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 .
DETAILED ACTION
This office action is in response to correspondence 07/01/26 regarding application 18/777,414, in which claims 1-20 were amended. Claims 1-20 are pending in the application and have been considered.
Response to Arguments
The examiner agrees with Applicant on page 8 that no new matter was added via the amendments to claims 1-20.
On page 8, Applicant argues that the rejections under 35 U.S.C. 101 of claims 15-20 should be withdrawn because claims 15-20 are allegedly amended to recite that the medium is non-transitory. However, the text of the amendments submitted 07/01/26 apparently indicates that no such amendments were made. Claims 15-20 are still directed to “A computer-readable storage medium” and do not mention “non-transitory” whatsoever, and so the rejections are maintained.
On pages 8-9, regarding the 35 U.S.C. 103 rejections based partly on Gupta, Applicant argues:
“Rather than using a predefined table of condition groups, nor using an AI model to select a matching group of conditions from the table, Gupta uses machine learning models to directly classify conversation data into intent. The system of Gupta uses the intent to predict customer experience metrics, determine business opportunity values, and generate other analytical outputs. Therefore, in Gupta, the machine learning model produces analytics based on the conversation data.
In contrast, in Claim 1, the AI model identifies a group of conditions from among multiple predefined condition groups already stored within a table, thereby separating the AI classification stage from the subsequent table-driven processing stage. Gupta neither discloses nor suggests this intermediate condition-group selection architecture or a table that stores multiple predefined groups of conditions for AI-based selection.”
In response, as Applicant points out, Gupta uses machine learning models to directly classify conversation data into intent. These intents are mapped to business opportunities using “Intent-Business Opportunity Value Map”, see [0045]. This explicitly corresponds to the claimed “…execute a trained artificial intelligence model including a neural network capability on the conversation content and the previous conversation content to determine from among a plurality of groups of conditions within the table, a group of conditions that matches the conversation content…” because Gupta uses a neural network to determine which intents from among intents in the table, the conversation utterances match. The intents in the table are fairly considered “groups of conditions” since, for example, their matching of the conversation utterances is conditional on the content of the conversation utterances. The examiner does not see the alleged contrast with Applicant’s separating the AI classification stage from the subsequent table-driven processing stage, since Gupta classifies intents from the conversation utterances and looks up opportunities that are mapped to these using Intent-Business Opportunity Value Map.
Applicant’s arguments on page 9 regarding “selecting an attribute associated with that parameter based on content within the profile” have been considered but are moot in view of the new grounds for rejection, based in part on the newly discovered reference to Punukollu et al. (US 20150193840 A1), which appears to display offers generated from customer profile information similarly to Applicant (compare Applicant’s Fig. 7B to Punukollu Fig. 9). The new grounds for rejection based in part on Punukollu are necessitated by Applicant’s amendments.
Missing Oath/Declaration
Applicant’s attention is directed to the notice 07/31/24 informing Applicant that a properly executed oath or declaration for the inventor has not been received. Applicant’s assistance in submitting the properly executed oath or declaration for the inventor is respectfully requested in order to avoid delays should the application otherwise be found in condition for allowance.
Claim Objections
In claim 8, line 11, should “presenting identifying” be “identifying”?
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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 15-20 are directed to a “computer-readable storage medium” comprising “instructions”. On page 52, para [0170], the specification discusses a computer-readable storage medium, stating "A computer program may be embodied on a computer readable medium, such as a storage medium. For example, a computer program may reside in random access memory (RAM), flash memory, read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disk, a removable disk, a compact disk read-only memory (CD-ROM), or any other form of storage medium known in the art." Thus, the specification factors against eligibility for the claimed “medium” because the defined scope of the medium is not limited (i.e., the specification only gives examples of media types in an open-ended list which includes “any other form of storage medium known in the art”). Because the scope is open ended, the claims as a whole include non-statutory medium types, e.g. carrier waves, which do not fall into a category of patent eligible subject matter.
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 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 of this title, 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.
Claims 1, 4-8, 11-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 20200311204 A1) in view of Punukollu et al. (US 20150193840 A1).
