DETAILED ACTION
Introduction
1. A response was filed in this application on 05/04/2026 after the non-final rejection of 11/05/2025. Claims 1, 9-10 are amended while claims 5, 14, 19 are cancelled and no new claims added in this latest submission by the Applicant. Thus, claims 1-4, 6-13 and 15-20 are currently pending for reconsideration by the Examiner and are examined below. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to arguments
2. The Applicant’s arguments have been fully considered but are unpersuasive for at least the reasons outlined below.
The Applicant first argues (on page 8 of their remarks) that a first classification output is the same as “already generated classification output”.
The Examiner respectfully disagrees and argues that these two phrases cannot be presumed to be equivalent. The Applicant is arguing limitations which have not been claimed and as such their discussion is moot and not pertinent to the instant claims. The Applicant is welcome to incorporate this language into the instant independent claims in order for it to be given patentable weight.
The Applicant thereafter argues that para 89 of Terry does not teach user input based on first classification output as the system of Terry according to the Applicant relates to configuration of classification of categories and does not constitute user input derived from evaluation of specific classification output.
The Examiner once again respectfully disagrees and argues that Terry teaches an insight manager that allows the user to manage insights. As previously discussed, insights are a collection of categories used to answer some question about a document. For example, a question for the document could include “is the lead looking to purchase a car in the next month?” Answering this question can have direct and significant importance to a car dealership. Certain categories that the AI system generates may be relevant toward the determination of this question. These categories are the ‘insight’ to the question, and may be edited or newly created via the insight manager. These teachings clearly satisfy the metes and bounds of the current claim language as it is written with a very high level of generality and thus can be given a broad but reasonable interpretation.
Furthermore, the Applicant argues that there is no disclosure in Terry that would inherently result in evaluation of a classification output using predefined labels (as user input) or generation of a second classification output based on such evaluation. According to the Applicant amended claim 1 requires that predefined labels (obtained as user input) are used to evaluate a generated first classification output. This constitutes, as per the Applicant, an output-level evaluation mechanism in which a particular classification result is assessed using structured labels. Applicant alleges that Terry does not disclose such an evaluation step. The labels in Terry, according to the Applicant, are not used to assess correctness or quality of a classification output instance, but rather to define or modify classification categories themselves. Accordingly, as per the Applicant, Terry fails to disclose or suggest the claimed use of predefined labels provided as user input for evaluation of the first classification output.
The Examiner once again respectfully disagrees and argues that the instant independent claims neither recite “an output-level evaluation mechanism in which a particular classification result is assessed using structured labels” nor “assessing correctness or quality of a classification output instance”. Once again, these limitations are not part of the instant claims and hence cannot be given patentable weight. Although the claims are read in light of the specification, the specification cannot be read into the claims and the claims must be given a broad but reasonable interpretation. The Applicant’s current claim scope is not commensurate with their arguments. The Applicant is once again invited to incorporate these limitations into the instant independent claims in order for them to be given patentable weight.
The Applicant argues that the Examiner appears to equate Terry's manual classification override with the claimed second classification output. Such an interpretation is incorrect. A manual override merely replaces or corrects an existing classification and does not constitute generation of a second classification output derived from evaluation of a first classification output. Terry therefore fails to disclose the claimed generation of a second classification output based on user input i.e. the predefined labels.
The Examiner once again respectfully disagrees and argues that para 90 of terry (which was used to teach generating a second classification output) never mentioned “manual classification override”. Instead, para 90 of Terry teaches that the knowledge base manager enables the management of knowledge sets by the user. A knowledge set is set of tokens with their associated category weights used by an aspect (AI algorithm) during classification. A category may include “continue contact?”, and associated knowledge set tokens could include statements such as “stop”, “do no contact”, “please respond” and the like. The knowledge base manager enables the user to build new knowledge sets, or edit exiting ones.
The Applicant thereafter contends that the Examiner asserts that para 90 of Terry discloses generation of a second classification output. Applicant argues that there is no disclosure of reprocessing the same input data to generate a second classification output based on user input.
The Examiner once again respectfully disagrees and argues that the instant independent claims do not recite “reprocessing the same input data to generate a second classification output based on user input”. Once again, these limitations are not part of the instant claims and hence cannot be given patentable weight. Although the claims are read in light of the specification, the specification cannot be read into the claims and the claims must be given a broad but reasonable interpretation. The Applicant’s current claim scope is not commensurate with their arguments. The Applicant is once again invited to incorporate these limitations into the instant independent claims in order for them to be given patentable weight. The Applicant mischaracterizes the teachings of Terry. As it stands, para 90 of Terry teaches that the knowledge base manager enables the management of knowledge sets by the user. A knowledge set is set of tokens with their associated category weights used by an aspect (AI algorithm) during classification. A category may include “continue contact?”, and associated knowledge set tokens could include statements such as “stop”, “do no contact”, “please respond” and the like. The knowledge base manager enables the user to build new knowledge sets, or edit exiting ones.
