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. This Office Action is sent in response to Applicant’s Communication received on 04/14/2026 for application number 17/436,281.
Response to Amendments
3. The Amendment filed 04/14/2026 has been entered. Claims 1, 22, and 29 have been amended. Claims 1, 6, 10-12, 14, 18, 20, 22-25, 27, and 29-35 remain pending in the application.
4. Applicant’s amendments to claims 1, 22, 29 have been fully considered and are persuasive. The amendments provided to overcome the 101 rejection issued in the last office action is sufficient. The 35 U.S.C § 101 rejection of claims 1, 6, 10-12, 14, 18, 20, 22-25, 27, and 29-35 is respectfully withdrawn.
Response to Arguments
Applicant argues that Singaraju fails to anticipate the amended independent claims. However, the argument is moot since this is a newly presented limitation, thus changes the scope of the claim. However, newly found references, Victoroff, and Farrar, are applied.
Claim Rejections – 35 USC § 103
5. The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
6. Claims 1, 6, 10-12, 14, 18, 20, 22-25, 27, and 29-35 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Singaraju et al. (U.S. Patent Application Pub. No. US 20190103095 A1) in view of Victoroff et al. (U.S. Patent Pub. No. US 10956790 B1), and further in view of Farrar et al. (U.S. Patent Application Pub. No. US 20200082300 A1).
Claim 1: Singaraju teaches a computer-implemented method for assisting a developer in improving an accuracy of a classification model (i.e. This disclosure related generally to improving quality of classification models, and more particularly, to improving quality of classification models for differentiating different end user intents by improving the quality of training samples used to train the classification models; para. [0024]), the computer implemented method comprising:
providing, by a computing device, the classification model including a plurality of features corresponding with a set of classes, wherein one or more features of the plurality of features correspond with at least one class of the set of classes (i.e. a computer-implemented technique may determine a distinguishability score for each respective combination of two intents (forming a pair) within a plurality of intents that is defined by a developer of a bot system. The computer-implemented technique may then identify each pair of intents that is difficult to differentiate by a classification model trained using given training samples (e.g., user utterances) based upon the distinguishability score (e.g., F-score), such as based upon the distinguishability score being lower than a threshold; para. [0026, 0062]), the utterances and their attributes are the operative inputs used by the classifier;
selecting the one or more features of the plurality of features (i.e. pairs of intents that are difficult to differentiate based upon the given training samples (e.g., user utterances) may be identified from a plurality of intents based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with one intent and a training sample associated with the other intent are ranked based upon a similarity score (e.g., a Jaccard similarity score or a Levenshtein distance) between the two training samples in each pair; para. [0070, 0072]);
extracting one or more values for the one or more features selected (i.e. pairs of intents that are difficult to differentiate based upon the given training samples (e.g., user utterances) may be identified from a plurality of intents based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with one intent and a training sample associated with the other intent are ranked based upon a similarity score (e.g., a Jaccard similarity score or a Levenshtein distance) between the two training samples in each pair. The identified pairs of intents and the corresponding pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options for improving the training samples; para. [0070, 0073]);
determining at least one correlation of the one or more features with the set of classes respectively (i.e. pairs of intents that are difficult to differentiate based upon the given training samples (e.g., user utterances) may be identified from a plurality of intents based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with one intent and a training sample associated with the other intent are ranked based upon a similarity score (e.g., a Jaccard similarity score or a Levenshtein distance) between the two training samples in each pair. The identified pairs of intents and the corresponding pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options for improving the training samples; para. [0070, 0075]);
generating at least one diagnostic example for the correlation (i.e. pairs of intents that are difficult to differentiate based upon the given training samples (e.g., user utterances) may be identified from a plurality of intents based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with one intent and a training sample associated with the other intent are ranked based upon a similarity score (e.g., a Jaccard similarity score or a Levenshtein distance) between the two training samples in each pair. The identified pairs of intents and the corresponding pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options for improving the training samples; para. [0070]);
wherein the at least one diagnostic example requires the developer to one of validate or invalidate a correctness of the correlation produced by the at least one diagnostic example (i.e. the computing system may provide, through a graphic user interface (GUI), information that may be used by the developer of the bot system to improve the intent classification. The information may include the identified pairs of intents that are determined to be difficult to distinguish, at least a portion of the corresponding pairs of training samples (e.g., pairs with the highest similarity scores) for each identified pair of intents, and user-selectable options for improving classification of utterances associated with the plurality of intents. In some embodiments, the information may be displayed on one or more GUI screens. For example, some information may be displayed on a new GUI screen when the developer selects a selectable item on a GUI screen, such as a link, a button, a user menu item, and the like. In some embodiments, the user-selectable options may include removing, adding, or modifying some training samples, and/or adding, deleting, or updating some intents; para. [0027, 0076]); and
