Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements filed May 16, 2023, August 16, 2024, and December 09, 2025, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 20 are rejected under 35 U.S.C. 101 because the claimed
invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a system type claim. Claim 19 is a process type claim. Claim 20 is a
CRM claim. Therefore, claims 1-20 are directed to either a process, machine manufacture, or
composition of matter.
As per claim 1,
2A Prong 1:
“selecting, from a plurality of pretrained chatbot, a selected chatbot for the specific domain based on the domain-specific data,” The user, mentally or with pencil and paper, makes a selection of a chatbot for the specified domain with the domain data included from the request prior to selection.
“modifying, based on the user-specific data, one or more parameters of the pretrained machine-learned model to generate a customized machine-learned mode;” The user, mentally or with pencil and paper, uses user specific-data to modify the parameters of a pretrained machine-learning model for customization and generation of a trained model.
“accessing, based on the selected chatbot and the request, user-specific data;” The user, mentally or with pencil and paper, makes a request for the selected chatbot where user-specific data is accessed for the pre-trained machine learning model.
2A Prong 2:
Additional elements:
“A computing system, comprising: one or more processors;” (mere instructions to apply an exception using a generic computer component).
“and one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors” (mere instructions to apply an exception using a generic computer component).
“receiving, from a user device of a first user, a request for a chatbot that specializes in a specific domain, the request including domain-specific data;” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
“and in response to the request, deploying an expert chatbot having the customized machine-learned model.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
2B:
Additional elements:
“A computing system, comprising: one or more processors;” (mere instructions to apply an exception using a generic computer component).
“and one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors” (mere instructions to apply an exception using a generic computer component).
“receiving, from a user device of a first user, a request for a chatbot that specializes in a specific domain, the request including domain-specific data;” (MPEP 2106.05(d)(II) indicate that merely "transmitting or receiving data" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
“and in response to the request, deploying an expert chatbot having the customized machine-learned model.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
As per claim 2,
2A Prong 1:
“The computing system of claim 1, wherein the operations further comprise: processing the user-specific data…..to generate a prediction;” The user, mentally or with pencil and paper, performs an operation to create a prediction with using user-specific data.
“evaluating a loss function based on the prediction;” The user, mentally or with pencil and paper, uses the prediction to evaluate a loss function for the model.
“and wherein the one or more parameters of the….is modified based on the loss function.” The user, mentally or with pencil and paper, uses the loss function to modify the parameters of the model.
2A Prong 2:
Additional elements:
“…with the pretrained machine-learned model…” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a pretrained model to generate a prediction.)
“….the pretrained machine-learned model….” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a pretrained model where the parameters of the model is modified using a loss function.)
2B:
Additional elements:
“…with the pretrained machine-learned model…” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a pretrained model to generate a prediction.)
“….the pretrained machine-learned model….” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a pretrained model where the parameters of the model is modified using a loss function.)
As per claim 3,
2A Prong 1:
“…to generate a prediction;” The user, mentally or with pencil and paper, creates a prediction using the inference input.
2A Prong 2:
Additional elements:
“The computing system of claim 1, wherein the operations further comprise: receiving, from a user interface of the user device, an inference input;” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
“processing the inference input with the expert chatbot” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of an expert chatbot to create a prediction as output.)
“and providing, on the user interface of the user device, the prediction as an output.” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
2B:
Additional elements:
“The computing system of claim 1, wherein the operations further comprise: receiving, from a user interface of the user device, an inference input;” (MPEP 2106.05(d)(II) indicate that merely "transmitting or receiving data" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
“processing the inference input with the expert chatbot” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of an expert chatbot to create a prediction as output.)
“and providing, on the user interface of the user device, the prediction as an output.” (MPEP 2106.05(d)(II) indicate that merely "Presenting offers and gathering statistics" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer)
As per claim 4,
2A Prong 1: A judicial exception is not recited in the claims as they do not recite an
abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes, law of nature or natural phenomenon).
2A Prong 2:
Additional elements:
“The computing system of claim 3, wherein the operations comprise: receiving a user interaction in response to providing the output on the user interface;” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
“and updating historical data associated with the first user based on the user interaction.” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
2B:
Additional elements:
“The computing system of claim 3, wherein the operations comprise: receiving a user interaction in response to providing the output on the user interface;” (MPEP 2106.05(d)(II) indicate that merely "transmitting or receiving data" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
“and updating historical data associated with the first user based on the user interaction.” (MPEP 2106.05(d)(II) indicate that merely “Storing and retrieving information in memory” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 5,
2A Prong 1:
“The computing system of claim 3, wherein the inference input is processed with the user-specific data to generate the prediction.” The user, mentally or with pencil and paper, creates a prediction using the inference input.
2A Prong 2: The claim does not recite any additional elements beyond the judicial. exception.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As per claim 6,
2A Prong 1:
“The computing system of claim 3, wherein the operations further comprise: processing the inference input with a second expert chatbot to generate a second prediction,” The user, mentally or with pencil and paper, creates a second prediction using the inference input.
2A Prong 2:
Additional elements:
“and providing, on the user interface of the user device, the second prediction as a second output,” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
“and wherein the first output and the second output are presented concurrently on the user interface.” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
2B:
Additional elements:
“and providing, on the user interface of the user device, the second prediction as a second output,” (MPEP 2106.05(d)(II) indicate that merely "Presenting offers and gathering statistics" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
“and wherein the first output and the second output are presented concurrently on the user interface.” (MPEP 2106.05(d)(II) indicate that merely "Presenting offers and gathering statistics" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 7,
2A Prong 1: A judicial exception is not recited in the claims as they do not recite an
abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes, law of nature or natural phenomenon).
2A Prong 2:
Additional elements:
“The computing system of claim 1, wherein the request includes authorization to access to historical data associated with the first user, the operations comprise:” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
“accessing, based on the authorization, the historical data associated with the first user, and wherein the user-specific data includes the historical data.” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
2B:
Additional elements:
“The computing system of claim 1, wherein the request includes authorization to access to historical data associated with the first user, the operations comprise:” (MPEP 2106.05(d)(II) indicate that merely "Restricting public access to media by requiring a consumer to view an advertisement." is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
“accessing, based on the authorization, the historical data associated with the first user, and wherein the user-specific data includes the historical data.” (MPEP 2106.05(d)(II) indicate that merely "Restricting public access to media by requiring a consumer to view an advertisement. " is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 8,
2A Prong 1: A judicial exception is not recited in the claims as they do not recite an
abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes, law of nature or natural phenomenon).
2A Prong 2:
Additional elements:
“The computing system of claim 1, wherein the request includes login credentials for a social media account of the first user, the operations comprise: accessing, using the login credentials, social media data of the first user, and wherein the user-specific data includes the social media data of the first user.” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
2B:
Additional elements:
“The computing system of claim 1, wherein the request includes login credentials for a social media account of the first user, the operations comprise: accessing, using the login credentials, social media data of the first user, and wherein the user-specific data includes the social media data of the first user.” (MPEP 2106.05(d)(II) indicate that merely "Restricting public access to media by requiring a consumer to view an advertisement." is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 9,
2A Prong 1: A judicial exception is not recited in the claims as they do not recite an
abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes, law of nature or natural phenomenon).
2A Prong 2:
Additional elements:
“The computing system of claim 1, wherein the request includes read access to local data stored locally on the user device, the operations comprise: accessing, using the read access, the local data, and wherein the user-specific data includes the local data.” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
accessing, using the read access, the local data, and wherein the user-specific data includes the local data.” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
2B:
Additional elements:
“The computing system of claim 1, wherein the request includes read access to local data stored locally on the user device, the operations comprise: accessing, using the read access, the local data, and wherein the user-specific data includes the local data.” (MPEP 2106.05(d)(II) indicate that merely “Storing and retrieving information in memory” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
accessing, using the read access, the local data, and wherein the user-specific data includes the local data.” (MPEP 2106.05(d)(II) indicate that merely “Storing and retrieving information in memory” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 10,
2A Prong 1: A judicial exception is not recited in the claims as they do not recite an
abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes, law of nature or natural phenomenon).
2A Prong 2:
Additional elements:
“The computing system of claim 1, wherein the request includes a website that is selected by the first user, and wherein the domain-specific data includes data obtained from the website.” (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
2B:
Additional elements:
“The computing system of claim 1, wherein the request includes a website that is selected by the first user, and wherein the domain-specific data includes data obtained from the website.” (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
As per claim 11,
2A Prong 1: A judicial exception is not recited in the claims as they do not recite an
abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes, law of nature or natural phenomenon).
