DETAILED ACTION
This action is in response to the reply filed 17 October 2025.
Claims 1–20 are pending. Claims 1, 12, and 18 are independent.
Claims 1–20 are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Arguments
The objection to the specification regarding the use of trademarks is withdrawn in light of the amendment and accompanying arguments (remarks, p. 1).
Applicant's arguments with respect to the rejections of claims 1–20 under § 101 have been fully considered and are persuasive. Therefore, the rejections are withdrawn.
Applicant's arguments with respect to the rejection(s) of claim(s) 1–20 under § 103 have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Freed et al.
Claim Rejections—35 U.S.C. § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1–6, 9–12, and 15–18 are rejected under 35 U.S.C. § 103 as being unpatentable over Vishnoi et al. (US 2022/0100961 A1) [hereinafter Vishnoi] in view of Goyal et al. (US 2022/0021630 A1) [hereinafter Goyal] and Freed et al. (US 2021/0067470 A1) [hereinafter Freed].
Regarding independent claim 1, Vishnoi teaches [a] system for determining a priority service context specific (SCS) channel among multiple SCS channels according to a priority status metric (PSM) and sending an advisory message thereto, the system comprising: at least one processor; A digital assistant/chat bot system implemented using a computer system, including a processing subsystem comprising one or more processors (Vishnoi, ¶¶ 230 and 232). a communication interface communicatively coupled to the at least one processor; and The computer system includes a communications subsystem (Vishnoi, ¶¶ 244–248). a memory device storing executable code that, when executed, configures the processor to: The computer system includes a storage subsystem for storing executable instructions (Vishnoi, ¶¶ 238–242). […] deploy the AI algorithm and determine thereby, for each particular SCS channel and at least one user device, a respective PSM comprising a ranking of priority of the particular SCS channel for messaging; A confidence score [PSM] is generated for each skill based on how likely the skill bot [SCS channel] can perform the task input by the user (Vishnoi, ¶¶ 87, 103). The input is processed by a trained model [AI algorithm] that classifies the user’s utterance (Vishnoi, ¶ 79). determine, for the at least one user device, a priority SCS channel, of the multiple SCS channels, having a PSM for a security matter higher than at least some of the other SCS channels; The confidence scores are evaluated to determine whether there is a confidence score that exceeds the next highest score by a win margin (Vishnoi, ¶ 103). The skill bots may include, e.g., bots with bank-related skills such as transferring balances, checking balances, etc. [security matters] (Vishnoi, ¶¶ 147, 149, 153). generate an advisory message for the security matter for the priority SCS channel; and The skill bot invoker determines what to provide as input [an advisory message] for the identified bot (Vishnoi, ¶ 104). send the advisory message to the respective system device of the priority SCS channel. The determined input is sent to the identified bot (Vishnoi, ¶ 104).
Vishnoi teaches a user interacting with a digital assistant comprising multiple bots (Vishnoi, ¶ 51), but does not expressly teach multiple system devices. However, Goyal teaches: monitor signals in multiple bidirectional SCS channels between multiple system devices and multiple user devices, each SCS channel for conveying signals within a respective context range to and from a respective system device of the multiple system devices; A client device [user device] communicates, via a network, with a primary bot and secondary bots [SCS channels] that may be located on a single server or on separate servers [multiple system devices] (Goyal, ¶¶ 13–16). The communication is bidirectional between the primary bot and secondary bots, between the client and server(s), and between a live agent and secondary bot (Goyal, ¶¶ 13–14, figs. 1 and 2). The system may include multiple clients [multiple user devices] (Goyal, ¶ 19).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Vishnoi with those of Goyal. Doing so would have been a matter of simple substitution of one known element [the bots being located locally or on a single server] for another [the bots being located remotely on multiple servers] to obtain predictable results [a service that routes messages among multiple chat bots, wherein the bots are located on different remote servers].
