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 – 4 are pending in this Office Correspondence.
Information Disclosure Statement
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Claim Objections
Claims 1 and 3 are objected to because of the following informalities:
With respect to claim 1, 2nd line of claim 1, limitations recites (a. a processor, . . )
Term “a.” represents a paragraph.
Please delete “a.” as there is/are no subsequent paragraph(s) b. or c.
With respect to claim 3, please delete numbers from claim limitations, such as (100), (101A), (104, 104A-104D), etc.
Please submit clear copy of claim 3 with the number(s).
With respect to claim 3, after line 19 and before last paragraph, termed limitation states, “characterized by that,” it is not clear as to what was previous limitation or steps followed “characterized by that,”.
Appropriate correction is required.
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 – 4 are rejected under 35 U.S.C. 103 as being unpatentable over Wang Fan (CN1144991058A) in view of USPGPUB 2012/0239643 issued to Michael Ekstrand et al. (“Ekstrand”) and further in view of USPGPUB 2021/0304749 issued to Brajesh Singh et al. (“Singh”).
With respect to claims 1 and 2, Wang Fan (CN1144991058A) teaches a system, non-transitory computer readable medium (CRM) and method (Para [0001]: methods, systems, computers, and storage media for extracting keywords from occupational knowledge graphs) for generating key vocabulary phrases, comprising: perform an operation that generates phrases (Para [0007]: annotating the preprocessed recruitment data to generate a text dictionary includes: manually annotating the descriptive information in the preprocessed recruitment data, marking the beginning, middle and end characters of the keywords, and finally generating a text dictionary, wherein the annotation adopts the BMES general format, and the text dictionary source document and annotation document), comprising:
receiving a user-specified term or phrases that involves text cleaning (Para [n0009]: the step of filtering the initial keywords to obtain the final keywords includes: filtering the initial keywords extracted by the final keyword extraction model using the TFIDF algorithm to obtain the final keywords);
deriving a knowledge graph using the relationship of plurality of key phrases (Para n0003]: knowledge graphs incorporate the concepts of semantic web and ontology in knowledge organization and expression, making it easier for knowledge to be exchanged, circulated, and processed between computers and between computers and humans. Knowledge graphs are a large-scale application of knowledge representation in industry. They connect identifiable objects on the Internet to form a
knowledge base of entities and relationships in the objective world. Knowledge graphs have significant vertical domain attributes, and the distribution of text vocabulary varies greatly across different domains).
improving the quality of the key phrases using the transformer models (Para [n0049]: the keywords extracted by the Flat-Lattice Transformer model are further filtered using the TFIDF algorithm to remove common words and improve the final keyword extraction effect);
storing phrases, document ids and statistical details about phrases such as frequency, TF-IDF in a knowledge graph database (Para [n0068 – n0069]: the TFIDF algorithm tends to filter out common words while retaining important words that have greater class distinguishing ability. The keywords extracted by the Flat-Lattice Transformer model are further filtered using the TF-IDF algorithm to remove common words and improve the final keyword extraction effect. After keyword extraction, the keywords in the recruitment data are transformed and relationships are generated according to certain rules, and the results are finally stored in the graph database).
Wang Fan teaches claimed invention substantially as claimed, however, Wang Fan does not explicitly teach ordering the phrases identified using the ranking criteria based on the phrases statistics; selecting and returning best key phrases as search results; and showing the search results as word cloud for the users to quickly identify the key vocabulary.
Ekstrand discloses ordering the phrases identified using the ranking criteria based on the phrases statistics (Ekstrand, claim 4: the step of ranking comprises ordering the resources listed in the first search results and the resources listed in the second search results based on the scores computed for the resources);
selecting and returning best key phrases as search results; and showing the search results as word cloud for the users to quickly identify the key vocabulary (Ekstrand, Para [0029]: the search engine returns the search results for the context-based queries and the context search components ranks and merges the various resources that are returned by the search, generating the merged context-based search results. The context search components may also be configured to generate the context-specific augmentation. The context-specific augmentation relates the merged context-based search results to the current context, enabling users to better identify useful results, or at least make better use of the merged context-based search results).