2A Prong 2:
Additional elements:
“The computing system of claim 1, wherein the request includes an audio sharing platform that is selected by the first user, and wherein the domain-specific data includes audio data obtained from an audio sharing platform that is associated with the domain.” (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
2B:
Additional elements:
“The computing system of claim 1, wherein the request includes an audio sharing platform that is selected by the first user, and wherein the domain-specific data includes audio data obtained from an audio sharing platform that is associated with the domain.” (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
As per claim 12,
2A Prong 1: A judicial exception is not recited in the claims as they do not recite an
abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes, law of nature or natural phenomenon).
2A Prong 2:
Additional elements:
“The computing system of claim 1, wherein the request includes a video sharing platform that is selected by the first user, and wherein the domain-specific data includes media data obtained from a video sharing platform that is associated with the domain.” (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
2B:
Additional elements:
“The computing system of claim 1, wherein the request includes a video sharing platform that is selected by the first user, and wherein the domain-specific data includes media data obtained from a video sharing platform that is associated with the domain.” (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
As per claim 13,
2A Prong 1: A judicial exception is not recited in the claims as they do not recite an
abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes, law of nature or natural phenomenon).
2A Prong 2:
Additional elements:
“The computing system of claim 1, wherein the request includes a social media platform that is selected by the first user, and wherein the domain-specific data includes public data obtained from a social media platform that is associated with the domain.” (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
2B:
Additional elements:
“The computing system of claim 1, wherein the request includes a social media platform that is selected by the first user, and wherein the domain-specific data includes public data obtained from a social media platform that is associated with the domain.” (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
As per claim 14,
2A Prong 1: A judicial exception is not recited in the claims as they do not recite an
abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes, law of nature or natural phenomenon).
2A Prong 2:
Additional elements:
“The computing system of claim 1, wherein the operations further comprise: sharing access of the expert chatbot with a second user based on a sharing request from the first user;” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
“and enabling subscription to the expert chatbot to a subscriber based on a subscription request from the subscriber.” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
2B:
Additional elements:
“The computing system of claim 1, wherein the operations further comprise: sharing access of the expert chatbot with a second user based on a sharing request from the first user;” (MPEP 2106.05(d)(II) indicate that merely "transmitting or receiving data" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
“and enabling subscription to the expert chatbot to a subscriber based on a subscription request from the subscriber.” (MPEP 2106.05(d)(II) indicate that merely "Restricting public access to media by requiring a consumer to view an advertisement." is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 15,
2A Prong 1: A judicial exception is not recited in the claims as they do not recite an
abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes, law of nature or natural phenomenon).
2A Prong 2:
Additional elements:
“The computing system of claim 1, wherein the pretrained machine-learned model of the selected chatbot…” ” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model for an expert chatbot.)
“…includes a first variable, and wherein the user-specific data accessed is based on the first variable.” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
2B:
Additional elements:
“The computing system of claim 1, wherein the pretrained machine-learned model of the selected chatbot” ” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model for an expert chatbot.)
“includes a first variable, and wherein the user-specific data accessed is based on the first variable.” (MPEP 2106.05(d)(II) indicate that merely "transmitting or receiving data" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 16,
2A Prong 1:
“wherein the provided user-specific data modifies the one or more parameters of the….” The user, mentally or with pencil and paper, modifies the parameters of a pre-trained machine learning model to generate a customize model.
2A Prong 2:
Additional elements:
“The computing system of claim 15, the operations comprising: providing the user-specific data that is accessed based on the first variable to the selected chatbot,” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
“pretrained machine-learned model to generate the customized machine-learned model.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to use user-specific data to modify the parameters of the pre-trained model to get a customized machine learned model.)
2B:
Additional elements:
“The computing system of claim 15, the operations comprising: providing the user-specific data that is accessed based on the first variable to the selected chatbot,” (MPEP 2106.05(d)(II) indicate that merely "transmitting or receiving data" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
“pretrained machine-learned model to generate the customized machine-learned model.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to use user-specific data to modify the parameters of the pre-trained model to get a customized machine learned model.)
As per claim 17,
2A Prong 1: A judicial exception is not recited in the claims as they do not recite an
abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes, law of nature or natural phenomenon).
2A Prong 2:
Additional elements:
“The computing system of claim 15, wherein the first variable is an attribute of the first user, and wherein the attribute is a location, an age, or a language associated with the first user.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a pretrained machine model where this limitation further describes the first variable recited in claim 15 where the attributes represent data associated with the first user for the pre-trained machine model.)
2B:
Additional elements:
“The computing system of claim 15, wherein the first variable is an attribute of the first user, and wherein the attribute is a location, an age, or a language associated with the first user.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a pretrained machine model where this limitation further describes the first variable recited in claim 15 where the attributes represent data associated with the first user for the pre-trained machine model.)
As per claim 18,
2A Prong 1:
“The computing system of claim 15, wherein the one or more parameters of….is modified based on the first attribute to...” The user, mentally or with pencil and paper, modifies the parameters of a pre-trained machine learning model to generate a customize model.
2A Prong 2:
Additional elements:
“the pretrained machine-learned model… generate the customized machine-learned model” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to use user-specific data to modify the parameters of the pre-trained model to get a customized machine learned model.)
2B:
Additional elements:
“the pretrained machine-learned model… generate the customized machine-learned model” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to use user-specific data to modify the parameters of the pre-trained model to get a customized machine learned model.)
As per claim 19,
2A Prong 1:
“selecting, from a plurality of pretrained chatbot, a selected chatbot for the specific domain based on the domain-specific data,” The user, mentally or with pencil and paper, makes a selection of a chatbot for the specified domain with the domain data included from the request prior to selection.
“modifying, based on the user-specific data, one or more parameters of the pretrained machine-learned model to generate a customized machine-learned mode;” The user, mentally or with pencil and paper, uses user specific-data to modify the parameters of a pretrained machine-learning model for customization and generation of a trained model.
“accessing, based on the selected chatbot and the request, user-specific data;” The user, mentally or with pencil and paper, makes a request for the selected chatbot where user-specific data is accessed for the pre-trained machine learning model.
2A Prong 2:
Additional elements:
“receiving, from a user device of a first user, a request for a chatbot that specializes in a specific domain, the request including domain-specific data;” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
“and in response to the request, deploying an expert chatbot having the customized machine-learned model.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
2B:
Additional elements:
“receiving, from a user device of a first user, a request for a chatbot that specializes in a specific domain, the request including domain-specific data;” (MPEP 2106.05(d)(II) indicate that merely "transmitting or receiving data" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
“and in response to the request, deploying an expert chatbot having the customized machine-learned model.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
As per claim 20,
2A Prong 1:
“selecting, from a plurality of pretrained chatbot, a selected chatbot for the specific domain based on the domain-specific data,” The user, mentally or with pencil and paper, makes a selection of a chatbot for the specified domain with the domain data included from the request prior to selection.
“modifying, based on the user-specific data, one or more parameters of the pretrained machine-learned model to generate a customized machine-learned mode;” The user, mentally or with pencil and paper, uses user specific-data to modify the parameters of a pretrained machine-learning model for customization and generation of a trained model.
“accessing, based on the selected chatbot and the request, user-specific data;” The user, mentally or with pencil and paper, makes a request for the selected chatbot where user-specific data is accessed for the pre-trained machine learning model.
2A Prong 2:
Additional elements:
“One or more non-transitory computer-readable media that store instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising:” (mere instructions to apply an exception using a generic computer component).
“and one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors” (mere instructions to apply an exception using a generic computer component).
“receiving, from a user device of a first user, a request for a chatbot that specializes in a specific domain, the request including domain-specific data;” (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)).
“and in response to the request, deploying an expert chatbot having the customized machine-learned model.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
2B:
Additional elements:
“One or more non-transitory computer-readable media that store instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising:” (mere instructions to apply an exception using a generic computer component).
“and one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors” (mere instructions to apply an exception using a generic computer component).
“receiving, from a user device of a first user, a request for a chatbot that specializes in a specific domain, the request including domain-specific data;” (MPEP 2106.05(d)(II) indicate that merely "transmitting or receiving data" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
“and in response to the request, deploying an expert chatbot having the customized machine-learned model.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 4, 5, 9, 14, 19, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sampat et al (US 11113475 B2), hereinafter references as Sampat, in view of Lee et al., (US 20230259761 A1), hereinafter references as Lee.
