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 .
Status of Claims
The present application is being examined under the claims filed on 10/17/2023.
Claims 1-20 are rejected.
Claims 1-20 are pending.
Specification
The specification filed on 10/17/2023 is acceptable for examination purposes.
Drawings
The drawings filed on 10/17/2023 are acceptable for examination purposes.
Claim Objections
Claims 13 and 20 are objected to because of the following informalities:
In claim 13, line 2, “comprising deep” should read “comprising the deep machine learning classification problem” to be consistent with similar limitation in claim 4.
In claim 13, line 3, “comprising deep natural language machine learning model” should read “comprising a deep natural language machine learning model.”
In claim 20, line 13, “comprising deep” should read “comprising the deep machine learning classification problem” to be consistent with similar limitation in claim 4.
In claim 20, line 14, “comprising deep natural language machine learning model” should read “comprising a deep natural language machine learning model.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention.
Claims 1 and 20 recite the limitation "the shallow-deep learning classifier" without any prior recitation of such a shallow-deep learning classifier in Independent Claim 1 and Independent Claim 20. There is insufficient antecedent basis for this limitation in the claim.
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.
Regarding Claim 1,
Step 1: Claim 1 is a method claim. Therefore, Claims 1-13 are directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
generating an input vector by performing vectorization on the natural language query (mental process - generating an input vector by performing vectorization on the natural language query may be performed manually by a user with the aid of pen and paper by observing/performing vectorization on the natural language query. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
receiving a natural language query from a user interface of a chatbot (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
inputting the input vector to a shallow-deep classifier, wherein the shallow-deep learning classifier comprises a classification machine learning model programmed to classify the input vector as being one of a shallow machine learning classification problem and a deep machine learning classification problem (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
outputting, by the shallow-deep classifier, an output label, wherein the output label comprises one of the shallow machine learning classification problem and the deep machine learning classification problem (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
receiving a natural language query from a user interface of a chatbot (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
inputting the input vector to a shallow-deep classifier, wherein the shallow-deep learning classifier comprises a classification machine learning model programmed to classify the input vector as being one of a shallow machine learning classification problem and a deep machine learning classification problem (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
outputting, by the shallow-deep classifier, an output label, wherein the output label comprises one of the shallow machine learning classification problem and the deep machine learning classification problem (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-13. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2,
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 2 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
responsive to the output label comprising the shallow machine learning classification problem, inputting the input vector to a topic classifier (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
outputting, by the topic classifier, a topic of the natural language query (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
responsive to the output label comprising the shallow machine learning classification problem, inputting the input vector to a topic classifier (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
outputting, by the topic classifier, a topic of the natural language query (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 3,
Step 2A Prong 1:
selecting, based on the topic, a selected chatbot from among a plurality of chatbots (mental process - selecting, based on the topic, a selected chatbot from among a plurality of chatbots may be performed manually by a user with the aid of pen and paper by observing/analyzing the topic. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
inputting the input vector to the selected chatbot (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
returning a chatbot response (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
inputting the input vector to the selected chatbot (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
returning a chatbot response (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 4,
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 4 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
inputting, responsive to the output label comprising the deep machine learning classification problem, the input vector to a deep classifier comprising a deep natural language machine learning model (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
outputting, by the deep classifier, an intent classification that represents an intent of the natural language query (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
inputting, responsive to the output label comprising the deep machine learning classification problem, the input vector to a deep classifier comprising a deep natural language machine learning model (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
outputting, by the deep classifier, an intent classification that represents an intent of the natural language query (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 5,
Step 2A Prong 1:
See the rejection of Claim 4 above, which Claim 5 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
transmitting the natural language query and the intent classification to a display device of an agent (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
transmitting the natural language query and the intent classification to a display device of an agent (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 6,
Step 2A Prong 1:
classifying, by the topic classifier and using the input vector, a topic of the natural language query (mental process – classifying, by the topic classifier and using the input vector, a topic of the natural language query may be performed manually by a user with the aid of pen and paper by observing/analyzing the topic classifier and the input vector. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
transmitting the input vector and the intent classification to a topic classifier comprising a topic classification machine learning model (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
transmitting the input vector and the intent classification to a topic classifier comprising a topic classification machine learning model (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 7,
Step 2A Prong 1:
generating, automatically and based on the intent classification and the topic, a chatbot response to the natural language query (mental process – generating, automatically and based on the intent classification and the topic, a chatbot response to the natural language query may be performed manually by a user with the aid of pen and paper by observing/analyzing the intent classification and the topic. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
transmitting the chatbot response to a user device (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
transmitting the chatbot response to a user device (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 8,
Step 2A Prong 1:
classifying, by the topic classification machine learning model, a topic of the natural language query (mental process – classifying, by the topic classification machine learning model, a topic of the natural language query may be performed manually by a user with the aid of pen and paper by observing/analyzing the topic classification machine learning model. See MPEP 2106.04(a)(2)(III)(C).)
selecting, from among a plurality of chatbots and based on the topic, a selected chatbot (mental process – selecting, from among a plurality of chatbots and based on the topic, a selected chatbot may be performed manually by a user with the aid of pen and paper by observing/analyzing a plurality of chatbots and the topic. See MPEP 2106.04(a)(2)(III)(C).)
