Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. The preliminary amendment filed on 08/13/2025 is acknowledged. Claims 1-20 are cancelled. Claims 21-40 are present for examination.
Information Disclosure Statement
3. The information disclosure statement (IDS) filed on 08/13/2025 was considered by the examiner.
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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5. Claims 21, 27, 29, 35, 37 and 38 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4 of U.S. Patent No. 12,393,623. Although the claims at issue are not identical, they are not patentably distinct from each other because the claimed invention of the instant application is a similar version of the claimed invention of the above identified U.S. Patent with the similar intended scope as shown below:
Instant Application
Patent No. 12,393,623
Claim 1. A method comprising:
receiving, from a user device and by the parameter generation platform, an utterance as a parameter of an API call to an API method published by the parameter generation platform;
converting, by an orchestration layer of the parameter generation platform, an audio file comprising the utterance into a text string;
tokenizing, by a tokenization engine of the parameter generation platform, the utterance into a plurality of tokens, each token of the plurality of tokens comprising a portion of the utterance separated by a space, wherein tokenizing includes identifying one or more tokens of the plurality of tokens to be operators and symbols within the utterance;
transforming, by a featurizer engine of the parameter generation platform, the plurality of tokens into a plurality of feature vectors, the featurizer engine comprising a sparse featurizer and a dense featurizer, the sparse featurizer providing a count of frequent individual words that are filtered based on individual words occurring in a plurality of received utterances, the dense featurizer providing a semantic meaning in context by converting one or more word strings of the utterance into a real valued feature vector, the plurality of feature vectors comprising the count of frequent individual words and the real valued feature vector;
determining, by a machine learning model of the parameter generation platform, an intent classification of the utterance based on the plurality of feature vectors;
classifying, by the machine learning model, the text string into query parameters;
determining, by the machine learning model, a database of a plurality of databases to query based on the plurality of feature vectors;
assigning, by the machine learning model, an entity label to each of the plurality of feature vectors;
resolving, by a disambiguation engine of the parameter generation platform, each feature vector of the plurality of feature vectors to a corresponding standardized value of a database query language, wherein resolving each feature vector of the plurality of feature vectors to a corresponding standardized value of a database query language includes processing each feature vector of the plurality of feature vectors with a string-based algorithm computing a Levenshtein distance; and
Claim 27. The method of claim 21, comprising: scoring each feature vector of the plurality of feature vectors with respect to candidates from reference data table.
determining, by the disambiguation engine, a conflict between the corresponding standardized value and the entity label and overriding the entity label based on the determination.
Claim 1. A method comprising:
receiving, by a user device, an utterance as an audio file;
receiving, by an orchestration layer of a parameter generation platform, the utterance;
converting, by the orchestration layer, audio file of the utterance into a text string;
tokenizing, by a tokenization engine of the parameter generation platform, the utterance into a plurality of tokens, each token of the plurality of tokens comprising a portion of the utterance separated by a space, wherein tokenizing includes identifying one or more tokens of the plurality of tokens to be operators and symbols within the utterance;
transforming, by a featurizer engine of the parameter generation platform, the plurality of tokens into a plurality of feature vectors, the featurizer engine comprising a sparse featurizer and a dense featurizer, the sparse featurizer providing a count of frequent individual words that are filtered based on individual words occurring in a plurality of received utterances, the dense featurizer providing a semantic meaning in context by converting one or more word strings of the utterance into a real valued feature vector, the plurality of feature vectors comprising the count of frequent individual words and the real valued feature vector;
determining, by a machine learning model of the parameter generation platform, an intent classification of the utterance based on the plurality of feature vectors;
classifying, by the machine learning model, the text string into query parameters;
determining, by the machine learning model, a database of a plurality of databases to query based on the plurality of feature vectors;
assigning, by the machine learning model, an entity label to each of the plurality of feature vectors;
resolving, by a disambiguation engine of the parameter generation platform, each feature vector of the plurality of feature vectors to a corresponding standardized value of a database query language, wherein resolving the each feature vector of the plurality of feature vectors to a corresponding standardized value of a database query language includes processing the each feature vector of the plurality of feature vectors with a string-based algorithm computing a Levenshtein distance;
scoring, by the disambiguation engine, the each feature vector of the plurality of feature vectors with respect to a plurality of candidates from a reference data table;
mapping, by the disambiguation engine, the each feature vector of the plurality of feature vectors to a key value, wherein the key value corresponds to a candidate of the plurality of candidates with a highest score, the highest score calculated based on the Levenshtein distance;
determining, by the disambiguation engine, a conflict between the corresponding standardized value and the entity label; and
overriding, by the disambiguation engine, the entity label based on the determination.
Claim Rejections - 35 USC § 103
6. 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.