As per claim 1, Sampat teaches the following limitations:
A computing system, comprising: one or more processors; and one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations, the operations comprising: ([Column 1, particularly L33 - 35] – “According to some implementations, a device may include one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to:” and [Column 1, particularly L33 - 35] – “According to some implementations, a non-transitory computer-readable medium may store one or more instructions. The one or more instructions, when executed by one or more processors of a device, may cause the one or more processors to:”, Sampat discloses a device/system that receives a request to generate a chatbot, where the chatbots characteristics is based on the request by the user.)
receiving, from a user device of a first user, a request for a chatbot that specializes in I set ta specific domain, the request including domain-specific data; ([Column 1, particularly L36 – 38] – “receive, from a user device, a request to generate a chatbot, wherein the request identifies a characteristic of the chatbot; determine a chatbot template for the chatbot based on the characteristic of the chatbot;”, Sampat teaches a computing system of the chatbot generator platform that receives a request to generate a chatbot, where the chatbots characteristics is based on the request by the user.)
accessing, based on the selected chatbot and the request, user-specific data; ([Column 13, particularly L7 - 24] – “For example, the chatbot generator platform may configure the chatbot to access one or more platforms (e.g., a natural language processing platform, an application platform, an authorization platform, a user interface platform, a spell check platform, a speech-to-text (STT) platform, a QnA platform, and/or the like) that are associated with the services to permit the chatbot to interact with a user during operation. In some implementations, to permit the chatbot to utilize and/or access the one or more platforms, the chatbot generator platform may configure one or more APIs for the chatbot when building the chatbot. Such APIs may be configured to permit the chatbot to automatically interact with and/or exchange information (e.g., via API calls) between such platforms to enable operation of the chatbot during an interaction with a user. For example, an API call to a QnA platform may be made to request and/or configure a QnA pair according to the chatbot information.”, Sampat discloses a platform that includes an authorization platform that allows the chatbot to interact with the user and access data between the platforms during interact with the user.)
modifying, based on the user-specific data, one or more parameters of the pretrained machine-learned model to generate a customized machine-learned model; ([Column 10, particularly L28 - 39] – “The chatbot generator platform may train the QnA generator model using historical data associated with generating sets of QnA pairs according to the one or more QnA generation parameters. Using the historical data to train the QnA generator model and the one or more QnA generation parameters as inputs to the QnA generator model, the chatbot generator platform may generate a most useful set of QnA pairs (according to the trained QnA generation model) to enable the chatbot to determine a most accurate response to a user and/or from the user (according to the trained QnA generation model) in order to correspondingly facilitate the interaction, as described herein.” and [Column 15, particularly L7 - 14] – “According to some implementations, prior to deploying and/or launching the chatbot, the chatbot generator platform may provide the chatbot to the chatbot developer to permit the chatbot developer to further develop and/or customize (or extend capabilities) of the chatbot. In such instances, the chatbot developer may be provided with instructions to permit the chatbot developer to customize the chatbot.”, Sampat discloses the question and answer (QnA) model that is trained by historical data and interactions for increasing response accuracy of a model for the generated chatbot.)
and in response to the request, deploying an expert chatbot having the customized machine-learned model. ([Column 14, particularly L26 - 33] – “As further shown in FIG. 1B, and by reference number 160, the chatbot generator platform, via the chatbot deployment manager, may deploy and/or launch the chatbot to a chatbot host platform. The chatbot host platform may be associated with a source identified in the request to generate the chatbot. In some implementations, the chatbot host platform may include and/or may be a web-based platform (e.g., a website).” and [Column 15, particularly L21 - 26] – “In this way, the chatbot generator platform may deploy the generated chatbot and/or permit the chatbot to be launched by a chatbot host platform to permit operation of the chatbot.”, Sampat discloses an implementation that allows the user to deploy the chatbot with the customized model.).
Sampat fails to explicitly disclose this limitation:
“selecting, from a plurality of pretrained chatbot, a selected chatbot for the specific domain based on the domain-specific data, the selected chatbot being associated with a pretrained machine-learned model;”.
However, Lee teaches this limitation, ([2018, Pg. 1, paragraph 0016] – “In another aspect of the present invention, there is provided a transfer learning method for a deep neural network including: a pre-trained model storing step of storing a plurality of pre-trained models that are deep neural network models learned using a plurality of pre-training datasets; a transfer learning data input step of inputting transfer learning data; a pre-trained model selecting step of selecting a pre-trained model corresponding to the input transfer learning data from among the plurality of stored pre-trained models; and a transfer learning step of generating a plurality of transfer learning models by performing transfer learning using the selected pre-trained model and the transfer learning data.”, Sampat discloses an operation of using preconfigured templates and training machine learning models for the chatbot. However, Lee discloses a selection from a plurality of stored pretrained machine learning models that can be combined with Sampats chatbot generator to select, store, and use pretrained models for the pretrained chatbots.)
Lee is analogous art with respect to Sampat because they are from the same field of endeavor, namely training machine learning models with domain data and using pretrained machine learning models. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Lee to implement the system where the chatbot generator can select from a plurality of stored pre-trained machine learning models with pre-trained chatbots to create an expert chatbot while allowing the storage of the chatbots and models for future use by other users. One would have been motivated to make such combination to improve the training process and user access for a selection of pre-trained models for an expert chatbot and a chatbot generator platform that specializes in a domain or topic based on the user request.
As per claim 3, Sampat teaches the entire limitation:
The computing system of claim 1, wherein the operations further comprise: receiving, from a user interface of the user device, an inference input; ([Column 1, particularly L5 - 11] – “A chatbot is a user interface that is capable of conducting a conversation via audio and/or text. Accordingly, a chatbot may simulate how a user might interact during the conversation. The chatbot may use one or more analyses including speech-to-text, natural language processing, and/or the like to analyze a user input and/or determine an appropriate response to the user input.”.), Chatting with the chatbot is the inference input where a prediction is made based on the user inputs. Sampat discloses the user to chatbot interaction operations.)
processing the inference input with the expert chatbot to generate a prediction; ([Column 11, particularly L56; Column 12 L1 - 9] – “As further shown in FIG. 4, process 400 may include deploying the chatbot to a chatbot host platform to enable operation of the chatbot wherein, during the operation of the chatbot, the language analysis model is trained based on the interaction with the user (block 480). For example, the chatbot generator platform (e.g., using processor 320, memory 330, storage component 340, input component 350, output component 360, communication interface 370 and/or the like) may deploy the chatbot to a chatbot host platform to enable operation of the chatbot, as described above. In some implementations, during the operation of the chatbot, the language analysis model is trained based on the interaction with the user.” Sampat discloses the interaction between the user and the chatbot where the input from the user is used to train a language analysis model for providing an output from the chatbot to the user.)
and providing, on the user interface of the user device, the prediction as an output. ([Column 5, particularly L28 – 34 ] – “In some implementations, the chatbot generator interface may receive the request as a series of user inputs from the chatbot developer. For example, the chatbot generator interface may receive one or more of the above characteristics of the chatbot and/or one or more of the characteristics of the chatbot developer in a plurality of messages from the chatbot developer.” Sampat discloses the user interface that receives input from the user and displays the prediction or response from the chatbot platform.)