when the developer invalidates the correctness of the at least one correlation of the one or more features with the set of classes respectively produced by the at least one diagnostic example (i.e. The developer may edit (e.g., modify or remove) any utterance by clicking an icon 722. In some embodiments, the developer may also add new utterance by clicking an icon 724. After the editing, the developer may validate the modified utterances by clicking a “Validate” button 730; para. [0089]):
locating existing examples in the training set having the at least one correlation of the one or more features with the set of classes respectively (i.e. At 1060, the computing system may select one or more pairs of training samples that have the highest similarity scores among the pairs of training samples. In some embodiments, the computing system may select one or more pairs of training samples having similarity scores greater than a certain threshold value; para. [0099]), and
automatically modifying the existing examples in the training set having the at least one correlation in the training set to suppress the at least one correlation (i.e. the developer may also add new utterance by clicking an icon 724. After the editing, the developer may validate the modified utterances by clicking a “Validate” button 730, and/or retrain the classification model for distinguishing the pair of intents by clicking a “Train” button 740; para. [0088, 0089]), “automatically modifying” can include system-perform modification of stored training samples once the developer selects an option, the modification is executed by the computer rather than manually rewriting a dataset outside the tool, improving distinguishability by editing training samples/labels so the model stops relying on the problematic similarity pattern, thereby, “suppress the correlation”.
Singaraju does not explicitly teach automatically, in response to the invalidation of the correctness of the at least one correlation, locating existing examples in the training set having the at least one correlation of the one or more features with the set of classes respectively, and automatically, in response to the invalidation of the correctness of the at least one correlation, modifying the existing examples automatically located in the training set having the at least one correlation of the one or more features with the set of classes in the training set to suppress the at least one correlation.
However, Victoroff teaches when the developer invalidates the correctness of the at least one correlation (i.e. The graphical user interface of concept 45, further comprising: a second user interface arranged for allowing the user to accept as valid or reject as invalid the likelihoods displayed on said second user display; the accepted likelihoods being considered as a label association to be used as a basis for the data recognition model; col. 8, lines 18-23) of the one or more features with the set of classes respectively (i.e. A graphical user interface comprising: a data interface arranged for receiving a first set of text documents; a calculator arranged for transforming each received text document into a vector of a n-dimensional manifold, n being an integer larger than 2; a first user display arranged for successively displaying each text document of the first set of text documents; a first user interface arranged for allowing a user to associate each displayed text document to a label selected by the user; the calculator being arranged to associate said label selected by the user to the n-dimensional manifold vector that corresponds to the displayed text document; col. 7, lines 42-55) produced by the at least one diagnostic example (i.e. a first user display for displaying any discrepancy between the estimated and known associated labels for all the test documents; a first user interface arranged for allowing a user to select any of the test documents having a displayed discrepancy between its estimated and known associated labels; col. 7, lines 10-20): automatically, in response to the invalidation of the correctness of the at least one correlation, locating existing examples in the training set (i.e. a first user interface arranged for allowing a user to select any of the test documents having a displayed discrepancy between its estimated and known associated labels; a calculator arranged for using a distance metric to calculate a distance between the selected test documents and each document of the set of learning documents; and a second user display for displaying a predetermined number of the learning documents of said set of learning documents that are the closest to the selected test documents; col. 7, lines 15-24) having the at least one correlation of the one or more features with the set of classes respectively (i.e. the training document that is closest to the mis-recognized test document, thus allowing a user to locate rapidly mistaken training documents; col. 14, lines 20-23), and in response to the invalidation of the correctness of the at least one correlation, modifying the existing examples automatically located in the training set having the at least one correlation of the one or more features with the set of classes in the training set (i.e. The graphical user interface of concept 41, comprising a second user interface arranged for allowing a user to correct the set of learning documents by canceling from the set of learning documents any learning document displayed on the second user display; wherein the data recognition model is arranged for updating said data recognition model based on the corrected set of learning documents; col. 7, lines 25-31) to suppress the at least one correlation (i.e. the GUI 10 can then be arranged to update the data recognition model (for example run in calculator 20) based on the corrected set of learning documents 16′, and eventually display any remaining discrepancy between the estimated and known associated labels for all the test documents, allowing the user to repeat the operations above until no discrepancies are found between the estimated and known associated labels for all the test documents; col. 14, lines 29-37).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Singaraju to include the feature of Victoroff. One would have been motivated to make this modification because it allows the developer not only to view problematic diagnostic examples, but also to trace a detected classification error back to the particular training examples most likely responsible for the erroneous feature/class association, thereby enabling targeted correction of the training set and improving the accuracy and reliability of the classification model.
