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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/30/2026 and 9/28/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
The amendments filed on 07/23/2026 have been considered. Claims 1-5, 7-12, and 14-19 have been amended. Thus, claims 1-20 are pending and presented for examination.
Applicant's arguments filed on 07/23/2026 with respect to the 35 U.S.C. 101 rejections have been fully considered and are unpersuasive for the reasons set forth below.
Applicant's amendments filed on 07/23/2026 with respect to the 35 U.S.C. 112(b) rejections have been fully considered and are persuasive. Thus, the 35 U.S.C. 112(b) rejections have been withdrawn.
Applicant's arguments filed on 07/23/2026 with respect to the 35 U.S.C. 103 rejections have been fully considered but are moot because of the new ground of rejection.
Response to Arguments
In pg.13-14, the Applicant argues in regards to the 101 rejection of claim 1,
Thus, creating such a knowledgebase is technically challenging at least due to the breadth of information required and the conflict resolution associated with data inconsistencies for individual topics.
Thus, at a minimum, claim 1 is directed to a specific improvement in how the computing system operates, as the machine learning chatbot is re-trained on superseding or correcting data so that the model's outputs track the current state of the communication system's information, rather than perpetuating outdated or inaccurate information. This is the type of improvement the USPTO has recognized as patent-eligible, when indicating an “[i]mprovement[] to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams,” may show an improvement in computer-functionality.
For similar reasons, Applicant further respectfully submits that claim 1 is directed to a specific improvement in how the machine learning chatbot itself operates. Specifically, re-training the ML chatbot with the second set of data adjusts the model's internal weightings and/or other parameters in accordance with that superseding or correcting data, such that the re-trained ML chatbot no longer surfaces the superseded or incorrect information reflected in the prior set of data and instead generates the recited “updated predicted response” consistent with the second set of data.
In response, the Examiner maintains the rejection as shown below. The features relied upon are not recited in the claim. Claim 1 recites only re-training "the ML chatbot with the second set of data." It doesn’t recite any conflict resolution, any identification of the superseded portion, or any weighting or parameter adjustment. Based on the claim, the updated predicted response only needs to be "based upon the second set of data." The phrase "that supersedes or corrects at least a portion of the set of data" describes the content of the received data, and the re-training is the same whether the new data corrects the old data or just adds to it. The specification itself describes re-training "with new, or different, information" as a general characteristic of machine learning (para. [0072]). This is unlike Ex parte Desjardins, where the claim recited how the parameters were adjusted, through per-parameter importance measures and a penalty term, so that the model learned a new task without losing performance on the prior one. In the instant claim, giving answers that reflect the latest information is what a human expert does after reading an updated manual or email, and keeping the answers up to date is an improvement to the abstract idea, not to computer component or system performance.
In pg.13 and 15, the Applicant further argues in regards to the 101 rejection of claim 1,
Applicant respectfully submits that this characterization oversimplifies the claim and fails to account for the specific requirements of claim 1 as amended.
Accordingly, Applicant respectfully submits claim 1 recites “a specific implementation of a solution to a problem in the software arts,” and integrates any alleged abstract idea into a practical application.
In response, the Examiner maintains the rejection as shown below. Each limitation of amended claim 1 is individually addressed in the updated rejection below, including what the receiving, re-training, and generating limitations do and do not recite. Unlike the self-referential table in Enfish (MPEP 2106.05(a)(I)), claim 1 recites a desired outcome, updated answers from a re-trained generic chatbot, rather than a specific way of achieving it (MPEP 2106.05(f)). Claims 8 and 15 recite the same limitations and are rejected for the same reasons, and the dependent claims are addressed individually below.
In pg.16-17, the Applicant further argues in regards to the 101 rejection of claim 2,
Thus, the outputs recited in claim 2 are verifiable, in that the machine learning chatbot's predicted response is bound to, and checkable against, the specific portion of the underlying communication system data that provides its explicit support and carries a quantified confidence value reflecting the likelihood that the response is accurate. This specific functionality renders the machine learning chatbot's outputs self-documenting within the system rather than opaque and consequently improves the functioning of the computing system itself.
In response, the Examiner maintains the rejection as shown below. As shown in the updated rejection below, citing the part of a document that supports an answer and stating how confident one is in that answer are mental steps a human expert routinely performs. Making an answer easier for a user to verify improves the information presented to the user, not the functioning of the computer, and claim 2 does not recite how the citation or the confidence value is generated. Improvements to the abstract idea are not relevant to determination under U.S.C. 101.
The Applicant did not address the rejection of claims 15-20 as directed to non-statutory subject matter, and claim 15 was not amended to exclude transitory signals, so that rejection is maintained.
Applicant's arguments with respect to claims 1-20 have been considered but are either repetitions of the arguments, or moot in view of the new ground(s) of rejection.
Claim Objections
Claim 1, 8, 15 objected to because of the following informalities:
Claim 1, 8, and 15 recites “a ML chatbot” twice. Once in the preamble “… a machine learning (ML) chatbot” and in the connecting limitation “… an ML chatbot”. In the connecting limitation it should say “… the ML chatbot”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
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.
Examiner’s Note: Some rejections will include an Examiner’s Note (labeled ‘EN’) to provide additional context or rationale explaining the basis for the rejection.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract
idea without significantly more.
Additionally, claims 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 15 recites a tangible machine-readable medium. Both said claim and the respective specification fail to disclose whether said tangible machine-readable medium is considered to read on a transitory propagating signal. Reading said claim under the broadest reasonable interpretation, tangible machine-readable medium is considered to read on a transitory propagating signal. Thus, both said claim and said specification fail to define said tangible machine-readable medium to be statutory media. Therefore, independent claim 15 and corresponding dependent claims 16-20 are not patent eligible.
Claim 1
Step 1: Claim 1 recites a method, therefore, it is directed to the statutory category of process.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the "Mathematical Concepts" grouping of abstract ideas. The claim recites the following abstract ideas:
aggregating, (…), a set of data corresponding to the communication system from a plurality of communications… (a person mentally or with a pen and paper aggregates data from communications by recognizing recurring themes, averaging, etc.)
… generate predicted responses to live prompts (…) (a person mentally or with a pen and paper comes up with predicted responses to live prompts.)
generating, (…), an updated predicted response to a live prompt (…) based upon the second set of data. (a person mentally or with a pen and paper comes up with an updated response to a live prompt based on newer information, such as answering with the solution in a newer email rather than the outdated solution in an older manual.)
Step 2A prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
A computer-implemented method for training and updating a machine learning (ML) chatbot to function as an interactive knowledge base in a communication system, the method comprising: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – Examiner’s note: high level recitation of applying a generic “computer-implemented”, “machine learning (ML) chatbot”, and “communication system” to an abstract idea. )
connecting, by one or more processors, a training module configured to train an ML chatbot to a communication channel associated with the communication system, wherein the communication channel transmits data corresponding to the communication system; (Adding insignificant extra- solution activity to the judicial exception -- MPEP 2106.05(g) – Examiner’s note: Connecting a training module to a communication channel in order to transmit data is recited at a high level of generality and could constitute mere receiving of transmitted data.)
aggregating, by the one or more processors executing the training module, (…) utilizing the communication channel; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – Examiner’s note: high level recitation of applying generic of the shelf “processors” and “communication channel” to an abstract idea.)