The Applicant thereafter alleges that amended claim 1 discloses a structured two-stage classification pipeline in which a first classification output is generated, evaluated using the user input, for example, the predefined labels, and then used to generate a second classification output based on that evaluation. Applicant alleges that Terry does not disclose such a pipeline. As per the Applicant, Thus, Terry fails to disclose a 2-step classification in the claimed sequence which is a closed-loop, instance-specific reclassification process. The claimed invention according to the Applicant operates at the level of individual classification outputs, enabling supervised refinement through labelled evaluation and script-based reprocessing. Applicant also alleges that Terry operates at the level of system configuration and does not provide any mechanism for producing a second classification output corresponding to the same input data based on evaluated feedback.
The Examiner once again respectfully disagrees and argues that the instant independent claims do not recite “two-stage classification pipeline”, “a closed-loop, instance-specific reclassification process”, “operation at the level of individual classification outputs, enabling supervised refinement through labelled evaluation and script-based reprocessing” or “a mechanism for producing a second classification output corresponding to the same input data based on evaluated feedback”. Once again, these limitations are not part of the instant claims and hence cannot be given patentable weight. Although the claims are read in light of the specification, the specification cannot be read into the claims and the claims must be given a broad but reasonable interpretation. The Applicant’s current claim scope is not commensurate with their arguments. The Applicant is once again invited to incorporate these limitations into the instant independent claims in order for them to be given patentable weight.
The Applicant further argues that Terry’s "transparency labels" are descriptive or display-oriented elements and are not used to evaluate correctness or quality of a classification output.
The Examiner once again respectfully disagrees and argues that the intended use of the “label” in the instant claims cannot be given patentable weight. Further the instant claims language nowhere recites “correctness” or “quality” insofar as the evaluation is concerned. The Applicant is once again invited to incorporate these phrases into the instant independent claims in order for them to be given patentable weight. Para 154 of Terry, further teaches that in the machine learning approach, we treat each question category as a class and each variation within the category as an instance labeled to belonging to that class and apply all the machine learning algorithms listed in the previous section to develop a model that is trained and applied in online mode.
Further, regarding claim 8, the Applicant argues that para 90 of Terry merely describes management of knowledge sets comprising tokens and associated weights. According to the Applicant, Terry does not disclose any script-based execution or programmatic reprocessing of input data.
The Examiner once again respectfully disagrees and argues that “script-based execution or programmatic reprocessing of input data” is not claimed anywhere. Further, a compute language script is written with a very high level of generality and could be given a broad but reasonable interpretation of being an AI algorithm. Terry is replete with various instances of disclosure for such algorithms.
The Applicant has not presented any other arguments for the remaining claims and those are therefore also deemed addressed by the discussion above. The prior art rejection is therefore sustained.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) The claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
3. Claims 1-4, 6-13 and 15-20 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Terry (U.S. Patent Application Publication # 2018/0373696 A1).
With regards to claim 1, Terry teaches a computer-implemented method for training a classifier for use in a voice recognition (VR) assistance system, the method comprising collecting data which comprises one or more natural language queries to the VR assistance system (Para 16, teaches method for natural language processing and classification including responding to simple question using natural language processing. This includes receiving, from a campaign manager, a set of training questions linked to facts answering the associated training question. The facts are stored in a third-party database);
processing the data using a natural language processing (NLP) algorithm (Para 88, teaches processing of data by an AI powered NLP algorithm);
generating a first classification output, based on results of the processing using the NLP algorithm (Para 88, further teaches that the AI model or multiple AI models as part of the AI manager make first classification of the question response data);
obtaining a user input based on the first classification output wherein the user input comprises predefined labels for evaluation of the first classification output (Para 89, teaches that in the next step the insight manager allows the user to manage insights. Insights are a collection of categories used to answer some question about a document. For example, a question for the document could include “is the lead looking to purchase a car in the next month?” Answering this question can have direct and significant importance to a car dealership. Certain categories that the AI system generates may be relevant toward the determination of this question. These categories are the ‘insight’ to the question, and may be edited or newly created via the insight manager. Figure 18 along with paragraphs 37 and 144, teach an example illustration of the message being overlaid with transparency labels);
and generating a second classification output, based on the user input, for training the classifier (Para 90, teaches that at the next step the knowledge base manager enables the management of knowledge sets by the user. A knowledge set is set of tokens with their associated category weights used by an aspect or AI algorithm, during classification. For example, a category may include “continue contact?”, and associated knowledge set tokens could include statements such as “stop”, “do no contact”, “please respond” and the like. The knowledge base manager enables the user to build new knowledge sets, or edit exiting ones).
