DETAILED ACTION
This action is in response to the RCE filed 02 July 2026.
Claims 1–6, 9–12, 16–18, and 21 are pending. Claims 1, 12, and 18 are independent.
Claims 1–6, 9–12, 16–18, and 21 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.
Continued Examination
A request for continued examination under 37 C.F.R. § 1.114, including the fee set forth in 37 C.F.R. § 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 C.F.R. § 1.114, and the fee set forth in 37 C.F.R. § 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 C.F.R. § 1.114. Applicant's submission filed on 14 May 2026 has been entered.
Response to Arguments
Applicant's arguments, see remarks, filed 14 May 2026, with respect to the rejection(s) of claim(s) 1–6, 9–12, 16–18 under § 103 have been fully considered and are persuasive, with the exception of the argument directed to the Byun reference. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Gilbert.
With regard to the arguments against the Byun reference (remarks, p. 5), the examiner notes that the “thereby” clause upon which the arguments depend expresses an intended result, and therefore does not limit the claim.
Claim Objections
Claim 18 is objected to because of the following informalities:
Claim 18 contains a grammatical error: “deploying the AI algorithm and determining thereby, for each particular SCS, a respective priority status metric…” [missing “channel” after “SCS”].
Claim 18 contains a grammatical error: “generating an advisory message for the regarding the security matter…” [“for the” should be deleted].
Claim 18 contains a grammatical error: “wherein the advisory message guides guiding using the advisory message…” [“wherein the advisory message guides” should be deleted].
Appropriate correction is required.
Claim Interpretation
Regarding claims 1–6, 9–12, 16–18, and 21, these claims recite (either explicitly or via dependence) the limitation “send[ing] at least a notification of the sent advisory message to each of the ordered SCS channels other than the determined highest priority SCS channel thereby preventing repetitive dialogs with a user of the particular user account regarding the security matter of the particular user account”. The “thereby” clause expresses an intended result of the preceding action (sending the notification) and therefore does not limit the claims. See, e.g., MPEP § 2111.04.
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, 16–18, and 21 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], Freed et al. (US 2021/0067470 A1) [hereinafter Freed], Gilbert (US 2017/0064083 A1), and Byun et al. (US 2019/0267001 A1) [hereinafter Byun].
Regarding independent claim 1, Vishnoi teaches [a] system for determining a particular service context specific (SCS) channel among multiple SCS channels as having a highest priority according to a priority status metric and sending an advisory message to a system device thereof, 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, a respective priority status metric comprising a ranking of priority of the particular SCS channel with regard to a particular user account; 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). […] generate an advisory message regarding the security matter of the particular user account; 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 determined highest priority SCS channel; and 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).
Vishnoi/Goyal/Freed teaches prioritizing a channel, e.g., a skill bot, based on a confidence score, but does not expressly teach ordering a plurality of channels by priority. However, Gilbert teaches: order the SCS channels each according to the determined priority status metric thereof such that the SCS channels are ordered by what SCS channels take priority over other SCS channels for receipt of advisory messaging with regard to the particular user account; Predictive data is gathered and analyzed by a computer system to determine an optimal channel for contact with a customer (Gilbert, ¶ 24). The predictive data analysis may be performed by a deep learning model (Gilbert, ¶ 50). The channels are ranked in order, starting from the most optimal (Gilbert, ¶¶ 24, 49). determine, for the particular user account, a highest priority SCS channel, of the ordered SCS channels, having a highest priority for receipt of advisory messaging regarding a security matter of the particular user account; The predictive analytics may determine the optimal channels based on the purpose of the contact (Gilbert, ¶ 42). The ranking of optimal channels is determined for a particular customer [account] and may be based on aspects of the account, e.g., demographic information of the customer or the value of the account to the provider (Gilbert, ¶¶ 27, 33, 52, 63, 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/Freed with those of Gilbert. One would have been motivated to do so in order to choose the channel that best fits the needs of the customer or the provider (Gilbert, ¶ 24).
Vishnoi/Goyal/Freed/Gilbert 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: send at least a notification of the sent advisory message to each of the ordered SCS channels other than the determined highest priority SCS channel thereby preventing repetitive dialogs with a user of the particular user account regarding the security matter of the particular user account. 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). The sharing prevents duplicative user interactions [repetitive dialogs] (Byun, ¶ 10).
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/Gilbert 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 2, the rejection of claim 1 is incorporated and Vishnoi/Goyal/Freed/Gilbert 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). The contact may be with a virtual computerized agent (Gilbert, ¶ 25).
Regarding dependent claim 3, the rejection of claim 2 is incorporated and Vishnoi/Goyal/Freed/Gilbert/Byun 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/Gilbert/Byun 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). The customer devices may be smart phones, tablets, computers, etc. (Gilbert, ¶ 39).
Regarding dependent claim 5, the rejection of claim 3 is incorporated and Vishnoi/Goyal/Freed/Gilbert/Byun 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/Gilbert/Byun 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/Gilbert/Byun 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/Gilbert/Byun 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/Gilbert/Byun 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). The channels may be email or text/SMS exchanges (Gilbert, ¶ 25).
