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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 21 August 2026 has been entered.
Response to Amendment
The amendment filed 21 August 2026 has been accepted and considered in this office action. Claims 1-3,7-9.14-16, and 21 have been amended.
Response to Arguments
Applicant’s arguments with respect to claim(s) 21 and 22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 21 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Offer et al. (US PAP 2019/0147109) in view of Nguyen et al. (A Robust Transformation- Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging) and further in view of Zhang et al. (US PAP 2019/0362003).
Consider claim 21, Offer teaches a computer-implemented method comprising:
receiving a first plurality of tokens generated from a document to be indexed (0044, tokenization of document text);
executing computer code to assign first part-of-speech tags to the first plurality of tokens (0046, part of speech tagging);
performing a lemmatization of the first plurality of tokens using the first part-of-speech tags to determine root words for the first plurality of tokens (0047, identifying lemma of each word based on POS tagging); and
indexing the document, indexing the document based on the root words determined for the first plurality of tokens (0049-51, generating index based on morphological (lemma) analysis.).
Offer does not specifically teach executing optimized computer code to assign first part-of-speech tags to the first plurality of tokens, the optimized computer code embodying ripple down rules generated by a machine learning ripple down rules generator that is trained on tagged training to generate single classification ripple down rules.
In the same field of part of speech tagging, Nguyen teaches executing optimized computer code to assign first part-of-speech tags to the first plurality of tokens, the optimized computer code embodying ripple down rules generated by a machine learning ripple down rules generator that is trained on tagged training to generate single classification ripple down rules (section 3, specifically section 3.1, ripple down rules for POS tagging is learned via machine learning, Section 3.2, rules are executed (which requires computer code) to POS tag incoming text, section 3, gold standard training corpus).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use machine learned ripple down rules as taught by Nguyen in the system of Offer in order to generate accurate POS tags automatically with minimal training time (Nguyen Abstract).
Offer and Nguyen do not specifically teach indexing the document, indexing the document comprising adding the root words determined for the first plurality of tokens to an index.
In the same field of indexing documents, Zhang teaches indexing the document, indexing the document comprising adding the root words determined for the first plurality of tokens to an index (0033, documents are directly indexed by the lemmas of keywords).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to index by lemmas as taught by Zhang in the system of Offer and Nguyen in order to reduce computing requirements of mapping terms to concepts.
Consider claim 22, Offer and Nguyen and Zhang teach The computer-implemented method of claim 21, further comprising:
receiving a second plurality of tokens, the second plurality of tokens generated from a search query (Offer 0054, receiving query, applying NPL processing used on documents including 0044, tokenization of document text);
executing the optimized computer code to assign second part-of-speech tags to the second plurality of tokens (Offer 0054, receiving query, applying NPL processing used on documents including 0046, part of speech tagging,);
performing a lemmatization of the second plurality of tokens using the second part-of-speech tags to determine root words for the second plurality of tokens (Offer 0054, receiving query, applying NPL processing used on documents including 0047, identifying lemma of each word based on POS tagging); and
searching the index using the root words determined for the second plurality of tokens (Offer, 0054-57, searching the index for concepts determining from processing including lemmas, Zhang, 0033 searching by Lemmas of queries).
Allowable Subject Matter
Claims 1-20 are allowed. The following is an examiner’s statement of reasons for allowance:
Consider claim 1, the closest prior art of record, Nguyen et al, teaches A computer-implemented method for a machine learning based rules compiler (abstract), the method comprising:
processing a corpus of documents using a machine learning rules generator to generate ripple down rules for part-of-speech tagging for a language, the ripple down rules comprising exception rules for tags in a tag set, the exception rules comprising tag string comparisons (section 3.1, using a corpus of documents to learn ripple down rules for POS tagging. Section 1 provides for exception rules and string comparisons).
However the prior art does not specifically teach or fairly suggest the limitations of
“compiling the ripple down rules into optimized computer code, further comprising:
generating an enumeration statement for an enumeration containing the tag set;
translating the exception rules for each tag in the tag set into if-else statements for the tag, translating the exception rules further comprising replacing the tag string comparisons with the enumeration; and
generating a switch case statement for a current tag, the switch case statement having a plurality of cases, each case in the plurality of cases corresponding to a respective tag from the tag set and including the if-else statements for the respective tag, wherein the optimized computer code comprises the enumeration statement and the switch case statement.” Rather, the prior art implements ripple down rules in a programing language such as JAVA, so there is no need to further compile them into computer code. Therefore claim 1 contains allowable subject matter.
Claims 7 and 14 contain allowable subject matter as claim 1 and therefore contains allowable subject matter as well.
Claims 2-6, 8-13, and 15-20 depend on and further limit claims 1, 7, and 14 and therefore contain allowable subject matter as well.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS C GODBOLD whose telephone number is (571)270-1451. The examiner can normally be reached 6:30am-5pm Monday-Thursday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at (571)272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
DOUGLAS GODBOLD
Examiner
Art Unit 2655
/DOUGLAS GODBOLD/Primary Examiner, Art Unit 2655
Prosecution Insights