Notice of Pre-AIA or AIA Status
The present application 89/746,230, filed on 6/18/2024 (or after March 16, 2013), is being examined under the first inventor to file provisions of the AIA (First Inventor to File).
In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
Claims 1-20 are pending in this application.
Drawings
The Drawings filed on 6/18/2024 are acceptable for examination purpose.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/23/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim4, 10-11 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 4, it is unclear what is meant by “the matching customer action comprising ………….. to have a “most similar semantic” meaning to the customer action of the present turn set, particularly the term “most similar” is a relative term that makes the claim indefinite
As to Claim 10, it is unclear what is meant by “wherein the foundational LLM comprises a neural network model having at least “1 billion parameters” that is configured to take in text as an input and produce text as an output, particularly the term 1 billion parameters is a relative term that makes the claim indefinite, as such neural network model dose not define parameters configurat[ion] capacity
As to Claim 11, it is unclear what is meant by “wherein the foundational LLM comprises a neural network model having at least 3 billion parameters that is configured to take in text as an input and produce text as an output”, particularly the term 3 billion parameters is a relative term that makes the claim indefinite, as such neural network model dose not define parameters configurat[ion] capacity
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 of US Application No. 18/746,230 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,598,254. . Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claims perform the same steps as the claims in the instant application
Instant US application: 18/746,230
US Patent No. 12,598,254
Claim 1,18, A computer-implemented method in a contact center for generating an action classifier model and use thereof in selectively initiating turn set queries of a knowledge base to assist agents in real time during ongoing conversations with customers, wherein the method comprises the steps of:
generating, via an automated modeling process, an action classifier model, wherein the automated modeling process comprises:
creating a training dataset by generating training samples in relation to respective turn sets selected from stored conversation data related to previous conversations, wherein, when described in relation to an exemplary first turn set from which a first training sample is generated, each training sample is created by:
receiving an encoder prompt that comprises a question asking what action a customer takes;
receiving a foundational large language model (LLM) that is configured to take as input a given turn set and the encoder prompt and generate output text describing a customer action that answers the encoder prompt based on content contained in the given turn set;
providing, as input to the foundational LLM, the first turn set and the encoder prompt;
generating the output text describing a customer action via an operation of the foundational LLM;
storing, as the first training sample in the training dataset, the first turn set in association with the customer action described by the generated output text;
training the action classifier model on the training samples of the training dataset; receiving classification data that classifies:
a first plurality of the customer actions found in the training samples as belonging to a first action category for which a knowledge base search is deemed needed; and
a second plurality of the customer actions as belonging to a second action category for which a knowledge base search is deemed not needed;
using the trained action classifier model and the received classification data to perform a query filtering routine in relation a present turn set occurring in an ongoing conversation between an agent and customer, wherein the query filtering routine comprises selectively initiating a turn set query of a knowledge base in relation to the present turn set based on whether a customer action for the present turn set is determined by the action classifier model to belong to the first action category type or the second action category type
Claim 13,1, A computer-implemented method for generating a customer bot and using the customer bot to train agents in a contact center, the method comprising: a first process, performed by a bot generating module performing, that generates the customer bot; and a second process, performed by an automated training module, that uses the customer bot to train the agents;
wherein the first process comprises the steps of:
gathering conversation data, the conversation data comprising data derived from natural language conversations occurring in the contact center during interactions between the agents and customers;
mining intents from the conversation data, wherein each mined intent comprises an intent label and a set of utterances associated with the intent label, the intent label identifying an issue found to be recurring within the interactions of the conversation data, and the set of utterances comprising utterances used to raise, discuss, or resolve the issue, wherein the utterances of the set of utterances comprise statements or phrases made by both the customers and agents;
selecting one or more mined intents of the mined intents, the one or more mined intents selected based on a relatedness to an interaction type found within the interactions;
constructing, from sets of utterances of the selected one or more mined intents, a dialog engine simulating the interaction type, the dialog engine defining a dialog flow for navigating the one or more issues associated with the selected one or more mined intents, the dialog flow including both customer-side statements and agent-side statements;
generating the customer bot with the dialog engine, the customer bot being configured in accordance with the customer-side statements of the dialog flow so to mimic a customer;
uploading the customer bot to the automated training module for use thereby to train the agents pursuant to the second process; and
periodically repeating the previous steps of the first process so that the customer bot uploaded to the automated training module is updated with conversation data that has been gathered since a previous time of repeating the steps of the first process;
wherein the second process comprises the steps of:
monitoring for one or more triggering events that determine whether a first agent of the agents should receive training related to the interaction type;
in response to detecting the one or more triggering events, initiating the training by initiating a virtual communication to a user device of the first agent;
connecting the virtual communication to the customer bot in response to establishing a communication connection with the user device;
conducting a simulated interaction of the interaction type by determining a scenario as a function of an evaluation of a performance of the first agent with respect to the interaction type in one or more previous interactions prior to the determination of the scenario and transmitting one or more customer-statements generated by the customer bot in accordance with the determined scenario to the first agent and receiving one or more statements made by the first agent in response thereto;
analyzing the one or more statements received from the first agent to derive a performance assessment of the first agent.