7. 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.
8. Claims 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0237934 (hereinafter Matcham) in view of US 2020/0152179 (hereinafter van Hout), further in view of US 10,721,242 (hereinafter Jones).
Regarding claims 21, 29 and 37, Matcham discloses a method comprising:
receiving, from a user device and by the parameter generation platform, an utterance as a parameter of an API call to an API method published by the parameter generation platform; converting, by an orchestration layer of the parameter generation platform, an audio file comprising the utterance into a text string ([0050, 0089 and 0096]; “…The session data 530 is obtained by a classifier optimizer 534 that outputs configuration data and the configuration data 536 is the same as the classifier optimizer 334 and the configuration data 336 in FIGS. 3A and 3B…”; and “…a user speaking to a voice assistant and having their utterance converted from speech to text using a known speech-to-text system…”);
tokenizing, by a tokenization engine of the parameter generation platform, the utterance into a plurality of tokens, each token of the plurality of tokens comprising a portion of the utterance separated by a space, wherein tokenizing includes identifying one or more tokens of the plurality of tokens to be operators and symbols within the utterance ([0045-0046]; “…For example, the tokenize 230 may be configured to partition the unstructured text data into sets of grouped character symbols (e.g., sub-strings) based on one or more punctuation character symbols (such as ‘’ – space- or ‘,’, and ‘.’). These sets of grouped character symbols may correspond to words or word portions…”);
transforming, by a featurizer engine of the parameter generation platform, the plurality of tokens into a plurality of feature vectors, the featurizer engine comprising a sparse featurizer and a dense featurizer, the sparse featurizer providing a count of frequent individual words that are filtered based on individual words occurring in a plurality of received utterances, the dense featurizer providing a semantic meaning in context by converting one or more word strings of the utterance into a real valued feature vector, the plurality of feature vectors comprising the count of frequent individual words and the real valued feature vector ([0035, 0042-0044, 0046, 0049, 0051-0052 and 0093]; “A bag-of-words model may count the frequency of unique sets of grouped characters (i.e., unique tokens). A bag-of-words model may be defined with reference to a corpus of token sets, which in the present case may comprise a database of unstructured text data that forms the training data for the classifier 218…”; and “…The vector reduction or transformation component 238 may apply one or more of at least three functions. Singular Value Decomposition (SVD – also known as Latent Semantic Analysis or Indexing – LSA/LSI)…”);
determining, by a machine learning model of the parameter generation platform, an intent classification of the utterance based on the plurality of feature vectors; classifying, by the machine learning model, the text string into query parameters ([0043, 0086-0087]; “…The text scenario may be generated by a generative machine learning model….In both cases, a user of the client computing device 405 acts to validate the text scenario as relating to their initial legal query, in a similar manner to the previously describe class validation…”);
determining, by the machine learning model, a database of a plurality of databases to query based on the plurality of feature vectors ([0005, 0041-0042 and 0086]; “The text pre-processor 214 is configured to apply one or more text pre-processing functions to the unstructured text data as received and output by the test interface 212 to output a structured numeric representation 216 of the unstructured text data…convert this to a structured numeric representation 216 in the form of a numeric input vector of a predefined length…”; and “…For example, a text scenario may be generated based on a validated domain and sub-domain and samples in the database of training data 435 that have the same validated domain and sub-domain…”);
assigning, by the machine learning model, an entity label to each of the plurality of feature vectors ([0039, 0041 and 0111]; “…The classification data 220 may comprise a determined class label and/or a classification vector as described above…”); and
wherein resolving each feature vector of the plurality of feature vectors to a corresponding standardized value of a database query language includes processing each feature vector of the plurality of feature vectors [with a string-based algorithm] ([0041 and 0051]; “…The unstructured text data 210 may comprise the data 112 that is transmitted over the network 120 in FIG. 1. The classification data 220 may comprise a determined class label and/or a classification vector as described above…”; and “If the vectorization component 236 implements a bag-of-words model and provides a vector for the unstructured text data based on token frequencies, then in certain cases, it may further process this vector using a perform a term-frequency inverse-document-frequency (TF-IDF) computation. …”); and
determining, by the disambiguation engine, a conflict between the corresponding standardized value and the entity label and overriding the entity label based on the determination ([0045 and 0073]; “…For example, a selection of a first value from a first list of options may rule out selection of a second value from a second list. In the example of FIG. 3B, the frequency ranking component 350 is configured to order the selectable options in a series of listed options based on the frequency of prior selection, e.g. following selection of the first value from the first list, a positively correlated value in the second list may have a high rank or order and a negatively correlated value in the second list may have low rank or order, where the relative rank or order is determined based on the strength of the historical correlations…”).
