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 .
This action is in response to the Application filed on 08/29/2023. Claims 2-21 are pending in the case. All claims are examined and rejected accordingly.
Information Disclosure Statement
3. As required by MPEP 609 (c), the Applicants’ submission of the Information Disclosure Statement(s) filed on 08/29/2023 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
Claim Interpretation
4. The following is a quotation of 35 U.S.C. 112(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.
4. 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(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
5. 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:
a data extraction and consumption (DEC) module to perform (claim 1)
an intent discovery core engine (claim 1)
an intent element feature extractor (claim 1)
an intent concept relationship discovery module (claim 1)
an intent discovery module (claim 1)
intent language model (ILM) management system(claim 1)
a services module for providing web services (claim 1)
a visualization module to format (claim 3)
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.
As per Applicant’s specifications, Figure 4 and 5 showing the algorithm and showing the system architecture and Figure 8, [0092]-[0096]:
[0092] a system diagram for Intent Discovery Core 200 in FIG. 1. The Input Data Source 214 to this system is any textual data, such as domain manuals 214.2 or service orders 214.1 specific to a domain. [0093] , The input data sources 214 is parsed by Data Extraction/Consumption: DEC 215 module and Content Segmentation 802 loads and deconstructs the content into Segments 803 which retains structural information in its representation.[0094] Segments 803 are iterated over to discover intent within the segments using Intent Discovery Module 1200 and Segments 803 are iterated by managing the intent discovered across the segments in Intent Language Model Management System 204. [0095]Intent Language Model Management System 204 uses the Intent Knowledge Controller 805 module to update elements of Intent 302 and Intent to Intent Language Model ILM 700. [0096] Data from a training data set is used to create Segments 803 using Content Segmentation 802 that can be processed by Intent Language Model Management System204 to find intent elements.
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 USC § 101
6. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
7. Claims 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea, without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claim is directed to a computer implemented method, which is a process and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 2 and 12,
At step 2A, prong 1, Does the claim recite a judicial exception?
Claim 2 further recites the steps of :
a data extraction and consumption (DEC) module to perform domain modeling and domain training to analyze, process, and present enterprise data. (This step relies on data processing that consist generic data manipulation which falls into the “Mathematical concepts” grouping of abstract ideas.),
an intent discovery core engine including an intent element feature extractor, an intent concept relationship discovery module, an intent discovery module, and an intent language model (ILM) management system (This step relies on model training and data classification, which falls into the “Mathematical Concepts” and “Mental process” grouping of abstract ideas.)
The claim recites mathematical concepts (neural-network computations) producing “anomaly scores” and “scaled anomaly scores” and mathematical process (identifying data and providing recommendation). Accordingly, the claims recite an abstract idea.
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application?
Further, the claim does not recite any additional element which could integrate this abstract idea into a practical application, because the additional elements recited of consist of:
“… a server system including at least one processor, a communications interface, and a data storage storing instructions to configure the at least one processor to perform functions …” (claim1), “a server system including at least one processor, a communications interface, and a data storage storing instructions to configure the at least one processor to perform functions …”, (claim 12) (Generic computer components on which to implement the math abstract idea (see MPEP 2106.05(f));
a services module for providing web services to send information to and receive information from at least one service enterprise (This step is sending and receiving information which is considered and extra-solution activity)
The additional elements are recited at a high level of generality and do not amount to significantly more than the abstract idea (MPEP 2106.05(f)). The claim use a computer to perform a math and does not improve the function of the computer or other technology. Accordingly, the claim does not integrate the abstract idea into practical application.
Thus, the claim is directed towards the abstract idea.
Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
No, as shown above with respect to integration of the abstract idea into a practical application, the additional element of “… a server system including at least one processor, a communications interface, and a data storage storing instructions to configure the at least one processor to perform functions …” (claim1), “a server system including at least one processor, a communications interface, and a data storage storing instructions to configure the at least one processor to perform functions …”, (claim 12) (Generic computer components on which to implement the math abstract idea (see MPEP 2106.05(f));
a services module for providing web services to send information to and receive information from at least one service enterprise (This step is sending and receiving information which is considered and extra-solution activity)
The additional elements, alone and in combination, fail to integrate the abstract idea into a practical application or add “significantly more.” Thus, the claims are not patent eligible. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Neither can insignificant extra-solution activity. All of these additional elements as generically claimed are thus considered well-understood, routine, and conventional. Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea.
Thus, these independent claims are not patent eligible.