It would have been obvious to a person of ordinary skill was made to modify and/or to omit the additional elements of claim 1-20 of U.S. Patent No. 12,598,254 to arrive at the claims 1-20 of the instant application 18/746,230 because the ordinary skilled person would have realized that the remaining element(s) would perform the same function as before and the only difference particularly claim 1,18 of instant application 18/604,868 using the trained action classifier model and the received classification data to perform a query filtering routine in relation a present turn set occurring in an ongoing conversation between an agent and customer, wherein the query filtering routine comprises selectively initiating a turn set query of a knowledge base in relation to the present turn set based on whether a customer action for the present turn set is determined by the action classifier model to belong to the first action category type or the second action category type while claim 13,1 of U.S. Patent No. 12,598,254, conducting a simulated interaction of the interaction type by determining a scenario as a function of an evaluation of a performance of the first agent with respect to the interaction type in one or more previous interactions prior to the determination of the scenario and transmitting one or more customer-statements generated by the customer bot in accordance with the determined scenario to the first agent and receiving one or more statements made by the first agent in response thereto limitation(s) is/are absent of the limitation from instant application 18/604,868 claim 1,18, Omission and/or addition of elements and its function in combination is obvious expedient if the remaining elements perform same functions as before, as such instant application claim 1,18 are broader
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify, add or omit the additional elements of claims 13,1 (US Pat. No. 12,598,254) to arrive at the claims 1,10 of the instant application (18/604,868 ) because the person would have realized that the remaining element would perform the same functions as before. "Omission of element and its function in combination is obvious expedient if the remaining elements perform same functions as before." See In re Karlson (CCPA) 136 USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U. S. Court of Customs and Patent Appeals.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application.
Claim 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, Federal Register (84 FR 50) on January 7, 2019 hereinafter 2019 PEG
Step 1. In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the method of claim 1,18, directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. In accordance with Step 2A prong one of the 2019 PEG, the limitations reciting the abstract idea are highlighted, and the limitations directed to additional elements are highlighted, as set forth in exemplary claim 1
Claim 1,18. A computer-implemented method in a contact center for generating an action classifier model and use thereof in selectively initiating turn set queries of a knowledge base to assist agents in real time during ongoing conversations with customers, wherein the method comprises the steps of:
generating, via an automated modeling process, an action classifier model, wherein the automated modeling process comprises:
creating a training dataset by generating training samples in relation to respective turn sets selected from stored conversation data related to previous conversations, wherein, when described in relation to an exemplary first turn set from which a first training sample is generated, each training sample is created by:
receiving an encoder prompt that comprises a question asking what action a customer takes;
receiving a foundational large language model (LLM) that is configured to take as input a given turn set and the encoder prompt and generate output text describing a customer action that answers the encoder prompt based on content contained in the given turn set;
providing, as input to the foundational LLM, the first turn set and the encoder prompt;
generating the output text describing a customer action via an operation of the foundational LLM;
storing, as the first training sample in the training dataset, the first turn set in association with the customer action described by the generated output text;
training the action classifier model on the training samples of the training dataset; receiving classification data that classifies:
a first plurality of the customer actions found in the training samples as belonging to a first action category for which a knowledge base search is deemed needed; and
a second plurality of the customer actions as belonging to a second action category for which a knowledge base search is deemed not needed;
using the trained action classifier model and the received classification data to perform a query filtering routine in relation a present turn set occurring in an ongoing conversation between an agent and customer, wherein the query filtering routine comprises selectively initiating a turn set query of a knowledge base in relation to the present turn set based on whether a customer action for the present turn set is determined by the action classifier model to belong to the first action category type or the second action category type”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, contact center for generating an action classifier model, generating, via an automated modeling process, creating a training dataset by generating training samples, receiving an encoder prompt, receiving a foundational large language model, providing, as input, generating output, storing, as the first training sample, training the action classifier model, a first plurality of the customer actions, a second plurality of the customer actions, using the trained action classifier model”, in the context of this claim, this limitation encompasses the user thinking of data collection of customer action such as monitoring, select[ion] of model corresponding to the input data from the set of models is a mental process, is merely an instruction to apply an exception on ordinary computer components, i.e, merely uses a computer as a tool to perform an abstract idea having input and generating output.