As per claim 4, Sampat teaches the entire limitations:
The computing system of claim 3, wherein the operations comprise: receiving a user interaction in response to providing the output on the user interface; ([Column 4, particularly L8 - 23] – “As shown in FIG. 1A, and by reference number 110, the chatbot generator platform receives a request to generate a chatbot from the chatbot developer. For example, the chatbot generator platform may receive the request via the chatbot generator interface and determine that a chatbot is to be generated for the chatbot developer based on receiving the request. As described herein, the chatbot generator platform enables a chatbot developer to quickly and efficiently generate a chatbot by simplifying the process for generating a chatbot. Accordingly, the chatbot developer may be an individual with chatbot development experience (e.g., a software engineer) or may not be an individual with chatbot development experience. Accordingly, the chatbot developer may be any user (e.g., a customer of an entity associated with the chatbot generator platform) that desires that a new chatbot be created for any purpose.”, Sampat discloses a user interface that displays user interaction with the chatbot.)
and updating historical data associated with the first user based on the user interaction. ([Column 4, particularly L8 - 23] – “In some implementations, the chatbot generator platform may use a machine learning model, such as a chatbot corpus generator model, to generate a chatbot corpus for a chatbot. For example, the chatbot generator platform may train the chatbot corpus generator model based on one or more chatbot corpus generation parameters used to generate a chatbot corpus for a chatbot, such as characteristics of the chatbot, characteristics of one or more topics of the chatbot, characteristics of one or more contexts of the chatbot, one or more sets of simulated user inputs and/or sets of simulated responses in training chatbot corpuses, and/or the like. The chatbot generator platform may train the chatbot corpus generator model using historical data associated with generating a chatbot corpus (e.g., including one or more corpuses in the training corpus data structure) according to the one or more chatbot corpus generation parameters. Using the historical data to train the chatbot corpus generator model and the one or more chatbot corpus generation parameters as inputs to the chatbot corpus generator model, the chatbot generator platform may generate a chatbot corpus to enable generation of a corresponding chatbot that can be used to facilitate an interaction between the chatbot and a user, as described herein”, Sampat discloses the operation of receiving inputs from the user to train the language analysis model from improving learning based on new interactions and a chatbot corpus database that stores historical data for training a corpus model associated with creating a chatbot that facilitates interaction with the user.)
As per claim 5, Sampat teaches the entire limitation:
The computing system of claim 3, wherein the inference input is processed with the user-specific data to generate the prediction. ([Column 10, particularly L28 - 39] – “The chatbot generator platform may train the QnA generator model using historical data associated with generating sets of QnA pairs according to the one or more QnA generation parameters. Using the historical data to train the QnA generator model and the one or more QnA generation parameters as inputs to the QnA generator model, the chatbot generator platform may generate a most useful set of QnA pairs (according to the trained QnA generation model) to enable the chatbot to determine a most accurate response to a user and/or from the user (according to the trained QnA generation model) in order to correspondingly facilitate the interaction, as described herein.” and [Column 12, particularly L47 - 58] – “Accordingly, the chatbot generator platform may configure the language analysis model to learn (e.g., be trained to determine) whether or not certain sets of responses to user inputs of interactions are appropriate (e.g., accurate, up to date, adequate to facilitate an interaction with the user, and/or the like). For example, the language analysis model may be trained during operation of the chatbot, in real-time, according to the interaction with the user. In some implementations, the language analysis model may be trained based on one or more other interactions associated with the chatbot, or one or more other interactions associated with one or more other chatbots.” and [Column 11, particularly L30 - 45] – “Additionally, or alternatively, the chatbot generator platform may configure the language analysis model to use a naive Bayesian classifier technique. In this case, the language analysis model may be configured to perform binary recursive partitioning to split data of the minimum feature set into partitions and/or branches and use the partitions and/or branches to perform predictions (e.g., that the chatbot is or is not to respond in a particular manner, that the user is or is not going to provide a certain user input, and/or the like).”, Sampat discloses the language analysis model using the QnA pairs, assocaited with historical, interaction, other context related data received from the user, to generate a prediction.)
As per claim 9, Sampat teaches the entire limitation:
The computing system of claim 1, wherein the request includes read access to local data stored locally on the user device, the operations comprise: accessing, using the read access, the local data, and wherein the user-specific data includes the local data. ([Column 18, particularly L60 - 67] – “Storage component 340 stores information and/or software related to the operation and use of device 300. For example, storage component 340 may include a hard disk (e.g., a magnetic disk, an optical disk, and/or a magneto-optic disk), a solid state drive (SSD), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and/or another type of non-transitory computer-readable medium, along with a corresponding drive”, Sampat discloses a storage component used by the chatbot generator platofrm, host platform, application platform, and authorization platform that can be locally stored on the user device or external storage.)
As per claim 14, Sampat teaches the entire limitation:
The computing system of claim 1, wherein the operations further comprise: sharing access of the expert chatbot with a second user based on a sharing request from the first user; ([Column 13, particularly L60 - 64] – “In some implementations, when building the chatbot, the chatbot may generate the chatbot to have certain restrictions (e.g., a certain number of users can access the chatbot, a certain number of uses of the chatbot, a certain number of topics can be associated with the chatbot, and/or the like)”, Sampat discloses that the chatbot platform and the generated chatbots can be used by multiple users. The chatbots can be customized to restrict access for the number of users and topics associated with the chatbot.)
and enabling subscription to the expert chatbot to a subscriber based on a subscription request from the subscriber. ([Column 14, particularly L1 - 22] – “According to some implementations, the full version chatbot may be generated based on the results of using the trial chatbot. In such cases, the chatbot generator platform may perform an operation to reconfigure the language analysis model of the trial version of the chatbot to be trained according to interactions associated with the chatbot or interactions associated with one or more other chatbots. In some implementations, the adaptation may be made based on the chatbot developer providing credentials that indicate that the chatbot is to be upgraded from the trial version to the full version (e.g., based on a change to the subscription status or SLA of the chatbot developer).”, Sampat discloses a subscription based system for the generated chatbots where a trial and full version is provided to the users.
As per claim 19, Sampat teaches the following limitations:
A computer-implemented method to deploy an expert chatbot, the method comprising: ([C1, particularly L15 - 32] – “According to some implementations, a method may include receiving a request to generate a chatbot, wherein the request identifies a type of the chatbot; determining a chatbot template for the chatbot based on the type of the chatbot; obtaining chatbot information according to the chatbot template; generating, based on the chatbot template and chatbot information, a chatbot corpus for the chatbot; generating a set of question and answer (QnA) pairs; configuring, based on the chatbot corpus, a language analysis model for the chatbot, wherein the language analysis model is configured to select one or more QnA pairs of the set of QnA pairs during an interaction between the chatbot and a user; building the chatbot according to the set of QnA pairs and the language analysis model; and deploying the chatbot to a chatbot host platform to enable operation of the chatbot, wherein, during the operation of the chatbot, the language analysis model is trained based on the interaction with the user.”, The method claim of the system that is disclosed and recites the same limitations in claim 1.)
receiving, from a user device of a first user, a request for a chatbot that specializes in a specific domain, the request including domain-specific data; ([Column 1, particularly L36 – 38] – “receive, from a user device, a request to generate a chatbot, wherein the request identifies a characteristic of the chatbot; determine a chatbot template for the chatbot based on the characteristic of the chatbot;”, Sampat teaches a computing system of the chatbot generator platform that receives a request to generate a chatbot, where the chatbots characteristics is based on the request by the user.)
accessing, based on the selected chatbot and the request, user-specific data; ([Column 13, particularly L7 - 24] – “For example, the chatbot generator platform may configure the chatbot to access one or more platforms (e.g., a natural language processing platform, an application platform, an authorization platform, a user interface platform, a spell check platform, a speech-to-text (STT) platform, a QnA platform, and/or the like) that are associated with the services to permit the chatbot to interact with a user during operation. In some implementations, to permit the chatbot to utilize and/or access the one or more platforms, the chatbot generator platform may configure one or more APIs for the chatbot when building the chatbot. Such APIs may be configured to permit the chatbot to automatically interact with and/or exchange information (e.g., via API calls) between such platforms to enable operation of the chatbot during an interaction with a user. For example, an API call to a QnA platform may be made to request and/or configure a QnA pair according to the chatbot information.”, Sampat discloses a platform that includes an authorization platform that allows the chatbot to interact with the user and access data between the platforms during interact with the user.)
modifying, based on the user-specific data, one or more parameters of the pretrained machine-learned model to generate a customized machine-learned model; ([Column 10, particularly L28 - 39] – “The chatbot generator platform may train the QnA generator model using historical data associated with generating sets of QnA pairs according to the one or more QnA generation parameters. Using the historical data to train the QnA generator model and the one or more QnA generation parameters as inputs to the QnA generator model, the chatbot generator platform may generate a most useful set of QnA pairs (according to the trained QnA generation model) to enable the chatbot to determine a most accurate response to a user and/or from the user (according to the trained QnA generation model) in order to correspondingly facilitate the interaction, as described herein.” and [Column 15, particularly L7 - 14] – “According to some implementations, prior to deploying and/or launching the chatbot, the chatbot generator platform may provide the chatbot to the chatbot developer to permit the chatbot developer to further develop and/or customize (or extend capabilities) of the chatbot. In such instances, the chatbot developer may be provided with instructions to permit the chatbot developer to customize the chatbot.”, Sampat discloses the question and answer (QnA) model that is trained by historical data and interactions for increasing response accuracy of a model for the generated chatbot.)
and in response to the request, deploying an expert chatbot having the customized machine-learned model. ([Column 14, particularly L26 - 33] – “As further shown in FIG. 1B, and by reference number 160, the chatbot generator platform, via the chatbot deployment manager, may deploy and/or launch the chatbot to a chatbot host platform. The chatbot host platform may be associated with a source identified in the request to generate the chatbot. In some implementations, the chatbot host platform may include and/or may be a web-based platform (e.g., a website).” and [Column 15, particularly L21 - 26] – “In this way, the chatbot generator platform may deploy the generated chatbot and/or permit the chatbot to be launched by a chatbot host platform to permit operation of the chatbot.”, Sampat discloses an implementation that allows the user to deploy the chatbot with the customized model.).