Consider claim 1, Gupta discloses an apparatus (apparatus, [0001]), comprising:
a memory configured to store a table which contains parameters that are mapped to groups of condition (Intent-Business Opportunity value Map 414, Fig 4, which maps groups of intents to business opportunities, [0036-0037]; stored in memory of computing system, [0031], Fig. 1)); and
a processor, wherein the processor and memory are communicably coupled (processor executing program tangibly embodied in a machine-readable storage device, which in the Fig. 1 computers is memory of computing system, [0067], [0031], Fig. 1), and configured to:
receive conversation content from an ongoing communication session with a computing device (computing architecture 200 provides a virtual assistant with which a user via a chatroom that facilitates a conversation including user queries and VA replies, [0032], Fig. 2, ongoing conversation shown in the left pane of Fig. 8 screenshot, [0066]),
obtain previous conversation content from a database (chats from chat logs database 112A, Fig. 1, [0031], including historical chat data 404, [0036], Fig. 4, and previous conversation content shown on the left pane of Fig. 8, [0066]),
execute a trained artificial intelligence model including a neural network capability on the conversation content and the previous conversation content to determine from among a plurality of groups of conditions within the table, a group of conditions that matches the conversation content (neural network matches user queries from conversations to intent & intent variations in table 410, [0036], [0048], Fig. 4, Long Short-term Memory based neural network is trained to predict intents over chat sessions, [0048], by matching conversations to intent & intent variations in table 410, [0036], Fig. 4; this matches user queries from the conversation to intents and intent variations, i.e. groups of conditions), and
identify a parameter mapped to the matching group of conditions within the table via a graphical user interface (GUI) of the computing device at a point in time during the ongoing communication session (monitoring dashboard displays visual indicators, e.g. an “untapped wallet” metric based on the mapped business opportunity value, 0064], [0066]).
Gupta does not specifically mention a computing device associated with a profile; conversation content of a profile; select at least one attribute associated with a parameter based on content within the profile; and present the parameter and the at least one selected attribute.
Punukollu discloses a computing device associated with a profile (event processing application on financial institution server generates and updates customer context profiles, [0049]);
conversation content of a profile (customer context profiles include previous communications and inquiries, [0062], [0068]);
select at least one attribute associated with a parameter based on content within the profile (customer events associated with top opportunities, Fig 9, [0086], [0099]);
present the parameter and the at least one selected attribute (displaying the customer events and offers, Fig 9, [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including a computing device associated with a profile; conversation content of a profile; select at least one attribute associated with a parameter based on content within the profile; and present the parameter and the at least one selected attribute in order to assist customers more quickly, as suggested by Punukollu ([0001]). Doing so would have led to predictable results of providing fast, accurate customer service when the customer communicates, as suggested by Punukollu ([0001]) . The references cited are analogous art in the same field of customer service.
Consider claim 8, Gupta discloses a method (method, [0001]) comprising:
receiving conversation content from an ongoing communication session with a computing device (computing architecture 200 provides a virtual assistant with which a user via a chatroom that facilitates a conversation including user queries and VA replies, [0032], Fig. 2, ongoing conversation shown in the left pane of Fig. 8 screenshot, [0066]),
obtaining previous conversation content from a database (chats from chat logs database 112A, Fig. 1, [0031], including historical chat data 404, [0036], Fig. 4, and previous conversation content shown on the left pane of Fig. 8, [0066]),
executing a trained artificial intelligence model including a neural network capability on the conversation content and the previous conversation content to determine from among a plurality of groups of conditions within the table, a group of conditions that matches the conversation content (neural network matches user queries from conversations to intent & intent variations in table 410, [0036], [0048], Fig. 4, Long Short-term Memory based neural network is trained to predict intents over chat sessions, [0048], by matching conversations to intent & intent variations in table 410, [0036], Fig. 4; this matches user queries from the conversation to intents and intent variations, i.e. groups of conditions), and
identifying a parameter mapped to the matching group of conditions within the table via a graphical user interface (GUI) of the computing device at a point in time during the ongoing communication session (monitoring dashboard displays visual indicators, e.g. an “untapped wallet” metric based on the mapped business opportunity value, 0064], [0066]).
Gupta does not specifically mention a computing device associated with a profile; conversation content of a profile; selecting at least one attribute associated with a parameter based on content within the profile; and presenting the parameter and the at least one selected attribute.
Punukollu discloses a computing device associated with a profile (event processing application on financial institution server generates and updates customer context profiles, [0049]);
conversation content of a profile (customer context profiles include previous communications and inquiries, [0062], [0068]);
selecting at least one attribute associated with a parameter based on content within the profile (customer events associated with top opportunities, Fig 9, [0086], [0099]);
presenting the parameter and the at least one selected attribute (displaying the customer events and offers, Fig 9, [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including a computing device associated with a profile; conversation content of a profile; selecting at least one attribute associated with a parameter based on content within the profile; and presenting the parameter and the at least one selected attribute for reasons similar to those for claim 1.