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 set forth in the 2019 PEG. Accordingly, the claim recites an abstract idea.
With respect to Step 2A prong two of the 2019 PEG, the judicial exception is not integrated into a practical application. The additional elements are directed to method steps, however, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular data structure of for generating an action classifier model, generating, via an automated modeling process, creating a training dataset by generating training samples, receiving an encoder prompt, receiving a foundational large language model, providing, as input, generating output, storing, as the first training sample, training the action classifier model, a first plurality of the customer actions, a second plurality of the customer actions, using the trained action classifier model, to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Furthermore, although these elements have been fully considered, they are directed to the use of generic computing elements (fig 1, fig 7, 0019-0024,0065-0072, of the instant specification make it clear that the disclosed functionality is implemented on well-known computing systems and general purpose computing devices) to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in the 2019 PEG) and is amount to simply saying "apply it" using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment computer based operating environment) by using the computer as a tool to perform the abstract idea.
Since the analysis of Step 2A prong one and prong two results in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional method limitations are directed to a generic computer, at a very high level of generality and without imposing meaningful limitations on the scope of the claim. In addition fig 1, fig 7, 0019-0024,0065-0072 of the instant specification describe generic off-the-shelf computer-based elements for implementing the claimed invention which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. The claim(s) fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".)
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well-understood, routine, and conventional manner.
MPEP § 2106.05 (d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g. at a high level of generality) as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec...; TLI Communications LLC v. AV Auto. LLC...; OIP Techs., Inc., v. Amazon.com, Inc... ; buySAFE, Inc. v. Google, Inc...;
Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life...;
Electronic recordkeeping, Alice Corp...; Ultramercial... ;
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc...;
Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank...; and
A web browser's back and forward button functionality, Internet Patent Corp. v. Active Network, Inc.
Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
Claim 2,19, further elaborates “wherein the query filtering routine further comprises:
providing, as inputs to the action classifier model, the present turn set and the encoder prompt;
generating, via an operation of the action classifier model, output text comprising a customer action for the present turn set; and
determining in which of the first plurality of the customer actions or the second plurality of the customer actions that a matching customer action for the customer action of the present turn set appears by: comparing the customer action of the present turn set against the customer actions found in the first plurality of customer actions and the second plurality of customer actions, the matching customer action comprising a one of the customer actions in either the first plurality of customer actions or the second plurality of customer actions revealed by the comparison to have an output text that matches the output text of the customer action of the present turn set”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 3,20, further elaborates “wherein the query filtering routine further comprises:
initiating a turn set query of the knowledge base in relation to the present turn set when the matching customer action is determined to appear in the first plurality of the customer actions; and
prohibiting a turn set query of the knowledge base in relation to the present turn set when the matching customer action is determined to appear in the second plurality of the customer actions”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 4, further elaborates
“generating, via a sentence transformer comprising an embeddings language-model, vector embeddings for each of the customer actions found in the first and second plurality of customer actions;
wherein the query filtering routine further comprises:
providing, as inputs to the action classifier model, the present turn set and the encoder prompt;
generating, via an operation of the action classifier model, output text comprising a customer action for the present turn set;
generating, via the sentence transformer comprising the embeddings language-model, a vector embedding for the customer action of the present turn set; and
determining in which of the first plurality of the customer actions or the second plurality of the customer actions that a matching customer action for the customer action of the present turn set appears by:
comparing the vector embedding of the customer action of the present turn set against the vector embeddings of the customer actions found in both the first plurality of customer actions and the second plurality of customer actions, the matching customer action comprising a one of the customer actions in either the first plurality of customer actions or the second plurality of customer actions revealed by the comparison to have a most similar semantic meaning to the customer action of the present turn set”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 5, further elaborates “wherein the query filtering routine further comprises:
initiating a turn set query of the knowledge base in relation to the present turn set when the matching customer action is determined to appear in the first plurality of the customer actions; and