The dependent claims respectively recite a judicial exception in limitations of: “the data storage stores further instructions to configure the at least one processor to perform further functions including that of a visualization module to format and package data and other information for display to an end user..”(claims 3/13), “the data storage stores further instructions to configure the at least one processor to perform further functions including that of a query engine to access results data based on a query and provide feedback in response thereto.” (claims 4/14), “the intent discovery core engine includes a feature extraction engine, a classifier and a clutterer to analyze a data stream and store metadata; and the data extraction and consumption (DEC) module further translates input data sources into defined abstractions for consumption by the feature extraction engine.”(claim 5/15), “the data storage stores further domain specific databases including data related to one or more domains.” (claims 6/16), “the intent element feature extractor is associated with a domain and includes domain- specific feature extraction parameters to extract data from enterprise data documents.. (claims 7/17), “the intent element feature extractor uses one or more of natural language processing (NLP) algorithms, machine learning and neural network algorithms for feature extraction.”(Claim 8/18), “the intent concept relationship engine is coupled in communication with the intent element feature extractor to receive and process the extracted data and integrate word to word relationships to generate a domain knowledge graph database.”(claim 9/19), and “the intent discover module includes an intent pattern recognizer to discover domain specific intent within segments of the enterprise data documents based on an intent language model.” (Claims 10/20), “the intent language model management system is used to find intent elements in training data sets to train and enrich the intent language model. (claim 11/21),
These additional limitations (in claims 3-11 and 13-21) also constitute concepts performed Mathematical concept or mathematical operation groupings of abstract ideas.
This judicial exception is not integrated into a practical application. Additional elements “computer readable medium comprising: computer program code (in claims 3-11 and 13-21), all amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of non-transitory computer readable medium comprising: computer program code are again insignificant extra-solution activity steps that cannot provide an inventive concept. All of these additional elements as generically claimed are considered well-understood, routine, and conventional.
Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, all of the dependent claims are also not patent eligible.
Examiner Comments
13. 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 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.
Claim Rejections - 35 USC § 103
14. 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.
15. Claims 2-21 are rejected under 35 U.S.C. 103 as being unpatentable over COSTELLO (Pub. No. US 20200065384 B1, Pub. Date 2018-11-22) in view of BANGALORE (US 20180174578 A1, 2018-06-21).
Regarding independent Claim 2,
COSTELLO teaches an adaptable system for analyzing enterprise data the adaptable system ( see Abstract illustrating the method of intent classification), comprising:
a server system including at least one processor, a communications interface, and a data storage storing instructions to configure the at least one processor to perform functions (see COSTELLO: Fig.1, [0027 system comprises a computer-readable memory storing executable instructions and one or more processors in communication with the computer-readable memory, … [0155], “system comprises a computer-readable memory storing executable instructions and one or more processors in communication with the computer-readable memory.”), including that of:
a data extraction and consumption (DEC) module to perform domain modeling and domain training to analyze, process, and present enterprise data (see COSTELLO: Fig.2, [0077], “upon receiving the data from the dataset, the training module 100 processes the data to allow the dataset to be usable for the purpose of model training, and it can be realized by a dataset processing portion (not shown in the drawings) in the training module 100 of the intent classification system 001.” … [0078], “under the action of the training module 100, each of the plurality of models is trained for a total of m times (m≥1), each time with a different initialization condition, to thereby obtain a total of m trained models which together form a trained model set corresponding to the each of the plurality of models.”).
an intent discovery core engine including an intent element feature extractor, an intent concept relationship discovery module, an intent discovery module, and an intent language model (ILM) management system (see COSTELLO: Fig.1A, [0063], “the intent classification system 001 (an intent discovery core engine) includes a training module 100 and a hierarchical prediction module 200 (intent element feature extractor).” … [0076], “The final prediction result (an intent discovery module) to be outputted by the hierarchical prediction module 200 in the intent classification system 001 disclosed herein can include a class (or an identifier/label thereof) to which the input data most likely belong.” … [0085], “the plurality of models employed by the intent classification system 001 include the CNN model, the GRU model, and the ABiCNN model, among others.” (intent language model (ILM) management system”); and
COSTELLO does not teach the method wherein:
an intent concept relationship discovery module system; and
a services module for providing web services to send information to and receive information from at least one service enterprise.
However, BANGALORE teaches the method wherein:
an intent concept relationship discovery module system (see BANGALORE: Fig. 1, [0021], “Domain hierarchies, stored within the repository of domain hierarchies 205, are hierarchical graphs having leaf nodes and non-leaf nodes (hereinafter referred to as “ancestor nodes” of the leaf nodes, in that they may be parents of leaf nodes, parents of parents, etc.). Each node represents a possible user intent. Each leaf node represents a different specific user intent (the most specific that the domain hierarchy in question recognizes), and the ancestor nodes represent related but more general user intents.”… see also Fig.3A, [0022], a sample domain hierarchy of intents) ;
a services module for providing web services to send information to and receive information from at least one service enterprise (see BANGALORE: Fig. 1, [0013], “A natural language processing system 100, being also connected to the network 140, provides natural language interpretation services on behalf of a merchant system 110. Based on its interpretation of user utterances, the natural language processing system 100 can determine an appropriate response to send to the user and can provide rational feedback to the user via the client device 120.”)