generating, automatically by the selected chatbot and based on the intent classification of the natural language query, a chatbot response to the natural language query (mental process – generating, automatically by the selected chatbot and based on the intent classification of the natural language query, a chatbot response to the natural language query may be performed manually by a user with the aid of pen and paper by observing/analyzing the selected chatbot and the intent classification of the natural language query. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
transmitting the input vector to a topic classifier comprising a topic classification machine learning model (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
transmitting the chatbot response to a user device (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
transmitting the input vector to a topic classifier comprising a topic classification machine learning model (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
transmitting the chatbot response to a user device (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 9,
Step 2A Prong 1:
generating a weighted classification by applying a weight to the intent classification (mental process – generating a weighted classification by applying a weight to the intent classification may be performed manually by a user with the aid of pen and paper by observing/applying a weight to the intent classification. See MPEP 2106.04(a)(2)(III)(C).)
generating a comparison by comparing the weighted classification to a threshold (mental process – generating a comparison by comparing the weighted classification to a threshold may be performed manually by a user with the aid of pen and paper by observing/comparing the weighted classification to a threshold. See MPEP 2106.04(a)(2)(III)(C).)
routing the intent classification based on the comparison (mental process – routing the intent classification based on the comparison may be performed manually by a user with the aid of pen and paper by observing/analyzing the comparison. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2 & Step 2B:
There are no additional elements.
Regarding Claim 10,
Step 2A Prong 1:
See the rejection of Claim 9 above, which Claim 10 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
responsive to the comparison failing to satisfy the threshold, transmitting the natural language query and the intent classification to a display device of an agent (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
responsive to the comparison failing to satisfy the threshold, transmitting the natural language query and the intent classification to a display device of an agent (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 11,
Step 2A Prong 1:
generating, automatically using the topic classifier, a chatbot response to the natural language query (mental process - generating, automatically using the topic classifier, a chatbot response to the natural language query may be performed manually by a user with the aid of pen and paper by observing/analyzing the topic classifier. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
responsive to the comparison satisfying the threshold, transmitting the input vector and the intent classification to a topic classifier comprising a topic classification machine learning model (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
transmitting the chatbot response to a user device (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
responsive to the comparison satisfying the threshold, transmitting the input vector and the intent classification to a topic classifier comprising a topic classification machine learning model (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
transmitting the chatbot response to a user device (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 12,
Step 2A Prong 1:
classifying, by the topic classifier, a topic of the natural language query (mental process - classifying, by the topic classifier, a topic of the natural language query may be performed manually by a user with the aid of pen and paper by observing/analyzing the topic classifier. See MPEP 2106.04(a)(2)(III)(C).)
selecting, from among a plurality of chatbots and based on the topic, a selected chatbot (mental process – selecting, from among a plurality of chatbots and based on the topic, a selected chatbot may be performed manually by a user with the aid of pen and paper by observing/analyzing a plurality of chatbots and the topic. See MPEP 2106.04(a)(2)(III)(C).)
generating, automatically by the selected chatbot and based on the intent classification of the natural language query, a chatbot response to the natural language query (mental process – generating, automatically by the selected chatbot and based on the intent classification of the natural language query, a chatbot response to the natural language query may be performed manually by a user with the aid of pen and paper by observing/analyzing the selected chatbot and the intent classification of the natural language query. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
responsive to the comparison satisfying the threshold, transmitting the input vector and the intent classification to a topic classifier comprising a topic classification machine learning model (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
transmitting the chatbot response to a user device (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
responsive to the comparison satisfying the threshold, transmitting the input vector and the intent classification to a topic classifier comprising a topic classification machine learning model (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
transmitting the chatbot response to a user device (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 13,
Step 2A Prong 1:
generating a weighted classification by applying a weight to the intent classification (mathematical concept - generating a weighted classification by applying a weight to the intent classification may be performed by mathematical process, multiplying the intent classification by a weight. See MPEP 2106.04(a)(2)(I)(C).)
generating a comparison by comparing the weighted classification to a threshold (mental process - generating a comparison by comparing the weighted classification to a threshold may be performed manually by a user with the aid of pen and paper by observing/comparing the weighted classification to a threshold. See MPEP 2106.04(a)(2)(III)(C).)
classifying, by the topic classifier, a topic of the natural language query (mental process - classifying, by the topic classifier, a topic of the natural language query may be performed manually by a user with the aid of pen and paper by observing/analyzing the topic classifier. See MPEP 2106.04(a)(2)(III)(C).)
selecting, from among a plurality of chatbots and based on the topic, a selected chatbot (mental process - selecting, from among a plurality of chatbots and based on the topic, a selected chatbot may be performed manually by a user with the aid of pen and paper by observing/analyzing a plurality of chatbots and the topic. See MPEP 2106.04(a)(2)(III)(C).)