prohibiting a turn set query of the knowledge base in relation to the present turn set when the matching customer action is determined to appear in the second plurality of the customer actions”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 6, further elaborates “wherein the vector embedding of the customer action of the present turn set are compared against the vector embeddings of the customer actions found in both the first plurality of customer actions and the second plurality of customer actions via a computed cosine similarity”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 7, further elaborates “wherein the embeddings language-model of the sentence transformer comprises a pretrained neural networks configured to encode sentences into embedding vectors such that, once encoded, the embedding vectors of semantically similar sentences comprise a cosine similarity that is greater than a cosine similarity of the embedding vectors from semantically dissimilar sentences”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 8, further elaborates “wherein the query filtering routine is performed repeatedly in relation to respective successively occurring turn sets derived in real time from the ongoing conversation”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 9, further elaborates “wherein the turn set is defined as a turn pair having two consecutively occurring conversational turns in which a first turn is an conversational turn of the agent and a second turn is a conversational turn of the customer”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 10, further elaborates “wherein the foundational LLM comprises a neural network model having at least 1 billion parameters that is configured to take in text as an input and produce text as an output”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 11, further elaborates “wherein the foundational LLM comprises a neural network model having at least 3 billion parameters that is configured to take in text as an input and produce text as an output”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 12, further elaborates “wherein the action classifier model comprises a machine learning model configured as a sequence-to-sequence model”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 13, further elaborates “wherein the action classifier model is trained via a machine learning algorithm until the action classifier model outputs predicted customer actions from the respective turn sets found in the training samples that mimic the actual customer actions output by the foundational LLM to within an acceptable threshold”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 14, further elaborates “wherein, when described in relation to the first training sample in the training dataset, which is representative of how each of the training samples in the training dataset are used to train the action classifier model, the step of training the action classifier model comprises:
providing, as input to the action classifier model, the first turn set and the encoder prompt;
generating output text describing a predicted customer action via an operation of the action classifier model;
comparing an actual customer action of the first training sample to the predictive customer action and, via the comparison, determining a difference therebetween; and
adjusting parameters of the action classifier model to reduce the determined difference”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 15, further elaborates “wherein the previous conversations and the ongoing conversation each comprise conversations conducted via a voice channel;
further comprising the step of transcribing via automatic speech recognition the previous conversations and the ongoing conversation”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 16, further elaborates “wherein the step of receiving classification data comprises:
generating a user interface on a display that lists the customer actions found in the training samples and facilitates a human operator to provide input selecting customer actions of the listed customer actions for inclusion in the first action category and the second action category; and
receiving input from the human operator classifying the first plurality of the customer actions as belonging to the first action category and the second plurality of the customer actions as belonging to the second action category”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 17. The method of claim 3, further comprising the step of generating the classification data by:
“recording, in relation to each of the turn sets of the stored conversation data related to previous conversations, search outcome data regarding whether a given turn set generated a successful knowledge base search, which is defined as a knowledge base search based on the given turn set that returns a knowledge base article used by the agent to assist the customer, or an unsuccessful knowledge base search, which is defined as a knowledge base search based on the given turn set that did not return a knowledge base article used by the agent to assist the customer;
sorting the customer actions in the training samples into same customer action sets, wherein each of the same customer action sets comprises the turn sets that resulted in a same customer action and the respective associated search outcome data;
calculating, for each of the same customer action sets, a percentage of the turn sets within a given same customer action set that generated a successful knowledge base search; and
classifying:
the customers action of each of the same customer action sets calculated as having a percentage that satisfies a predetermined threshold as belonging to the first action category for which the knowledge base search is deemed needed; and
the customers action of each of the same customer action sets calculated as having a percentage that does not satisfy the predetermined threshold as belonging to the second action category for which the knowledge base search is deemed not needed”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Furthermore, “calculating, for each…….. customer action sets calculated as having a percentage………. customer action sets calculated as having a percentage that does not satisfy” i.e, , other than reciting “by a processor”, general-purpose computing, nothing in the claim element precludes the step from practically being performed in the mind. Consistent with the instant specification as at para 0069,0105, one can mentally calculate[ing] ……..percentage……… covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional method limitations are directed to a generic computer, at a very high level of generality and without imposing meaningful limitations on the scope of the claim. In addition fig 1, fig 7, 0019-0024,0065-0069, 0072, 0105 of the instant specification describe generic off-the-shelf computer-based elements for implementing the claimed invention which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".)
