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 .
Claims 1 – 10 are pending in this Office Correspondence.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over “Crafting a Knowledge Graph: The Semantic Data Modeling Way” by Teodora Petkova (“Petkova”) and in view of “Code Search Net Challenge Evaluating the State of Semantic Code Search” by Hamel Husain et al. (“Husain”).
With respect to claims 1 and 9, Petkova teaches a system and method for identifying domain-specific contextual information within an enterprise computing environment to recommend a storyboard for creating a new knowledge asset in response to a search query, which comprises of pre-processing the domain-specific articles received from the information retrieval server by extraction of text and unstructured components (“Ontotext's 10 Steps of Crafting a Knowledge Graph With Semantic Data Modeling”: steps 2 and 3: Gather and analyze relevant data: discover what datasets, taxonomies and other information (proprietary, open or commercially
available) would serve you best to achieve your goal in terms of domain, scope, provenance, maintenance, etc. Clean data to ensure data quality: Correct any data quality issues to make the data most applicable to your task. This includes removing invalid or meaningless entries, adjusting data fields to accommodate multiple values, fixing inconsistencies, etc.),
generation of a domain-specific semantic model using the machine learning model (refer step 4 in section 'Ontotext's 10 Steps of Crafting a Knowledge Graph With Semantic Data Modeling': Create your semantic data model: Analyze thoroughly the different data schemata to prepare for harmonizing the data. Reuse or engineer ontologies, application profiles, RDF shapes or some other mechanism on how to use them together. Formalize your data model using standards like RDF Schema and OWL),
transmission of the domain-specific semantic model to the information retrieval server (refer step 5 in section 'Ontotext's 10 Steps of Crafting a Knowledge Graph With Semantic Data Modeling': Integrate data with ETL tools or virtualization approaches: Apply ETL tools to convert your data to RDF or use data virtualization to access it via technologies such as NoETL, OBDA, GraphQL Federation, etc. Generate semantic metadata to make the data easier to update, discover and reuse),
processing of search query (refer steps 7-8 in section 'Ontotext's 10 Steps of Crafting a Knowledge Graph With Semantic Data Modeling': Architect the data management and search layer Merge different graphs flawlessly using the RDF data model. For locally stored data GraphDB can efficiently enforce the semantics of the data model via reasoning, consistency checking and validation. It can scale in a cluster and synchronize with search engines like Elasticsearch to match the anticipated usage and performance requirements. Augment your graph via reasoning, analytics and text analysis: Enrich your data extracting new entities and relationships from text. Apply inference and graph analytics to uncover new information. Now your graph has more data than the sum of its constituent datasets. It is also better interconnected, which brings more content and enables deeper analytics) and
generation of new knowledge (refer steps 8-10 in section 'Ontotext's 10 Steps of Crafting a Knowledge Graph With Semantic Data Modeling': Augment your graph via reasoning, analytics and text analysis” Enrich your data extracting new entities and relationships from text. Apply inference and graph analytics to uncover new information. Now your graph has more data than the sum of its constituent datasets. It is also better interconnected, which brings more content and enables deeper analytics.
Maximize the usability of your data: Start delivering the answers to your original questions through different knowledge discovery tools such as powerful SPARQL queries, easy to use GraphQL interface, semantic search, faceted search, data visualization, etc. Also, ensure that your data is FAIR (findable, accessible, interoperable and reusable). Make your KG easy to maintain and evolve: after you have crafted your knowledge graph and people have started using it, keep it live by setting up your maintenance procedures - the way it would evolve and updates from the different sources will be consumed while maintaining high data quality).
Petkova does not explicitly teach processing of extracted text from articles into paragraphs, sentences and words, storing the extracted text in a database, creation of a domain specific semantic model based on the articles, updating of the model by the addition and deletion of articles and search operation on the model.
However, Husain discloses processing of extracted text from articles into paragraphs, sentences and words (section 2), storing the extracted text in a database (section 2), creation of a domain specific semantic model based on the articles (sections 3 and 4), updating of the model by the addition and deletion of articles (sections 3 and 4) and search operation on the model (sections 3 and 4).
Both of Petkova and Husain are same field of endeavor and they are both in the data processing art and therefore, they are combinable/modifiable.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teachings of Petkova with the teachings of Husain in order to efficiently use rare term, which often appear in code. Researching neural methods that can efficiently and accurately represent rare terms will improve performance. Using code quality as an additional signals that allows for filtering of bad results could substantially improve satisfaction of search users.
As to claim 3, the information retrieval server is configured to tokenize the search query into one or more sentences and one or more words; and identify whether the search query is related to identifying search results including enterprise knowledge assets or to create a new knowledge asset (Husain: Section 2, Code search Net Corpus Collection: tokenizing all Go, Java, JavaScript, Python, PHP and Ruby function or method using tree sitter - . . .documentation text using a heuristic regular expression).
As to claim 8, the central semantic model generating unit is further configured to update the domain-specific semantic model based on new domain-specific articles and assets across the world wide web (Petkova, “Ontotext's 10 Steps of Crafting a knowledge Graph With Semantic Data Modeling”: Step 3, Clean data to ensure data quality: Correct any data quality issues to make the data most applicable to your task. This includes removing invalid or meaningless entries, adjusting data fields to accommodate multiple values, fixing inconsistencies, etc.).
Allowable Subject Matter
Claims 2, 4 – 7 and 10 are objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art made of record does not explicitly teach or fairly suggest, “central semantic model generating unit is configured to (i) transmit the domain-specific semantic model (M) to the information retrieval server that is specific for the enterprise; and(ii) generate an updated domain specific semantic model (M') by updating the domain specific semantic model, using a model update pipeline, based on a plurality of updated domain- specific articles and assets (A') comprising an addition or deletion of one or more articles and assets received from the first database on periodic intervals, thereafter transmit the updated domain specific semantic model (M') to execute on the information retrieval server” – as recited in claims 2 and 10.