“training, by the one or more processors executing the training module, the ML chatbot with the set of data corresponding to the communication system as inputs to generate a plurality of training responses as outputs, wherein the plurality of training responses represents responses to hypothetical prompts related to the data corresponding to the communication system; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s note: high level recitation of applying generic training to a ML chatbot to get an output. )
connecting, by the one or more processors the ML chatbot to one or more live communication channels of the communication system to (…) (Adding insignificant extra- solution activity to the judicial exception -- MPEP 2106.05(g) – Examiner’s note: Connecting a generic ML chatbot to a communication channel in order to transmit data is recited at a high level of generality and could constitute mere receiving of transmitted data. )
received across the one or more live communication channels; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
receiving, by the one or more processors, a second set of data corresponding to the communication system that supersedes or corrects at least a portion of the set of data; (Adding insignificant extra-solution activity to the judicial exception -- MPEP 2106.05(g) - Examiner’s note: mere data gathering. The phrase "that supersedes or corrects at least a portion of the set of data" describes the content of the data that is received; it does not recite any operation performed by the one or more processors on the set of data.)
re-training, by the one or more processors executing the training module, the ML chatbot with the second set of data; and (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of applying generic training/re-training to a ML chatbot to get an output. The claim does not recite how the re-training is performed, how any weighting or parameter of the ML chatbot is adjusted, or how the superseded or corrected portion of the set of data is identified or handled. The ML chatbot is re-trained in the same generic manner whether the second set of data supersedes, corrects, or merely adds to the set of data.)
generating, by the one or more processors executing the re-trained ML chatbot, (…) received across the one or more live communication channels (…) (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of using a generic re-trained ML chatbot as a tool to output a response. The claim does not require the updated predicted response to exclude, or differ from, the superseded or corrected information; it only requires that the response be based upon the second set of data.)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
A computer-implemented method for training and updating a machine learning (ML) chatbot to function as an interactive knowledge base in a communication system, the method comprising: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – Examiner’s note: high level recitation of applying a generic “computer-implemented”, “machine learning (ML) chatbot”, and “communication system” to an abstract idea. )
connecting, by one or more processors, a training module configured to train an ML chatbot to a communication channel associated with the communication system, wherein the communication channel transmits data corresponding to the communication system; (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
aggregating, by the one or more processors executing the training module, (…) utilizing the communication channel; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). –Examiner’s note: high level recitation of applying generic of the shelf “processors” and “communication channel” to an abstract idea.)
“training, by the one or more processors executing the training module, the ML chatbot with the set of data corresponding to the communication system as inputs to generate a plurality of training responses as outputs, wherein the plurality of training responses represents responses to hypothetical prompts related to the data corresponding to the communication system; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s note: high level recitation of applying generic training to a ML chatbot to get an output.)
connecting, by the one or more processors the ML chatbot to one or more live communication channels of the communication system to (…) (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.)
received across the one or more live communication channels; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)))
receiving, by the one or more processors, a second set of data corresponding to the communication system that supersedes or corrects at least a portion of the set of data; (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.)
re-training, by the one or more processors executing the training module, the ML chatbot with the second set of data; and (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of applying generic training/re-training to a ML chatbot to get an output. The claim does not recite how the re-training is performed, how any weighting or parameter of the ML chatbot is adjusted, or how the superseded or corrected portion of the set of data is identified or handled. The ML chatbot is re-trained in the same generic manner whether the second set of data supersedes, corrects, or merely adds to the set of data.)
generating, by the one or more processors executing the re-trained ML chatbot, (…) received across the one or more live communication channels (…) (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of using a generic re-trained ML chatbot as a tool to output a response. The claim does not require the updated predicted response to exclude, or differ from, the superseded or corrected information; it only requires that the response be based upon the second set of data.)
Claim 2
Step 1: A method, as above.
Step 2A prong 1: See the rejection of claim 1 above, which claim 2 depends on. Claim 2 further recites:
generating, (…), a predicted response to the user query, wherein the predicted response includes a citation to a first piece of data from the set of data, (a person mentally or with a pen and paper comes up with predicted response to a user query and provides a citation to it.)
wherein the citation identifies a portion of the first piece of data that provides explicit support for the predicted response, and wherein the predicted response further comprises a confidence value associated with a likelihood that the predicted response is accurate. (a person mentally or with a pen and paper points to the part of the data that supports the response, such as a page of a manual, and states how confident they are that the response is accurate, such as 80 percent.)
Step 2A prong 2:
connecting, by the one or more processors, a user to a live agent across a first communication channel to which the ML chatbot is connected; (Adding insignificant extra- solution activity to the judicial exception -- MPEP 2106.05(g) – EN: connecting a user to a live agent which is connected to a chatbot is interpreted as a basic communication setup in order for sending and receiving data.)
receiving, at the one or more processors a user query from the user; and (Adding insignificant extra- solution activity to the judicial exception -- MPEP 2106.05(g) – EN: mere data gathering)
by the one or more processors executing the ML chatbot. (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)))
Step 2B:
connecting, by the one or more processors, a user to a live agent across a first communication channel to which the ML chatbot is connected; (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer. )
receiving, at the one or more processors a user query from the user; and (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
by the one or more processors executing the ML chatbot. (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)))
Claim 3
Step 1: a method, as above.
Step 2A prong 1: See the rejection of claim 1 above, which claim 3 depends on. Claim 3 further recites:
generating, (…) one or more embeddings associated with the user query; (a person mentally or with a pen and paper breaks down the text in to various character strings (tokens), assigning a numerical score to represent particular semantic characteristics of those strings to form an n-dimensional value.)
comparing, (…) the one or more embeddings to a library of embeddings, by determining a geometric distance between the one or more embeddings and embeddings of the library of embeddings within an n-dimensional space; (a person mentally or with a pen and paper performs a comparison by calculating the geometric distance between the embedding values, which is also a mathematical calculation.)
determining, based upon the comparing, one or more closest embeddings from the library of embeddings to the one or more embeddings; and (a person mentally or with a pen and paper selects the embeddings having the smallest calculated distance.)
determining, (…) the predicted response based upon the one or more closest embeddings. (a person mentally or with a pen and paper comes up with a predicted response based on the closest embeddings.)
Step 2A prong 2 & step 2B:
by the one or more processors (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Claim 4
Step 1: a method, as above.
Step 2A prong 1: See the rejection of claim 1 above, which claim 4 depends on. Claim 4 further recites:
evaluating, (…) a predicted relevance of the one or more prior responses based upon the user query; (a person mentally or with a pen and paper performs evaluates a predicted relevance of the one or more prior responses based on the user query)
determining, (…) at least one relevant prior response from the one or more prior responses; and (a person mentally or with a pen and chooses the most relevant response from prior responses)
generating, (…) the predicted response based upon the user query and the at least one relevant prior response. (a person mentally or with a pen and paper comes up with a predicted response based on the user query and the at least one relevant prior response).
Step 2A prong 2:
retrieving, by the one or more processors, one or more prior responses from a response database; (Adding insignificant extra- solution activity to the judicial exception -- MPEP 2106.05(g) – EN: mere data gathering)
by the one or more processors executing the ML chatbot (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)))
Step 2B:
retrieving, by the one or more processors, one or more prior responses from a response database; (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
by the one or more processors executing the ML chatbot (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)))
Claim 5
Step 1: a method, as above.