Sampat fails to explicitly disclose this limitation:
“selecting, from a plurality of pretrained chatbot, a selected chatbot for the specific domain based on the domain-specific data, the selected chatbot being associated with a pretrained machine-learned model;”.
However, Lee teaches this limitation, ([2018, Pg. 1, paragraph 0016] – “In another aspect of the present invention, there is provided a transfer learning method for a deep neural network including: a pre-trained model storing step of storing a plurality of pre-trained models that are deep neural network models learned using a plurality of pre-training datasets; a transfer learning data input step of inputting transfer learning data; a pre-trained model selecting step of selecting a pre-trained model corresponding to the input transfer learning data from among the plurality of stored pre-trained models; and a transfer learning step of generating a plurality of transfer learning models by performing transfer learning using the selected pre-trained model and the transfer learning data.”, Sampat discloses an operation of using preconfigured templates and training machine learning models for the chatbot. However, Lee discloses a selection from a plurality of stored pretrained machine learning models that can be combined with Sampats chatbot generator to select, store, and use pretrained models for the pretrained chatbots.)
Lee is analogous art with respect to Sampat because they are from the same field of endeavor, namely training machine learning models with domain data and using pretrained machine learning models. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Lee to implement the system where the chatbot generator can select from a plurality of stored pre-trained machine learning models with pre-trained chatbots to create an expert chatbot while allowing the storage of the chatbots and models for future use by other users. One would have been motivated to make such combination to improve the training process and user access for a selection of pre-trained models for an expert chatbot and a chatbot generator platform that specializes in a domain or topic based on the user request.
As per claim 20, Sampat teaches the following limitations:
One or more non-transitory computer-readable media that store instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising: ([C1, particularly L15 - 32] – “According to some implementations, a non-transitory computer-readable medium may store one or more instructions. The one or more instructions, when executed by one or more processors of a device, may cause the one or more processors to: receive a request to generate a chatbot; determine a chatbot template for the chatbot based on the request; obtain chatbot information according to the chatbot template; generate, using a first machine learning model, a chatbot corpus for the chatbot, wherein the first machine learning model generates the chatbot corpus using the chatbot information and the chatbot template. generate, using a second machine learning model, a set of question and answer (QnA) pairs based on the chatbot corpus; configure, using a third machine learning model, a language analysis model for the chatbot; build the chatbot according to the set of QnA pairs and the language analysis model; and deploy the chatbot to a chatbot host platform for operation, wherein the chatbot is built to, while under operation: engage in an interaction with a user via the chatbot host platform, use the language analysis model to select one or more QnA pairs from the set of QnA pairs during the interaction, and train the language analysis model based on the interaction.”, Sampat discloses the CRM claim, a non-transitory computer readable medium that stores and performs the operations with the same limitations recited in the system of claim 1.)
receiving, from a user device of a first user, a request for a chatbot that specializes in a specific domain, the request including domain-specific data; ([Column 1, particularly L36 – 38] – “receive, from a user device, a request to generate a chatbot, wherein the request identifies a characteristic of the chatbot; determine a chatbot template for the chatbot based on the characteristic of the chatbot;”, Sampat teaches a computing system of the chatbot generator platform that receives a request to generate a chatbot, where the chatbots characteristics is based on the request by the user.)
accessing, based on the selected chatbot and the request, user-specific data; ([Column 13, particularly L7 - 24] – “For example, the chatbot generator platform may configure the chatbot to access one or more platforms (e.g., a natural language processing platform, an application platform, an authorization platform, a user interface platform, a spell check platform, a speech-to-text (STT) platform, a QnA platform, and/or the like) that are associated with the services to permit the chatbot to interact with a user during operation. In some implementations, to permit the chatbot to utilize and/or access the one or more platforms, the chatbot generator platform may configure one or more APIs for the chatbot when building the chatbot. Such APIs may be configured to permit the chatbot to automatically interact with and/or exchange information (e.g., via API calls) between such platforms to enable operation of the chatbot during an interaction with a user. For example, an API call to a QnA platform may be made to request and/or configure a QnA pair according to the chatbot information.”, Sampat discloses a platform that includes an authorization platform that allows the chatbot to interact with the user and access data between the platforms during interact with the user.)
modifying, based on the user-specific data, one or more parameters of the pretrained machine-learned model to generate a customized machine-learned model; ([Column 10, particularly L28 - 39] – “The chatbot generator platform may train the QnA generator model using historical data associated with generating sets of QnA pairs according to the one or more QnA generation parameters. Using the historical data to train the QnA generator model and the one or more QnA generation parameters as inputs to the QnA generator model, the chatbot generator platform may generate a most useful set of QnA pairs (according to the trained QnA generation model) to enable the chatbot to determine a most accurate response to a user and/or from the user (according to the trained QnA generation model) in order to correspondingly facilitate the interaction, as described herein.” and [Column 15, particularly L7 - 14] – “According to some implementations, prior to deploying and/or launching the chatbot, the chatbot generator platform may provide the chatbot to the chatbot developer to permit the chatbot developer to further develop and/or customize (or extend capabilities) of the chatbot. In such instances, the chatbot developer may be provided with instructions to permit the chatbot developer to customize the chatbot.”, Sampat discloses the question and answer (QnA) model that is trained by historical data and interactions for increasing response accuracy of a model for the generated chatbot.)
and in response to the request, deploying an expert chatbot having the customized machine-learned model. ([Column 14, particularly L26 - 33] – “As further shown in FIG. 1B, and by reference number 160, the chatbot generator platform, via the chatbot deployment manager, may deploy and/or launch the chatbot to a chatbot host platform. The chatbot host platform may be associated with a source identified in the request to generate the chatbot. In some implementations, the chatbot host platform may include and/or may be a web-based platform (e.g., a website).” and [Column 15, particularly L21 - 26] – “In this way, the chatbot generator platform may deploy the generated chatbot and/or permit the chatbot to be launched by a chatbot host platform to permit operation of the chatbot.”, Sampat discloses an implementation that allows the user to deploy the chatbot with the customized model.).
Sampat fails to explicitly disclose this limitation:
“selecting, from a plurality of pretrained chatbot, a selected chatbot for the specific domain based on the domain-specific data, the selected chatbot being associated with a pretrained machine-learned model;”.
However, Lee teaches this limitation, ([2018, Pg. 1, paragraph 0016] – “In another aspect of the present invention, there is provided a transfer learning method for a deep neural network including: a pre-trained model storing step of storing a plurality of pre-trained models that are deep neural network models learned using a plurality of pre-training datasets; a transfer learning data input step of inputting transfer learning data; a pre-trained model selecting step of selecting a pre-trained model corresponding to the input transfer learning data from among the plurality of stored pre-trained models; and a transfer learning step of generating a plurality of transfer learning models by performing transfer learning using the selected pre-trained model and the transfer learning data.”, Sampat discloses an operation of using preconfigured templates and training machine learning models for the chatbot. However, Lee discloses a selection from a plurality of stored pretrained machine learning models that can be combined with Sampats chatbot generator to select, store, and use pretrained models for the pretrained chatbots.)
Lee is analogous art with respect to Sampat because they are from the same field of endeavor, namely training machine learning models with domain data and using pretrained machine learning models. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Lee to implement the system where the chatbot generator can select from a plurality of stored pre-trained machine learning models with pre-trained chatbots to create an expert chatbot while allowing the storage of the chatbots and models for future use by other users. One would have been motivated to make such combination to improve the training process and user access for a selection of pre-trained models for an expert chatbot and a chatbot generator platform that specializes in a domain or topic based on the user request.