The prior art made of record does not explicitly teach or fairly suggest, “the information retrieval server is configured to recommend a storyboard for creating the new knowledge asset in response to the search query by (i) identifying a type knowledge asset to create the new knowledge asset if an identified intent of the user is to create the new knowledge asset;(ii) providing the one or more sentences or the one or more words as an input to the plurality of enterprise specific semantic models if the identified intent of the user is to create the new knowledge asset; (iii) retrieving relevant enterprise knowledge assets or articles, based on an output from the plurality of enterprise specific semantic models; (iv) extracting relevant information snippets from the enterprise knowledge assets or articles that are retrieved; and(v) ordering the extracted information snippets in a sequence flow according to a relevance or a relationship between the extracted information snippets, thereafter providing the information snippets in the sequence flow as the storyboard” as recited in claim 4.
The dependent claim 5, being definite, further limiting, and fully enabled by the specification may also be allowable.
The prior art made of record does not explicitly teach or fairly suggest, “the information retrieval server generates the plurality of enterprise-specific semantic models by (i) receiving the domain-specific semantic model (M) from the central semantic model generating unit (ii) receiving the one or more articles and assets within the enterprise from a second database, and (iii) generating the plurality of enterprise specific semantic models for a plurality of organizations by fine tuning the domain specific semantic model (M) with the one or more articles and assets from the enterprise” as recited in claim 6.
The dependent claim 7, being definite, further limiting, and fully enabled by the specification may also be allowable.
Examiner Notes
The examiner has considered the applicant's claims in light of the disclosure. However, the examiner respectfully reminds the applicant that during prosecution before the USPTO, claims are to be given their broadest reasonable interpretation, and the scope of a claim cannot be narrowed by reading disclosed limitations into the claim. See In re Morris, 127 F.3d 1048, 1054 (Fed. Cir. 1997). The Office must apply the broadest reasonable meaning to the claim language, taking into account any definitions presented in the specification. In re Am. Acad. of Sci. Tech Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004) (citing In re Bass, 314 F.3d 575,577(Fed. Cir. 2002)); “[i]t is the claims that measure the invention.” SRIInt’l v. Matsushita Elec. Corp. of Am., 775 F.2d 1107, 1121 (Fed. Cir. 1985) (enbanc). Written description may not be read into a claim when the claim language is broader than the embodiment. SuperGuide Corp. v. DirecTV Enters, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (citing Electro Med. Sys. S.A. v. Cooper Life Sci., Inc., 34 F.3d 1048, 1054 (Fed. Cir. 1994))
Note that “limitations appearing in the specification will not be read into the claims, and … interpreting what is meant by a word in a claim is not to be confused with adding an extraneous limitation appearing in the specification, which is improper.” Intervet Am., v. Kee-Vet Labs., 887 F.2d 1050, 1053, 12 USPQ2d 1474 1476 (fed. Cir. 1989).
“The ordinary and customary meaning of a claim term is the meaning that the term would have to a person of ordinary skill in the art in question at the time of the invention, i.e., as of the effective filing date of the patent application.” Phillips v. AWH Corp,. 415 F.3d 1303, 1313, 75 USPQ2d 1321, 1326 (fed. Cir. 2005).
“One purpose for examining the specification is to determine if the patentee has limited the scope of the claims.’… For example, an inventor may choose to be his own lexicographer is he defines the specific terms used to describe the invention’ with reasonable clarity, deliberateness, and precision.” Such a definition may appear in the written description, … or in the prosecution history, …” Teleflex, Inc. v. Ficosa N. Am Corp., 299 F.3d 1313, 1325, 63 USPQ2d 1374, 1381 (Fed. Cir. 2002).
Prior art pertinent to the disclosed invention is also cited and Applicants are reminded that they must consider all cited art under Rule 111(c) when amending the claims to conform with 35 U.S.C. 112.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Listed prior art could be used as an obviousness type Office correspondence.
Pierre (USPAT 9,110,977): involves obtaining multiple topic terms. A set of information that is related to topic terms is automatically acquired. The linguistic analysis is performed on set of information to determine a set of linguistic structures that are represented in set of information. Each linguistic structure in the set of linguistic structures includes a data element and identifiers that respectively identify multiple linguistic categories which are associated with data element. A set of relevant linguistic structures that are represented in set of information is determined.
Konarov (USPGPUB 2013/0268533): discloses receiving a graph search query specifying user-selected graph search elements; generating graph search query metadata representing the graph search elements of the graph search query and relationships of the graph search elements; comparing the graph search query metadata to content metadata sets, wherein each content metadata set represents content elements of a content item presented on a respective web resource and relationships of the content elements; determining similarity scores between the content metadata sets and the graph search query metadata based on the comparison; and selecting web resources on which the content items are presented based on the similarity scores of the respective content metadata sets.
Banda (USPGPUB 2021/0334300): a processor executes the machine-readable instructions to tokenize text included in a query for content into text portions. The processor encodes the text portions into respective vectors and organizes the text portions based on natural language similarity of the text portions. The natural language similarity is based on the respective vectors. The processor generates one or more search results based on the organized text portions and ranks one or more search for presentation on a computing device. Preferably, the processor obtains the query from the computing device via a network.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID AL ALAM whose telephone number is (571)272-4030. The examiner can normally be reached on M-F 8:00 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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September 19, 2026
/SHAHID A ALAM/Primary Examiner, Art Unit 2161