Consider claim 15, Gupta discloses a computer-readable storage medium comprising instructions which when executed by a computer (processor executing program tangibly embodied in a machine-readable storage device, [0067], [0031], Fig. 1 cause a processor to perform:
receiving conversation content from an ongoing communication session with a computing device (computing architecture 200 provides a virtual assistant with which a user via a chatroom that facilitates a conversation including user queries and VA replies, [0032], Fig. 2, ongoing conversation shown in the left pane of Fig. 8 screenshot, [0066]),
obtaining previous conversation content from a database (chats from chat logs database 112A, Fig. 1, [0031], including historical chat data 404, [0036], Fig. 4, and previous conversation content shown on the left pane of Fig. 8, [0066]),
executing a trained artificial intelligence model including a neural network capability on the conversation content and the previous conversation content to determine from among a plurality of groups of conditions within the table, a group of conditions that matches the conversation content (neural network matches user queries from conversations to intent & intent variations in table 410, [0036], [0048], Fig. 4, Long Short-term Memory based neural network is trained to predict intents over chat sessions, [0048], by matching conversations to intent & intent variations in table 410, [0036], Fig. 4; this matches user queries from the conversation to intents and intent variations, i.e. groups of conditions), and
identifying a parameter mapped to the matching group of conditions within the table via a graphical user interface (GUI) of the computing device at a point in time during the ongoing communication session (monitoring dashboard displays visual indicators, e.g. an “untapped wallet” metric based on the mapped business opportunity value, 0064], [0066]).
Gupta does not specifically mention a computing device associated with a profile; conversation content of a profile; selecting at least one attribute associated with a parameter based on content within the profile; and presenting the parameter and the at least one selected attribute.
Punukollu discloses a computing device associated with a profile (event processing application on financial institution server generates and updates customer context profiles, [0049]);
conversation content of a profile (customer context profiles include previous communications and inquiries, [0062], [0068]);
selecting at least one attribute associated with a parameter based on content within the profile (customer events associated with top opportunities, Fig 9, [0086], [0099]);
presenting the parameter and the at least one selected attribute (displaying the customer events and offers, Fig 9, [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including a computing device associated with a profile; conversation content of a profile; selecting at least one attribute associated with a parameter based on content within the profile; and presenting the parameter and the at least one selected attribute for reasons similar to those for claim 1.
Consider claim 4, Gupta discloses the processor is configured to implement a second trained AI model configured to determine a tone of a conversation, and execute the second trained AI model on the conversation content to determine a current tone of the ongoing communication session (a trained emotional state classifier predicts an emotional state of the user during the conversation based on the textual messages, i.e. the tone of the messages, [0011], [0012]).
Consider claim 5, Gupta discloses the processor is configured to output the parameter based on the current tone of the ongoing communication session (generating a display signal including a status indicator based on the user experience score, predicted based on tone of the user messages, [0011], [0012], [0019]).
Gupta does not specifically mention the at least one selected attribute.
Punukollu discloses the at least one selected attribute (customer events associated with top opportunities, Fig 9, [0086], [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including at least one selected attribute for reasons similar to those for claim 1.
Consider claim 6, Gupta discloses the processor is configured to generate a model feedback record which includes at least one of the conversation content, previous conversation content, an identifier of the parameter, and an indication of whether the parameter was accepted, and retrain the trained AI model based on the model feedback record (feedback signal used to re-train the machine learning models of the VA, [0034]).
Gupta does not specifically mention the at least one selected attribute.
Punukollu discloses the at least one selected attribute (customer events associated with top opportunities, Fig 9, [0086], [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including at least one selected attribute for reasons similar to those for claim 1.
Consider claim 7, Gupta discloses the processor is configured to output a description of the parameter to a second graphical user interface (GUI) with a visual indicator which indicates output of the parameter via the GUI (visual indicators are output with textual descriptions in second window pane, i.e. “second GUI”,, Fig. 8, [0066]), wherein an AI agent performs an action related to the parameter (virtual assistant such as a chatbot, e.g. AI agent, performs one or more VA replies 204B, [0032], which are “related to” the visual indicators since they are derived from the ongoing conversation, [0064], [0066]).
Consider claim 11, Gupta discloses the implementing a second trained AI model configured to determine a tone of a conversation, and executing the second trained AI model on the conversation content to determine a current tone of the ongoing communication session (a trained emotional state classifier predicts an emotional state of the user during the conversation based on the textual messages, i.e. the tone of the messages, [0011], [0012]).
Consider claim 12, Gupta discloses outputting the parameter based on the current tone of the ongoing communication session (generating a display signal including a status indicator based on the user experience score, predicted based on tone of the user messages, [0011], [0012], [0019]).