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well-understood, routine, and conventional manner.
MPEP § 2106.05 (d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g. at a high level of generality) as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec...; TLI Communications LLC v. AV Auto. LLC...; OIP Techs., Inc., v. Amazon.com, Inc... ; buySAFE, Inc. v. Google, Inc...;
Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life...;
Electronic recordkeeping, Alice Corp...; Ultramercial... ;
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc...;
Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank...; and
A web browser's back and forward button functionality, Internet Patent Corp. v. Active Network, Inc.
Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ghoche et al., (hereafter Ghoche), US Pub. No. 2024/0386213 filed Jul, 2023 ].
As to Claim 1,18, Ghoche teaches a system which including “a computer-implemented method in a contact center for generating an action classifier model and use thereof in selectively initiating turn set queries of a knowledge base to assist agents in real time during ongoing conversations with customers, wherein the method comprises the steps of: (Ghoche: fig 1, fig 2A, fig 32,-34- Ghoche teaches customer support services in association with customer support application for example CRM/helpdesk bidirectionally connected with AI augmented customer support module as detailed in fig 1, further, AI services supporting service agent workflow, “discovery” or knowledge based to assist agents including agent information assistant classifier generation and customer support solution classifier generation as detailed in fig 2 and conversations of the customers fine tuned to modify and/or update answers based on voice analysis as detailed in fig 32-34 and it should be noted that the prior art of Ghoche teaches AI/ML training engine connected bidirectionally to the knowledge base and the service agent modules as detailed in fig 2A, where machine learning, turn-set training data are specific types of data for example train conversational AI for example AI Chatbot, fig 46A, element 4604)
PNG
media_image1.png
171
244
media_image1.png
Greyscale
PNG
media_image2.png
314
201
media_image2.png
Greyscale
PNG
media_image3.png
229
164
media_image3.png
Greyscale
PNG
media_image4.png
254
158
media_image4.png
Greyscale
“generating, via an automated modeling process, an action classifier model, wherein the automated modeling process comprises” (Ghoche: fig 2A , fig 27, element 235,0077– Ghoche teaches AI services including automatic customer support solution classifier generation element 235, particularly classifier automatically classifies and generating data);
PNG
media_image5.png
84
316
media_image5.png
Greyscale
“creating a training dataset by generating training samples in relation to respective turn sets selected from stored conversation data related to previous conversations, wherein, when described in relation to an exemplary first turn set from which a first training sample is generated, each training sample is created by (Ghoche: fig 5, element 515, 0130, fig 34, 0195, element 3410 – Ghoche teaches generating training datasets in training ML classifier particularly associated with conversations including generating training sets for human labelled conversations that including train generative model)
receiving an encoder prompt that comprises a question asking what action a customer takes (Ghoche: fig 6, 0111, 0133- Ghoche teaches encoder for question and encoder for answer particularly producing performing prompt, embedding are learned using encoder framework) ;
PNG
media_image6.png
200
230
media_image6.png
Greyscale
“receiving a foundational large language model (LLM) that is configured to take as input (Ghoche: fig 47 – Ghoche teaches large language model associated with AI chatbot) a given turn set and the encoder prompt and generate output text describing a customer action that answers the encoder prompt based on content contained in the given turn set” (Ghoche: fig 6, 0111,0133, fig 49, element 4912, 0233-0234, fig 50 – Ghoche teaches AI chatbot and large language model in handling conversation and relevant answer(s)), and Ghoche’s language model or LLM corresponds to foundational language model)
PNG
media_image7.png
264
289
media_image7.png
Greyscale
PNG
media_image8.png
192
292
media_image8.png
Greyscale
“providing, as input to the foundational LLM, the first turn set and the encoder prompt” (Ghoche: 0111-0112,0230-232 – Ghoche teaches large language model used in generative AI models for example ChatGPT and user interface provides input to the LLM);
“generating the output text describing a customer action via an operation of the foundational LLM” (Ghoche: fig 52-53, 0236-0237 – Ghoche teaches customer actions and interactions between AI chatbot and customer as detailed in fig 52-53 is identical to instant specification drawing fig 9);
PNG
media_image9.png
207
135
media_image9.png
Greyscale
PNG
media_image10.png
270
194
media_image10.png
Greyscale
“storing, as the first training sample in the training dataset, the first turn set in association with the customer action described by the generated output text” (Ghoche: 0070-0071, 0112, fig 1 – Ghoche teaches storing in a database information related to training data, knowledge based records and like);
PNG
media_image1.png
171
244
media_image1.png
Greyscale