generating, automatically by the selected chatbot and based on the intent classification of the natural language query, a chatbot response to the natural language query (mental process - generating, automatically by the selected chatbot and based on the intent classification of the natural language query, a chatbot response to the natural language query may be performed manually by a user with the aid of pen and paper by observing/analyzing the selected chatbot and the intent classification of the natural language query. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
inputting, responsive to the output label comprising deep, the input vector to a deep classifier comprising deep natural language machine learning model (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
outputting, by the deep classifier, an intent classification that represents an intent of the natural language query (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
transmitting, responsive to the comparison satisfying the threshold, the input vector and the intent classification to a topic classifier comprising a topic classification machine learning model (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
transmitting the chatbot response to a user device (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
inputting, responsive to the output label comprising deep, the input vector to a deep classifier comprising deep natural language machine learning model (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
outputting, by the deep classifier, an intent classification that represents an intent of the natural language query (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
transmitting, responsive to the comparison satisfying the threshold, the input vector and the intent classification to a topic classifier comprising a topic classification machine learning model (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
transmitting the chatbot response to a user device (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 14,
Step 1: Claim 14 is a system claim. Therefore, Claims 14-19 are directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
generate the output label by executing the shallow-deep classifier on the input vector (mental process – generating the input vector by performing vectorization on the natural language query may be performed manually by a user with the aid of pen and paper by observing/analyzing the shallow-deep classifier and the input vector. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
a processor (recited at a high-level of generality (i.e., as generic a processor) such that it amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f).)
a data repository in communication with the processor and storing: a natural language query, an input vector, an output label comprising one of a shallow machine learning classification problem and a deep machine learning classification problem (recited at a high-level of generality (i.e., as generic a data repository) such that it amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f).)
a shallow-deep classifier executable by the processor, wherein the shallow-deep classifier comprises a classifier machine learning model programmed to determine whether the natural language query represents the shallow machine learning classification problem or the deep machine learning classification problem (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).)
a server controller which, when executed by the processor, is programmed to receive the natural language query (recited at a high-level of generality (i.e., as generic a server controller) such that it amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f).)
generate the input vector by performing vectorization on the natural language query (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).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
a processor (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
a data repository in communication with the processor and storing: a natural language query, an input vector, an output label comprising one of a shallow machine learning classification problem and a deep machine learning classification problem (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
a shallow-deep classifier executable by the processor, wherein the shallow-deep classifier comprises a classifier machine learning model programmed to determine whether the natural language query represents the shallow machine learning classification problem or the deep machine learning classification problem (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
a server controller which, when executed by the processor, is programmed to receive the natural language query (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
generate the input vector by performing vectorization on the natural language query (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
For the reasons above, Claim 14 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 15-19. The additional limitations of the dependent claims are addressed below.
Regarding Claim 15,
Step 2A Prong 1:
generate the intent classification (mental process – generating the intent classification may be performed manually by a user with the aid of pen and paper by observing/analyzing the deep classifier. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
a deep classifier executable by the processor, wherein the deep classifier is trained to take, as input, the input vector and to generate, as output, an intent classification of the natural language query (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).)
wherein the server controller is further programmed to execute the deep classifier (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).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
a deep classifier executable by the processor, wherein the deep classifier is trained to take, as input, the input vector and to generate, as output, an intent classification of the natural language query (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
wherein the server controller is further programmed to execute the deep classifier (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Regarding Claim 16,
Step 2A Prong 1:
generate a weighted classification by applying a weight to the intent classification (mathematical concept - generating a weighted classification by applying a weight to the intent classification may be performed by mathematical process, multiplying the intent classification by a weight. See MPEP 2106.04(a)(2)(I)(C).)
generate a comparison by comparing the weighted classification to a threshold (mental process – generating a comparison by comparing the weighted classification to a threshold may be performed manually by a user with the aid of pen and paper by observing/comparing the weighted classification to a threshold. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the intent classification comprises the deep machine learning classification problem (recited at a high-level of generality (i.e., as generic the intent classification) such that it amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f).)
wherein the server controller is further programmed to (recited at a high-level of generality (i.e., as generic the server controller) such that it amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f).)
route the intent classification based on the comparison (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the intent classification comprises the deep machine learning classification problem (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
wherein the server controller is further programmed to (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
route the intent classification based on the comparison (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 17,
Step 2A Prong 1:
See the rejection of Claim 16 above, which Claim 17 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the server controller is further programmed to perform one of: routing the intent classification and the natural language query to a display device of an agent (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).)
route the intent classification and the input vector to a topic classifier (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the server controller is further programmed to perform one of: routing the intent classification and the natural language query to a display device of an agent (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
route the intent classification and the input vector to a topic classifier (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Regarding Claim 18,
Step 2A Prong 1:
generate the topic by executing the topic classifier on the input vector (mental process – generating the topic by executing the topic classifier on the input vector may be performed manually by a user with the aid of pen and paper by observing/analyzing the topic classifier and the input vector. See MPEP 2106.04(a)(2)(III)(C).)
select, based on the topic, the selected chatbot (mental process – selecting, based on the topic, the selected chatbot may be performed manually by a user with the aid of pen and paper by observing/analyzing the topic and the selected chatbot. See MPEP 2106.04(a)(2)(III)(C).)
generate, by executing the selected chatbot on the input vector, a chatbot response (mental process – generating, by executing the selected chatbot on the input vector, a chatbot response may be performed manually by a user with the aid of pen and paper by observing/analyzing the selected chatbot and the input vector. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
a topic classifier executable by the processor, wherein the topic classifier is trained to take, as input, the input vector and to generate, as output, a topic of the natural language query (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).)