However, Farrar teaches automatically (i.e. a method for rejecting biased data using a machine learning model; para. [0003]), modifying the existing examples automatically located in the training set having the at least one correlation of the one or more features with the set of classes in the training set to suppress the at least one correlation (i.e. Referring to FIG. 2D, the adjuster 220 compares a training data set weight 218 and a bias cluster weight 214 that share a common data characteristic (e.g., a bias sensitive variable) or a combination of data characteristics. When the ML training data set 302 over represents a bias sensitive variable, the training data set weight 218 exceeds (e.g., is greater than) the cluster weight 214 (e.g., the training data set 302 indicates a 20% greater white racial makeup) for the data characteristic corresponding to the bias sensitive variable. In response to this over representation, the process 226 executing by the adjuster 220 may correspond to a data removal adjustment process that adjusts the training data set weight 218 by removing data from the training data set 302 until the training data set weight 218 matches the cluster weight 214; para. [0034, 0038, 0045, 0046]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Singaraju and Victoroff to include the feature of Farrar. One would have been motivated to make this modification because it provides an efficient way for a system automatically suppresses the invalidated feature/class association and improves classification accuracy.
Claim 6: Singaraju, Victoroff, and Farrar teach the computer-implemented method as claimed in claim 1. Singaraju further teaches wherein determining the at least one correlation includes at least one of computing the at least one correlation over a set of examples or extracting the at least one correlation from the classification model (i.e. pairs of intents that are difficult to differentiate based upon the given training samples (e.g., user utterances) may be identified from a plurality of intents based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with one intent and a training sample associated with the other intent are ranked based upon a similarity score (e.g., a Jaccard similarity score or a Levenshtein distance) between the two training samples in each pair. The identified pairs of intents and the corresponding pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options for improving the training samples; para. [0070, 0073, 0082, 0086]).
Claim 10: Singaraju, Victoroff, and Farrar teach the computer-implemented method as claimed in claim 1. Singaraju further teaches wherein the at least one diagnostic example includes at least one of: a text-based question, an image-based question, an audio-based question, a video-based question, or a data-based question for the developer to at least one of validate or invalidate the correctness of the correlation produced (i.e. pairs of intents that are difficult to differentiate based upon the given training samples (e.g., user utterances) may be identified from a plurality of intents based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with one intent and a training sample associated with the other intent are ranked based upon a similarity score (e.g., a Jaccard similarity score or a Levenshtein distance) between the two training samples in each pair. The identified pairs of intents and the corresponding pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options for improving the training samples; para. [0070, 0076]).