Vishnoi/Goyal teaches training a bot to infer an intent (Vishnoi, e.g., ¶ 46) but does not expressly teach a training loop based on monitoring multiple channels as claimed. However, Freed teaches: train an AI algorithm using a computer-implemented iterative training loop into which training data is inserted, the training data comprising at least a portion of the monitored signals in multiple bidirectional SCS channels between multiple system devices and multiple user devices; A chatbot system uses a classifier to classify an intent of a user based on utterances (Freed, ¶ 15). The training data is generated from previous multiple-utterance interactions between users and chatbots (Freed, ¶ 20). The data may be collected for multiple chatbot systems deployed on multiple devices [multiple system devices] and utilized my multiple users [multiple user devices] (Freed, ¶ 73). The models are retrained based on newly validated utterances [i.e., the training is performed multiple times, or is “looped”] (Freed, ¶ 78).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Vishnoi/Goyal with those of Freed. One would have been motivated to do so in order to, over time, improve the accuracy of the classification of intents [and therefore the routing to the correct bot] by retraining the model as new utterances are received (Freed, ¶ 23).
Regarding dependent claim 2, the rejection of claim 1 is incorporated and Vishnoi/Goyal/Freed further teaches: wherein at least one system device communicates, via at least one of the SCS channels, with a user of the at least one user device via a virtual agent using conversational artificial intelligence (AI). The primary and secondary bots are configured to conduct conversations with users (Goyal, ¶ 14). Intelligent bots, powered by AI, that perform conversations with end users (Vishnoi, ¶¶ 3, 115).
Regarding dependent claim 3, the rejection of claim 2 is incorporated and Vishnoi/Goyal/Freed further teaches: wherein the at least one processor executes a machine learning algorithm configured to guide, via at the at least one SCS channel, dialog or actions during a phone call or a chat session with a user concerning a user matter via the virtual agent using the conversational artificial intelligence (AI). A machine learning model is used to determine the intent in the conversational context (Vishnoi, ¶ 115). The intent is matched to a dialog flow (Vishnoi, ¶ 40). The dialog flows determine how the bots react to user input, e.g., the next actions the bot will take (Vishnoi, ¶¶ 73–83).
Regarding dependent claim 4, the rejection of claim 3 is incorporated and Vishnoi/Goyal/Freed further teaches: wherein the phone call or the chat session transpires between the at least one user device and the at least one system device over a network connection via the communication interface, wherein the at least one user device is one of a mobile phone, a non-mobile phone, a tablet device, a computer or a display screen with a virtual or physical keyboard. The user device(s) may be desktop or laptop computers, tablets, smartphones [mobile phones], or other devices; the chat bots are accessed using, e.g., a web browser or mobile app (Goyal, ¶ 12). The client device(s) may be portable handheld devices such as cellular phones, smartphones, or tablets (Vishnoi, ¶ 201). The input device(s) may include a keyboard, touch screen, or gesture recognition device (Vishnoi, ¶¶ 235–236). The digital assistant may be available via Short Message Service [SMS] (Vishnoi, ¶ 30). The client(s) and server(s) communicate using a network (Goyal, ¶ 13; Vishnoi, ¶ 197).
Regarding dependent claim 5, the rejection of claim 3 is incorporated and Vishnoi/Goyal/Freed further teaches: wherein the virtual agent conducts the phone call or a chat session with the user by steps including: asking an initial question of the user and receiving a response from the user via the at least one user device; The bot may ask the user a clarification question (Goyal, ¶ 39). The bot may ask the user a question related to the intent (Vishnoi, ¶ 40). determining a next question to ask of the user or a next action to take based on the response; and The communication channel remains open between the user and the secondary bot to handle follow-up chat messages with the user (Goyal, ¶ 32). The bot uses natural language understanding and natural language generation to perform a conversation with the user and, e.g., guide the user through a process by requesting information required to complete the process [next questions] (Vishnoi, ¶ 46). connecting a human agent into the phone call or the chat session when connecting the human agent is determined as the next action. The secondary bot may determine that a live human agent is required to assist the user, and establishes a communication channel between the user and the live human agent (Goyal, ¶ 21).
Regarding dependent claim 6, the rejection of claim 1 is incorporated and Vishnoi/Goyal/Freed further teaches: wherein, via each of the multiple SCS channels, at least one system device communicates with a user of the at least one user device via a respective human agent or virtual agent using conversational artificial intelligence (AI), and wherein the advisory message guides the human agent or virtual agent of the priority SCS channel in a system-wide next dialog with the user. The user interacts with the digital assistant and skill bots using natural language conversations (Vishnoi, ¶¶ 30–31). The user input causes the digital assistant/bot to take appropriate actions according to a conversation flow, including requesting user input [i.e., a next step in a dialog between the user and the assistant/bot] (Vishnoi, ¶¶ 39, 46). The routing system may switch between different skills based on the user input, e.g., if the user input is better handled by a different skill bot [i.e., the next step, which may be switching to a different bot, is considered on a system-wide basis] (Vishnoi, ¶¶ 132–134). The chat bots conduct a conversation with a human user (Goyal, ¶¶ 1, 14–15). The system may switch to a different secondary chat bot if the subject matter provided by the user cannot be handled by the current chat bot (Goyal, ¶ 32).