Claim(s) 2, 15, 16, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sampat et al (US 11113475 B2), hereinafter references as Sampat, in view of Lee et al., (US 20230259761 A1), hereinafter references as Lee, in further view of Polleri et al., (US 20210081819 A1), hereinafter referenced as Polleri.
As per claim 2, Sampat teaches the following limitations:
The computing system of claim 1, wherein the operations further comprise: processing the user-specific data …. to generate a prediction; ([Column 11, particularly L30 - 45] – “The computing system of claim 1, wherein the operations further comprise: processing the user-specific data with the pretrained machine-learned model to generate a prediction;”, Sampat discloses the chatbot uses language analysis model that that uses a classifier technique for configuring the model to perform predictions. The specification for this case mentions a gradient boosting machine (not a loss function.)
Sampat fails to explicitly disclose this limitation:
“…with the pretrained machine-learned model”
However, Lee discloses this limitation, ([2018, Pg. 1, paragraph 0016] – “In another aspect of the present invention, there is provided a transfer learning method for a deep neural network including: a pre-trained model storing step of storing a plurality of pre-trained models that are deep neural network models learned using a plurality of pre-training datasets; a transfer learning data input step of inputting transfer learning data; a pre-trained model selecting step of selecting a pre-trained model corresponding to the input transfer learning data from among the plurality of stored pre-trained models; and a transfer learning step of generating a plurality of transfer learning models by performing transfer learning using the selected pre-trained model and the transfer learning data.”, Lee discloses a selection of pre-trained machine learning models that can be stored and accessed.)
Lee and Sampat are analogous art and in the same field of invention because both references pertain to creating and training machine learning models with domain data that relates to a specific area of knowledge or topic. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Lee to include a selection of pretrained machine learning models associated with a pretrained chatbot. One would have been motivated to make such combination to improve the generation of an expert chatbot by having a selection of pre-trained models that can be accessed by the user where the system is further improved through transfer learning for better accuracy and reducing the need for extensive data during training.
Sampat fails to explicitly disclose this limitation:
“evaluating a loss function based on the prediction; and wherein the one or more parameters of the pretrained machine-learned model is modified based on the loss function.”.
However, Polleri teaches this limitation, ([2021, paragraph 0073] – “The third user input can include training metrics. The training metrics help evaluate the performance of the model. Example training metrics can include classification accuracy, logarithmic loss, area under curve, F1 Score, mean absolute error, and mean squared error. The accuracy metric is a ratio of the number of correct predictions divided by the number of predictions made. The logarithmic loss metric works by penalizing false classifications. Area Under Curve (AUC) can be used for binary classification problem. AUC of a classifier is equal to the probability that the classifier will rank a randomly chosen positive example higher than a randomly chosen negative example. F1 Score is used to measure a test's accuracy. F1 Score is the Harmonic Mean between precision and recall. The range for F1 Score is [0, 1]. F1 Score can inform the user how precise a classifier is (how many instances it classifies correctly), as well as how robust it is (it does not miss a significant number of instances).”, Sampat discloses the process of using the interactions of the user and historical data as input parameters to the machine learning model to faciliate interaction. Polleri provides a loss function that can be implemented in a machine learning model.)
Polleri and Sampat are analogous art and in the same field of invention because both references pertain to creating and training machine learning models while improving chatbot responses. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to use Sampat as modified by Lee in view of Polleri to optimize the training of machine learning models by including a loss function and an output related to evaluating the loss function on a user interface. One would have been motivated to make such combination to improve the responses of the chatbot when interacting with the user while implementing metrics of output information pertaining to the process of training the machine learning model.
As per claim 15, Sampat in view of Polleri fails to disclose the following limitation:
“the pretrained machine-learned model of the selected chatbot”
However, Lee discloses this limitation, ([2018, Pg. 1, paragraph 0016] – “In another aspect of the present invention, there is provided a transfer learning method for a deep neural network including: a pre-trained model storing step of storing a plurality of pre-trained models that are deep neural network models learned using a plurality of pre-training datasets; a transfer learning data input step of inputting transfer learning data; a pre-trained model selecting step of selecting a pre-trained model corresponding to the input transfer learning data from among the plurality of stored pre-trained models; and a transfer learning step of generating a plurality of transfer learning models by performing transfer learning using the selected pre-trained model and the transfer learning data.”, Lee discloses a selection of pre-trained machine learning models that can be stored and accessed.)
Lee and Sampat are analogous art and in the same field of invention because both references pertain to creating and training machine learning models with domain data that relates to a specific area of knowledge or topic. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Lee to include a selection of pretrained machine learning models associated with a pretrained chatbot. One would have been motivated to make such combination to improve the generation of an expert chatbot by having a selection of pre-trained models that can be accessed by the user where the system is further improved through transfer learning for better accuracy and reducing the need for extensive data during training.
Sampat in view of Lee fails to disclose the following limitation:
“The computing system of claim 1, wherein…..includes a first variable, and wherein the user-specific data accessed is based on the first variable.”
However, Polleri discloses this limitation, ([2021, Pg. 13, paragraph 0161] – “Context section—The skill bot designer can define variables that are used in a conversation flow in the context section. Other variables that may be named in the context section include, without limitation: variables for error handling, variables for built-in or custom entities, user variables that enable the skill bot to recognize and persist user preferences, and the like.”, Sampat teaches a platform for chatbots and user interaction with data access. Polleri discloses variables that contain attributes of data that are recognized by a chatbot.)
Polleri and Sampat are analogous art and in the same field of invention because both references pertain to machine learning and chatbots. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Polleri to include variables that can access user data. One would have been motivated to make such combination to improve the chatbot system to maintain context of conversations and data through variables while the dynamic information from the variables provides relevant responses to the user.
As per claim 16, Sampat teaches this part of the limitation:
The computing system of claim 15, the operations comprising: providing the user-specific data that is accessed… ([2021, Pg. 13, paragraph 0161] – “For example, the chatbot generator platform may configure the chatbot to access one or more platforms (e.g., a natural language processing platform, an application platform, an authorization platform, a user interface platform, a spell check platform, a speech-to-text (STT) platform, a QnA platform, and/or the like) that are associated with the services to permit the chatbot to interact with a user during operation. In some implementations, to permit the chatbot to utilize and/or access the one or more platforms, the chatbot generator platform may configure one or more APIs for the chatbot when building the chatbot. Such APIs may be configured to permit the chatbot to automatically interact with and/or exchange information (e.g., via API calls) between such platforms to enable operation of the chatbot during an interaction with a user. For example, an API call to a QnA platform may be made to request and/or configure a QnA pair according to the chatbot information.”, Sampat teaches a platform for a chatbot and user interaction with data access.)
wherein the provided user-specific data modifies the one or more parameters of the pretrained machine-learned model to generate the customized machine-learned model. ([2021, Pg. 13, paragraph 0161] – “In some implementations, the chatbot generator platform may use a machine learning model, such as a QnA generator model, to generate a set of QnA pairs for the chatbot. For example, the chatbot generator platform may train the QnA generator model based on one or more QnA generation parameters, such as the format of the chatbot, the topic of the chatbot, the context of the chatbot, the chatbot corpus, and/or the like. The chatbot generator platform may train the QnA generator model using historical data associated with generating sets of QnA pairs according to the one or more QnA generation parameters. Using the historical data to train the QnA generator model and the one or more QnA generation parameters as inputs to the QnA generator model, the chatbot generator platform may generate a most useful set of QnA pairs (according to the trained QnA generation model) to enable the chatbot to determine a most accurate response to a user and/or from the user (according to the trained QnA generation model) in order to correspondingly facilitate the interaction, as described herein.”, Sampat teaches an operation to modify parameters of the QnA model to generate an improved model for the chatbot.)
However, Polleri discloses this part of the limitation:
…based on the first variable to the selected chatbot ([2021, Pg. 13, paragraph 0161] – “Context section—The skill bot designer can define variables that are used in a conversation flow in the context section. Other variables that may be named in the context section include, without limitation: variables for error handling, variables for built-in or custom entities, user variables that enable the skill bot to recognize and persist user preferences, and the like.”, Polleri discloses variables used for parameters in a machine learning model.)
Polleri and Sampat are analogous art and in the same field of invention because both references pertain to machine learning and chatbots. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Polleri to use variables for user data access and modifying the parameters of a pretrained machine learning model to generate a customized machine learning model. One would have been motivated to make such combination to improve the process of developing a trained and customized machine learning model with user data and context specific knowledge for personalized interactions.