Step 2A prong 1: See the rejection of claim 1 above, which claim 5 depends on. Claim 5 further recites:
wherein the second set of data is aggregated (…) and includes one or more of: (i) an internal document, (ii) an archived email, (iii) a recorded verbal conversation, or (iv) a recorded live chat. (a person mentally or with a pen and paper aggregates a second set of data which includes one or more of the 4 options.)
Step 2A prong 2 & Step 2B:
from a plurality of internal subsystems of the communication system (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)))
Claim 6
Step 1: a method, as above.
Step 2A prong 1: See the rejection of claim 1 above, which claim 6 depends on.
Step 2A prong 2 & step 2B:
the communication channel is at least one of: (i) a live webchat, (ii) a phone call, (iii) an email, or (iv) a text message; and the set of data corresponding to the communication system includes one or more of: (i) a text string, (ii) an audio stream, (iii) a video stream, (iv) a file, or (v) a document. (The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Claim 7
Step 1: a method, as above.
Step 2A prong 1: See the rejection of claim 1 above, which claim 7 depends on. The claim further recites:
converting, (…), the verbal communication to a first text string; (a person mentally or with a pen and paper transcribes spoken words into written text.)
determining, (…), a first predicted response to the verbal communication based upon the first text string; (a person mentally or with a pen and paper comes up with a predicted response to the verbal communication based on the first text string)
generating, (…), the first predicted response to the verbal communication as a second text string; (a person mentally or with a pen and paper comes up with a predicted response to the verbal communication as a second text string)
converting, (…), the second text string to a second verbal communication; and (a person mentally or with a pen and paper translates the written answer back into speech.)
Step 2A prong 2:
receiving, at the one or more processors, a verbal communication across the audio communication channel; (Adding insignificant extra- solution activity to the judicial exception -- MPEP 2106.05(g) – EN: mere data gathering.)
causing, by the one or more processors, the second verbal communication to be conveyed across the audio communication channel. (Adding insignificant extra- solution activity to the judicial exception -- MPEP 2106.05(g))
Step 2B:
receiving, at the one or more processors, a verbal communication across the audio communication channel; (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
causing, by the one or more processors, the second verbal communication to be conveyed across the audio communication channel. (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
Claim 8
Step 1: Claim 8 recites a system, therefore it is directed to the statutory category of machine.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the "Mathematical Concepts" grouping of abstract ideas. The claim recites the following abstract ideas:
aggregate, (…), a set of data corresponding to the communication system from a plurality of communications… (a person mentally or with a pen and paper aggregates data from communications by recognizing recurring themes, averaging, etc.)
… generate predicted responses to live prompts (…) (a person mentally or with a pen and paper comes up with predicted responses to live prompts.)
generate, (…), an updated predicted response to a live prompt (…) based upon the second set of data. (a person mentally or with a pen and paper comes up with an updated response to a live prompt based on newer information, such as answering with the solution in a newer email rather than the outdated solution in an older manual.)
Step 2A prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
A system for training and updating a machine learning (ML) chatbot to function as an interactive knowledge base in a communication system, comprising: one or more processors; and a non-transitory computer-readable memory coupled to the one or more processors, the memory storing instructions thereon that, when executed by the one or more processors, cause the one or more processors to: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – Examiner’s note: high level recitation of applying a generic “processors”, “memory”, and “machine learning (ML) chatbot” to an abstract idea.)
connect a training module configured to train an ML chatbot to a communication channel associated with the communication system, wherein the communication channel transmits data corresponding to the communication system, (Adding insignificant extra- solution activity to the judicial exception -- MPEP 2106.05(g) – Examiner’s note: Connecting a training module to a communication channel in order to transmit data is recited at a high level of generality and could constitute mere receiving of transmitted data.)
aggregate, by executing the training module, (…) utilizing the communication channel; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – Examiner’s note: high level recitation of applying generic of the shelf “processors” and “communication channel” to an abstract idea. )
train, by executing the training module, the ML chatbot with the set of data corresponding to the communication system as inputs to generate a plurality of training responses as outputs, wherein the plurality of training responses represents responses to hypothetical prompts related to the data corresponding to the communication system, (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). --Examiner’s note: high level recitation of applying generic training to a ML chatbot to get an output.)
connect the ML chatbot to one or more live communication channels of the communication system to (…) (Adding insignificant extra- solution activity to the judicial exception -- MPEP 2106.05(g) – Examiner’s note: Connecting a generic ML chatbot to a communication channel in order to transmit data is recited at a high level of generality and could constitute mere receiving of transmitted data.)
received across the one or more live communication channels, Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
receive a second set of data corresponding to the communication system that supersedes or corrects at least a portion of the set of data, (Adding insignificant extra-solution activity to the judicial exception -- MPEP 2106.05(g) - Examiner’s note: mere data gathering. The phrase "that supersedes or corrects at least a portion of the set of data" describes the content of the data that is received; it does not recite any operation performed by the one or more processors on the set of data.)
re-train, by executing the training module, the ML chatbot with the second set of data, and (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of applying generic training/re-training to a ML chatbot to get an output. The claim does not recite how the re-training is performed, how any weighting or parameter of the ML chatbot is adjusted, or how the superseded or corrected portion of the set of data is identified or handled. The ML chatbot is re-trained in the same generic manner whether the second set of data supersedes, corrects, or merely adds to the set of data.)
generate, by executing the re-trained ML chatbot, (…) received across the one or more live communication channels (…) (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of using a generic re-trained ML chatbot as a tool to output a response. The claim does not require the updated predicted response to exclude, or differ from, the superseded or corrected information; it only requires that the response be based upon the second set of data.)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
A system for training and updating a machine learning (ML) chatbot to function as an interactive knowledge base in a communication system, comprising: one or more processors; and a non-transitory computer-readable memory coupled to the one or more processors, the memory storing instructions thereon that, when executed by the one or more processors, cause the one or more processors to: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – Examiner’s note: high level recitation of applying a generic “processors”, “memory”, and “machine learning (ML) chatbot” to an abstract idea.)
connect a training module configured to train an ML chatbot to a communication channel associated with the communication system, wherein the communication channel transmits data corresponding to the communication system, (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
aggregate, by executing the training module, (…) utilizing the communication channel; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – Examiner’s note: high level recitation of applying generic of the shelf “processors” and “communication channel” to an abstract idea. )
train, by executing the training module, the ML chatbot with the set of data corresponding to the communication system as inputs to generate a plurality of training responses as outputs, wherein the plurality of training responses represents responses to hypothetical prompts related to the data corresponding to the communication system, (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s note: high level recitation of applying generic training to a ML chatbot to get an output.)
connect the ML chatbot to one or more live communication channels of the communication system to (…) (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
received across the one or more live communication channels, (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)))
receive a second set of data corresponding to the communication system that supersedes or corrects at least a portion of the set of data, (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.)
re-train, by executing the training module, the ML chatbot with the second set of data, and (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of applying generic training/re-training to a ML chatbot to get an output. The claim does not recite how the re-training is performed, how any weighting or parameter of the ML chatbot is adjusted, or how the superseded or corrected portion of the set of data is identified or handled. The ML chatbot is re-trained in the same generic manner whether the second set of data supersedes, corrects, or merely adds to the set of data.)
generate, by executing the re-trained ML chatbot, (…) received across the one or more live communication channels (…) (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of using a generic re-trained ML chatbot as a tool to output a response. The claim does not require the updated predicted response to exclude, or differ from, the superseded or corrected information; it only requires that the response be based upon the second set of data.)
