine learning model ([0079]: “The apparatus, wherein to determine the intent of the user from the natural language input, the intent extraction module is to apply a machine learning classifier to convert the natural language input into structured output arguments, wherein the structured output arguments comprise the intent of the user, at least one object of the intent, a location of the intent, and a day-time of the intent”.). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Vainas teachings in the Somnez, Gardner, and Cooper system. Skilled artisan would have been motivated to incorporate applying machine learning algorithms in mappings between queries and various user intents taught by Vainas in the Somnez, Gardner, and Cooper system to increase the efficiency of the process through automation and increases the predictive analytics of the system. This close relation between both of the references highly suggests an expectation of success.
Somnez in view of Gardner, Cooper, and Vainas does not explicitly teach shifting the focus of retrieval of search results to the search query to a particular ontology of the plurality of new ontologies based on the predicted semantic intent.
Fang teaches shifting the focus of retrieval of search results to the search query to a particular ontology of the plurality of new ontologies based on the predicted semantic intent ([0030]: “Computing device 102 uses semantic search engine 108 to identify highly ranked ontologies (semantic data sets) that are related to keywords W. In one example, computing device 102 queries semantic search engine 106 to return ontologies that are related to keywords W, such as ontologies 110 (FIG. 1), and then selects the top n ontologies”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Fang teachings in the Somnez, Gardner, Cooper, and Vainas system. Skilled artisan would have been motivated to incorporate selecting highly ranked ontologies for a particular search taught by Fang in the Somnez, Gardner, Cooper, and Vainas system to retrieve relevant results for particular queries, thus can enhance user experience with the system. This close relation between the references highly suggests an expectation of success.
Regarding claim 3, note the rejections of claims 1. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 5, note the rejections of claims 1. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 7, note the rejections of claims 1. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 2, Somnez in view of Gardner, Cooper, Vainas and Fang teaches all of the limitations of claim 1. Somnez further teaches wherein the plurality of new ontologies are used to perform semantic searches ([0031]: “The search user interface module may allow end users to search an entity-centric knowledge base and present the results on an enriched user interface or experience”...[0058]: “A search may no longer need to be based only on keywords, but may also involve semantics, entity relationship, and other contexts. For example, an enterprise knowledge graph may help enterprise users on various aspects such as knowledge discovery, multi-facet search, the optimization of search result ranking algorithms, query extension, recommendation, and summarization”.); and wherein the one or more hardware processors are further configured for: receiving search queries from users ([0038]: “The system has an API server that connects to a search engine which allows sending a query by the API user, and returning structured results back to an API user”); providing context-based search results to the user in response to the search query ([0038]: “The system returns structured and integrated search results back to the API user)”… [0058]: “An enterprise knowledge graph may help enterprise users on various aspects such as knowledge discovery, multi-facet search, the optimization of search result ranking algorithms, query extension, recommendation, and summarization”).
Somnez in view of Gardner does not explicitly teach obtaining contextual information about the search query and the user making the query; predicting the user's intent based on a contextual analysis of the search query itself and the user making the query; comparing the predicted user intent to the searchable index of the plurality of new ontologies from the automated index subsystem.
Cooper teaches obtaining contextual information about the search query and the user making the query([0012]: “When a querier, such as a customer, asks a question, the system analyzes the language patterns and concepts via a dictionary, such as a set of either rules or concepts, or both. The system also adds to query processing a contextual awareness of the question by using, for example, user profile and session information”... [0031]: “Along with these elements and/or annotations, contextual information from peripheral information repository is provided to rules engine. Contextual information includes peripheral information (other than the query itself) that is used to prepare a response by satisfying business conditions of a rule”…[0058]: “Further to the language condition is the business condition. According to one embodiment a business condition module of rules engine is configured to match peripheral information against one or more business conditions. Business condition module acquires peripheral information for use by system to provide a context in which a query has been initiated. This peripheral information can include a profile of the querier, including past questions and answers, time at which query was initiated (thus providing context to retrieve information that may be time-sensitive), session information, a web page and/or content with which the query coincides, etc”… Examiner’s note: Thus, the system adds contextual information along with the query based on the user profile and session information of the query); predicting the user's intent based on a contextual analysis of the search query itself and the user making the query ([0051]: “The language conditions are derived from a query submitted to system in a natural language and business conditions arise from data that describes the context of the query, including information about the querier. In processing a query, rules engine determines whether a rule in rule dictionary specifies any of these conditions”… [0053]: “IML is a regulars expression language designed to match elements against questions, sentences, documents, or any other body of textual content, and can be used to match certain user intents (when matched against the user's question), or to match documents containing specific terms (when matched against answer content)”); comparing the predicted user intent to the searchable index of plurality of new ontologies from the automated index subsystem ([0053]: “IML is a regulars expression language designed to match elements against questions, sentences, documents, or any other body of textual content, and can be used to match certain user intents (when matched against the user's question), or to match documents containing specific terms (when matched against answer content)”… [0056]: “In operation, when a user submits a question. NLM determines whether the set of positive and negative question examples should allow the user's question to match or not to match. To make this determination, system 100 examines the concepts that occur both in the user's question and the question examples. The ontology of multi-layered concept repository can be used to determine whether the user's question is identical to, or shares a common ancestor with, one of the question examples by using a configurable number of generations with which to test common ancestry”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Cooper teachings in the Somnez and Gardner and Vainas system. Skilled artisan would have been motivated to incorporate comparing user intent to the ontologies taught by Cooper in the Somnez and Gardner and Vainas system to increase the relevancy that the user is expected based on their intents. This close relation between both of the references highly suggests an expectation of success.
