DETAILED ACTION
Introduction
1. This office action is in response to Applicant's submission filed on 01/25/2024. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are currently pending and examined below.
Drawings
2. The drawings filed on 01/25/2024 have been accepted and considered by the Examiner.
Information Disclosure Statement
3. The Information Statements (IDSs) filed on 01/25/2024, 06/25/2025 have been accepted/considered and are in compliance with the provisions of 37 CFR 1.97.
Priority
4. The Applicants priority to International Patent Application # PCT/RU2021/0003 18, filed July 28, 2021, has been accepted and considered in this office action.
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.
5. Claims 1-20 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 (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);
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 5, Terry teaches the method of claim 1, wherein the user input comprises predefined labels for evaluation of the first classification output (Figure 18 along with paragraphs 37 and 144, teach an example illustration of the message being overlaid with transparency labels).
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 and 11-15, these are system claims for the corresponding method claims 1-8. 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 and 11-15 are similarly rejected under the same rationale as applied above with respect to method claims 1-8.
With regards to claims 10 and 16-20, these are computer readable medium (CRM) claims for the corresponding method claims 1-8. 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 and 16-20 are similarly rejected under the same rationale as applied above with respect to method claims 1-8.
Conclusion
6. The following prior art, made of record but not relied upon, is considered pertinent to applicant's disclosure: Mahler (U.S. Patent Application Publication # 2014/0279738 A1), Anisimovich (U.S. Patent # 10474756 B2). These references are also included in the PTO-892 form attached with this office 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)