Matcham does not explicitly disclose the features of resolving, by a disambiguation engine of the parameter generation platform, each feature vector of the plurality of feature vectors to a corresponding standardized value of a database query language. However, van Hout explicitly discloses the feature of utilizing the sparse solution; and processing each feature vector of the plurality of feature vectors with a string-based algorithm ([0108]). Also, van Hout discloses that “…and wherein the computation engine comprises circuitry configured to determine, based on the query feature vector and the reference feature vector, at least one detection score corresponding to a level of confidence that the reference audio signal contains the query” ([0005 and 0038]). Further, van Hout discloses that “DTW module 404 may use one of several distance measures to build joint distance matrix 218. For example, the distance measures may include Euclidean distance, correlation, city block distance, cosine distance, dot product, minus log dot product, and so on. In such examples, DTW module 404 may consider each feature in query feature vector 206 and each feature in reference feature vector 208 as a point in a two-dimensional space…” ([0061]). Furthermore, van Hout discloses that “…Accordingly, DTW module 404 may normalize detection scores based on distribution in order to determine a standardized detection score across queries…” ([0074]) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of van Hout in the system of Matcham in view of the desire to enhance the classification of the unstructured text data system by utilizing the query feature vector process resulting in improving the efficiency of outputting the structured numeric representation of the input.
While Matcham in view of van Hout discloses the method wherein the string-based algorithm as explained above, the references do not explicitly disclose the feature of utilizing a Levenshtein distance. However, such feature is well known in the art as disclosed by Jones (col. 14, lns. 36-56) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Jones in the modified system of Matcham in view of the desire to enhance the classification of the unstructured text data system by utilizing the particular algorithm resulting in improving the efficiency of outputting the structured numeric representation of the input.
Regarding claims 22 and 30, Matcham in view of van Hout and Jones disclose the method comprising: mapping a feature vector of the plurality of feature vectors to a type field (Matcham: [0003, 0032 and 0051]), (van Hout: [0038]) and (Jones: col. 6, lns. 54-65). Therefore, the limitations of claims 22, and 30 are rejected in the analysis of claims 21 or 29, and the claims are rejected on that basis.
Regarding claims 23, 31 and 39, Matcham in view of van Hout and Jones disclose the method comprising: formatting a database query in the database query language including each corresponding standardized value (Matcham: [0025]) and (van Hout: [0074]). Therefore, the limitations of claims 23, 31 and 39 are rejected in the analysis of claims 21, 29 or 37, and the claims are rejected on that basis.
Regarding claims 24 and 32, Matcham in view of van Hout and Jones disclose the method comprising: mapping a feature vector of the plurality of feature vectors to a merchant entity (Matcham: [0039 and 0069]), (van Hout: [0037]) and (Jones: col. 6, lns. 5-14). Therefore, the limitations of claims 24, and 32 are rejected in the analysis of claims 21 or 29, and the claims are rejected on that basis.
Regarding claims 25 and 33, Matcham in view of van Hout and Jones disclose the method comprising: resolving a feature vector of the plurality of feature vectors to a valid entity (Matcham: [0046]), (van Hout: [0063]) and (Jones: col. 14, lns. 34-col. 15, lns. 3). Therefore, the limitations of claims 25, and 33 are rejected in the analysis of claims 21 or 29, and the claims are rejected on that basis.
Regarding claims 26, 34 and 40, Matcham in view of van Hout and Jones disclose the method wherein resolving each feature vector of the plurality of feature vectors to a corresponding standardized value of a database query language includes processing each feature vector of the plurality of feature vectors with a probabilistic model (Matcham: [0059 and 0091]) and (van Hout: [0056 and 0074]). Therefore, the limitations of claims 26, 34 and 40 are rejected in the analysis of claims 21, 29 or 37, and the claims are rejected on that basis.
Regarding claims 27, 35 and 38, Matcham in view of van Hout and Jones disclose the method comprising: scoring each feature vector of the plurality of feature vectors with respect to candidates from reference data table (Matcham: [0047]). Therefore, the limitations of claims 27, 35 and 38 are rejected in the analysis of claims 21, 37 or 29, and the claims are rejected on that basis.
Regarding claims 28 and 36, Matcham in view of van Hout and Jones disclose the method comprising: mapping each feature vector of the plurality of feature vectors to a key value, (Matcham: [0021 and 0096]) and (van Hout: [0096]). Therefore, the limitations of claims 28 and 36 are rejected in the analysis of claims 21 or 29, and the claims are rejected on that basis.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONICA M PYO whose telephone number is (571)272-8192. The examiner can normally be reached Monday-Friday 8am-4pm.
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/MONICA M PYO/ Primary Examiner, Art Unit 2161
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