a selected chatbot selected from among a plurality of chatbots, wherein the plurality of chatbots comprise natural language processing machine learning models (recited at a high-level of generality (i.e., as generic a chatbot) such that it amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f).)
wherein the server controller is further programmed to (recited at a high-level of generality (i.e., as generic the server controller) such that it amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f).)
returning the chatbot response (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
a topic classifier executable by the processor, wherein the topic classifier is trained to take, as input, the input vector and to generate, as output, a topic of the natural language query (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
a selected chatbot selected from among a plurality of chatbots, wherein the plurality of chatbots comprise natural language processing machine learning models (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
wherein the server controller is further programmed to (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
returning the chatbot response (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Regarding Claim 19,
Step 2A Prong 1:
See the rejection of Claim 14 above, which Claim 19 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
a vector generator executable by the processor, wherein the vector generator is programmed to transform the natural language query to the input vector (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).)
wherein the server controller is further programmed to generate, using the vector generator, the input vector (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).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
a vector generator executable by the processor, wherein the vector generator is programmed to transform the natural language query to the input vector (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
wherein the server controller is further programmed to generate, using the vector generator, the input vector (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Regarding Claim 20,
Step 1: Claim 20 is a method claim. Therefore, Claim 20 is directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
generating an input vector by performing vectorization on the natural language query (mental process - generating an input vector by performing vectorization on the natural language query may be performed manually by a user with the aid of pen and paper by observing/performing vectorization on the natural language query. See MPEP 2106.04(a)(2)(III)(C).)
generating a weighted classification by applying a weight to the intent classification (mental process – generating a weighted classification by applying a weight to the intent classification may be performed manually by a user with the aid of pen and paper by observing/applying a weight to the intent classification. See MPEP 2106.04(a)(2)(III)(C).)
generating a comparison by comparing the weighted classification to a threshold (mental process – generating a comparison by comparing the weighted classification to a threshold may be performed manually by a user with the aid of pen and paper by observing/comparing the weighted classification to a threshold. See MPEP 2106.04(a)(2)(III)(C).)
classifying, by the topic classifier executing on the input vector, a topic of the natural language query (mental process – classifying, by the topic classifier executing on the input vector, a topic of the natural language query may be performed manually by a user with the aid of pen and paper by observing/analyzing the topic classifier and the input vector. See MPEP 2106.04(a)(2)(III)(C).)
selecting, from among a plurality of chatbots and based on the topic, a selected chatbot (mental process – selecting, from among a plurality of chatbots and based on the topic, a selected chatbot may be performed manually by a user with the aid of pen and paper by observing/analyzing a plurality of chatbots and the topic. See MPEP 2106.04(a)(2)(III)(C).)
generating, automatically by the selected chatbot, a chatbot response to the natural language query, wherein the selected chatbot also uses the intent classification to generate the chatbot response when the intent classification is present (mental process – generating, automatically by the selected chatbot, a chatbot response to the natural language query, wherein the selected chatbot also uses the intent classification to generate the chatbot response when the intent classification is present may be performed manually by a user with the aid of pen and paper by observing/analyzing the selected chatbot and the intent classification. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
receiving a natural language query from a user interface of a chatbot (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
inputting the input vector to a shallow-deep classifier, wherein the shallow-deep learning classifier comprises a classification machine learning model programmed to classify the input vector as being one of a shallow machine learning classification problem and a deep machine learning classification problem (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
outputting, by the shallow-deep classifier, an output label, wherein the output label comprises one of the shallow machine learning classification problem and the deep machine learning classification problem (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
inputting, responsive to the output label comprising deep, the input vector to a deep classifier comprising deep natural language machine learning model (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
outputting, by the deep classifier, an intent classification that represents an intent of the natural language query (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
transmitting the input vector and the intent classification to a topic classifier comprising a topic classification machine learning model when the comparison satisfies the threshold (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
transmitting the input vector to the topic classifier when the output label comprises the shallow machine learning classification problem (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
transmitting the chatbot response to a user device (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
receiving a natural language query from a user interface of a chatbot (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
inputting the input vector to a shallow-deep classifier, wherein the shallow-deep learning classifier comprises a classification machine learning model programmed to classify the input vector as being one of a shallow machine learning classification problem and a deep machine learning classification problem (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
outputting, by the shallow-deep classifier, an output label, wherein the output label comprises one of the shallow machine learning classification problem and the deep machine learning classification problem (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
inputting, responsive to the output label comprising deep, the input vector to a deep classifier comprising deep natural language machine learning model (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
outputting, by the deep classifier, an intent classification that represents an intent of the natural language query (MPEP 2106.05(d)(II) indicates 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
transmitting the input vector and the intent classification to a topic classifier comprising a topic classification machine learning model when the comparison satisfies the threshold (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
transmitting the input vector to the topic classifier when the output label comprises the shallow machine learning classification problem (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
transmitting the chatbot response to a user device (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” 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 limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 20 is rejected as being directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al. (Lu and Ni, “BERT-CNN: a Hierarchical Patent Classifier Based on a Pre-Trained Language Model” (https://arxiv.org/abs/1911.06241), 2019) (hereinafter Lu), in view of Chung et al. (US 20170068888 A1) (hereinafter Chung).