Claim 11: Singaraju, Victoroff, and Farrar teach the computer-implemented method as claimed in claim 1. Singaraju further teaches wherein generating the at least one diagnostic example comprises: accessing a plurality of examples within a training set (i.e. a computing system may receive training samples for training one or more classifiers to distinguish inputs associated with a plurality of intents. The plurality of intents may be generated or identified by a developer of a bot system as described above. The training samples may include examples of end user utterances that users may communicate with the bot system. The training samples may also include the end user intents associated with the end user utterances. For example, the training samples may include annotations or labels indicating the end user intents associated with respective end user utterances; para. [0072]); extracting the one or more features for each example of the plurality of examples (i.e. for each pair of intents that is identified as difficult to distinguish, the computing system may rank pairs of training samples that each include a training sample associated with one intent and a training sample with the other intent in the pair of intents based upon a similarity score between the two training samples in each pair of training samples. The pairs of training samples may include any pair of training samples that may include a training sample associated with a first intent in the pair of intents and a training sample associated with a second intent in the pair of intents. In some embodiments, the similarity score may include a Jaccard similarity score (or Jaccard distance) or a Levenshtein distance as described in detail below; para. [0075, 0076]); and generating the at least one diagnostic example based upon, at least in part, the extracted features (i.e. the computing system may provide, through a graphic user interface (GUI), information that may be used by the developer of the bot system to improve the intent classification. The information may include the identified pairs of intents that are determined to be difficult to distinguish, at least a portion of the corresponding pairs of training samples (e.g., pairs with the highest similarity scores) for each identified pair of intents, and user-selectable options for improving classification of utterances associated with the plurality of intents; para. [0076]).
Claim 12: Singaraju, Victoroff, and Farrar teach the computer-implemented method as claimed in claim 1. Singaraju further teaches wherein each of the one or more features comprise at least one of a word, a part of a word, a phrase, a sentence, a paragraph, a combination of words, a portion of an image, a portion of an audio, a portion of a video, or a portion of data (i.e. an “utterance” may refer to any sentence a customer or end user uses to communicate with a bot system. An “intent” may refer to an action that an end user intends to take or intends the bot system to take, or a goal that the end user would like to accomplish, when communicating with the bot system using one or more utterances; para. [0028, 0076]).
Claim 14: Singaraju, Victoroff, and Farrar teach the computer-implemented method as claimed in claim 1. Singaraju further teaches comprising: receiving an input from the developer that one of validates or invalidates the correctness of the correlation in response to the at least one diagnostic example (i.e. techniques disclosed herein can be used to debug and/or optimize classification models used by a bot system to determine end user intents based upon user utterances. For example, the techniques may identify possible root causes of misclassifications by a classification model, such as identifying specific training samples that are associated with different intents but are very similar, or specific intents that may need to be better defined. Thus, a developer may only need to review and edit the identified training samples or intents. In some embodiments, only classification models associated with the updated intents or the updated training samples may be retrained. Thus, the developer can quickly verify the effectiveness of the editing for the optimization using techniques disclosed herein, without having to retrain all intent classification models for the bot system; para. [0027]); when the developer invalidates the correctness of the correlation selected, at least one of: recommending the developer provide an additional set of examples used as training data for adjusting the classification model to suppress the correlation selected between the one or more features selected and the set of classes; and receiving the additional set of examples (i.e. the information may be displayed on one or more GUI screens. For example, some information may be displayed on a new GUI screen when the developer selects a selectable item on a GUI screen, such as a link, a button, a user menu item, and the like. In some embodiments, the user-selectable options may include removing, adding, or modifying some training samples, and/or adding, deleting, or updating some intents. In some embodiments, modifying a training sample may include modifying the utterance associated with the training sample. In some embodiments, modifying a training sample may include modifying the annotation or label of the end user intent associated with the training sample. In some embodiments, adding an intent may include adding training samples associated with the intent. In some embodiments, modifying an intent may include modifying the description of the intent; para. [0076]); automatically generating an additional set of examples used as training data for adjusting the classification model to suppress the correlation selected between the one or more features selected and the set of classes; automatically generating an additional set of examples and recommending at least one of the developer revise or approve the additional set of examples such that the additional set of examples are used as training data for adjusting the classification model to suppress the correlation selected between the one or more features selected and the set of classes; or adjusting the classification model by modifying at least one parameter of the classification model (i.e. fig. 3, the information may be displayed on one or more GUI screens. For example, some information may be displayed on a new GUI screen when the developer selects a selectable item on a GUI screen, such as a link, a button, a user menu item, and the like. In some embodiments, the user-selectable options may include removing, adding, or modifying some training samples, and/or adding, deleting, or updating some intents. In some embodiments, modifying a training sample may include modifying the utterance associated with the training sample. In some embodiments, modifying a training sample may include modifying the annotation or label of the end user intent associated with the training sample. In some embodiments, adding an intent may include adding training samples associated with the intent. In some embodiments, modifying an intent may include modifying the description of the intent; para. [0070, 0075, 0076]).
