DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/14/2025 has been entered.
Response to Arguments
Applicant’s arguments, see Applicant’s argument ,filed 11/14/2025 with respect to the rejection of claims 1-20 have been fully considered and are persuasive. The rejection has been withdrawn.
Double Patenting
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No.12,393,785. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Current application 18/066,321
1. A system for generating recommendations for labeling chatbot data, the system comprising: one or more processors; and one or more non-transitory, computer-readable media comprising instructions that, when executed by the one or more processors cause operations comprising: receiving storing, within a label record database, text data for an unlabeled training datum corresponding to chatbot messages from a chatbot user, wherein a training dataset comprising the text data is used for a machine learning model, wherein the text data comprises language-based data with syntax information; inputting the text data to a natural language processing model to generate a first vector representation of the text data, wherein the first vector representation preserves syntax from the text data retrieving storing, within the label record database, a plurality of label records for a plurality of previously-labeled textual datasets corresponding to a plurality of label records from a label record database, wherein the plurality of label records comprises a plurality of dataset identifiers of the plurality of previously-labeled textual datasets and corresponding labels previously assigned to the plurality of dataset identifiers, and wherein the plurality of previously- labeled textual datasets comprise chatbot text data previously processed using one or more trained machine learning models; generating inputting the plurality of label records for the plurality of previously- labeled textual datasets into the natural language processing model to generate a plurality of vector representation of the text data and a plurality of vector representations of the plurality of previously-labeled textual datasets, wherein the plurality of vector representation preserves representations preserve syntax from the chatbot text data; in response to inputting generating the first vector representation and the plurality of vector representations into a natural language processing model, determining executing, using the natural language processing model, a distance calculation to measure a plurality of distances in an n-dimensional vector space between the first vector representation and each vector representation of the plurality of vector representations; generating, using the natural language processing model, a plurality of average similarity metrics between the text data and each previously-labeled textual dataset of the plurality of previously-labeled textual datasets based on the plurality of distances; in response to determining that querying the label record database to determine a label record of a previously-labeled textual dataset of the plurality of previously-labeled textual datasets [[has]] having a highest average similarity metric of the plurality of average similarity metrics, wherein the plurality of average similarity metrics measures lexical and syntactic similarity between text represented by the text data and respective text represented by the plurality of previously-labeled textual datasets, respectively, determining a the label record for the previously-labeled textual dataset comprising comprises a first label name, a modification timestamp, and a dataset identifier; generating a first recommendation for a first label for the text data based on the label record; generating and storing, in the label record database, a new label record comprising the first label and an identifier of the text data a second label name based on the label record of the previously-labeled textual dataset; generating a first feature input for the machine learning model based on the [[first]] new label record and the unlabeled training datum; and generating, using inputting the first feature input to the machine learning model; based on the first feature input being input to the machine learning model, to generate a first output for the machine learning model comprising sentiment analysis relating to satisfaction of the chatbot user.
Patent No.: 12,393,785
1.A system for labeling data for artificial neural network models when information used to determine those labels may be received in using both real-time data and batch-processed data streams that reduces computations resulting from labeling discrepancies, the system comprising: one or more processors; and[[a]] one or more non-transitory, computer-readable medium media comprising instructions that when executed by the one or more processors cause operations comprising: receiving, at a device in a computer network, a first data stream, wherein the first data stream comprises first comprising real-time processed data for populating a [[first]] label for a [[first]] training datum in a [[first]] training dataset for an artificial neural network model, wherein the [[first]] real-time processed data comprises initial textual chatbot data from an artificial intelligence engine for a chatbot user; generating a first vector representation of the [[first]] real-time processed data for use in a natural language processing model, wherein the first vector representation represents the first real-time processed data using a bag of words structure; based on processing determining, based on the first vector representation through the, using a natural language processing model, determining the a first label for the [[first]] training datum; in response to receiving assigning, based on a determination that the first data stream terminated prior to receipt of a completion message, determining whether all data used to generate a final label for the first training datum has been received, wherein the completion message comprises an indication of an end of the first real-time processed data; in response to not receiving the completion message upon termination of the first data stream, assigning a first label type to the [[first]] label; storing, in a label record database, for the [[first]] training datum, a record comprising the first label and a first indication that the first label for the first training datum has the first label type, the record tracking labeling changes of the training datum; generating a first feature input [[for]] based on the training datum, the first label, and the first label type and inputting the first feature input to the artificial neural network model based on the first label, the first training datum, and the first indication; and generating a first output for the artificial neural network model based on the first label, the first training datum, and the first indication, wherein the to generate a first output comprises sentiment prediction for a satisfaction level for the chatbot user comprising a first classification of the chatbot user based on the initial textual chatbot data; receiving a second data stream comprising batch-processed data for populating the label, the batch-processed data comprising completed textual chatbot data from the artificial intelligence engine for the chatbot user including the completion message; generating a second vector representation of the batch-processed data and determining, using the natural language processing model, a second label for the training datum based on the second vector representation; responsive to determining, based on a similarity metric computed using the first vector representation and the second vector representation being less than a label similarity threshold, that the second label differs from the first label: updating the record to comprise the first label with the first label type and the second label with a second label type assigned based on the completion message being received; and preventing a second feature input generated based on the training datum, the label, and the second label type from being input to the artificial neural network model.