As per claim 18, Sampat teaches this part of the limitation:
The computing system of claim 15, wherein the one or more parameters of the pretrained machine-learned model is modified… ([C11, particularly L28 - 40] – “In some implementations, the chatbot generator platform may use a machine learning model, such as a QnA generator model, to generate a set of QnA pairs for the chatbot. For example, the chatbot generator platform may train the QnA generator model based on one or more QnA generation parameters, such as the format of the chatbot, the topic of the chatbot, the context of the chatbot, the chatbot corpus, and/or the like. The chatbot generator platform may train the QnA generator model using historical data associated with generating sets of QnA pairs according to the one or more QnA generation parameters. Using the historical data to train the QnA generator model and the one or more QnA generation parameters as inputs to the QnA generator model, the chatbot generator platform may generate a most useful set of QnA pairs (according to the trained QnA generation model) to enable the chatbot to determine a most accurate response to a user and/or from the user (according to the trained QnA generation model) in order to correspondingly facilitate the interaction, as described herein.”, Sampat teaches the modification of the machine learning model parameters to create a customized model..)
However, Polleri teaches this part of the limitation:
…based on the first attribute to generate the customized machine-learned model. ([2021, Pg. 13, paragraph 0161] – “Context section—The skill bot designer can define variables that are used in a conversation flow in the context section. Other variables that may be named in the context section include, without limitation: variables for error handling, variables for built-in or custom entities, user variables that enable the skill bot to recognize and persist user preferences, and the like.” and [2021, Pg. 7, paragraph 0097] – “At 320, the functionality includes providing controls to adjust model. The controls can be executed through a Chatbot, a graphical user interface, or one or more user selectable menus. Controls allow a user to adjust the outcome of the model by adjusting the variables used for the selected algorithm. In various embodiments, the control display the outcome values as the model is adjusted.”, Polleri provides the variables that are attributes to the user and both references disclose modifying the parameters for a customized machine learning model.)
Polleri and Sampat are analogous art and in the same field of invention because both references pertain to machine learning and chatbots. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Polleri to incorporate a first attribute and using the first attribute to modify parameters of a pretrained model to generate a customized machine learning model. One would have been motivated to make such combination to improve the adaptation of the pre-trained model for better accuracy based on the desired attribute for accuracy and personalization of responses by the chatbot.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sampat et al (US 11113475 B2), hereinafter referenced as Sampat, in view of Lee et al., (US 20230259761 A1), hereinafter referenced as Lee, in view of Gatti (US 20180025726 A1), hereinafter referenced as Gatti.
As per claim 6, Sampat in view of Lee in view of Polleri fails to disclose the following limitations:
The computing system of claim 3, wherein the operations further comprise: processing the inference input with a second expert chatbot to generate a second prediction, and providing, on the user interface of the user device, the second prediction as a second output, and wherein the first output and the second output are presented concurrently on the user interface.
However, Gatti discloses this limitation: ([2018, Pg. 4, paragraph 0007] – “Exemplary embodiments of the present disclosure provide a system and method to create coordinated multi-chatbots using natural dialog systems. Embodiments of the disclosure use a distributed and decentralized conceptual framework and cookbook for creating a hybrid rule and machine learning-based system where the coordination rules can be manually defined or learned using machine learning algorithms. A framework according to an embodiment can define the entities, relationships and behaviors needed for the creation of coordinated multi-chatbots that react or pro-actively act using natural dialogue. Further embodiments provide one or more chatbots with the role of mediator in a chat group and the mediator chatbot can invite one or more chatbots into the chat group while interacting with users based on users' utterances. A mediator according to an embodiment can also redirect topics based on users' utterances and to enforce that the chatbots only send allowed messages.”, Gatti discloses a group chat with one or more chatbots, that can interact with the user or more than one user and chatbot or with one or more chatbots. The group chat of chat bots Gatti reference can facilitate the predictions made by the Sampat reference in this limitation while outputting the information on the user interface.)
Gatti and Sampat are analogous art and in the same field of invention because both references pertain to training machine learning models and chatbots with user interactions. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Gatti to have more than one chatbot operating concurrently on one screen to facilitate and create predictions. One would have been motivated to make such combination to improve user experience by including a multi-chatbot coordinated system to allow multiple responses with different variations based on the request for the user.
Claim(s) 7, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sampat et al (US 11113475 B2), hereinafter referenced as Sampat, in view of Lee et al., (US 20230259761 A1), hereinafter referenced as Lee, in view of Kaizer et al., (US 10904212 B1), hereinafter reference as Kaizer.
As per claim 7, Sampat discloses this part of the limitation:
“and wherein the user-specific data includes the historical data.” ([Column 13, particularly L7 - 24] – “For example, the chatbot generator platform may configure the chatbot to access one or more platforms (e.g., a natural language processing platform, an application platform, an authorization platform, a user interface platform, a spell check platform, a speech-to-text (STT) platform, a QnA platform, and/or the like) that are associated with the services to permit the chatbot to interact with a user during operation. In some implementations, to permit the chatbot to utilize and/or access the one or more platforms, the chatbot generator platform may configure one or more APIs for the chatbot when building the chatbot. Such APIs may be configured to permit the chatbot to automatically interact with and/or exchange information (e.g., via API calls) between such platforms to enable operation of the chatbot during an interaction with a user. For example, an API call to a QnA platform may be made to request and/or configure a QnA pair according to the chatbot information”, Sampat discloses the requesting operation, a cloud environment, and authorization platform that allows the chatbot to interact with the user and access related data.)
However, Kaizer discloses this part of the limitation:
“accessing, based on the authorization, the historical data associated with the first user,” ([Column 6, particularly L45 - 51] – “According to some embodiments, at 208 the chatbot 204 may request permission of user 202 to access a chat history of user 202. The chat history may include a history of chatroom messages, sent to and from user 202, within the chatroom of chatbot 204, or within another chatroom. Whether the present or a different chatroom, chatbot 204 may request credentials from user 202, e.g., a username and password to log into the chatroom or confirm access authorization. Such credentials may be retrieved automatically according to some embodiments, e.g., using OAuth.”, Kaizer discloses the authorization process with required login credentials to access related data.)
Kaizer and Sampat are analogous art and in the same field of invention because both references pertain to chatbots. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Kaizer to apply an authorization process during the user request for the chatbot to obtain historical data associated with the user. One would have been motivated to make such combination to improve the access control and security to selectively regulate how the system can use historical data from the user. Another improvement from the combination of Kaizer and Sampat is the learning of the chatbot by accessing related historical data from the user to generate more accurate and specific responses based on the user requests.
As per claim 8, Sampat fails to explicitly disclose the following limitation:
“The computing system of claim 1, wherein the request includes login credentials for a social media account of the first user, the operations comprise: accessing, using the login credentials, social media data of the first user, and wherein the user-specific data includes the social media data of the first user.”
However, Kaizer discloses this limitation, ([Column 6, particularly L45 - 51] – “According to some embodiments, at 208 the chatbot 204 may request that user 202 provide credentials, such as a user name and password, for an account of user 202 with a third-party platform, such as a social media profile or an email account. Other types of accounts are also possible, not limited to accounts with social media websites and email accounts.”, Sampat discloses a request from a user to create a chatbot. Kaiser discloses a chatbot that requests username and password credentials for a user account and social media data.)
Kaizer and Sampat are analogous art and in the same field of invention because both references pertain to chatbots. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Kaizer to include login credentials for a social media account of the user during the user request. The accessed social media data can provide user interests, preferences, and real time insights to user behaviors for training the chatbot. One would have been motivated to make such combination to improve the data collection of the chatbot system and apply additional learning for providing more effective responses based on data pertaining to their social media accounts.
Claim(s) 10 - 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sampat et al (US 11113475 B2), hereinafter referenced as Sampat, in view of Lee et al., (US 20230259761 A1), hereinafter referenced as Lee, in view of Kelly et al., (US 20250355901 A1), hereinafter reference as Kelly.
As per claim 10, Sampat in view of Lee in view of Polleri fails to explicitly disclose the following limitation:
“The computing system of claim 1, wherein the request includes a website that is selected by the first user, and wherein the domain-specific data includes data obtained from the website.”