Both of Wang Fan and Ekstrand 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 Wang Fan’s extracting keywords from occupational knowledge graphs with the teachings of Ekstrand’s generating context-based queries in order to enable improving a user's ability to successfully search internet-based documentation by generating search results based on context information in combination with the user-specified terms. The modification further enable facilitating user-specified prioritization that changes weight assigned to the queries, thus increasing weight for the queries in which priority is increased, and reducing the weight for the queries in which priority is decreased.
Modified Wang Fan and Ekstrand do not explicitly recite relationship of plurality of key phrases, thereby deriving a lemma.
Singh discloses the key-term and synonyms extracting system further comprises the pre-processor configured for pre-processing the plurality of resolved raw text, wherein the pre-processing comprises at least one of tokenizing, part of speech (POS) tagging, filtering punctuations, filtering stop-words, filtering any excluded POS tags, lemmatizing, filtering domain specific words as in Para [0038]. Further to the above, Singh teaches the raw text is extracted from the text documents using a known text extraction technique that include two column parsing techniques, Optical character recognition (OCR), OCR with pre-defined standard templates of a specific domain, pre-defined standard template based removal wherein the pre-defined boilerplate statement/texts are removed and only relevant content is retained. The plurality of text documents obtained from a plurality of sources can be in a variety of formats that include Portable Document Format (PDF), txt (text) format or in document format. During the extraction of raw text from the input documents, a few challenges such as two-column format, text as image (for PDFs) and standard template documents are addressed. Further for two-column format sentence boundaries and cases are identified where a word is split across lines and optionally joined by hyphenating while for documents with standard templates, only specific sections of interest are identified and blank templates are used as to identify relevant phases for further parsing and OCR techniques are used for PDF documents (please see Singh, Para [0027-0028]).
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 further modify the modified Wang Fan and Ekstrand in order to enable extracting relevant information quickly and accurately from the available digital/electronic information with continuous growth of digital or electronic information. Modification would ensure that the key-terms are determined in several stages using frequency based techniques based on a relevancy scoring of the keys-terms, and the set of synonyms are determined for the identified key-term based on the language-based approach or domain specific approach, so that the method is independent of any specific supervised training and does not require a huge amount of training data.
Claim 3 is essentially the same as claim 1 and the subject matter of claim 3 is rejected in the analysis above in claim 1. Since each and every limitations of claim 3 has been addressed above in the rejection of claim 1, claim 3 is rejected for the same reasons as applied to claim 1 above.
As to claim 4, the improving step of the quality of the key phrases uses BERT variants (Wang Fan CN1144991058A): Para [n0039]: Transformer is an important achievement in the field of natural language processing in recent years. The BERT algorithm proposed by the Google team for generating word vectors has achieved significant improvements in 11 NLP tasks, making it arguably the most exciting news in the field of deep learning).
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.
Tong (USPAT 7,945,579): involves receiving search query including several terms and generating set of context data based on search query. Several terms of search query are identified as potential stopwords. Another set of context data is generated based on search query without terms identified as potential stopwords. Set of context data are compared. Search results are identified based on search query and classification of terms.
Cava (USPAT 9,015,176): involves collecting data related to search queries performed using a search engine, where the data identifies search terms from associated search queries. A particular keyword is identified. A candidate keyword related to the particular keyword is identified based on the data. The associated search queries are determined by identifying the search queries performed within a search session. A set of search queries is identified based on each of the search queries in the set being received from a particular location, where the search session comprises the set of search queries.
Kunc (USPAT 10,540,347): involves receiving a natural language input from a user by a computing device. Multiple hypotheses are identified from the natural language input. The hypotheses are mapped to concepts of an ontology. An output is presented to the user based on disambiguation. A natural language input from the user is a verbal utterance spoken by the user. The natural language input is sent to a remote recognizer from the computing device.
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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July 22, 2026
/SHAHID A ALAM/Primary Examiner, Art Unit 2161
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