Gupta does not specifically mention the at least one selected attribute.
Punukollu discloses the at least one selected attribute (customer events associated with top opportunities, Fig 9, [0086], [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including at least one selected attribute for reasons similar to those for claim 1.
Consider claim 13, Gupta discloses generating a model feedback record which includes at least one of the conversation content, previous conversation content, an identifier of the parameter, and an indication of whether the parameter was accepted, and retraining the trained AI model based on the model feedback record (feedback signal used to re-train the machine learning models of the VA, [0034]).
Gupta does not specifically mention the at least one selected attribute.
Punukollu discloses the at least one selected attribute (customer events associated with top opportunities, Fig 9, [0086], [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including at least one selected attribute for reasons similar to those for claim 1.
Consider claim 14, Gupta discloses outputting a description of the parameter to a second graphical user interface (GUI) with a visual indicator which indicates output of the parameter via the GUI (visual indicators are output with textual descriptions in second window pane, i.e. “second GUI”, Fig. 8, [0066]), where an AI agent performs an action related to the parameter (virtual assistant such as a chatbot, e.g. AI agent, performs one or more VA replies 204B, [0032], which are “related to” the visual indicators since they are derived from the ongoing conversation, [0064], [0066]).
Consider claim 18, Gupta discloses the processor performs implementing a second trained AI model configured to determine a tone of a conversation, and executing the second trained AI model on the conversation content to determine a current tone of the ongoing communication session (a trained emotional state classifier predicts an emotional state of the user during the conversation based on the textual messages, i.e. the tone of the messages, [0011], [0012]).
Consider claim 19, Gupta discloses the processor performs outputting the parameter based on the current tone of the ongoing communication session (generating a display signal including a status indicator based on the user experience score, predicted based on tone of the user messages, [0011], [0012], [0019]).
Gupta does not specifically mention the at least one selected attribute.
Punukollu discloses the at least one selected attribute (customer events associated with top opportunities, Fig 9, [0086], [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including at least one selected attribute for reasons similar to those for claim 1.
Consider claim 20, Gupta discloses the processor performs generating a model feedback record which includes at least one of the conversation content, previous conversation content, an identifier of the parameter, and an indication of whether the parameter was accepted, and retraining the trained AI model based on the model feedback record (feedback signal used to re-train the machine learning models of the VA, [0034]).
Gupta does not specifically mention the at least one selected attribute.
Punukollu discloses the at least one selected attribute (customer events associated with top opportunities, Fig 9, [0086], [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including at least one selected attribute for reasons similar to those for claim 1.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 20200311204 A1) in view of Punukollu et al. (US 20150193840 A1), in further view of Sivasubramanian et al. (US 20210158234 A1).
Consider claim 2, Gupta does not specifically mention the at least one selected attribute.
Punukollu discloses the at least one selected attribute (customer events associated with top opportunities, Fig 9, [0086], [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including at least one selected attribute for reasons similar to those for claim 1.
Gupta and Punukollu do not specifically mention the ongoing communication session comprises a telephone call conducted via a software application, and the processor is configured to receive speech from the telephone call which has been converted to text and output the parameter during the telephone call via the software application.
Sivasubramanian discloses the ongoing communication session comprises a telephone call conducted via a software application (call routed to agents conducted over VOIP, for which a software application is inherent, [0166], [0167]), and the processor is configured to receive speech from the telephone call which has been converted to text (calls are transcribed to text, [0073]), and output the parameter during the telephone call via the software application (displaying a “next best action” in the dashboard for the call center agent, [0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta and Punukollu such that the ongoing communication session comprises a telephone call conducted via a software application, and the processor is configured to receive speech from the telephone call which has been converted to text, and output the parameter during the telephone call via the software application in order to improve speed and accuracy of communication tools, as suggested by Sivasubramanian ([0002]). Doing so would have led to predictable results of improved data analytics, as suggested by Sivasubramanian ([0002]). The references cited are analogous art in the same field of natural language processing.
Consider claim 9, Gupta does not specifically mention the at least one selected attribute.
Punukollu discloses the at least one selected attribute (customer events associated with top opportunities, Fig 9, [0086], [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including at least one selected attribute for reasons similar to those for claim 1.
Gupta and Punukollu do not specifically mention the ongoing communication session comprises a telephone call conducted via a software application, and the processor is configured to receive speech from the telephone call which has been converted to text and output the parameter during the telephone call via the software application.