“training the action classifier model on the training samples of the training dataset; receiving classification data that classifies” (Ghoche: fig 1, 0077, AI customer support module element 140 maintains trained models and classifiers):
“a first plurality of the customer actions found in the training samples as belonging to a first action category for which a knowledge base search is deemed needed” (Ghoche: fig 1, fig 2A, 0068-0069 – Ghoche teaches customer support solution trained AI/ML ,model and Agent information assistant trained AI/ML model particularly connected to the discovery associated with service agent support module as detailed in fig 2A because AI services supports trained customer support samples processed and automatically classified); ; and
“a second plurality of the customer actions as belonging to a second action category for which a knowledge base search is deemed not needed” (Ghoche: 0089-0090,0175 – Ghoche teaches AI/MI model trained to identify a macro to answer question, particularly it may turn high accuracy likely the correct macro is selected thereby knowledge base search may not needed when the correct answer of the customer actions delivered from the category);
using the trained action classifier model (Ghoche : fig 1, element 140 trained models and classifiers) and
PNG
media_image11.png
177
152
media_image11.png
Greyscale
the received classification data to perform a query filtering routine in relation a present turn set occurring in an ongoing conversation between an agent and customer,(Ghoche: fig 33-34, 0193-0195 – Ghoche teaches template answer workflow particularly performing query filtering provided to a generative model on conversations, customer sentiment based on voice and training data) wherein the query filtering routine comprises selectively initiating a turn set query of a knowledge base in relation to the present turn set based on whether a customer action for the present turn set is determined by the action classifier model to belong to the first action category type or the second action category type” (Ghoche: fig 2A-2B, fig 14, 0077-0078, 0093-0094,0145-0146).
As to Claim 2,19, Ghoche disclosed:
“providing, as inputs to the action classifier model, the present turn set and the encoder prompt” (Ghoche: 0111-0112);
“generating, via an operation of the action classifier model, output text comprising a customer action for the present turn set” (Ghoche: fig 14, 0146-0147); and
PNG
media_image12.png
206
193
media_image12.png
Greyscale
determining in which of the first plurality of the customer actions or the second plurality of the customer actions that a matching customer action for the customer action of the present turn set appears by: (Ghoche: fig 1, fig 2A, 0068-0069) , comparing the customer action of the present turn set against the customer actions found in the first plurality of customer actions and the second plurality of customer actions, (Ghoche : 0090,0175 ) the matching customer action comprising a one of the customer actions in either the first plurality of customer actions or the second plurality of customer actions revealed by the comparison to have an output text that matches the output text of the customer action of the present turn set” (Ghoche: 0110,0114-0115) .
As to Claim 3,20, Ghoche disclosed:
“initiating a turn set query of the knowledge base in relation to the present turn set when the matching customer action is determined to appear in the first plurality of the customer actions” (Ghoche:0070-0071, 0094-0096,0176, fig 27) ; and
PNG
media_image13.png
207
208
media_image13.png
Greyscale
“prohibiting a turn set query of the knowledge base in relation to the present turn set when the matching customer action is determined to appear in the second plurality of the customer actions” (Ghoche: 0087-0089)
As to Claim 4, Ghoche disclosed:
“generating, via a sentence transformer comprising an embeddings language-model, vector embeddings for each of the customer actions found in the first and second plurality of customer actions” (Ghoche: 0108-0111);
“wherein the query filtering routine further comprises” (Ghoche: 0119,0156, fig 16)
“providing, as inputs to the action classifier model, the present turn set and the encoder prompt” (Ghoche: fig 1, element 140 trained models and classifiers, 0112) and
PNG
media_image11.png
177
152
media_image11.png
Greyscale
“generating, via an operation of the action classifier model, output text comprising a customer action for the present turn set” (Ghoche: 0114-0115);
“generating, via the sentence transformer comprising the embeddings language-model, a vector embedding for the customer action of the present turn set” (Ghoche: fig 17, 0162-0163) and
PNG
media_image14.png
185
201
media_image14.png
Greyscale
“determining in which of the first plurality of the customer actions or the second plurality of the customer actions that a matching customer action for the customer action of the present turn set appears by” (Ghoche: fig 1, fig 2A, 0068-0069):
comparing the vector embedding of the customer action of the present turn set against the vector embeddings of the customer actions found in both the first plurality of customer actions and the second plurality of customer actions (Ghoche : 0090,0109, 0175 ), the matching customer action comprising a one of the customer actions in either the first plurality of customer actions or the second plurality of customer actions revealed by the comparison to have a most similar semantic meaning to the customer action of the present turn set (Ghoche: 0110,0114-0115,0162-0163).