Claims 9-14 recite substantially the same limitations as method claims 2-7 respectively. Therefore, claim 9-14 is rejected under the same rationale as claim 2-7.
Claim 15
Step 1: Claim 15 recites a tangible machine-readable medium. Assuming the applicant corrects the signal per se issue as identified above, the abstract idea analysis would still apply. See below.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the "Mathematical Concepts" grouping of abstract ideas. The claim recites the following abstract ideas:
aggregate, (…), a set of data corresponding to the communication system from a plurality of communications… (a person mentally or with a pen and paper aggregates data from communications by recognizing recurring themes, averaging, etc.)
… generate predicted responses to live prompts (…) (a person mentally or with a pen and paper comes up with predicted responses to live prompts.)
generate, (…), an updated predicted response to a live prompt (…) based upon the second set of data. (a person mentally or with a pen and paper comes up with an updated response to a live prompt based on newer information, such as answering with the solution in a newer email rather than the outdated solution in an older manual.)
Step 2A prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
A tangible machine-readable medium comprising instructions for training and updating a machine learning (ML) chatbot to function as an interactive knowledge base in a communication system that, when executed, cause a machine to at least: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – Examiner’s note: high level recitation of applying a generic “tangible machine-readable medium” and “machine learning (ML) chatbot” to an abstract idea.)
connect a training module configured to train an ML chatbot to a communication channel associated with the communication system, wherein the communication channel transmits data corresponding to the communication system; (Adding insignificant extra- solution activity to the judicial exception -- MPEP 2106.05(g) – Examiner’s note: Connecting a training module to a communication channel in order to transmit data is recited at a high level of generality and could constitute mere receiving of transmitted data.)
aggregate, by executing the training module, (…) utilizing the communication channel; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – Examiner’s note: high level recitation of applying generic of the shelf “processors” and “communication channel” to an abstract idea. )
train, by executing the training module, the ML chatbot with the set of data corresponding to the communication system as inputs to generate a plurality of training responses as outputs, wherein the plurality of training responses represents responses to hypothetical prompts related to the data corresponding to the communication system; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). --Examiner’s note: high level recitation of applying generic training to a ML chatbot to get an output.)
connect the ML chatbot to one or more live communication channels of the communication system to (…) (Adding insignificant extra- solution activity to the judicial exception -- MPEP 2106.05(g) – Examiner’s note: Connecting a generic ML chatbot to a communication channel in order to transmit data is recited at a high level of generality and could constitute mere receiving of transmitted data.)
received across the one or more live communication channels; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
receive a second set of data corresponding to the communication system that supersedes or corrects at least a portion of the set of data; (Adding insignificant extra-solution activity to the judicial exception -- MPEP 2106.05(g) - Examiner’s note: mere data gathering. The phrase "that supersedes or corrects at least a portion of the set of data" describes the content of the data that is received; it does not recite any operation performed by the one or more processors on the set of data.)
re-train, by executing the training module, the ML chatbot with the second set of data; and (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of applying generic training/re-training to a ML chatbot to get an output. The claim does not recite how the re-training is performed, how any weighting or parameter of the ML chatbot is adjusted, or how the superseded or corrected portion of the set of data is identified or handled. The ML chatbot is re-trained in the same generic manner whether the second set of data supersedes, corrects, or merely adds to the set of data.)
generate, by executing the re-trained ML chatbot, (…) received across the one or more live communication channels (…) (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of using a generic re-trained ML chatbot as a tool to output a response. The claim does not require the updated predicted response to exclude, or differ from, the superseded or corrected information; it only requires that the response be based upon the second set of data.)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
A tangible machine-readable medium comprising instructions for training and updating a machine learning (ML) chatbot to function as an interactive knowledge base in a communication system that, when executed, cause a machine to at least: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – Examiner’s note: high level recitation of applying a generic “tangible machine-readable medium” and “machine learning (ML) chatbot” to an abstract idea.)
connect a training module configured to train an ML chatbot to a communication channel associated with the communication system, wherein the communication channel transmits data corresponding to the communication system; (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
aggregate, by executing the training module, (…) utilizing the communication channel; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – Examiner’s note: high level recitation of applying generic of the shelf “processors” and “communication channel” to an abstract idea. )
train, by executing the training module, the ML chatbot with the set of data corresponding to the communication system as inputs to generate a plurality of training responses as outputs, wherein the plurality of training responses represents responses to hypothetical prompts related to the data corresponding to the communication system; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s note: high level recitation of applying generic training to a ML chatbot to get an output.)
connect the ML chatbot to one or more live communication channels of the communication system to (…) (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
received across the one or more live communication channels; (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)))
receive a second set of data corresponding to the communication system that supersedes or corrects at least a portion of the set of data; (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, e.g., using the Internet to gather data, is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.)
re-train, by executing the training module, the ML chatbot with the second set of data; and (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of applying generic training/re-training to a ML chatbot to get an output. The claim does not recite how the re-training is performed, how any weighting or parameter of the ML chatbot is adjusted, or how the superseded or corrected portion of the set of data is identified or handled. The ML chatbot is re-trained in the same generic manner whether the second set of data supersedes, corrects, or merely adds to the set of data.)
generate, by executing the re-trained ML chatbot, (…) received across the one or more live communication channels (…) (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). - Examiner’s note: high level recitation of using a generic re-trained ML chatbot as a tool to output a response. The claim does not require the updated predicted response to exclude, or differ from, the superseded or corrected information; it only requires that the response be based upon the second set of data.)
Claims 16-20 recite substantially the same limitations as method claims 2-6 respectively. Therefore, claim 16-20 is rejected under the same rationale as claim 2-6.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Examiner’s Note: Some rejections will include an Examiner’s Note (labeled ‘EN’) to provide additional context or rationale explaining the basis for the rejection.
Claims 1, 5-8, 12-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yannam et al. (US 20230379273 A1), hereinafter “Yannam” in view of Bajaj et al. (US 20190228099 A1), hereinafter “Bajaj”, and further in view of Mazza et al. (US 11676044 B1), hereinafter “Mazza”.