Sivasubramanian discloses the ongoing communication session comprises a telephone call conducted via a software application (call routed to agents conducted over VOIP, for which a software application is inherent, [0166], [0167]), and the processor is configured to receive speech from the telephone call which has been converted to text (calls are transcribed to text, [0073]), and output the parameter during the telephone call via the software application (displaying a “next best action” in the dashboard for the call center agent, [0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta and Punukollu such that the ongoing communication session comprises a telephone call conducted via a software application, and the processor is configured to receive speech from the telephone call which has been converted to text, and output the parameter during the telephone call via the software application for reasons similar to those for claim 2.
Consider claim 16, Gupta does not specifically mention the at least one selected attribute.
Punukollu discloses the at least one selected attribute (customer events associated with top opportunities, Fig 9, [0086], [0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta by including at least one selected attribute for reasons similar to those for claim 1.
Gupta and Punukollu do not specifically mention the ongoing communication session comprises a telephone call conducted via a software application, and the processor is configured to receive speech from the telephone call which has been converted to text and output the parameter during the telephone call via the software application.
Sivasubramanian discloses the ongoing communication session comprises a telephone call conducted via a software application (call routed to agents conducted over VOIP, for which a software application is inherent, [0166], [0167]), and the processor is configured to receive speech from the telephone call which has been converted to text (calls are transcribed to text, [0073]), and output the parameter during the telephone call via the software application (displaying a “next best action” in the dashboard for the call center agent, [0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta and Punukollu such that the ongoing communication session comprises a telephone call conducted via a software application, and the processor is configured to receive speech from the telephone call which has been converted to text, and output the parameter during the telephone call via the software application for reasons similar to those for claim 2.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 20200311204 A1) in view of Punukollu et al. (US 20150193840 A1), in further view of Cruz Huertas et al. (US 20180225279 A1).
Consider claim 3, Gupta discloses the processor is configured to execute the trained AI model on the conversation content, previous conversation content, and at least one future correspondence (processing historical data and text from ongoing VA chat to predict future intents via intent classifier, [0046], Fig. 5).
Gupta and Punukollu do not specifically mention determining unwanted content to be removed from the at least one future correspondence, and in response, delete the unwanted content from the at least one future correspondence to generate a modified at least one future correspondence.
Cruz Huertas discloses determining unwanted content to be removed from the at least one future correspondence, and in response, delete the unwanted content from the at least one future correspondence to generate a modified at least one future correspondence (modifying a proposed message by removing content that does not contextually fit the messaging session, [0103]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta and Punukollu by determining unwanted content to be removed from the at least one future correspondence, and in response, delete the unwanted content from the at least one future correspondence to generate a modified at least one future correspondence in order to ensure messages are contextually appropriate, as suggested by Cruz Huertas ([0001]), leading to predictable results of avoiding innocent jokes which would be poorly received, as suggested by Cruz Huertas ([0001]). The references cited are analogous art in the same field of natural language processing.
Consider claim 10, Gupta discloses executing the trained AI model on the conversation content, previous conversation content, and at least one future correspondence (processing historical data and text from ongoing VA chat to predict future intents via intent classifier, [0046], Fig. 5).
Gupta and Punukollu do not specifically mention determining unwanted content to be removed from the at least one future correspondence, and in response, delete the unwanted content from the at least one future correspondence to generate a modified at least one future correspondence.
Cruz Huertas discloses determining unwanted content to be removed from the at least one future correspondence, and in response, delete the unwanted content from the at least one future correspondence to generate a modified at least one future correspondence (modifying a proposed message by removing content that does not contextually fit the messaging session, [0103]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta and Punukollu by determining unwanted content to be removed from the at least one future correspondence, and in response, delete the unwanted content from the at least one future correspondence to generate a modified at least one future correspondence for reasons similar to those for claim 3.
Consider claim 17, Gupta discloses the processor performs executing the trained AI model on the conversation content, previous conversation content, and at least one future correspondence (processing historical data and text from ongoing VA chat to predict future intents via intent classifier, [0046], Fig. 5).
Gupta and Punukollu do not specifically mention determining unwanted content to be removed from the at least one future correspondence, and in response, delete the unwanted content from the at least one future correspondence to generate a modified at least one future correspondence.
Cruz Huertas discloses determining unwanted content to be removed from the at least one future correspondence, and in response, delete the unwanted content from the at least one future correspondence to generate a modified at least one future correspondence (modifying a proposed message by removing content that does not contextually fit the messaging session, [0103]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Gupta and Punukollu by determining unwanted content to be removed from the at least one future correspondence, and in response, delete the unwanted content from the at least one future correspondence to generate a modified at least one future correspondence for reasons similar to those for claim 3.
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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/Jesse S Pullias/
Primary Examiner, Art Unit 2655 08/19/26
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