Claim 18: Singaraju, Victoroff, and Farrar teach the computer-implemented method as claimed in claim 1. Singaraju further teaches comprising: adjusting the classification model (i.e. The identified pairs of intents and the corresponding pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options for improving the training samples. The user-selectable options may include adding, deleting, or modifying some training samples, or adding, deleting, or modifying some intents. The updated training samples and end user intents may be used to retrain one or more classification models for differentiating the end user intents that are difficult to differentiate. The above-described processing may be performed recursively until no pair of end user intents may be identified as being difficult to differentiate; para. [0070]); and re-determining the at least one correlation of the one or more features selected upon adjusting the classification model (i.e. The identified pairs of intents and the corresponding pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options for improving the training samples. The user-selectable options may include adding, deleting, or modifying some training samples, or adding, deleting, or modifying some intents. The updated training samples and end user intents may be used to retrain one or more classification models for differentiating the end user intents that are difficult to differentiate. The above-described processing may be performed recursively until no pair of end user intents may be identified as being difficult to differentiate; para. [0070]).
Claim 20: Singaraju, Victoroff, and Farrar teach the computer-implemented method as claimed in claim 1. Singaraju further teaches comprising: iteratively generating another diagnostic example for the developer for another correlation selected from the at least one correlation, wherein the another diagnostic example requires the developer to one of validate or invalidate the correctness of another correlation produced by the another diagnostic example (i.e. The identified pairs of intents and the corresponding pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options for improving the training samples. The user-selectable options may include adding, deleting, or modifying some training samples, or adding, deleting, or modifying some intents. The updated training samples and end user intents may be used to retrain one or more classification models for differentiating the end user intents that are difficult to differentiate. The above-described processing may be performed recursively until no pair of end user intents may be identified as being difficult to differentiate; para. [0027, 0070, 0076]).
Claim 22 is similar in scope to Claim 1 and is rejected under a similar rationale.
Singaraju teaches one or more processors configured to store instructions thereon for (i.e. Various inventive embodiments are described herein, including methods, systems, non-transitory computer-readable storage media storing programs, code, or instructions executable by one or more processors, and the like; para. [0003]).
Claims 23-25 and 27 are similar in scope to Claims 20, 10, 12, 14 and are rejected under a similar rationale.
Claim 29 is similar in scope to Claim 1 and is rejected under a similar rationale.
Singaraju teaches a computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations (i.e. Storage subsystem 1318 provides a repository or data store for storing information and data that is used by computer system 1300. Storage subsystem 1318 provides a tangible non-transitory computer-readable storage medium for storing the basic programming and data constructs that provide the functionality of some examples. Storage subsystem 1318 may store software (e.g., programs, code modules, instructions) that when executed by processing subsystem 1304 provides the functionality described above. The software may be executed by one or more processing units of processing subsystem 1304; para. [0071, 0145]) for assisting a developer in improving an accuracy of a classification model (i.e. This disclosure related generally to improving quality of classification models, and more particularly, to improving quality of classification models for differentiating different end user intents by improving the quality of training samples used to train the classification models; para. [0024]).
Claims 30-35 are similar in scope to Claims 6, 10-12, 14, 18 and are rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Aghdaie et al. (Pub. No. US 20170259177 A1), The model generation system 146 may, in some cases, also receive feedback data 154. This data may be received as part of a supervised model generation process that enables a user, such as an administrator, to provide additional input to the model generation system 146 that may be used to facilitate generation of the prediction model 160. For example, if an anomaly exists in the historical data 152, the user may tag the anomalous data enabling the model generation system 146 to handle the tagged data differently, such as applying a different weight to the data or excluding the data from the model generation process.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/TAN H TRAN/Primary Examiner, Art Unit 2141
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