Claim 1
Yannam teaches,
A computer-implemented method for training and updating a machine learning (ML) chatbot to function as an interactive knowledge base in a communication system, the method comprising: (Para 18, “Systems, methods, and apparatus are provided for integrating AI-powered conversational messaging with an agent interface during a live customer session.)” Para 27, “In response to a determination of intent and topic, the bot may retrieve answers associated with the topic from a knowledge store.” Para 5, “It would be desirable to continually improve the chatbot aid provided to agents by training an AI-powered bot using crowdsourced responses from successful agents.” Para 42, “During the task closure process, the session history from the first interface may be mined as feedback for ongoing training of the interactive response system AI engine.” – EN: this denotes training the AI-powered bot and continually improving (updating) it through ongoing training, where the trained bot retrieves answers from a knowledge store and thereby functions as an interactive knowledge base.)
connecting, by one or more processors, a training module configured to train an ML chatbot to a communication channel associated with the communication system, wherein the communication channel transmits data corresponding to the communication system; (Para 28, “The interactive response system may incorporate AI-based responses. The system may train an AI-powered communication engine.” Para 25, “The second communication interface may also be initiated in response to the customer request for communication with the agent. Customer inputs to the live chat may also be received by the interactive response system.” Para 42, “During the task closure process, the session history from the first interface may be mined as feedback for ongoing training of the interactive response system AI engine.” Para 32 “The system may form a training set for a neural network.” – EN: this denotes interactive response system components that form training sets and train the AI engine (neural network). This module is connected to the “second communication interface” (live chat channel) from which “customer inputs” and “session history” are received.)
aggregating, by the one or more processors executing the training module, a set of data corresponding to the communication system from a plurality of communications utilizing the communication channel; (Para 32, “Forming a topic-centric training set for a neural network may include mining information that relates to legacy user communications, legacy intelligence, and the plurality of outcomes as described above.” Para 28, “The system may designate a topic of user interest and retrieve legacy communications regarding the topic. The legacy communications are preferably retrieved from an electronically stored library of communications.” Para 29, “The system may preferably remove duplicative communications from retrieved list of legacy communications.” Para 44, “Crowdsourcing may involve identifying customer inputs along with corresponding bot-generated responses that were modified by the agents or responses generated entirely by the agents. Responses from different sessions may be mined and the data may be used to train the AI engine.” – EN: this denotes mining and retrieving “legacy user communications” from an “electronically stored library of communications” to form “topic-centric training set”.)
training, by the one or more processors executing the training module, the ML chatbot with the set of data corresponding to the communication system as inputs to generate a plurality of training responses as outputs, (…) (Para 35, “The formation of each neural network may use the topic-centric training set to assign individual weights to each of the plurality of nodes based on the legacy communications, legacy intelligence, and the plurality of outcomes.” Para 27, “In response to a determination of intent and topic, the bot may retrieve answers associated with the topic from a knowledge store.”)
connecting, by the one or more processors, the ML chatbot to one or more live communication channels of the communication system to generate predicted responses to live prompts received across the one or more live communication channels; (Para 70, “A customer may request a live chat at a mobile device application on a mobile device or via any suitable medium. At 302 , a live agent is assigned and a session is initiated.” Para 73, “At 316, the interactive response system may receive the customer input. At 318, the system may use AI to determine customer intent. At 320, the system may evaluate the determination of intent. At 324, based on the intent, the AI engine may create predictions and access stored knowledge to generate a response. At 326, a pop-up window may display a generated response at the agent interface… An accepted or modified response may be inserted into the ongoing live agent-customer interaction.” Para 22, “In some embodiments, communication with a chatbot and communication with a live agent may be implemented within a single mobile application chat function. Communication with both the chatbot and the live agent may occur within a single chat session.”
receiving, by the one or more processors, a second set of data corresponding to the communication system (…) (Para 42, “During the task closure process, the session history from the first interface may be mined as feedback for ongoing training of the interactive response system AI engine.” Para 10, “In response to the task closure entry, the agent interface may transmit a session history to the interactive response system. The interactive response system may mine the session history to generate a training set for the neural network.” – EN: this denotes the session history that is transmitted to the interactive response system and mined for ongoing training after the AI engine has been trained, which is a second set of data corresponding to the communication system.)
re-training, by the one or more processors executing the training module, the ML chatbot with the second set of data; and (Para 42, “During the task closure process, the session history from the first interface may be mined as feedback for ongoing training of the interactive response system AI engine.” Para 45, “Agent feedback may be used to train and tune the AI engine. In this way, intelligence from successful agents may be folded into the bot for a better customer experience across the board.” – EN: this denotes “ongoing training” of the AI engine using newly mined session history and agent feedback, this constitutes re-training the ML chatbot with the second set of data.)
Yannam does not explicitly teach:
wherein the plurality of training responses represents responses to hypothetical prompts related to the data corresponding to the communication system;
However, Bajaj teaches:
wherein the plurality of training responses represents responses to hypothetical prompts related to the data corresponding to the communication system; (Para 23, “The model-generated Q&A pairs may be generated from a deep learning framework having multiple sequence-to-sequence neural networks. Each deep learning model is trained to learn a conditional probability distribution that predicts the likelihood that a question y is more likely to be asked when a given text passage c is read. The questions generated from a respective deep learning model is then paired with its corresponding answer and consolidated into a lookup or dictionary that can be accessed upon demand.” Para 60, “The generated questions 312 and corresponding answers 304 are then formulated as one or more pairs consisting of a question and its corresponding answer, <Q, A> 130 . All the generated <Q, A> pairs are then formatted into a lookup table or dictionary and used as part of the knowledge base.” Para 26, “Both neural networks are utilized to generate questions for a given context and paired with an associated answer into a Q&A pair. The model-generated Q&A pairs are then stored in a knowledge base for use in answering user-query requests.”)
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the AI chatbot framework in a communication system of Yannam with the hypothetical Q&A pairs from system data of Bajaj during the training step. The motivation for doing so would be to enable the system to rapidly and accurately answer subsequent user queries on demand without requiring human experts to manually anticipate and draft every possible response. As Bajaj elaborates regarding the benefit of this automated generation and storage methodology in paragraph 26, “Both neural networks are utilized to generate questions for a given context and paired with an associated answer into a Q&A pair. The model-generated Q&A pairs are then stored in a knowledge base for use in answering user-query requests.”
Yannam and Bajaj do not explicitly teach:
[receiving another set of data] … that supersedes or corrects at least a portion of the set of data;
generating, by the one or more processors executing the re-trained ML chatbot, an updated predicted response to a live prompt received across the one or more live communication channels based upon the second set of data.
(Examiner’s Note: the bracketed portion is added for context)
However, Mazza teaches:
[receiving another set of data] … that supersedes or corrects at least a portion of the set of data; (Col. 3, lines 45-52, “Even after the chatbot is trained, the chatbot administrator may need to update the chatbot from time to time to account for changes in company source data (e.g., company policy changes). For example, the chatbot’s response to ‘How do I cancel my account,’ may need to be updated if the company adopts a new set of steps for canceling the account. The update may require, for example, retraining the chatbot based on the changes to the company’s source data.” Col. 13, lines 8-16, “FIG. 9 is a flow diagram of a process for retraining the chatbot according to one embodiment. The process starts, and in block 900, the training system 202 monitors and identifies change in the source data. The change may be for example, a change of a response to be provided to a frequently asked question. The change may be due to a change in company policy. For example, a return policy of the company may change, necessitating a change to the response on how to make returns.” Col. 7, lines 36-43, “In some embodiments, the training system 202 is configured to monitor the knowledge base 14 for changes in the source data. When a change is detected, the training system 202 may provide the updated source data (including context surrounding the source data), to the language model, for generating one or more training questions. The updated question-answer pair may then be used for retraining the inference models.” – EN: this denotes changed source data (e.g., a new set of steps for canceling an account, or a changed return policy) that is identified after the chatbot has been trained and that changes the response previously provided to a frequently asked question, i.e., data that supersedes or corrects at least a portion of the data on which the chatbot was trained. When combined with Yannam, the second set of data received for the ongoing training includes such changed data, and thereby supersedes or corrects at least a portion of the set of data.)