Regarding Claim 1,
Lu teaches:
“A method comprising:” (preamble)
“receiving a natural language query from a user interface of a chatbot” (Lu, Section 4.3.3, “Taking the patent classification in this paper as an example, input the following two sentences: Sentence A: This utility model discloses […]”; Examiner’s note: inputting the following two sentences can be done by using a user interface of a chatbot since chatbot is well-known to receive a natural language query.)
“generating an input vector by performing vectorization on the natural language query” (Lu, Fig. 2 and Section 3.1.2, “The output of each transformer layer in BERT can be used as a sentence vector […]”; Examiner’s note: transformer layer teaches generating an input vector.)
Lu does not explicitly teach the shallow-deep learning classifier comprising a classification machine learning model programmed to classify the input vector as being one of a shallow machine learning classification problem and a deep machine learning classification problem and outputting, by the shallow-deep classifier, an output label, wherein the output label comprises one of the shallow machine learning classification problem and the deep machine learning classification problem.
Chung teaches “Cost-sensitive Classification With Deep Learning Using Cost-aware Pre-training (title)” comprising:
“inputting the input vector to a shallow-deep classifier” (Chung, Paragraph [0021], “The cost-sensitive classifier 102 is shown to include an input layer 104 of neurons or nodes […] a cost-sensitive auto-encoder (CSAE) 110 is shown to provide pre-training of the classifier 102”; Examiner’s note: The cost-sensitive classifier teaches a shallow-deep classifier and a cost-sensitive auto-encoder (CSAE) providing pre-training of the classifier further teaches inputting the input vector to a shallow-deep classifier.)
“wherein the shallow-deep learning classifier comprises a classification machine learning model programmed to classify the input vector as being one of a shallow machine learning classification problem and a deep machine learning classification problem” (Chung, Paragraph [0020], “The cost-sensitive classifier 102 is programmed or otherwise configured to carry out cost-sensitive classification”; Examiner’s note: the cost-sensitive classifier teaches the shallow-deep learning classifier and the cost-sensitive classifier selects classification based on the classification error cost.)
“outputting, by the shallow-deep classifier, an output label, wherein the output label comprises one of the shallow machine learning classification problem and the deep machine learning classification problem” (Chung, Paragraph [0021], “The cost-sensitive classifier 102 is shown to include […] an output layer (or reconstruction layer) 108 of neurons or nodes”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the invention in Lu by applying the cost-sensitive classifier as taught in Chung to the shallow-deep classifier in Lu in order to “minimize[] total cost of errors” (Chung, Paragraph [0014]).
Regarding Claim 2,
The combination of Lu and Chung teaches:
“The method of claim 1, further comprising:” (preamble)
“responsive to the output label comprising the shallow machine learning classification problem, inputting the input vector to a topic classifier” (Lu, Fig. 2 and Section 3.1, “The vectors are jointly trained with the convolutional neural network“; Examiner’s note: the convolutional neural network teaches a topic classifier.)
“outputting, by the topic classifier, a topic of the natural language query” (Lu, Section 3.1.2, “the data is concatenated and then passed through a softmax layer to obtain the probability distribution for patent classification“; Examiner’s note: obtaining the probability distribution implies outputting a topic of the natural language query by the topic classifier.)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 14,
Claim 14 recites substantially the same limitations as Claim 1, in the form of a system, therefore,
it is rejected under the same rationale.
Claims 3 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lu in view of Chung as applied in claim 1, and further in view of Anderson et al. (US 20190140986 A1) (hereinafter Anderson).
Regarding Claim 3,
The combination of Lu and Chung teaches:
“The method of claim 2, further comprising:” (preamble)
Inputting the input vector (Chung - see supra claim 1)
Lu and Chung do not explicitly teach selecting, based on the topic, a selected chatbot from among a plurality of chatbots, inputting the input vector to the selected chatbot and returning a chatbot response.
Anderson teaches “CHATBOT ORCHESTRATION (title)” comprising:
“selecting, based on the topic, a selected chatbot from among a plurality of chatbots” (Anderson, 405 and 410 in Fig. 2, Paragraph [0002], “A ranking algorithm is employed to rank the master chatbot and a plurality of modular chatbots, the ranking algorithm scoring the master chatbot and the plurality of modular chatbots based upon the intent of the one or more chat messages and the entities contained […]”; Examiner’s note: ranking the master chatbot and the plurality of modular chatbots based on the intent and the entities implies selecting a selected chatbot based on the topic.)
inputting one or more chat messages to the selected chatbot (Anderson, 415 in Fig. 2, Paragraph [0002], “The master chatbot […] forwards automatically the one or more chat messages to a ranked modular chatbot”; Examiner’s note: a ranked modular chatbot teaches the selected chatbot.)
“returning a chatbot response” (Anderson, Paragraph [0016], “appropriate responses generated by the master chatbot 130 and modular chatbots 150 are transmitted back to the user computer 110 for display within the chat module”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Lu and Chung by applying the chatbot orchestration as taught in Anderson to the shallow-deep classifier in Lu in order to “efficiently respond to messages” (Anderson, Paragraph [0001]).
Regarding Claim 18,
Claim 18 recites substantially the same limitations as Claims 2 and 3, in the form of a system, therefore, it is rejected under the same rationale.