With regards to claim 2, Terry teaches the method of claim 1, wherein the VR assistance system is a multi- language VR assistance system (Para 98, teaches that the system may be a multi-language system).
With regards to claim 3, Terry teaches the method of claim 1, wherein processing the data using the NLP algorithm comprises processing of at least one of audio data or speech-to-text transcribed data (Para 82, teaches that the system is geared for both audio and written textual messages. Para 150, also teaches speech-to-text conversion).
With regards to claim 4, Terry teaches the method of claim 1, wherein the user input comprises an indication of a malfunction of NLP classification on aspects including at least one of auditive query analysis or query content recognition (Para 150, further teaches that the system initially undergoes a query to identify if the non-textual element is a movie or an image. If the element is a movie, the system may separate out any audio elements to the video and then perform a speech to text conversion. The textual output can then be run though a textual analysis).
With regards to claim 6, Terry teaches the method of claim 1, wherein the first and second classification outputs comprise one or more of a first natural language query from the one or more natural language queries (As shown previously, para 89, teaches that in the next step the insight manager allows the user to manage insights. Insights are a collection of categories used to answer some question about a document. For example, a question for the document could include “is the lead looking to purchase a car in the next month?” Answering this question can have direct and significant importance to a car dealership. Certain categories that the AI system generates may be relevant toward the determination of this question. These categories are the ‘insight’ to the question, and may be edited or newly created via the insight manager. Para 90, teaches that at the next step the knowledge base manager enables the management of knowledge sets by the user. A knowledge set is set of tokens with their associated category weights used by an aspect or AI algorithm, during classification. For example, a category may include “continue contact?”, and associated knowledge set tokens could include statements such as “stop”, “do no contact”, “please respond” and the like. The knowledge base manager enables the user to build new knowledge sets, or edit exiting ones);
language of the first natural language query; a data set comprising data which comprises one or more of the first natural language query, a part of the first natural language query, or a response of the VR assistance system to the first natural language query; an audio file transcript of the data set;
information about audio errors within the selected data set (Paragraphs 103-104, teach this aspect);
information about an accent of a speaker of the first natural language query; a profile of the speaker; classification of scope of the first natural language query; or classification of a scope of an answer by the VR assistance system given to the first natural language query.
With regards to claim 7, Terry teaches the method of any of claim 6, wherein the audio errors comprise errors regarding a wakeup word of the VR assistance system or errors regarding the first natural language query of the speaker (Paragraphs 103-104, teach regarding the first natural language query of the speaker).
With regards to claim 8, Terry teaches the method of claim 1, wherein the second classification output is generated based on a compute language script (Para 90, teaches that at the next step the knowledge base manager enables the management of knowledge sets by the user. A knowledge set is set of tokens with their associated category weights used by an aspect or AI algorithm, during classification. For example, a category may include “continue contact?”, and associated knowledge set tokens could include statements such as “stop”, “do no contact”, “please respond” and the like. The knowledge base manager enables the user to build new knowledge sets, or edit exiting ones).
With regards to claims 9, 11-13 and 15, these are system claims for the corresponding method claims 1-4 and 6-8. These two sets of claims are related as method and apparatus of using the same, with each claimed system element's function corresponding to the claimed method step. Accordingly, claims 9, 11-13 and 15 are similarly rejected under the same rationale as applied above with respect to method claims 1-4 and 6-8.
With regards to claims 10, 16-18 and 20, these are computer readable medium (CRM) claims for the corresponding method claims 1-4 and 6-8. These two sets of claims are related as method and CRM of using the same, with each claimed CRM element's function corresponding to the claimed method step. Accordingly, claims 10, 16-18 and 20 are similarly rejected under the same rationale as applied above with respect to method claims 1-4 and 6-8.
Conclusion
4. THIS ACTION IS MADE FINAL. The Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). The following prior art, made of record but not relied upon, is considered pertinent to applicant's disclosure: Sun (U.S. Patent Application Publication # 2021/0082402 A1), Giulianelli (U.S. Patent Application Publication # 2015/0149176 A1). These references are also included in the PTO-892 form attached with this office action.
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.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to NEERAJ SHARMA whose contact information is given below. The examiner can normally be reached on Monday to Friday 8 am to 5 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Louis-Desir can be reached on 571-272-7799 (Direct Phone). The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
/NEERAJ SHARMA/
Primary Examiner, Art Unit 2659
571-270-5487 (Direct Phone)
571-270-6487 (Direct Fax)
neeraj.sharma@uspto.gov (Direct Email)
Prosecution Insights