generating, by the one or more processors executing the re-trained ML chatbot, an updated predicted response to a live prompt received across the one or more live communication channels based upon the second set of data. (Col. 13, lines 17-19, “In act 902, the training system 202 engages in a process of automatically generating training questions based on the changed source data.” Col. 13, lines 31-33, “In act 904, the training system 202 may use the training questions and associated answers for re-training the inference models of the chatbot system 10.” Col. 13, lines 1-7, “In one embodiment, the training system 202 is configured to monitor for changes in the source data, and retrain the chatbot in order to ensure that the answers provided by the chatbot reflect the change. In this manner, the chatbot remains synchronized with the source data, and the answers provided by the chatbot are consistent with answers provided by the source data.” Col. 5, lines 9-16, “For example, the end user may formulate a query, and transmit the query to the chatbot system 10 as a chat message, text message, social media message, and/or the like. The chatbot system 10 may process the query and determine a user intent. … Once the intent is determined, the chatbot may output an answer in response to the query.” Col. 13, lines 47-51, “In the various embodiments, the terms ‘interaction’ and ‘communication’ are used interchangeably, and generally refer to any real-time and non-real time interaction using, for example, chats, text messages, social media messages, and/or the like.” – EN: this denotes the answers provided by the retrained chatbot reflecting the change in the source data, i.e., an updated predicted response generated by the re-trained ML chatbot based upon the second set of data, output in response to a query received as a real-time chat message. When combined with Yannam, the query is a live prompt received across the one or more live communication channels to which the ML chatbot is connected.)
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the AI chatbot framework in a communication system of Yannam and Bajaj with the taking into account the chatbot’s source data changes and then re-training of the chatbot on the changed source data of Mazza. The motivation for doing so would be to ensure that the answers provided by the chatbot to user queries reflect changes in the underlying data, such as a change in company policy, so that the chatbot remains synchronized and consistent with the data from which its answers are drawn. As Mazza elaborates regarding the benefit of this re-training methodology in column 13, lines 1-7, “In one embodiment, the training system 202 is configured to monitor for changes in the source data, and retrain the chatbot in order to ensure that the answers provided by the chatbot reflect the change. In this manner, the chatbot remains synchronized with the source data, and the answers provided by the chatbot are consistent with answers provided by the source data.”
Claim 5
Yannam further teaches:
The computer-implemented method of claim 1, wherein the second set of data is aggregated from a plurality of internal subsystems of the communication system and includes one or more of: (i) an internal document, (ii) an archived email, (iii) a recorded verbal conversation, or (iv) a recorded live chat. (Para 10, "In response to the task closure entry, the agent interface may transmit a session history to the interactive response system. The interactive response system may mine the session history to generate a training set for the neural network. The interactive response system may also mine crowdsourced session histories from highly rated agents to generate training sets for the neural network." Para 20, "The first interface may be an agent interface for an agent to interact with a customer… The agent interface may be a customer response manager (CRM)." Para 42, "During the task closure process, the session history from the first interface may be mined as feedback for ongoing training of the interactive response system AI engine." Para 44, "…Responses from different sessions may be mined and the data may be used to train the AI engine." Para 21, "The mobile device application may include a live chat function for interacting with an agent in real time." – EN: this denotes the session histories transmitted from the agent interfaces (customer response managers) of multiple agents and mined for ongoing training, i.e., a second set of data aggregated from a plurality of internal subsystems of the communication system. Each session history is the record of a live chat between a customer and an agent, thereby satisfying "(iv) a recorded live chat".).)
Claim 6
The computer-implemented method of claim 1, wherein:
the communication channel is at least one of: (i) a live webchat, (ii) a phone call, (iii) an email, or (iv) a text message; and (Para 7, “A customer may request a live session at a mobile device using a mobile application or via any suitable method. A live session may be initiated at an agent interface”)
the set of data corresponding to the communication system includes one or more of: (i) a text string, (ii) an audio stream, (iii) a video stream, (iv) a file, or (v) a document. (Para 10, “The interactive response system may mine the session history to generate a training set for the neural network. The interactive response system may also mine crowdsourced session histories from highly rated agents to generate training sets for the neural network.” Para 54, “Memory 115 may store software including the operating system 117 and application program(s) 119 along with any data 111 needed for the operation of the system 100 . Memory 115 may also store videos, text, and/or audio assistance files.”)
Claim 7
Yannam further teaches:
The computer-implemented method of claim 1, wherein the one or more live communication channels includes an audio communication channel, and the method further comprises: (Para 55, “ I/O module 109 may include connectivity to a microphone… through which input may be provided into computer 101”)
receiving, at the one or more processors, a verbal communication across the audio communication channel; (Para 2, “An enterprise customer may communicate with the enterprise through a variety of channels. For example, a customer may communicate by telephone, internet-based chat, or chat through a mobile application. In each of these mediums, the customer may engage with either an interactive response system or a with live agent.” Para 3, “Chatbots may simulate conversations with a human using text, text-to-speech, or speech-to-speech.” – EN: this denotes a customer communicating “by telephone” with the interactive response system constitutes receiving a verbal communication across an audio communication channel. The speech-to-speech mode confirms voice input is received.)
converting, by the one or more processors, the verbal communication to a first text string; (Para 3, “Chatbots may simulate conversations with a human using text, text-to-speech, or speech-to-speech.” Para 26, “The bot may use natural language understanding to determine intent and extract entities from the customer input.” – EN: Under BRI, the “speech-to-speech” involves an intermediate speech to text conversion step, in the form of parsing the speech to text strings for the bot to understand and digest the information.)
determining, by the one or more processors executing the ML chatbot, a first predicted response to the verbal communication based upon the first text string; (Para 26, “The bot may use natural language understanding to determine intent and extract entities from the customer input. For example, if a customer enters “I need help with a dispute,” the system may determine intent as “need assistance” and a topic/entity as “filing a dispute.” Para 73, “At 324 , based on the intent, the AI engine may create predictions and access stored knowledge to generate a response.” – EN: this denotes the AI engine determining a predicted response to the verbal communication by processing the text-converted customer input through intent detection and prediction generation.)
generating, by the one or more processors executing the ML chatbot, the first predicted response to the verbal communication as a second text string; (Para 73, “At 324 , based on the intent, the AI engine may create predictions and access stored knowledge to generate a response. At 326 , a pop-up window may display a generated response at the agent interface.” – EN: The generated response displayed as text in a “pop-up window” at the agent interface constitutes the first predicted response generated “as a second text string”.)
Additional Examiner’s Note: The above converting, determining, and generating limitations are disclosed in Yannam. Specifically, the “speech recognition applications” in combination with bots that use “natural language understanding”. This process must include a convert step that converts a received verbal communication into a text string for processing. The system then determines intent and generates a predicted response retrieved from the knowledge store before outputting it. See paragraphs 3, 26, 27, and 58 of Yannam.
converting, by the one or more processors, the second text string to a second verbal communication; and (Para 3, “Chatbots may simulate conversations with a human using text, text-to-speech, or speech-to-speech.” – EN: this denotes “text-to-speech” that chatbots may use, which involves converting text string to verbal communication.”)
causing, by the one or more processors, the second verbal communication to be conveyed across the audio communication channel. (Para 3, “Chatbots may simulate conversations with a human using text, text-to-speech, or speech-to-speech.” Para 9, “The interactive response system may generate an AI-based response to the customer input. The response may be displayed at the agent interface. Following agent approval, the response may be transmitted to the customer mobile device.”)