Regarding dependent claim 9, the rejection of claim 1 is incorporated and Vishnoi/Goyal/Freed further teaches: wherein the bidirectional SCS channels conduct respective dialogs between at least one system device and at least one user device. The chat bots are used in dialog systems (Goyal, ¶ 1). The chat bots may be located on the same server or different servers [system devices] (Goyal, ¶ 16). The user utilizes a chat interface on a client device [user device] connected to the bots via a network (Goyal, ¶ 12).
Regarding dependent claim 10, the rejection of claim 1 is incorporated and Vishnoi/Goyal/Freed further teaches: wherein the respective dialogs are conducted, at least in part, non-concurrently, and at least some of the dialogs are conducted intermittently. The bot may interact with the user using Short Message Service [SMS] (Vishnoi, ¶¶ 30, 35, 36). [Applicant defines intermittent dialogs to include dialogs conducted via SMS; see specification, para. 14.]
Regarding dependent claim 11, the rejection of claim 10 is incorporated and Vishnoi/Goyal/Freed further teaches: wherein each dialog of the dialogs conducted intermittently is conducted via SMS, text, email, or app push notification. The bot may interact with the user using Short Message Service [SMS] (Vishnoi, ¶¶ 30, 35, 36).
Regarding independent claim 12, Vishnoi teaches [a] system for determining a priority service context specific (SCS) channel among multiple SCS channels according to a priority status metric (PSM) and sending an advisory message thereto, the system comprising: at least one processor; A digital assistant/chat bot system implemented using a computer system, including a processing subsystem comprising one or more processors (Vishnoi, ¶¶ 230 and 232). a communication interface communicatively coupled to the at least one processor; and The computer system includes a communications subsystem (Vishnoi, ¶¶ 244–248). a memory device storing executable code that, when executed, configures the at least one processor to: The computer system includes a storage subsystem for storing executable instructions (Vishnoi, ¶¶ 238–242). […] deploy the AI algorithm and determine thereby, for each particular SCS channel and at least one user device, a respective PSM comprising a ranking of priority of the particular SCS channel for messaging; A confidence score [PSM] is generated for each skill based on how likely the skill bot [SCS channel] can perform the task input by the user (Vishnoi, ¶¶ 87, 103). The input is processed by a trained model [AI algorithm] that classifies the user’s utterance (Vishnoi, ¶ 79). determine, for the at least one user device, a priority SCS channel, of the multiple SCS channels, having a PSM for a security matter higher than at least some of the other SCS channels; The confidence scores are evaluated to determine whether there is a confidence score that exceeds the next highest score by a win margin (Vishnoi, ¶ 103). The skill bots may include, e.g., bots with bank-related skills such as transferring balances, checking balances, etc. [security matters] (Vishnoi, ¶¶ 147, 149, 153). generate an advisory message for the priority SCS channel; and The skill bot invoker determines what to provide as input [an advisory message] for the identified bot (Vishnoi, ¶ 104). send the advisory message to the respective system device of the priority SCS channel, The determined input is sent to the identified bot (Vishnoi, ¶ 104). wherein, via each of the multiple SCS channels, at least one system device communicates with a user of the at least one user device via a respective human agent or virtual agent using conversational artificial intelligence (AI), and wherein the advisory message guides the human agent or virtual agent of the priority SCS channel in a system-wide next dialog with the user. The user interacts with the digital assistant and skill bots using natural language conversations (Vishnoi, ¶¶ 30–31). The user input causes the digital assistant/bot to take appropriate actions according to a conversation flow, including requesting user input [i.e., a next step in a dialog between the user and the assistant/bot] (Vishnoi, ¶¶ 39, 46). The routing system may switch between different skills based on the user input, e.g., if the user input is better handled by a different skill bot [i.e., the next step, which may be switching to a different bot, is considered on a system-wide basis] (Vishnoi, ¶¶ 132–134). The chat bots conduct a conversation with a human user (Goyal, ¶¶ 1, 14–15). The system may switch to a different secondary chat bot if the subject matter provided by the user cannot be handled by the current chat bot (Goyal, ¶ 32).