The claims of the current application are not patentably distinct from the claims of patent No.: 12,393,785. Both claim sets are directed to the same general inventive concept of using label records and similarity-based analysis of training data to recommend or select labels for machine learning training data. More specifically, both claim sets recite limitations involving: a label record database label records associated with a datum or dataset identifier comparison of labels, including pre-existing labels and new labels vector representations and similarity metrics recommending a label based on the relative similarity of labeled data machine-learning model processing, including feature inputs and outputs model error indicators timestamps, update rates, and warnings the claims of the application differs from the patent, at most, by adding or emphasizing: real-time and batch-processed chatbot data streams; label type assignment based on completion/finality; downstream model output based on chatbot text; record updates based on later-received data. However, these additions are merely obvious variations of the same underlying label-selection and label-management framework already recited in the patent. The additional real-time/batch-processing and label-type terminology does not create a patentably distinct invention because it is implemented within the same record-based, similarity-driven labeling architecture.
Claims 1 -20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 20 of copending Application No. 18/066,337. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Current application: 18/066,321
1. (Currently Amended) A system for generating recommendations for labeling chatbot data, the system comprising: one or more processors; and one or more non-transitory, computer-readable media comprising instructions that, when executed by the one or more processors cause operations comprising: receiving storing, within a label record database, text data for an unlabeled training datum corresponding to chatbot messages from a chatbot user, wherein a training dataset comprising the text data is used for a machine learning model, wherein the text data comprises language-based data with syntax information; inputting the text data to a natural language processing model to generate a first vector representation of the text data, wherein the first vector representation preserves syntax from the text data retrieving storing, within the label record database, a plurality of label records for a plurality of previously-labeled textual datasets corresponding to a plurality of label records from a label record database, wherein the plurality of label records comprises a plurality of dataset identifiers of the plurality of previously-labeled textual datasets and corresponding labels previously assigned to the plurality of dataset identifiers, and wherein the plurality of previously- labeled textual datasets comprise chatbot text data previously processed using one or more trained machine learning models; generating inputting the plurality of label records for the plurality of previously- labeled textual datasets into the natural language processing model to generate a plurality of vector representation of the text data and a plurality of vector representations of the plurality of previously-labeled textual datasets, wherein the plurality of vector representation preserves representations preserve syntax from the chatbot text data; in response to inputting generating the first vector representation and the plurality of vector representations into a natural language processing model, determining executing, using the natural language processing model, a distance calculation to measure a plurality of distances in an n-dimensional vector space between the first vector representation and each vector representation of the plurality of vector representations; generating, using the natural language processing model, a plurality of average similarity metrics between the text data and each previously-labeled textual dataset of the plurality of previously-labeled textual datasets based on the plurality of distances; in response to determining that querying the label record database to determine a label record of a previously-labeled textual dataset of the plurality of previously-labeled textual datasets [[has]] having a highest average similarity metric of the plurality of average similarity metrics, wherein the plurality of average similarity metrics measures lexical and syntactic similarity between text represented by the text data and respective text represented by the plurality of previously-labeled textual datasets, respectively, determining a the label record for the previously-labeled textual dataset comprising comprises a first label name, a modification timestamp, and a dataset identifier; generating a first recommendation for a first label for the text data based on the label record; generating and storing, in the label record database, a new label record comprising the first label and an identifier of the text data a second label name based on the label record of the previously-labeled textual dataset; generating a first feature input for the machine learning model based on the [[first]] new label record and the unlabeled training datum; and generating, using inputting the first feature input to the machine learning model; based on the first feature input being input to the machine learning model, to generate a first output for the machine learning model comprising sentiment analysis relating to satisfaction of the chatbot user.