Claims 4-6, 9, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lu in view of Chung as applied in claim 1, and further in view of Costello (US 20200065384 A1).
Regarding Claim 4,
The combination of Lu and Chung teaches:
“The method of claim 1, further comprising:” (preamble)
“inputting, responsive to the output label comprising the deep machine learning classification problem, the input vector to a deep classifier comprising a deep natural language machine learning model” (Lu, Section 3.1.1, “The output of the Transformer layer is used as the input to the downstream CNN model”; Chung, Paragraph [0020], “The cost-sensitive classifier 102 is programmed or otherwise configured to carry out cost-sensitive classification”; Examiner’s note: the output of the transformer layer teaches the input vector, CNN is a deep classifier, and cost-sensitive classification teaches the output label comprising the deep machine learning classification problem.)
Lu and Chung do not explicitly teach outputting, by the deep classifier, an intent classification that represents an intent of the natural language query.
Costello teaches “Method And System For Intent Classification (title)” comprising:
“outputting, by the deep classifier, an intent classification that represents an intent of the natural language query” (Costello, Paragraph [0076], “The final prediction result to be outputted by the hierarchical prediction module 200 in the intent classification system”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Lu and Chung by applying the intent classification as taught in Costello to the shallow-deep classifier in Lu in order to “improve performance on intent classification” (Costello, Paragraph [0222]).
Regarding Claim 5,
The combination of Lu, Chung and Costello teaches:
“The method of claim 4, further comprising:” (preamble)
“transmitting the natural language query and the intent classification to a display device of an agent” (Costello, Paragraph [0155], “The intent class can then be transmitted back to the user or can be utilized for further analysis for, or for further interaction with, the user”)
The reasons of obviousness have been noted in the rejection of Claim 4 above and applicable herein.
Regarding Claim 6,
The combination of Lu, Chung and Costello teaches:
“The method of claim 4, further comprising:” (preamble)
transmitting the input vector to a topic classifier comprising a topic classification machine learning model (Lu, Fig. 2 and Section 3.1, “The vectors are jointly trained with the convolutional neural network“; Examiner’s note: the convolutional neural network teaches a topic classifier.)
transmitting the intent classification to a classifier (Costello, Fig. 3, Paragraph [0100], “The second ensemble layer 230 then performs at least one ensembled calculation with the plurality of first-layer ensembles 2201 as inputs”; Examiner’s note: the first-layer ensembles teach the intent classification and the second ensemble layer teaches a classifier.)
“classifying, by the topic classifier and using the input vector, a topic of the natural language query (Lu, Fig. 2 and Section 3.1, “The vectors are jointly trained with the convolutional neural network“; Examiner’s note: vectors being jointly trained teaches classifying and using the input vector and the convolutional neural network further teaches the topic classifier.)
The reasons of obviousness have been noted in the rejection of Claim 4 above and applicable herein.
Regarding Claim 9,
The combination of Lu, Chung and Costello teaches:
“The method of claim 4, further comprising:” (preamble)
“generating a weighted classification by applying a weight to the intent classification” (Costello, Paragraph [0032], “each of the plurality of models can be a neural networks-based model, and the different initialization condition can comprise different random initialization of weights”; Examiner’s note: neural network-based model comprising initialization of weights teaches generating a weighted classification by applying a weight to the intent classification.)
“generating a comparison by comparing the weighted classification to a threshold” (Costello, S700’ in Fig. 7, Paragraph [0145], “comparing a metric corresponding to each of the plurality of first-layer ensembles and each of the plurality of second-layer ensembles”; Examiner’s note: the first-layer ensembles teach the weighted intent classification and a metric teaches a threshold.)
“routing the intent classification based on the comparison” (Costello, Fig. 5, Paragraph [0184], “To obtain the second-layer ensembles, all possible combinations of the first-layer ensembles […]”; Examiner’s note: the second-layer ensemble teaches routing the first-layer ensemble.)
The reasons of obviousness have been noted in the rejection of Claim 4 above and applicable herein.
Regarding Claim 15,
Claim 15 recites substantially the same limitations as Claim 4, in the form of a system, therefore,
it is rejected under the same rationale.
Regarding Claim 16,
Claim 16 recites substantially the same limitations as Claim 9, in the form of a system, therefore,
it is rejected under the same rationale.
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Lu in view of Chung, and further in view of Costello as applied in claim 4, and further in view of Anderson.
Regarding Claim 7,
The combination of Lu, Chung and Costello teaches:
“The method of claim 6, further comprising:” (preamble)
generating, based on the intent classification, a chatbot response to the natural language query (Costello, Paragraph [0063], “make a prediction of what intent class the input data likely belong to, and then to output a final prediction result”)
generating, based on the topic, a chatbot response to the natural language query (Lu, Section 3.1.2, “the data is concatenated and then passed through a softmax layer to obtain the probability distribution for patent classification“; Examiner’s note: obtaining the probability distribution implies generating a response to the natural language query based on the topic.)
“transmitting the chatbot response to a user device” (Costello, Paragraph [0155], “The intent class can then be transmitted back to the user or can be utilized for further analysis for, or for further interaction with, the user”)
Lu, Chung and Costello do not explicitly teach generating, automatically, a chatbot response to the natural language query.