Vishnoi teaches a user interacting with a digital assistant comprising multiple bots (Vishnoi, ¶ 51), but does not expressly teach multiple system devices. However, Goyal teaches: monitor signals in multiple bidirectional SCS channels between multiple system devices and at least one user device, each SCS channel conveying signals to and from a respective system device of the multiple system devices; A client device [user device] communicates, via a network, with a primary bot and secondary bots [SCS channels] that may be located on a single server or on separate servers [multiple system devices] (Goyal, ¶¶ 13–16). The communication is bidirectional between the primary bot and secondary bots, between the client and server(s), and between a live agent and secondary bot (Goyal, ¶¶ 13–14, figs. 1 and 2).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Vishnoi with those of Goyal. Doing so would have been a matter of simple substitution of one known element [the bots being located locally or on a single server] for another [the bots being located remotely on multiple servers] to obtain predictable results [a service that routes messages among multiple chat bots, wherein the bots are located on different remote servers].
Vishnoi/Goyal teaches training a bot to infer an intent (Vishnoi, e.g., ¶ 46) but does not expressly teach training based on monitoring multiple channels as claimed. However, Freed teaches: train an AI algorithm using at least a portion of the monitored signals in multiple bidirectional SCS channels between multiple system devices and multiple user devices; A chatbot system uses a classifier to classify an intent of a user based on utterances (Freed, ¶ 15). The training data is generated from previous multiple-utterance interactions between users and chatbots (Freed, ¶ 20). The data may be collected for multiple chatbot systems deployed on multiple devices [multiple system devices] and utilized my multiple users [multiple user devices] (Freed, ¶ 73). The models are retrained based on newly validated utterances [i.e., the training is performed multiple times, or is “looped”] (Freed, ¶ 78).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Vishnoi/Goyal with those of Freed. One would have been motivated to do so in order to, over time, improve the accuracy of the classification of intents [and therefore the routing to the correct bot] by retraining the model as new utterances are received (Freed, ¶ 23).
Regarding dependent claim 15, the rejection of claim 13 is incorporated and Vishnoi/Goyal/Freed further teaches: wherein the at least one processor executes a machine learning algorithm configured to guide, via the at least one SCS channel, dialog or actions during a phone call or a chat session with a user concerning a user matter via the virtual agent using the conversational artificial intelligence (Al). The user interacts with the digital assistant and skill bots using natural language conversations (Vishnoi, ¶¶ 30–31). The user input causes the digital assistant/bot to take appropriate actions according to a conversation flow, including requesting user input [i.e., a next step in a dialog between the user and the assistant/bot] (Vishnoi, ¶¶ 39, 46). An intent analysis engine uses, e.g., a machine learning-based classifier to determine intents corresponding to user utterances and the skill bots take one or more actions based on the intent (Vishnoi, ¶ 70). The skill bot may be a trained machine learning model (Vishnoi, ¶ 73). The chat bots conduct a conversation with a human user (Goyal, ¶¶ 1, 14–15). The system uses a machine learning model to determine the mapping of the user utterances to intents (Goyal, ¶¶ 17–18, 29).
Regarding dependent claim 16, the rejection of claim 15 is incorporated and Vishnoi/Goyal/Freed further teaches: wherein the phone call or the chat session transpires between the at least one user device and the at least one system device over a network connection via the communication interface, wherein the at least one user device is one of a mobile phone, a non-mobile phone, a tablet device, a computer or a display screen with a virtual or physical keyboard. The client hardware device may be a smartphone [mobile phone], tablet, or desktop computer (Goyal, ¶ 12). The client connects to the chat bots using a network (Goyal, ¶¶ 12–13).
Regarding dependent claim 17, the rejection of claim 15 is incorporated and Vishnoi/Goyal/Freed further teaches: wherein the virtual agent conducts the phone call or a chat session with the user by steps including: asking an initial question of the user and receiving a response from the user; The bot may ask the user how it can help in response to the user saying “hello” [an initial question] (Vishnoi, ¶ 37). determining a next question to ask of the user or a next action to take based on the response; and The bot determines the next action based on a conversation flow (Vishnoi, ¶ 39). connecting a human agent into the phone call or the chat session when connecting the human agent is determined as the next action. A secondary bot may determine that a live/human agent is required to assist the user based on the conversation, and establishes a communication channel between the user and the live agent (Goyal, ¶ 21).