Regarding claim 4, note the rejections of claims 2. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 6, note the rejections of claims 2. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 8, note the rejections of claims 2. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 9, Somnez in view of Gardner, Cooper, Vainas, and Fang teaches all of the limitations of claim 1. Somnez further teaches wherein the plurality of new ontologies comprise both entities and relationships between entities, wherein the relationships specify semantic connections between the entities within each ontological domain (Fig.6 & [0040]: “FIG. 6 illustrates an embodiment of an entity-centric knowledge base, in which domain entities, such as actors, movies, cities, and other information, are linked to each other to provide enriched information. The knowledge base 600 may be implemented as the knowledge base 254 in FIG. 2. An entity may refer to any piece of information, such as a person, an event, a location, etc. The entity-centric knowledge base 600 comprises information about the entities and relationships between the entities. In an embodiment, the knowledge base 600 may be generated by integrating a number of graph databases including graph databases 610, 620, 630, and 640”...Examiner’s note: A new ontology such as a an entity-centric knowledge base can contain entities and edges that describe the relationships between the entities).
Regarding claim 13, note the rejections of claims 9. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 10, Somnez in view of Gardner, Cooper, Vainas, and Fang teaches all of the limitations of claim 1. Somnez further teaches analyzing the data and the additional information using one or more machine learning models further comprises: clustering identified entities to select appropriate ontologies for representing the data (Fig. 6& [0041]: “The collected data may be cleaned and filtered to enhance its quality and normalize data and filter duplicate and/or incomplete entities. The semantic data analysis may be performed in which the filtered data may be mapped to a corresponding domain ontology”); and translating the data into the selected ontologies by adding records for entities and relationships between them ([0040]: “The relationships may be discovered by performing analysis on collected text data using a semantic analysis unit. Thus, mapping relationships between the entities of different graph databases may lead to the entity-centric knowledge base 600 with enriched information related to its entities”… [0041]: “Data from various sources for each entity may be unified to yield a single result. Based on the extracted entities, discovered entities and/or relations may be added into a knowledge base. The added entities/relations may be linked to the existing entity relations/properties”).
Regarding claim 14, note the rejections of claims 10. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 11, Somnez in view of Gardner, Cooper, Vainas, and Fang teaches all of the limitations of claim 1. Somnez further teaches wherein automatically generating the plurality of new ontologies comprises representing the same data in multiple different ontological structures simultaneously, wherein each ontological structure provides a different semantic view of the underlying data (Fig. 6 & [0040]: “FIG. 6 illustrates an embodiment of an entity-centric knowledge base 600, in which domain entities, such as actors, movies, cities, and other information, are linked to each other to provide enriched information. The knowledge base 600 may be implemented as the knowledge base 254 in FIG. 2. An entity may refer to any piece of information, such as a person, an event, a location, etc. The entity-centric knowledge base 600 comprises information about the entities and relationships between the entities. In an embodiment, the knowledge base 600 may be generated by integrating a number of graph databases including graph databases 610, 620, 630, and 640…Thus, the graph databases 610 and 620 may be integrated by specifying relationships linking the graph databases, e.g., the fact that Brad Pitt has also casted in Ocean's Twelve. The relationships may be discovered by performing analysis on collected text data using a semantic analysis unit (e.g., the unit 233 in FIG. 2). Thus, mapping relationships between the entities of different graph databases may lead to the entity-centric knowledge base 600 with enriched information related to its entities”).
Regarding claim 15, note the rejections of claims 11. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 12, Somnez in view of Gardner, Cooper, Vainas, and Fang teaches all of the limitations of claim 1. Somnez in view of Gardner, Cooper does not explicitly teach wherein predicting the semantic intent of the user further comprises applying vector distance functions to measure semantic similarity between the search query and elements within the plurality of new ontologies.
Vainas teaches predicting the semantic intent of the user further comprises applying vector distance functions to measure semantic similarity between the search query and elements within the plurality of new ontologies ([0054]: “The vector space may be organized as an ontology. The ontology may comprise term/term clusters and senses thereof, organized in, for example, a glossary for each term, as well as a synonym set for each term and an antonym set for each term”…[0055]: “At block 520, intent extraction module 500 may apply one or more machine learning-based classifier(s) to one or more vector space representations of the term(s)/ term cluster(s) of block 515 to determine a likely sense of the term(s)/term cluster(s). The likely sense may be determined, for example, based on a distance, such as a cosine similarity distance, between the vector space representations of the term(s)/term cluster(s) in the natural language input). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Vainas teachings in the Somnez, Gardner, and Cooper system. Skilled artisan would have been motivated to incorporate vector space functionalities to ontologies taught by Vainas in the Somnez, Gardner, and Cooper system to increase the efficiency and accuracy of measuring similarities between different entities. This close relation between both of the references highly suggests an expectation of success.
Regarding claim 16, note the rejections of claims 12. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Boyle et al. (US PGPUB 20120303356) is directed to providing information to a user in response to a received user query. A natural language analysis generates substrings relevant to the user query. An ontology analysis outputs: terms of an ontology matching the relevant generated substrings; and relationships between the terms. A query analysis analyzes the user query regarding the outputted terms and relationships, including ascertaining whether the user query is more suitable for service than for an information search. If it is so ascertained, then service actions for the user to perform are identified to the user. If it is not so ascertained, then: the user query is refined based on the outputted terms and relationships; a search query is generated based on the refined user query, a search is initiated based on the search query, and results of the search are provided to the user.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in
this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/C.D.V./Examiner, Art Unit 2153 04/27/2026
/KAVITA STANLEY/Supervisory Patent Examiner, Art Unit 2153
Prosecution Insights