As to Claim 5, Ghoche disclosed:
“initiating a turn set query of the knowledge base in relation to the present turn set when the matching customer action is determined to appear in the first plurality of the customer actions” (Ghoche:0070-0071, 0094-0096,0176, fig 27) ; and
PNG
media_image13.png
207
208
media_image13.png
Greyscale
; and
“prohibiting a turn set query of the knowledge base in relation to the present turn set when the matching customer action is determined to appear in the second plurality of the customer actions” (Ghoche: 0087-0089).
As to Claim 6, Ghoche disclosed: “wherein the vector embedding of the customer action of the present turn set are compared against the vector embeddings of the customer actions found in both the first plurality of customer actions and the second plurality of customer actions via a computed cosine similarity” (Ghoche: 0109,0112-0113).
As to Claim 7, Ghoche disclosed “wherein the embeddings language-model of the sentence transformer comprises a pretrained neural networks configured to encode sentences into embedding vectors such that, once encoded, the embedding vectors of semantically similar sentences comprise a cosine similarity that is greater than a cosine similarity of the embedding vectors from semantically dissimilar sentences” (Ghoche: 0109,0112-0113,0159-0160).
As to Claim 8, Ghoche disclosed “wherein the query filtering routine is performed repeatedly in relation to respective successively occurring turn sets derived in real time from the ongoing conversation” (Ghoche: fig 32, 0192-0193).
PNG
media_image15.png
114
192
media_image15.png
Greyscale
As to Claim 9, Ghoche disclosed “wherein the turn set is defined as a turn pair having two consecutively occurring conversational turns in which a first turn is an conversational turn of the agent and a second turn is a conversational turn of the customer” (Ghoche: 0193-0195, fig 33-34)
PNG
media_image16.png
245
144
media_image16.png
Greyscale
As to Claim 10-11, Ghoche disclosed “wherein the foundational LLM comprises a neural network model having at least 1 billion parameters that is configured to take in text as an input and produce text as an output” (Ghoche: fig 1-2B, 0071,0175,0182,0230 – Ghoche teaches Artificial intelligence and machine learning model techniques used in classify data, generating answers and/or portions of answers particularly using AI/ML training engines element 278, further Ghoche teaches automatically generating workflow template answer engine, as such large language model or LLM is an AI model trained large amount of text for natural language processing particularly question and answer as natural language and in the context of one billion parameters, or more billion parameters just refers adjustable parameters to store in understanding of large language model(s) patterns and pretrained vast amount of data. The prior art of Ghoche teaches “ChatGPT” is a generative AI associated with large language model and Ghoche’s large language model may implement using generative AI models for example ChatGPT or GPT4 as detailed in 0230, therefore, 1 billion parameters, 3 billion parameters is adjustable values that process and generate text is supported by the Ghoche prior art
As to Claim 12, Ghoche disclosed “wherein the action classifier model comprises a machine learning model configured as a sequence-to-sequence model” (Ghoche: fig 1, element 140 AI/ML training module, trained AI/ML model, element 210 may use input sequence of data into an output sequence of data).
As to Claim 13, Ghoche disclosed “wherein the action classifier model is trained via a machine learning algorithm until the action classifier model outputs predicted customer actions from the respective turn sets found in the training samples that mimic the actual customer actions output by the foundational LLM to within an acceptable threshold” (Ghoche: fig 1, fig 2A,0074,0076, 0081,0087-0089,0175).