However, Kelly discloses this limitation, ([2025, Pg. 40, paragraph 0461] – “Social media platforms 3840 may include various social media websites and/or applications, such as TWITTER, REDDIT, FACEBOOK, YOUTUBE, and other such sites, various news sites (e.g., CNN, FOX, etc., video news channels on YOUTUBE, or any other source of news), various forums, various commenting mediums or blogging platforms, and/or other sources of social data. The social media platforms 3840 may provide social media data, either directly through social media websites and/or applications, or via various application programming interfaces (APIs). In some embodiments, the data collection system 3812 of the computing platform 3800 may interface with the social media platforms 3840 to obtain social media data, for example, by calling APIs provided by the social media platforms 3840.”, Sampat discloses the chatbot generator. Kelly discloses obtaining data from various websites such as Twitter, Facebook, YouTube, and other website mediums.)
Kelly and Sampat are analogous art and in the same field of invention because both references pertain to machine learning and data collection for training data. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Kelly to include a website selected by the first user and the obtained data from the website is associated with the domain data of the request. One would have been motivated to make such combination to improve data collection to enhance the training and learning process of the chatbot with website data that pertain to the domain requested by the user.
As per claim 11, Sampat fails to explicitly disclose the following limitation:
“The computing system of claim 1, wherein the request includes an audio sharing platform that is selected by the first user, and wherein the domain-specific data includes audio data obtained from an audio sharing platform that is associated with the domain.”
However, Kelly discloses this limitation, ([2025, Pg. 40, paragraph 0461] – “Social media platforms 3840 may include various social media websites and/or applications, such as TWITTER, REDDIT, FACEBOOK, YOUTUBE, and other such sites, various news sites (e.g., CNN, FOX, etc., video news channels on YOUTUBE, or any other source of news), various forums, various commenting mediums or blogging platforms, and/or other sources of social data. The social media platforms 3840 may provide social media data, either directly through social media websites and/or applications, or via various application programming interfaces (APIs). In some embodiments, the data collection system 3812 of the computing platform 3800 may interface with the social media platforms 3840 to obtain social media data, for example, by calling APIs provided by the social media platforms 3840.”, Sampat discloses the chatbot generator. Kelly discloses obtaining data from various websites such as twitter, facebook, youtube, and other website mediums that all have audio sharing capabilities.)
Kelly and Sampat are analogous art and in the same field of invention because both references pertain to machine learning and data collection for training data. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Kelly to include an audio sharing platform selected by the user and associating the obtained data from the audio sharing platform with the domain data from the request. One would have been motivated to make such combination to improve data collection to enhance the training and learning process of the chatbot with an audio sharing platform data insights that pertain to the domain requested by the user.
As per claim 12, Sampat fails to explicitly disclose the following limitation:
“The computing system of claim 1, wherein the request includes a video sharing platform that is selected by the first user, and wherein the domain-specific data includes media data obtained from a video sharing platform that is associated with the domain.”
However, Kelly discloses this limitation, ([2025, Pg. 40, paragraph 0461] – “Social media platforms 3840 may include various social media websites and/or applications, such as TWITTER, REDDIT, FACEBOOK, YOUTUBE, and other such sites, various news sites (e.g., CNN, FOX, etc., video news channels on YOUTUBE, or any other source of news), various forums, various commenting mediums or blogging platforms, and/or other sources of social data. The social media platforms 3840 may provide social media data, either directly through social media websites and/or applications, or via various application programming interfaces (APIs). In some embodiments, the data collection system 3812 of the computing platform 3800 may interface with the social media platforms 3840 to obtain social media data, for example, by calling APIs provided by the social media platforms 3840.”, Sampat discloses the chatbot generator. Kelly discloses obtaining data from Facebook, Twitter, Reddit, and Youtube, which are all platforms that include the capabilities of video sharing.)
Kelly and Sampat are analogous art and in the same field of invention because both references pertain to machine learning and data collection for training data. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Kelly to include a video sharing platform selected by the user and associating the obtained data from the video sharing platform with the domain data from the request. One would have been motivated to make such combination to improve data collection to enhance the training and learning process of the chatbot with a video sharing platform data insights that pertain to the domain requested by the user.
As per claim 13, Sampat fails to explicitly disclose the following limitation:
“The computing system of claim 1, wherein the request includes a social media platform that is selected by the first user, and wherein the domain-specific data includes public data obtained from a social media platform that is associated with the domain.”
However, Kelly discloses this limitation, ([2025, Pg. 40, paragraph 0461] – “Social media platforms 3840 may include various social media websites and/or applications, such as TWITTER, REDDIT, FACEBOOK, YOUTUBE, and other such sites, various news sites (e.g., CNN, FOX, etc., video news channels on YOUTUBE, or any other source of news), various forums, various commenting mediums or blogging platforms, and/or other sources of social data. The social media platforms 3840 may provide social media data, either directly through social media websites and/or applications, or via various application programming interfaces (APIs). In some embodiments, the data collection system 3812 of the computing platform 3800 may interface with the social media platforms 3840 to obtain social media data, for example, by calling APIs provided by the social media platforms 3840.”, Sampat discloses the chatbot generator. Kelly discloses obtaining data from Facebook, Twitter, Reddit, and YouTube. Both Facebook and Twitter are social media platforms that have accessible data based on the user request.)
Kelly and Sampat are analogous art and in the same field of invention because both references pertain to machine learning and data collection for training data. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Kelly to include a social media platform selected by the user and associating the obtained data from the social media platform with the domain data from the request. One would have been motivated to make such combination to improve data collection to enhance the training and learning process of the chatbot with a social media sharing platform data insights that pertain to the domain requested by the user.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sampat et al (US 11113475 B2), hereinafter referenced as Sampat, in view of Lee et al., (US 20230259761 A1), hereinafter referenced as Lee, in view of Polleri et al., (US 20210081819 A1), hereinafter referenced as Polleri, in view of Gustafson et al., US 9847084 B2, hereinafter referenced as Gustafson.
As per claim 17, Sampat in view of Lee fails to explicitly disclose the following limitation:
“The computing system of claim 15, wherein the first variable is an attribute of the first user, and wherein the attribute is a location, an age, or a language associated with the first user.”
However, Polleri teaches this part of the limitation:
wherein the first variable is an attribute of the first user, ([2021, Pg. 13, paragraph 0161] – “Context section—The skill bot designer can define variables that are used in a conversation flow in the context section. Other variables that may be named in the context section include, without limitation: variables for error handling, variables for built-in or custom entities, user variables that enable the skill bot to recognize and persist user preferences, and the like.”, Polleri discloses that a variable is an attribute to the user with the corresponding data associated with the user.)
Polleri and Sampat are analogous art and in the same field of invention because both references pertain to machine learning and chatbots. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Polleri to implement a variable with attributes that pertain to the user. One would have been motivated to make such combination to use variables that access user data to improve the chatbot regarding training for patterns and predictions based on the data with context specific knowledge for personalized interactions.
However, Sampat in view of Lee in view of Polleri fails to teach this part of the limitation:
and wherein the attribute is a location, an age, or a language associated with the first user.
Gustafon teaches this part of the limitation:
([C11, particularly L28 - 40] – “In some embodiments, the scoring module 142 further extracts other data attributes disclosed from the input, such as (without limitation) one or more of distress level, life events, engagement, state of mind, distress, purpose of contact/task, demographic data (race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, credit score, gender), or a combination thereof. In various embodiments, these other data attributes are combined with the personality type of the user 102 to determine the best response or output to provide the user 102. In one embodiment, these attributes are continually updated based on the inputs received so that a real-time response is provided.”, Gustafon provides attribute data of demographics.)
Gustafon and Sampat in view of Polleri are analogous art and in the same field of invention because both references pertain to machine learning and an application of chatbots. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify Sampat in view of Gustafon to include demographics such as location, age, and language as attributes. One would have been motivated to make such combination to improve the machine learned chatbot personalization through the attributes with operations of storing, updating, and recalling user-specific data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Liu et al., US 20190370629 A1, this reference discloses a gossip group of domains specific chatbots. Liu et al., is relevant to this case regarding the use of chatbots trained in a domain or area of knowledge and an application of machine learning. However, the reference was not relied upon as prior art.
Bax et al., US 20250385879 A1 this reference discloses systems and methods for chatbots. Bax et al., is relevant to this case regarding the use of chatbots with machine learning and data gathering for identifying engagements between a user and a chatbot. However, the reference was not relied upon as prior art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN OLIVER T DE GUZMAN whose telephone number is (571)272-9341. The examiner can normally be reached Monday-Friday 7:30 am - 5:00 pm, Alternate Fridays off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN OLIVER T DE GUZMAN/Examiner, Art Unit 2123 /ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123