Claim 8
Yannam teaches,
A system for training and updating a machine learning (ML) chatbot to function as an interactive knowledge base in a communication system, comprising:
one or more processors; and a non-transitory computer-readable memory coupled to the one or more processors, the memory storing instructions thereon that, when executed by the one or more processors, cause the one or more processors to: (Para 6, “Systems, methods, and apparatus are provided for integrating AI-powered conversational messaging with an agent interface during a live customer session.” Para 53, “Computer 101 may have a processor 103 for controlling the operation of the device and its associated components, and may include RAM 105 , ROM 107 , input/output (“I/O”) 109 , and a non-transitory or non-volatile memory 115 . Machine-readable memory may be configured to store information in machine-readable data structures. The processor 103 may also execute all software running on the computer. Other components commonly used for computers, such as EEPROM or Flash memory or any other suitable components, may also be part of the computer 101 .” Para 5, “It would be desirable to continually improve the chatbot aid provided to agents by training an AI-powered bot using crowdsourced responses from successful agents.”)
The remaining limitations of claim 8 are substantially the same as method claim 1, therefore claim 8 is rejected under the same rationale as claim 1.
Claims 12-14 recite substantially the same limitations as method claims 5-7 respectively. Therefore, claim 12-14 is rejected under the same rationale as claim 5-7.
Claim 15
Yannam teaches,
A tangible machine-readable medium comprising instructions for training and updating a machine learning (ML) chatbot to function as an interactive knowledge base in a communication system that, when executed, cause a machine to at least: (Para 6, “Systems, methods, and apparatus are provided for integrating AI-powered conversational messaging with an agent interface during a live customer session.” Para 53, “Computer 101 may have a processor 103 for controlling the operation of the device and its associated components, and may include RAM 105 , ROM 107 , input/output (“I/O”) 109 , and a non-transitory or non-volatile memory 115 . Machine-readable memory may be configured to store information in machine-readable data structures. The processor 103 may also execute all software running on the computer. Other components commonly used for computers, such as EEPROM or Flash memory or any other suitable components, may also be part of the computer 101.” Para 5, “It would be desirable to continually improve the chatbot aid provided to agents by training an AI-powered bot using crowdsourced responses from successful agents.”)
The remaining limitations of claim 15 are substantially the same as method claim 1, therefore claim 15 is rejected under the same rationale as claim 1.
Claims 19-20 recite substantially the same limitations as method claims 5-6 respectively. Therefore, claim 19-20 is rejected under the same rationale as claim 5-6.
Claims 2-4, 9-11, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Yannam in view of Bajaj and Mazza, and further in view of Mahmoud et al. (US 20220383153 A1), hereinafter “Mahmoud”.
Claim 2
Yannam in view of Bajaj and Mazza teaches all the limitations of claim 1, as discussed above.
Yannam further teaches:
The computer-implemented method of claim 1, further comprising:
connecting, by the one or more processors, a user to a live agent across a first communication channel to which the ML chatbot is connected; (Para 7, “A customer may request a live session at a mobile device using a mobile application or via any suitable method. A live session may be initiated at an agent interface. A parallel session may be initiated at an interactive response system. The interactive response system may include one or more neural networks.” Para 69, “FIG. 3 shows an illustrative process flow 300 in accordance with the principles of the disclosure. Process flow 300 shows integrating bot-generated responses with a live agent-customer interaction.” Para 22, “In some embodiments, communication with a chatbot and communication with a live agent may be implemented within a single mobile application chat function. Communication with both the chatbot and the live agent may occur within a single chat session. For example, a customer may begin interactions with the chatbot. In the course of communication with the chatbot, the customer may request a live agent. The same chat function may then present communication with a live agent.”
receiving, at the one or more processors, a user query from the user; and (Para 71, “At 304 , the agent may receive a customer input. At 306 , the agent may process the customer input.” Para 73, “At 316 , the interactive response system may receive the customer input.”)
generating, by the one or more processors executing the ML chatbot, a predicted response to the user query, (Para 73, “ At 324 , based on the intent, the AI engine may create predictions and access stored knowledge to generate a response. At 326 , a pop-up window may display a generated response at the agent interface.”)
wherein the predicted response includes a citation to a first piece of data from the set of data, (…) (Para 39, “The bot-generated responses may include next-step predictions. For example, the bot may identify documents and forms that may be helpful to the agent. An agent may select an option to view the document. An agent may select an option to transmit the document to the customer. In some embodiments, agent approval of a document or form may automatically insert a link for access to the document into the chat with the customer.” – EN: Under the broadest reasonable interpretation (BRI), the “citation to a first piece of data from the set of data” corresponds to “a link for access to the document into the chat.” The link to a specific document constitutes a citation to a piece of data from the training/knowledge data set. Additionally, Claim 2 of Yannam itself recites “generating a prediction based on the customer input; and based on the prediction, displaying a link to a document at the agent device.”
Yannam, Bajaj, and Mazza do not explicitly teach:
wherein the citation identifies a portion of the first piece of data that provides explicit support for the predicted response, and wherein the predicted response further comprises a confidence value associated with a likelihood that the predicted response is accurate.
However, Mahmoud teaches:
wherein the citation identifies a portion of the first piece of data that provides explicit support for the predicted response, (See Figure 4A; Para 70, “The UI 402 may further indicate a title 412 of the document, and highlight (or otherwise visually indicate) the subdocuments 414. Generally, the relevant subdocuments may be highlighted or otherwise distinguishes the relevant subdocument from the rest of the document.” Para 62, “… the retriever component 218 then feeds the top matches to the reader component 220, and the reader component 220 may be a machine reading comprehension model to extract the minimal relevant span of text in each subdocument to highlight for the agents 112 …” Para 63, “Once the reader component 220 outputs the highlights, the orchestrator sends a message to the client (UI) with each subdocument’s ID and its corresponding highlights (an array of pairs of start and end indices for the spans to highlight).” Para 75, “The event captured here is agent’s 112 clicking a recommendation’s link to get more details or read the entire help article.” – EN: this denotes the title of the recommended document and the recommendation’s link to the help article (a citation to a first piece of data), together with the highlighted subdocument and the minimal relevant span of text extracted by the reader (a portion of the first piece of data that provides explicit support for the recommended answer).)
and wherein the predicted response further comprises a confidence value associated with a likelihood that the predicted response is accurate. (Para 62, “The reader component 220 may use question-answering models (e.g., models based on Stanford Question Answering Dataset models). Such models also produce a confidence score of how well they answered a given query given a subdocument that may contain the answer (or indicate that the query is unanswerable given the subdocument). The reader component 220 can aggregate the score as well into the final score for each match, with its own coefficient; rescore, rerank, and apply thresholds, then return the matches.” Para 64, “The ranker component 222 may receive the answers and scores at ‘9,’ and send the ranked results to the agent-assist interface 214 at ‘10.’” See figure 4A, “CONFIDENCE 416”; Para 70, “Additionally, the UI 504 may indicate confidence values 416 indicating how confident the agent-assist system 118 is about the recommended document and/or subdocument 414.” – EN: this denotes the confidence values presented with the recommended document or subdocument (see figure 4A). The confidence value is associated with accuracy of the predicted response because when it calculates the final score for each recommended match, it takes into account the question-answering model’s score of how well it answered the user’s query from that subdocument, or whether the query cannot be answered from it (Para 62).)