Because both COSTELLO and BANGALORE in the same/similar field of endeavor of natural language processing and intent determination, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of COSTELLO to include a hierarchal intent modeling and system interaction feature that provide a web services to send information to and receive information from at least one service enterprise as taught by BANGALORE. One would have been motivated to make such a combination to improve user intent classification accuracy and enterprise level intent predictions so that robust, simple and scalable intent classification system are deployed.
Regarding Claim 3,
COSTELLO and BANGALORE and teaches all the limitations of claim 2. BANGALORE further teaches the system wherein:
the data storage stores further instructions to configure the at least one processor to perform further functions including that of a visualization module to format and package data and other information for display to an end user (see BANGALORE: Fig.4, [0045], “he computer 400 can lack certain illustrated components. In one embodiment, a computer 400 acting as a server may lack a graphics adapter 412, and/or display 418, as well as a keyboard or pointing device. Moreover, the storage device 408 can be local and/or remote from the computer 400 (such as embodied within a storage area network (SAN)).”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of COSTELLO to include a visualization module to format and package data and other information for display to an end user as taught by BANGALORE. One would have been motivated to make such a combination to improve user intent classification accuracy and enterprise level intent predictions so that robust, simple and scalable intent classification system are deployed.
Regarding Claim 4,
COSTELLO and BANGALORE and teaches all the limitations of claim 2. COSTELLO further teaches the system wherein :
data storage stores further instructions to configure the at least one processor to perform further functions including that of a query engine to access results data based on a query and provide feedback in response thereto (see COSTELLO: Fig.3, [0100], “The second ensemble layer 230 then performs at least one ensembled calculation with the plurality of first-layer ensembles 2201 as inputs, to thereby obtain at least one second-layer ensembled prediction result (short as second-layer ensemble hereafter), each corresponding to one of the at least one ensembled calculation. The at least one second-layer ensemble can form a final prediction result that is ultimately outputted from the hierarchical prediction module 200 of the intent classification system 001.”)
Regarding Claim 5,
COSTELLO and BANGALORE and teaches all the limitations of claim 2. COSTELLO further teaches the system wherein :
the intent discovery core engine includes a feature extraction engine, a classifier and a clusterer to analyze a data stream and store metadata (see COSTELLO: Fig.2, [0121], “the plurality of trained model sets are respectively built can be a machine learning algorithm (i.e. machine learning classifier), which can specifically be a deep learning classifier (e.g. CNNs, RNNs, multi-layer perceptron/deep neural networks, etc.) or a non-deep learning machine learning classifier (e.g. logistic regression, support vector machines (SVMs), and naive Bayes, etc.)”); and
the data extraction and consumption (DEC) module further translates input data sources into defined abstractions for consumption by the feature extraction engine (see COSTELLO: Fig.2, [0121], “The first ensemble layer 220 is further configured to receive the plurality of first-layer ensembled prediction results 2101 obtained from the calculation layer 210, and then to calculate a plurality of ensembled prediction results 2201 (short as a plurality of first-layer ensembles hereafter). Each of the plurality of first-layer ensembles is substantially obtained from those first-layer ensembled prediction results 2101 that correspond to a same trained model set,”)
Regarding Claim 6,
COSTELLO and BANGALORE and teaches all the limitations of claim 2. COSTELLO further teaches the system wherein:
the data storage stores further domain specific databases including data related to one or more domains (see COSTELLO: Fig.2, [0162], “For a particular banking-related application, the domain specific data is used. The proprietary banking dataset was collected as an initial in-house pilot study of 360 usable written utterances and increased to a total of 5,358 usable (clean) utterances by utilizing Mechanical Turk. An example utterance is: “I would like to open a checking account.” Data collection was based on 12 prompts representing different slot/intent combinations.”)
Regarding Claim 7,
COSTELLO and BANGALORE and teaches all the limitations of claim 2. COSTELLO further teaches the system wherein :
the intent element feature extractor is associated with a domain and includes domain- specific feature extraction parameters to extract data from enterprise data documents (see COSTELLO: Fig.2, [166], “”)The domains cover 31 categories, including Chit-chat and 30 task-oriented categories. The SMP dataset is skewed towards Chit-chat with around 20% of data in it, and the rest of the 30 categories are more evenly distributed. Since the Chinese data given is not tokenized, the Jieba tokenizer (github.com/fxsjy/jieba) was used to tokenize the sentences.
