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 .
Election/Restrictions
Applicant’s election without traverse in the reply filed on 05/09/2025 is acknowledged. All pending claims 12 - 15, not withdrawn, filed 07/06/2022 were examined. Claims 1 – 7, 8 – 11, 16 and 17 were withdrawn and claim 14 was amended.
Joint Inventors
3. Applicant is reminded that upon the cancellation of claims to a non-elected
invention, the inventorship must be corrected in compliance with 37 CFR 1 .48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 317 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 3,7 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
Examiner’s Amendment
4. An examiner’s amendment to the record is included as an attachment. Should the changes and/or additions be unacceptable to applicant, an amendment may be filed as provided by 37 CFR 1.312. To ensure consideration of such an amendment, it MUST be submitted no later than the payment of the issue fee.
Authorization for this examiner’s amendment was given in an interview with Applicant’s Representative, Nitin Kaushik (Reg. 80,417), on 9/10/2025.
Claim Interpretation
5. The following is a quotation of 35 U.S.C. l 12(f):
(f) Element in Claim for a Combination. - An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for
performing a specified function without the recital of structure, material or acts in support thereof, and such claim shall be construed to cover the corresponding
structure, material or acts described in the specification and equivalents thereof.
6. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material or acts for performing the claimed function.
Use of the word "means" ( or "step") in a claim with functional language creates a
rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, materiaL or acts to entirely perform the recited function.
Absence of the word "means" ( or "step") in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word "means" ( or "step") are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word "means" ( or "step") are not being interpreted under 35 U.S. C. 112(±) or preAIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
7. This application includes one or more claim limitations that do not use the word "means," but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
"an input engine (310) for receiving …” - corresponding to element 310 in Fig. 1, in claim 12
"a pre-processing engine (214) configured with input engine (310) to …” - corresponding to element 214 in Fig. 1, in claim 12
"a feature generation engine (330) configured with the high recall – high search filter (320) to …” - corresponding to element 330 in Fig. l, in claim 12
"a first model engine (340-1) configured with the feature generation engine (330) to …” - corresponding to element 340-1 in Fig. l in claim 12
"a second model engine (340-2) configured with the feature generation engine (330) to …” - corresponding to element 340-2 in Fig. l in claim 12
“an ensemble meta-learner model engine (350) coupled to first model engine (340-1) and the second model engine (340-2) to …” - corresponding to element 350 in Fig. l in claim 12
“output engine” in claim 15
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections – 35 U.S.C. §103
8. 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.
9. The factual inquiries set forth in Graham v John Deere Co., 383 U.S. 1, 148 USPQ
459 (1966), that are applied for establishing a background for determining obviousness
under 35 U.S.C. 103 are summarized as follows:
a. Determining the scope and contents of the prior art
b. Ascertaining the differences between the prior art and the claims at issue
c. Resolving the level of ordinary skill in the pertinent art
d. Considering objective evidence present in the application indicating
obviousness or nonobviousness
Claims 12 – 15 are rejected under 35 U.S.C. 103 as being unpatentable over Stuhee et al. (United States Patent Publication Number 20130332454), hereinafter Stuhee, in view of Lee et al. (United States Patent Publication Number 20210110259 ), hereinafter referred to as Lee .
Regarding claim 12 Stuhee teaches an ensemble meta-learner model system (100), (Fig. 5, system [0013]) the system (100) (Fig. 5, system [0013]) comprises: an input engine (310) (Fig. 5, (520) Den Generator [0042]) such as “input engine” for receiving an input data that includes individual and organizational names; (In particular, DEN generator 520 may be configured to receive input data or information on terms or names (e.g., from data base 530, automatic modeler 540 and other sources) for a DEN [0044]) a pre-processing engine (214) (canonical data model (CDM) [0021]) such as “pre-processing engine (214)” configured with the input engine (310) (Fig. 5, (520) Den Generator [0042]) such as “input engine” to standardize (attempts to standardize document structures or schemas by prescribing a common set of core components or elements in a document [0019]) SEE ALSO [0021] the input data; (input data [0026], [0044]) a high recall - high search filter (320) (linguistic analysis tools [0031]) such as “high recall - high search filter (320” configured with the pre-processing engine (214) (canonical data model (CDM) [0021]) such as “pre-processing engine (214)” to perform a search analysis (analysis [0023], [0044]) on the input data; (input data [0026], [0044]) a feature generation engine (330) (automatic modeler [0042]) such as “feature generation engine (330)” configured with the high recall - high search filter (320) (linguistic analysis tools [0031]) such as “high recall - high search filter (320)” of matched names; (candidate terms and DEN terms [0036]) such as “matched names” of matched names; (candidate terms and DEN terms [0036]) such as “matched names” of matched names(candidate terms and DEN terms [0036]) such as “matched names”
Stuhee does not fully disclose to transform and prepare the input data for training; a first model engine (340-1) configured with the feature generation engine (330) to provide first probability from the input data; a second model engine (340-2) configured with the feature generation engine (330) to provide second probability from the input data; and an ensemble meta-learner model engine (350) coupled the first model (340- 1) and the second model (340-2) to provide final probability from the input data.