As to Claim 14, Ghoche disclosed wherein, when described in relation to the first training sample in the training dataset, which is representative of how each of the training samples in the training dataset are used to train the action classifier model, the step of training the action classifier model comprises” (Ghoche: fig 1, fig 2A) :
PNG
media_image1.png
171
244
media_image1.png
Greyscale
PNG
media_image2.png
314
201
media_image2.png
Greyscale
“providing, as input to the action classifier model, the first turn set and the encoder prompt” (Ghoche: fig 1, element 140 trained models and classifiers, 0112) and
PNG
media_image11.png
177
152
media_image11.png
Greyscale
generating output text describing a predicted customer action via an operation of the action classifier model” (Ghoche: 0099-0100,0130-0131);
comparing an actual customer action of the first training sample to the predictive customer action and, via the comparison, determining a difference therebetween(Ghoche : 0090,0109, 0175 ); and
adjusting parameters of the action classifier model to reduce the determined difference” (Ghoche: 0089-0091).
As to Claim 15, Ghoche disclosed “wherein the previous conversations and the ongoing conversation each comprise conversations conducted via a voice channel” (Ghoche: 0156,0193);
“further comprising the step of transcribing via automatic speech recognition the previous conversations and the ongoing conversation”(Ghoche: 0160-0162).
As to Claim 16, Ghoche disclosed
“generating a user interface on a display that lists the customer actions found in the training samples and facilitates a human operator to provide input selecting customer actions of the listed customer actions for inclusion in the first action category and the second action category (Ghoche: fig 15, 0154-0155, fig 52-53,0237-0238); and
PNG
media_image10.png
270
194
media_image10.png
Greyscale
“receiving input from the human operator classifying the first plurality of the customer actions as belonging to the first action category and the second plurality of the customer actions as belonging to the second action category” (Ghoche: fig 11, 0139, fig 52-53,0237-0238).
PNG
media_image17.png
103
313
media_image17.png
Greyscale
As to Claim 17, Ghoche disclosed:
“recording, in relation to each of the turn sets of the stored conversation data related to previous conversations, search outcome data regarding whether a given turn set generated a successful knowledge base search, which is defined as a knowledge base search based on the given turn set that returns a knowledge base article used by the agent to assist the customer,
(Ghoche:0070-0071, 0094-0096,0176, fig 27) ; and
PNG
media_image13.png
207
208
media_image13.png
Greyscale
or an unsuccessful knowledge base search, which is defined as a knowledge base search based on the given turn set that did not return a knowledge base article used by the agent to assist the customer (Ghoche: fig 15, fig 27, 0149-0150.0153-0154);
PNG
media_image18.png
118
231
media_image18.png
Greyscale
“sorting the customer actions in the training samples into same customer action sets, wherein each of the same customer action sets comprises the turn sets that resulted in a same customer action and the respective associated search outcome data” (Ghoche: fig 21-22, 0166—167);
“calculating, for each of the same customer action sets, a percentage of the turn sets within a given same customer action set that generated a successful knowledge base search” (Ghoche: fig 21-23,0167-0168); and
PNG
media_image19.png
315
234
media_image19.png
Greyscale
classifying: (Ghoche: 0077, fig 2A)
the customers action of each of the same customer action sets calculated as having a percentage that satisfies a predetermined threshold as belonging to the first action category for which the knowledge base search is deemed needed (Ghoche: 0088-0090,0100,0126 fig 4); and
“the customers action of each of the same customer action sets calculated as having a percentage that does not satisfy the predetermined threshold as belonging to the second action category for which the knowledge base search is deemed not needed” (Ghoche: 0126-0127, 0147-0148,0152-0163).
Conclusion
The prior art made of record
a. US Pub. No. 2024/0386213
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201,73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure
Authorization for Internet Communications
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax (not Examiner's Fax), Regular postal mail, or EFS Web using PTO/SB/439.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Srirama Channavajjala whose telephone number is 571-272-4108. The examiner can normally be reached on Monday-Friday from 8:00 AM to 5:30 PM Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gorney, Boris, can be reached on (571) 270- 5626. The fax phone numbers for the organization where the application or proceeding is assigned is 571-273-8300 Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free)
/Srirama Channavajjala/Primary Examiner, Art Unit 2154