Regarding Claim 8,
COSTELLO and BANGALORE and teaches all the limitations of claim 7. COSTELLO further teaches the system wherein:
the intent element feature extractor uses one or more of natural language processing (NLP) algorithms, machine learning and neural network algorithms for feature extraction (see COSTELLO: Fig.2, [0168], “Intent classification is the task of correctly labeling a natural language utterance from a predetermined set of intents. It is treated as a multiclass classification task, and a discriminative machine learning model is trained to output a predicted classification for a given utterance. In cases where an utterance has multiple intents, the intents are concatenate and the result is treated as a single, distinct intent.”)
Regarding Claim 9,
COSTELLO and BANGALORE and teaches all the limitations of claim 7. COSTELLO further teaches the system wherein:
the intent concept relationship engine is coupled in communication with the intent element feature extractor to receive and process the extracted data and integrate word to word relationships to generate a domain knowledge graph database (see COSTELLO: Fig.2, [0155], “a user utterance can be recognized as, and converted into, an input text by a speech recognizer, which is configured as part of a cellular phone or a chatbot. The input text can then be uploaded into a cloud server having the hierarchical prediction module 200 in the intent classification system 001, where the intent class for the user utterance can be determined in an online and real-time mode. The intent class can then be transmitted back to the user or can be utilized for further analysis for, or for further interaction with, the user.”)
Regarding claim 10,
COSTELLO and BANGALORE and teaches all the limitations of claim 7. COSTELLO further teaches the system wherein:
the intent discover module includes an intent pattern recognizer to discover domain specific intent within segments of the enterprise data documents based on an intent language model (see COSTELLO: Fig.2, [0002], “algorithms to identify intent have progressed from utilizing approaches such as parts-of-speech tags with domain information as features, simple pattern matching, and bag-of-words model to state-of-the-art deep learning algorithms.”)
Regarding Claim 11,
COSTELLO and BANGALORE and teaches all the limitations of claim 10. COSTELLO further teaches the system wherein:
the intent language model management system is used to find intent elements in training data sets to train and enrich the intent language model (see COSTELLO: Fig.2, [0183], “The multi-layer ensemble approach is comprised of distinct first-layer and second-layer ensembles (FIG. 5). To obtain first-layer ensembles, each model was first trained three times with the same hyperparameters but different random initializations for the weights. Then a majority vote with confidence was utilized to find the ensemble predictions, and finally the F1 score was calculated. In Tables 1, 2, 3, and 6, 7, 8, the three individual model F1 scores (columns “1”, “2”, “3”), as well as their ensemble F1 (column “En”), are shown.”)
Regarding independent Claim 12,
Claim 12 is directed to a system claim and has similar/same limitation as claim 2 and is rejected under the same rationale.
Regarding Claim 13,
Claim 13 is directed to a system claim and has similar/same limitation as claim 3 and is rejected under the same rationale.
Regarding Claim 14,
Claim 14 is directed to a system claim and has similar/same limitation as claim 3 and is rejected under the same rationale.
Regarding Claim 15,
Claim 15 is directed to a system claim and has similar/same limitation as claim 5 and is rejected under the same rationale.
Regarding Claim 16,
Claim 16 is directed to a system claim and has similar/same limitation as claim 6 and is rejected under the same rationale.
Regarding Claim 17,
Claim 17 is directed to a system claim and has similar/same limitation as claim 7 and is rejected under the same rationale.
Regarding Claim 18,
Claim 18 is directed to a system claim and has similar/same limitation as claim 8 and is rejected under the same rationale.
Regarding Claim 19,
Claim 19 is directed to a system claim and has similar/same limitation as claim 9 and is rejected under the same rationale.
Regarding Claim 20,
Claim 20 is directed to a system claim and has similar/same limitation as claim 10 and is rejected under the same rationale.
Regarding Claim 21,
Claim 21 is directed to a system claim and has similar/same limitation as claim 11 and is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
NUMBER:
INVENTOR-INFORMATION:
TITLE / DESCRIPTION
US 20180191867 A1
Siebel; Thomas M.
Title: SYSTEMS, METHODS, AND DEVICES FOR AN ENTERPRISE AI AND INTERNET-OF-THINGS PLATFORM
Description: he present disclosure relates to big data analytics, data integration, processing, machine learning, and more particularly relates to an enterprise Internet-of-Things (IoT) application development platform.
US 10824818 B2
Peper; Joseph
Title: Systems And Methods For Machine Learning-based Multi-intent Segmentation And Classification
Description: he inventions herein relate generally to the machine learning and artificially intelligent dialogue systems fields, and more specifically to a new and useful system and method for intelligently synthesizing training data and training machine learning models of a machine learning-based conversational service in the machine learning field..
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm.
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/Zelalem Shalu/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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