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the AI chatbot framework in a communication system of Yannam, Bajaj, and Mazza with the presentation of the source document with its relevant subdocument highlighted, and the presentation of confidence values, of Mahmoud. The motivation for doing so would be to allow the live agent, who accepts, rejects, or modifies each generated response before it is inserted into the live interaction (Yannam, Para 73), to quickly identify the portion of the source document that supports the response and to see how confident the system is about the response, rather than sifting through dense documents. As Mahmoud elaborates regarding the benefit of this subdocument highlighting in paragraph 35, “In this way, the agent-assist system 118 may present documents and highlight subdocuments to help agents 112 quickly identify the relevant content of the documents.”
Claim 3
Mahmoud further teaches:
The computer-implemented method of claim 2, wherein generating the predicted response further comprises:
generating, by the one or more processors, one or more embeddings associated with the user query; (Para 56, “… the retriever component 218 may embed the query using an embedding model (that preserves the semantic relationships detailed earlier) into a vector q, and searches the vector space index that corresponds to the agent’s 112 knowledge base profile.” Para 114, “At 716, the agent-assist system 118 may generate a third embedding indicating a semantic associated with the query.” – EN: this denotes embedding the user’s query into a vector q, i.e., one or more embeddings associated with the user query.)
comparing, by the one or more processors, the one or more embeddings to a library of embeddings, by determining a geometric distance between the one or more embeddings and embeddings of the library of embeddings within an n-dimensional space; (Para 45, “The document processor 210 performs techniques to break down (using topic drift among other signals), transform, and index a collection contact-center knowledge base documents 206 into subdocuments represented by embeddings (points in a high-dimensional vector space) to enable semantic approximate nearest neighbor (ANN) search for a given query …” Para 56, “Each subdocument in the knowledge base profile, with references to the document to which it belongs, is represented as a point in the high-dimensional vector space (e.g., 1024 dimensions).” Para 67, “In this way, the queries received from users 104 can be embedded and represented as a vector 304, and then distances between the query vectors and stored subdocument vectors 310 to determine relevant subdocuments for the queries (e.g., semantic similarity).” Para 115, “For instance, a distance between the third embedding and the first embedding may be less than a distance between the third embedding and the second embedding.” – EN: this denotes comparing the query embedding to the indexed subdocument embeddings (library of embeddings), each represented as a point in a high-dimensional (e.g., 1024-dimensional) vector space, by the distances between the query vector and the stored subdocument vectors, i.e., determining a geometric distance within an n-dimensional space.)
determining, based upon the comparing, one or more closest embeddings from the library of embeddings to the one or more embeddings; and (Para 56, “The agent-assist system 118 finds the k nearest points, embedded using the same embedding model, to the input q.” Para 58, “Its direct generalization is the k nearest-neighbor search (the k-NN search), which looks for the k closest objects, which have k smallest distance values to the query q.” – EN: this denotes the k nearest points having the k smallest distance values to the query q, i.e., one or more closest embeddings from the library of embeddings to the one or more embeddings.)
determining, by the one or more processors, the predicted response based upon the one or more closest embeddings. (Para 62, “… the retriever component 218 then feeds the top matches to the reader component 220, and the reader component 220 may be a machine reading comprehension model to extract the minimal relevant span of text in each subdocument …” Para 42, “The retrieved matches are then fed a machine reading comprehension (reader) system, which extracts a minimal recommendation for the agent 112 that most pertains to the user’s 104 query; alternatively, a generative model generates a minimal recommendation text to show.” – EN: this denotes the reader extracting the minimal relevant span, or the generative model generating the minimal recommendation text, from the top matches returned by the nearest-neighbor search, i.e., the predicted response determined based upon the one or more closest embeddings.)
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the AI chatbot framework in a communication system of Yannam, Bajaj, and Mazza with the embedding-based nearest-neighbor search of Mahmoud. The motivation for doing so would be to identify the stored content whose meaning is closest to the user query, including content that expresses the same meaning in different words, so that the chatbot determines a relevant predicted response. As Mahmoud elaborates regarding the benefit of this embedding-based search in paragraph 46, the embeddings are indexed “such that embeddings closer to each other in the vector space have similar meaning (high relevance) and ones far apart have different meaning (less relevance)”, and in paragraph 83, “sentence-embedding methods capture semantic similarity between non-exact matches (different phrases and/or words whose meanings are similar)”.
Claim 4
Yannam further teaches:
The computer-implemented method of claim 2, wherein generating the predicted response further comprises:
retrieving, by the one or more processors, one or more prior responses from a response database; (Para 27, “In response to a determination of intent and topic, the bot may retrieve answers associated with the topic from a knowledge store.” Para 28, “The system may designate a topic of user interest and retrieve legacy communications regarding the topic. The legacy communications are preferably retrieved from an electronically stored library of communications.” – EN: this denotes “electronically stored library of communications” which reads on a “response database” because “legacy communications” are historical back-and-forth interactions between customers and agents/bots, thus they include prior responses.)
evaluating, by the one or more processors executing the ML chatbot, a predicted relevance of the one or more prior responses based upon the user query; (para 73, “At 324 , based on the intent, the AI engine may create predictions and access stored knowledge to generate a response.” Para 30, “The system may retrieve legacy intelligence relating to historical user selections regarding the topic.” Para 31, “The system may retrieve a plurality of outcomes based on the legacy intelligence. The plurality of outcomes may include outcomes associated with historical user selections.” – EN: this denotes the AI engine’s process of accessing “stored knowledge,” retrieving “legacy intelligence,” and evaluating historical outcomes to create predictions.)
determining, by the one or more processors executing the ML chatbot, at least one relevant prior response from the one or more prior responses; and (Para 36, “In response to a selection by a user of a topic of user interest, the system may generate a plurality of topic-related user options based on the neural network. These options may preferably have been formed with an associated priority score. The system may use the priority to score to rank and display the plurality of user options.” Para 27, “ In response to a determination of intent and topic, the bot may retrieve answers associated with the topic from a knowledge store.” – EN: this denotes “topic-related user options” and “answers” that are generated based on mining “legacy communications” and historical “session histories”. Therefore, the pool of options the neural network is ranking can include “prior responses”.)
generating, by the one or more processors executing the ML chatbot, the predicted response based upon the user query and the at least one relevant prior response. (Para 36, “In response to a selection by a user of a topic of user interest, the system may generate a plurality of topic-related user options based on the neural network.” Para 73, “At 324 , based on the intent, the AI engine may create predictions and access stored knowledge to generate a response. At 326 , a pop-up window may display a generated response at the agent interface.” – EN: this denotes the AI engine generates the response by combing the determined intent from the user query with accessed “stored knowledge” (which includes prior responses and legacy intelligence), thereby generating the predicted response based on the prior responses.)
Claims 9-11 recite substantially the same limitations as method claims 2-4 respectively. Therefore, claim 9-11 is rejected under the same rationale as claim 2-4.
Claims 16-18 recite substantially the same limitations as method claims 2-4 respectively. Therefore, claim 16-18 is rejected under the same rationale as claim 2-4.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/NAYMUR RAHMAN ALI/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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