Anderson teaches:
generating, automatically and based on the one or more chat messages, a chatbot response to the natural language query (Anderson, Paragraph [0031], “The message forwarding module 139 automatically forwards the one or more chat messages to the most appropriate modular chatbot 150 for response to the user computer”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Lu, Chung and Costello by applying the chatbot orchestration as taught in Anderson to the shallow-deep classifier in Lu in order to “efficiently respond to messages” (Anderson, Paragraph [0001]).
Regarding Claim 8,
The combination of Lu, Chung, Costello and Anderson teaches:
“The method of claim 4, further comprising:” (preamble)
“transmitting the input vector to a topic classifier comprising a topic classification machine learning model” (Lu, Fig. 2 and Section 3.1, “The vectors are jointly trained with the convolutional neural network“; Examiner’s note: the convolutional neural network teaches a topic classifier.)
“classifying, by the topic classification machine learning model, a topic of the natural language query” (Lu, Fig. 2, Section 3.1.2, “convolutional neural networks (CNNs) […] have also achieved good results in natural language processing.”; using convolutional neural networks in natural language processing teaches classifying a topic of the natural language query by the topic classification machine learning model.)
“selecting, from among a plurality of chatbots and based on the topic, a selected chatbot” (Anderson, 405 and 410 in Fig. 2, Paragraph [0002], “A ranking algorithm is employed to rank the master chatbot and a plurality of modular chatbots, the ranking algorithm scoring the master chatbot and the plurality of modular chatbots based upon the intent of the one or more chat messages and the entities contained […]”; Examiner’s note: ranking the master chatbot and the plurality of modular chatbots based on the intent and the entities implies selecting a selected chatbot from among a plurality of chatbots and based on the topic.)
“generating, automatically by the selected chatbot and based on the intent classification of the natural language query, a chatbot response to the natural language query” (Anderson, Paragraph [0031], “The message forwarding module 139 automatically forwards the one or more chat messages to the most appropriate modular chatbot 150 for response to the user computer”; Examiner’s note: the message forwarding module teaches generating, automatically by the selected chatbot, a chatbot response to the natural language query. Costello further teaches generating, based on the intent classification of the natural language query, a chatbot response to the natural language query – see supra claim 7)
“transmitting the chatbot response to a user device” (Costello – see supra claim 7)
The reasons of obviousness have been noted in the rejection of Claim 7 above and applicable herein.
Claims 10, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lu in view of Chung, and further in view of Costello as applied in claim 4, and further in view of Nishant et al. (US 20190012390 A1) (hereinafter Nishant).
Regarding Claim 10,
The combination of Lu, Chung and Costello teaches:
“The method of claim 9, wherein routing comprises,” (preamble)
Lu, Chung and Costello do not explicitly teach transmitting the natural language query and the intent classification to a display device of an agent, responsive to the comparison failing to satisfy the threshold.
Nishant teaches “ARTIFICIAL INTELLIGENCE SYSTEM FOR PROVIDING RELEVANT CONTENT QUERIES ACROSS UNCONNECTED WEBSITES VIA A CONVERSATIONAL ENVIRONMENT (title)” comprising:
“responsive to the comparison failing to satisfy the threshold, transmitting the natural language query and the intent classification to a display device of an agent” (Nishant, Paragraph [0165], “the confidence value is less than the second value, step 1218 may proceed to step 1224. At step 1224, chat bot 132 may display a message indicating that CQ platform 102 is unsure of the user's intent and, at step 1226, chat bot 132 may display one or more intention options on chat bot interface 150.”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Lu, Chung and Costello by using artificial intelligence system for providing relevant content queries as taught in Nishant to the shallow-deep classifier in Lu in order to “improve[] web-based data querying in network environments and, more particularly, to artificial intelligence systems and methods for providing relevant content” (Nishant, Paragraph [0001]).
Regarding Claim 11,
The combination of Lu, Chung, Costello and Nishant teaches:
“The method of claim 9, wherein routing comprises,” (preamble)
responsive to the comparison satisfying the threshold, transmitting the intent classification to a display device (Nishant, Paragraph [0164], “the confidence value is greater than the second value, step 1218 may proceed to step 1220 [...] At step 1220, CQ platform 102 display a message on chat bot interface 150 providing a guess of the user's intent.”)
transmitting the input vector to a topic classifier comprising a topic classification machine learning model (Lu – see supra claim 8)
generating, automatically using the query system, a chatbot response to the natural language query (Nishant, Paragraph [0029], “The query system may be configured to automatically query at least one source among the one or more content sources and obtain query results, based on the identified intent.”)
generating, using the topic classifier, a chatbot response to the natural language (Lu – see supra claim 7)
“transmitting the chatbot response to a user device” (Costello – see supra claim 7)
The reasons of obviousness have been noted in the rejection of Claim 10 above and applicable herein.
Regarding Claim 17,
Claim 17 recites substantially the same limitations as Claims 10 and 11, in the form of a system, therefore, it is rejected under the same rationale.
Claims 12, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lu in view of Chung, and further in view of Costello, and further in view of Anderson as applied in claim 7, and further in view of Nishant.