Regarding independent claim 18, Vishnoi teaches [a] method for determining, by a computing system, a priority service context specific (SCS) channel among multiple SCS channels according to a priority status metric (PSM) and sending an advisory message thereto, the system comprising at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing computer-readable instructions, the at least one processor configured to execute the computer-readable instructions, the method comprising, upon execution of the computer-readable instructions by the at least one processor: A digital assistant/chat bot system implemented using a computer system, including a processing subsystem comprising one or more processors (Vishnoi, ¶¶ 230 and 232). The computer system includes a communications subsystem (Vishnoi, ¶¶ 244–248). The computer system includes a storage subsystem for storing executable instructions (Vishnoi, ¶¶ 238–242). […] deploying the AI algorithm and determining thereby, for each particular SCS channel and at least one user device, a respective PSM comprising a ranking of priority of the particular SCS channel for messaging; A confidence score [PSM] is generated for each skill based on how likely the skill bot [SCS channel] can perform the task input by the user (Vishnoi, ¶¶ 87, 103). The input is processed by a trained model [AI algorithm] that classifies the user’s utterance (Vishnoi, ¶ 79). determining, for the at least one user device, a priority SCS channel, of the multiple SCS channels, having a PSM for a security matter higher than at least some of the other SCS channels; The confidence scores are evaluated to determine whether there is a confidence score that exceeds the next highest score by a win margin (Vishnoi, ¶ 103). The skill bots may include, e.g., bots with bank-related skills such as transferring balances, checking balances, etc. [security matters] (Vishnoi, ¶¶ 147, 149, 153). generating an advisory message for the security matter for the priority SCS channel; and The skill bot invoker determines what to provide as input [an advisory message] for the identified bot (Vishnoi, ¶ 104). sending the advisory message to the respective system device of the priority SCS channel, The determined input is sent to the identified bot (Vishnoi, ¶ 104). wherein, via each of the multiple SCS channels, at least one system device communicates with a user of the at least one user device via a respective human agent or virtual agent using conversational artificial intelligence (AI), and wherein the advisory message guides the human agent or virtual agent of the priority SCS channel in a system-wide next dialog with the user. The user interacts with the digital assistant and skill bots using natural language conversations (Vishnoi, ¶¶ 30–31). The user input causes the digital assistant/bot to take appropriate actions according to a conversation flow, including requesting user input [i.e., a next step in a dialog between the user and the assistant/bot] (Vishnoi, ¶¶ 39, 46). The routing system may switch between different skills based on the user input, e.g., if the user input is better handled by a different skill bot [i.e., the next step, which may be switching to a different bot, is considered on a system-wide basis] (Vishnoi, ¶¶ 132–134). The chat bots conduct a conversation with a human user (Goyal, ¶¶ 1, 14–15). The system may switch to a different secondary chat bot if the subject matter provided by the user cannot be handled by the current chat bot (Goyal, ¶ 32).
Vishnoi teaches a user interacting with a digital assistant comprising multiple bots (Vishnoi, ¶ 51), but does not expressly teach multiple system devices. However, Goyal teaches: monitoring signals in multiple bidirectional SCS channels between multiple system devices and at least one user device, each SCS channel conveying signals to and from a respective system device of the multiple system devices; A client device [user device] communicates, via a network, with a primary bot and secondary bots [SCS channels] that may be located on a single server or on separate servers [multiple system devices] (Goyal, ¶¶ 13–16). The communication is bidirectional between the primary bot and secondary bots, between the client and server(s), and between a live agent and secondary bot (Goyal, ¶¶ 13–14, figs. 1 and 2).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Vishnoi with those of Goyal. Doing so would have been a matter of simple substitution of one known element [the bots being located locally or on a single server] for another [the bots being located remotely on multiple servers] to obtain predictable results [a service that routes messages among multiple chat bots, wherein the bots are located on different remote servers].