Regarding independent claim 12, Vishnoi teaches [a] system for determining a particular service context specific (SCS) channel among multiple SCS channels as having a highest priority according to a priority status metric and sending an advisory message to a system device thereof, 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, a respective priority status metric comprising a ranking of priority of the particular SCS channel with regard to a particular user account; 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). […] generate an advisory message for the highest priority SCS channel, regarding the topic of the particular user account; 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 determined highest priority SCS channel; and The determined input is sent to the identified bot (Vishnoi, ¶ 104). […] wherein the advisory message guides a human agent or virtual agent of the determined highest priority SCS channel in a system-wide, with respect to the multiple system devices, 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).
Vishnoi/Goyal/Freed teaches prioritizing a channel, e.g., a skill bot, based on a confidence score, but does not expressly teach ordering a plurality of channels by priority. However, Gilbert teaches: order the SCS channels each according to the determined priority status metric thereof such that the SCS channels are ordered by what SCS channels take priority over other SCS channels for receipt of advisory messaging with regard to the particular user account; Predictive data is gathered and analyzed by a computer system to determine an optimal channel for contact with a customer (Gilbert, ¶ 24). The predictive data analysis may be performed by a deep learning model (Gilbert, ¶ 50). The channels are ranked in order, starting from the most optimal (Gilbert, ¶¶ 24, 49). determine, for the particular user account, a highest priority SCS channel, of the ordered SCS channels, having a highest priority for receipt of advisory messaging regarding a topic of the particular user account; The predictive analytics may determine the optimal channels based on the purpose of the contact (Gilbert, ¶ 42). The ranking of optimal channels is determined for a particular customer [account] and may be based on aspects of the account, e.g., demographic information of the customer or the value of the account to the provider (Gilbert, ¶¶ 27, 33, 52, 63, 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/Freed with those of Gilbert. One would have been motivated to do so in order to choose the channel that best fits the needs of the customer or the provider (Gilbert, ¶ 24).
Vishnoi/Goyal/Freed/Gilbert 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: send at least a notification of the sent advisory message to each of the ordered SCS channels other than the determined highest priority SCS channel thereby preventing repetitive dialogs with a user of the particular user account regarding the topic for the particular user account. 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). The sharing prevents duplicative user interactions [repetitive dialogs] (Byun, ¶ 10).
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/Gilbert 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 16, the rejection of claim 12 is incorporated and Vishnoi/Goyal/Freed/Gilbert/Byun further teaches: wherein the advisory message guides the human agent or virtual agent of the determined highest priority SCS channel in the system-wide next dialog with the user via a user device, and wherein the 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 16 is incorporated and Vishnoi/Goyal/Freed/Gilbert/Byun further teaches: wherein the virtual agent conducts the system-wide next dialog with the user via a user device as a 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 particular service context specific (SCS) channel among multiple SCS channels as having a highest priority according to a priority status metric and sending an advisory message to a system device thereof, 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, a respective priority status metric comprising a ranking of priority of the particular SCS channel with regard to a particular user account; 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). […] generating an advisory message for the regarding the security matter of the particular user account; 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 determined highest priority SCS channel; The determined input is sent to the identified bot (Vishnoi, ¶ 104). […] wherein the advisory message guides guiding using the advisory message a human agent or virtual agent of the determined highest priority SCS channel in a system-wide, with respect to the multiple system devices, 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).
Vishnoi/Goyal/Freed teaches prioritizing a channel, e.g., a skill bot, based on a confidence score, but does not expressly teach ordering a plurality of channels by priority. However, Gilbert teaches: order the SCS channels each according to the determined priority status metric thereof such that the SCS channels are ordered by what SCS channels take priority over other SCS channels for receipt of advisory messaging with regard to the particular user account; Predictive data is gathered and analyzed by a computer system to determine an optimal channel for contact with a customer (Gilbert, ¶ 24). The predictive data analysis may be performed by a deep learning model (Gilbert, ¶ 50). The channels are ranked in order, starting from the most optimal (Gilbert, ¶¶ 24, 49). determine, for the particular user account, a highest priority SCS channel, of the ordered SCS channels, having a highest priority for receipt of advisory messaging regarding a security matter of the particular user account; The predictive analytics may determine the optimal channels based on the purpose of the contact (Gilbert, ¶ 42). The ranking of optimal channels is determined for a particular customer [account] and may be based on aspects of the account, e.g., demographic information of the customer or the value of the account to the provider (Gilbert, ¶¶ 27, 33, 52, 63, 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/Freed with those of Gilbert. One would have been motivated to do so in order to choose the channel that best fits the needs of the customer or the provider (Gilbert, ¶ 24).
Vishnoi/Goyal/Freed/Gilbert 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: sending at least a notification of the sent advisory message to each of the ordered SCS channels other than the determined highest priority SCS channel thereby preventing repetitive dialogs with a user of the particular user account regarding the security matter of the particular user account. 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). The sharing prevents duplicative user interactions [repetitive dialogs] (Byun, ¶ 10).
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/Gilbert 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 21, the rejection of claim 18 is incorporated and Vishnoi/Goyal/Freed/Gilbert/Byun further teaches: wherein the virtual agent conducts the system-wide next dialog with the user via a user device as a 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 user device; 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).
Conclusion
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