Regarding Claim 12,
The combination of Lu, Chung, Costello and Anderson teaches:
“The method of claim 9, wherein routing comprises,” (preamble)
transmitting the input vector to a topic classifier comprising a topic classification machine learning model (Lu – see supra claim 8)
“classifying, by the topic classifier, a topic of the natural language query” (Lu, Fig. 2, Section 3.1.2, “convolutional neural networks (CNNs) […] have also achieved good results in natural language processing.”; Examiner’s note: using convolutional neural networks in natural language processing teaches classifying a topic of the natural language query by the topic classifier.)
“selecting, from among a plurality of chatbots and based on the topic, a selected chatbot” (Anderson – see supra claim 8)
“generating, automatically by the selected chatbot and based on the intent classification of the natural language query, a chatbot response to the natural language query” (Anderson and Costello – see supra claim 8)
“transmitting the chatbot response to a user device” (Costello – see supra claim 7)
Lu, Chung, Costello and Anderson do not explicitly teach transmitting the intent classification, responsive to the comparison satisfying the threshold.
Nishant teaches:
responsive to the comparison satisfying the threshold, transmitting the intent classification to a display device (Nishant – see supra claim 11)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Lu, Chung, Costello and Anderson by using artificial intelligence system for providing relevant content queries as taught in Nishant to the shallow-deep classifier in Lu in order to “improve[] web-based data querying in network environments and, more particularly, to artificial intelligence systems and methods for providing relevant content” (Nishant, Paragraph [0001]).
Regarding Claim 13,
The combination of Lu, Chung, Costello, Anderson and Nishant teaches:
“The method of claim 1, further comprising:” (preamble)
“inputting, responsive to the output label comprising deep, the input vector to a deep classifier comprising deep natural language machine learning model” (Lu, Section 3.1.1, “The output of the Transformer layer is used as the input to the downstream CNN model”; Chung, Paragraph [0020], “The cost-sensitive classifier 102 is programmed or otherwise configured to carry out cost-sensitive classification”; Examiner’s note: the output of the transformer layer teaches the input vector, CNN is a deep classifier, and cost-sensitive classification teaches the output label comprising deep.)
“outputting, by the deep classifier, an intent classification that represents an intent of the natural language query” (Costello – see supra claim 4)
“generating a weighted classification by applying a weight to the intent classification” (Costello – see supra claim 9)
“generating a comparison by comparing the weighted classification to a threshold” (Costello – see supra claim 9)
transmitting the input vector to a topic classifier comprising a topic classification machine learning model (Lu – see supra claim 8)
transmitting, responsive to the comparison satisfying the threshold, the intent classification to a display device (Nishant – see supra claim 11)
“classifying, by the topic classifier, a topic of the natural language query” (Lu – see supra claim 12)
“selecting, from among a plurality of chatbots and based on the topic, a selected chatbot” (Anderson – see supra claim 8)
“generating, automatically by the selected chatbot and based on the intent classification of the natural language query, a chatbot response to the natural language query” (Anderson and Costello – see supra claim 8)
“transmitting the chatbot response to a user device” (Costello – see supra claim 7)
The reasons of obviousness have been noted in the rejection of Claim 12 above and applicable herein.
Regarding Claim 20,
Claim 20 recites substantially the same limitations as Claims 1, 2, 4, 6, 8, 9 and 12, in the form of a system, therefore, it is rejected under the same rationale.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Lu in view of Chung as applied in claim 1, and further in view of Misiewicz et al. (US 20220138170 A1) (hereinafter Misiewicz).
Regarding Claim 19,
The combination of Lu and Chung teaches:
“The system of claim 14, further comprising:” (preamble)
Lu and Chung do not explicitly teach a vector generator executable by the processor, wherein the vector generator is programmed to transform the natural language query to the input vector, wherein the server controller is further programmed to generate, using the vector generator, the input vector.
Misiewicz teaches “VECTOR-BASED SEARCH RESULT GENERATION (title)” comprising:
“a vector generator executable by the processor, wherein the vector generator is programmed to transform the natural language query to the input vector, wherein the server controller is further programmed to generate, using the vector generator, the input vector” (Misiewicz, Figure 1 and Paragraph [0012], “The knowledge search system receives a search query associated with the entity from an end user. A set of search terms of the search query is identified and processed to generate embedding vectors for each search term […]“; Misiewicz, Paragraph [0023], “the knowledge search system 110 can include one or more software and/or hardware modules […], and a vector embedding generator 116, […] The components or modules of the knowledge search system 110 may be, for example, a hardware component, circuitry, dedicated logic, programmable logic, microcode, etc., that may be implemented in the processing device of the knowledge search system.”; Examiner’s note: the knowledge search system, containing a vector embedding generator, receives a search query and generates embedding vectors. Therefore, the knowledge search system teaches a vector generator executable by the processor, wherein the vector generator is programmed to transform the natural language query to the input vector. The knowledge search system also teaches the server controller further programmed to generate, using the vector generator, the input vector. See figure 1 below.
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It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the combination of Lu and Chung by applying the vector-based search result generation as taught in Misiewicz to the shallow-deep classifier in Lu in order to “provide for improved query comprehension and enhanced candidate search result recall” (Misiewicz, Paragraph [0040]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gupta et al. teaches an automated conversational tool for intent learning, curated information presenting, and fake news alerting.
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/YONG DOO RHO/Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
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