Vishnoi/Goyal teaches training a bot to infer an intent (Vishnoi, e.g., ¶ 46) but does not expressly teach training based on monitoring multiple channels as claimed. However, Freed teaches: training an AI algorithm using a computer-implemented iterative training loop into which training data is inserted, the training data comprising at least a portion of the monitored signals in multiple bidirectional SCS channels between multiple system devices and multiple user devices; A chatbot system uses a classifier to classify an intent of a user based on utterances (Freed, ¶ 15). The training data is generated from previous multiple-utterance interactions between users and chatbots (Freed, ¶ 20). The data may be collected for multiple chatbot systems deployed on multiple devices [multiple system devices] and utilized my multiple users [multiple user devices] (Freed, ¶ 73). The models are retrained based on newly validated utterances [i.e., the training is performed multiple times, or is “looped”] (Freed, ¶ 78).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Vishnoi/Goyal with those of Freed. One would have been motivated to do so in order to, over time, improve the accuracy of the classification of intents [and therefore the routing to the correct bot] by retraining the model as new utterances are received (Freed, ¶ 23).
Claims 7, 8, 13, 14, 19, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Vishnoi et al. (US 2022/0100961 A1) [hereinafter Vishnoi] in view of Goyal et al. (US 2022/0021630 A1) [hereinafter Goyal] and Freed et al. (US 2021/0067470 A1) [hereinafter Freed], further in view of Byun et al. (US 2019/0267001 A1) [hereinafter Byun].
Regarding dependent claim 7, the rejection of claim 1 is incorporated. Vishnoi/Goyal teaches routing messages between a user and a single bot, but does not expressly teach sending messages to each of the bots. However, Byun teaches: wherein the executable code, when executed, further configures the at least one processor to send at least a notification of the sent advisory message to each of the multiple SCS channels other than the priority SCS channel. Context information for processing a user utterance and providing a response is shared between a plurality of chat bots providing different services (Byun, ¶¶ 7, 10, 144, 145).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Vishnoi/Goyal/Freed with those of Byun. One would have been motivated to do so in order to prevent duplicative user interactions with the intelligent server hosting the chat bots (Byun, ¶ 10).
Regarding dependent claim 8, the rejection of claim 7 is incorporated and Vishnoi/Goyal/Freed/Byun further teaches: wherein sending at least the notification to each of the multiple SCS channels other than the priority SCS channels prevents repetitive dialogs with the user regarding a topic of the advisory message. The sharing prevents duplicative user interactions [repetitive dialogs] (Byun, ¶ 10).
Regarding dependent claim 13, the rejection of claim 12 is incorporated. Vishnoi/Goyal/Freed teaches routing messages between a user and a single bot, but does not expressly teach sending messages to each of the bots. However, Byun teaches: wherein the executable code, when executed, further configures the at least one processor to send at least a notification of the sent advisory message to each of the multiple SCS channels other than the priority SCS channel. Context information for processing a user utterance and providing a response is shared between a plurality of chat bots providing different services (Byun, ¶¶ 7, 10, 144, 145).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Vishnoi/Goyal/Freed with those of Byun. One would have been motivated to do so in order to prevent duplicative user interactions with the intelligent server hosting the chat bots (Byun, ¶ 10).
Regarding dependent claim 14, the rejection of claim 13 is incorporated and Vishnoi/Goyal/Freed/Byun further teaches: wherein sending at least the notification to each of the multiple SCS channels other than the priority SCS channels prevents repetitive dialogs with the user regarding a topic of the advisory message. The sharing prevents duplicative user interactions [repetitive dialogs] (Byun, ¶ 10).
Regarding dependent claim 19, the rejection of claim 18 is incorporated. Vishnoi/Goyal/Freed teaches routing messages between a user and a single bot, but does not expressly teach sending messages to each of the bots. However, Byun teaches: further comprising sending at least a notification of the sent advisory message to each respective system device of the multiple SCS channels other than the priority SCS channel. Context information for processing a user utterance and providing a response is shared between a plurality of chat bots providing different services (Byun, ¶¶ 7, 10, 144, 145).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Vishnoi/Goyal/Freed with those of Byun. One would have been motivated to do so in order to prevent duplicative user interactions with the intelligent server hosting the chat bots (Byun, ¶ 10).
Regarding dependent claim 20, the rejection of claim 19 is incorporated and Vishnoi/Goyal/Freed/Byun further teaches: further comprising sending at least the notification to each respective system device of the multiple SCS channels other than the priority SCS channel prevents repetitive dialogs with the user regarding a topic of the advisory message. The sharing prevents duplicative user interactions [repetitive dialogs] (Byun, ¶ 10).
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 C.F.R. § 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 C.F.R. § 1.17(a)) pursuant to 37 C.F.R. § 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.
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
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/Tyler Schallhorn/Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144