Application Number:18/066,337
1.A system for selecting labels based on dataset similarity for machine learning model training chatbot text data comprising: one or more processors; and a non-transitory, computer-readable medium comprising instructions that when executed by the one or more processors cause operations comprising: receiving, at a device on a computer network, a label modification request for a training datum for a machine learning model, wherein the label modification request comprises a new label and a datum identifier, and wherein the new label comprises a category of chatbot data; retrieving, from a label record database, a plurality of label records corresponding to the datum identifier, wherein the plurality of label records comprises a plurality of pre- existing labels previously applied to the training datum; comparing a pre-existing label from the plurality of pre-existing labels with the new label; based on determining that the pre-existing label from the plurality of pre-existing labels differs from the new label, generating, based on the label record database, (1) a first dataset labeled with the new label and (2) a second dataset labeled with the pre-existing label; calculating a first plurality of similarity metrics between the training datum and each datum of the first dataset and a second plurality of similarity metrics between the training datum and each datum of the second dataset; calculating a first average similarity metric based on an average of the first plurality of similarity metrics and a second average similarity metric based on an average of the second plurality of similarity metrics; comparing the first average similarity metric with the second average similarity metric; and based on determining that the second average similarity metric is higher than the first average similarity metric, generating a recommendation for a modified label based on the pre-existing label.
2. A method for selecting labels based on dataset similarity for machine learning model training data, the method comprising: receiving, at a device on a computer network, a label modification request for a training datum for a machine learning model, wherein the label modification request comprises a new label and a datum identifier; retrieving, from a label record database, a plurality of label records corresponding to the datum identifier; comparing a pre-existing label from the plurality of label records with the new label; based on determining that the pre-existing label from the plurality of label records differs from the new label, generating, based on the label record database, (1) a first dataset labeled with the new label and (2) a second dataset labeled with the pre-existing label; calculating (1) a first average similarity metric between the training datum and the first dataset and (2) a second average similarity metric between the training datum and the second dataset; comparing the first average similarity metric with the second average similarity metric; and based on comparing the first average similarity metric with the second average similarity metric, generating a recommendation for a modified label.
16. A non-transitory, computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving, at a device on a computer network, a label modification request for a training datum for a machine learning model, wherein the label modification request comprises a new label and a datum identifier; retrieving, from a label record database, a plurality of label records corresponding to the datum identifier; comparing a pre-existing label from the plurality of label records with the new label; based on determining that the pre-existing label from the plurality of label records differs from the new label, generating, based on the label record database, (1) a first dataset labeled with the new label and (2) a second dataset labeled with the pre-existing label; calculating (1) a first average similarity metric between the training datum and the first dataset and (2) a second average similarity metric between the training datum and the second dataset; comparing the first average similarity metric with the second average similarity metric; and based on comparing the first average similarity metric with the second average similarity metric, generating a recommendation for a modified label.
Claims 1-20 are not patentably distinct from claims 1-20 of application serial number 18/066,337. Both claim sets are directed to label management and label recommendation for machine-learning training data using label records stored in a label record database, label comparison, similarity-based analysis, and downstream model processing. The claims of the current application recite, among other things, receiving real-time processed data and batch-processed data for chatbot training data, generating vector representations, determining labels using an NLP model, assigning label types based on completion/finality, storing label records in a label record database, generating feature inputs and model outputs, and updating or preventing use of labels based on label differences or similarity determinations. The claims of serial number 18/066,337 recite receiving a label modification request, retrieving label records from a label record database, comparing a new label with a pre-existing label, generating datasets labeled with the respective labels, calculating similarity metrics, comparing average similarity metrics, and generating a recommendation for a modified label.
The differences between the claims are only superficial and amount to obvious variations of the same inventive concept. The present application merely applies the same label-record, similarity, and recommendation framework to real-time and batch-processed chatbot data and introduces label-type terminology associated with data completeness. These additional limitations do not render the present application patentably distinct from application 18/066,337.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Allowable Subject Matter
Claims 1 -20 are allowable over the prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892
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/RICHEMOND DORVIL/ Supervisory Patent Examiner, Art Unit 2658
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