Lee teaches to transform and prepare (convert the input into a vector [0054], [0057]) such as “transform and prepare” the input data for training; (received input for training [0068]) a first model engine (340-1) (Fig. 3, (310) first model [0085]) configured (configured [0056])with the feature generation engine (330) (an Adjuster 23 [079]) such as “feature generation engine (330)” to provide first probability (first probability [0085]) from the input data; (input for training [0068]) such as “input data” a second model engine (340-2) (Fig. 3, (320) second model [0086]) configured (configured [0056]) with the feature generation engine (330) (an Adjuster 23 [079]) such as “feature generation engine (330)” to provide second probability (second probability [0086]) from the input data; (input for training [0068]) such as “input data” and an ensemble meta-learner model engine (350) (Fig. 2 (250) Ensembler [0079]) coupled (coupled to [0047]) the first model (340- 1) (Fig. 3, (310) first model [0085]) and the second model (340-2) (Fig. 3, (320) second model [0086]) to provide final probability (adjusted probability [0077]) such as “final probability” from the input data (input for training [0068]) such as “input data”
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 have modified Stuhee to incorporate the teachings of Lee whereby to transform and prepare the input data for training; a first model engine (340-1) configured with the feature generation engine (330) to provide first probability of matched names from the input data; a second model engine (340-2) configured with the feature generation engine (330) to provide second probability of matched names from the input data; and an ensemble meta-learner model engine (350) coupled the first model (340- 1) and the second model (340-2) to provide final probability of matched names from the input data. By doing so for an ensembler to perform the beam search, it may need to normalize an adjusted second
probability. Lee [0104]
Regarding claim 13 Stuhee in view of Lee teaches the system (100) (Fig. 5, system [0013]) as claimed in claim 12,
Stuhee as modified further teaches wherein the high recall - high search filter (320) (linguistic analysis tools [0031]) such as “high recall - high search filter (320”
Stuhee does not fully disclose provides top 500 names from the input data.
Lee teaches provides top 500 names (top n candidates, [0090]) from the input data (input sequence [0054]; input speech [0055]) such as “input data”
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 have modified Stuhee to incorporate the teachings of Lee wherein provides top 500 names from the input data. By doing so probabilities can be highly predicted by the acoustic model. Lee [0090]
Regarding claim 14 Stuhee in view of Lee teaches the system (100) (Fig. 5, system [0013]) as claimed in claim 12,
Stuhee as modified further teaches wherein the feature generation engine (330) (automatic modeler [0042]) such as “feature generation engine (330) generates the feature (calculate domain or context-specific representations of the CDM [0043]) such as “features” on the input data (input data [0044])
Stuhee does not fully disclose and passed through the trained model by the first model engine (340-1), the [[first]] second model engine (340-2) and the ensemble meta-learner model engine (350) for scoring.
Lee teaches and passed through the trained model by the first model engine (340-1), (Fig. 3, (310) first model [0085]) the [[first]] second model engine (340-2) (Fig. 3, (320) second model [0086]) and the ensemble meta-learner model engine (350) (Fig. 2 (250) Ensembler [0079]) for scoring (provide a DEN similarity score, [0036])
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 have modified Stuhee to incorporate the teachings of Lee wherein and passed through the trained model by the first model engine (340-1), the [[first]] second model engine (340-2) and the ensemble meta-learner model engine (350) for scoring. By doing so the DEN generator 520 may deploy matching algorithms to obtain a DEN similarity score for a candidate ordering of terms, and use the similarity score to revise, refine, or validate the candidate ordering of terms. Lee [0045]
Regarding claim 15 Stuhee in view of Lee teaches the system (100) (Fig. 5, system [0013]) as claimed in claim 12,
Stuhee does not fully disclose wherein the final probability of matched names from the input data is stored in the output engine (360).
Lee teaches wherein the final probability (adjusted probability [0077]) such as “final probability” of matched names (candidate terms and DEN terms [0036]) such as “matched names” from the input data (input sequence [0054]; input speech [0055]) such as “input data” is stored in (stored in [0112]) the output engine (360). (memory [0112], [0116]) such as “output engine”
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 have modified Stuhee to incorporate the teachings of Lee wherein the final probability of matched names from the input data is stored in the output engine (360). By doing so volatile and nonvolatile memory may store the data processed by the processor. Lee [0116].
Conclusion
10. The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure.
Malhotra et al., (United States Patent Publication Number 20220058504) teaches “The model selector may also be coupled to the processor. The model selector may select a feature from the training data based on a variety of statistical feature models including a first statistical model and a second statistical model. The feature may be selected using the first statistical model to provide a first feature and the second statistical model to provide a second feature. The model selector may also train a probabilistic classifier to provide a variety of classification models including a first classification model and a second classification mode [0004]”
11. Any inquiry concerning this communication or earlier communications from the
examiner should be directed to Kweku Halm whose telephone number is (469) 295-
9144. The examiner can normally be reached on 7:30AM - 5:30PM Mon - Thur. If
attempts to reach the examiner by telephone are unsuccessful, the examiner's
supervisor, Sanjiv Shah can be reached on (571) 272-4098. The fax phone
number for the organization where this application or proceeding is assigned is 571-273-
8300.
Information regarding the status of an application may be obtained from the
Patent Application Information Retrieval (PAIR) system. Status information for published
applications may be obtained from either Private PAIR or Public PAIR. Status information
for unpublished applications is available through Private PAIR only. For more
information about the PAIR system, see http://pair-direct.uspto.gov. Should you have
questions on access to the Private PAIR system, contact the Electronic Business Center
(EBC) at 866-217-9197 (toll-free).
/KWEKU WILLIAM HALM/Examiner, Art Unit 2166
/VAN H OBERLY/Primary Examiner, Art Unit 2166