DETAILED ACTION
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 .
Claim Status
Claims 1-20 are pending in the application.
Claim Objections
Claims 8 and 16 are objected to because of the following informalities: the claims recite the limitation “a first sent of intent classifications”. This should be changed to read “a first set of intent classifications.” 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.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim 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.
Claim 4 recites circular reasoning. The first limitation states to “selecting the agent device based on the at least two predicted multi-level client intent classifications.” Then, conversely, the second limitation states “providing the at least two predicted multi-level client intent classifications based on the selection of the agent device.” The dependency between the “agent device” and the “at least two predicted multi-level client intent classifications” can only go in one direction. As such, the entire meaning of this claim is unclear. For the purposes of applying prior art, the second limitation will be ignored.
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:
Claims 1-8 are directed to a method, Claims 9-16 are directed to a non-transitory computer-readable medium, and Claims 17-20 are directed to a system. Therefore, each of the claims is directed to one of the four statutory categories of patent eligible subject matter.
Step 2A Prong 1:
Claims 1, 9, and 17 recite:
“generating, [utilizing a machine learning model] based on the features, a plurality of predicted multi-level client intent classifications for the client device, from a hierarchical intent architecture, and corresponding multi-level client intent classification probabilities”; generating prediction classifications and probabilities based on features is an evaluation that can be carried out by a human in the mind or with pen and paper, and is therefore a mental process
“selecting at least two predicted multi-level client intent classifications from the plurality of multi-level client intent classifications utilizing the multi-level client intent classification probabilities”; selecting classifications based on probabilities is an evaluation that can be carried out by a human in the mind or with pen and paper, and is therefore a mental process
Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“a non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer system to”; “A system comprising: at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to”; these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f)
“extracting features corresponding to a client device, in response to receiving a communication from the client device”; “providing, via a graphical user interface of an agent device, the at least two predicted multi-level client intent classifications for association with the communication”; these limitations amounts to insignificant extra solution activity, mere data gathering and outputting, as per MPEP 2106.05(g)
“utilizing a machine learning model”; utilizing a broadly recited machine learning model at a high level of generality amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f)
Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“a non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer system to”; “A system comprising: at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to”; these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f)
“extracting features corresponding to a client device, in response to receiving a communication from the client device”; “providing, via a graphical user interface of an agent device, the at least two predicted multi-level client intent classifications for association with the communication”; these limitations amounts to insignificant extra solution activity, mere data gathering and outputting, as per MPEP 2106.05(g); furthermore, this amounts to well-understood, routine, and conventional activity as per MPEP 2106.05(d) (“i. Receiving or transmitting data over a network”)
“utilizing a machine learning model”; utilizing a broadly recited machine learning model at a high level of generality amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f)
Dependent Claims
Claims 2, 10, and 18 recite: “receiving, via the graphical user interface of the agent device, a selection of a multi-level client intent classification of the at least two predicted multi-level client intent classifications”; this amounts to insignificant extra solution activity, mere data gathering and outputting, as per MPEP 2106.05(g) under Step 2A Prong 2; furthermore, this amounts to well-understood, routine, and conventional activity as per MPEP 2106.05(d) (“i. Receiving or transmitting data over a network”) under Step 2B
“based on the received selection, generating an association between the multi-level client intent classification and the communication”; a human can write down an association between a classification and a data point such as a communication, therefore this is a mental process
Claims 3, 11, and 19 recite: “updating the machine learning model utilizing the association between the multi-level client intent classification and the communication”; a broad recitation of updating a machine learning model, at a high level of generality, amounts to insignificant extra solution activity, (2) Whether the limitation is significant (i.e. it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention), as per MPEP 2106.05(g) under Step 2A Prong 2, as Examiner notes that any machine learning model has to be trained or retrained in order to be used, and therefore generically reciting such a process is only nominally or tangentially related to the invention; furthermore, this amounts to well-understood, routine, and conventional activity as per MPEP 2106.05(d) under Step 2B, as evidenced by Berkheimer reference Wu et al. (“DeltaGrad: Rapid retraining of machine learning models”), which states on Pages 1 and 2 that “Machine learning models are used increasingly often, and are rarely static. Models may need to be retrained on slightly changed datasets, for instance when datapoints have been added or deleted … There is a great deal of work on model retraining and updating”)
Claims 4, 12, and 20 recite: “selecting the agent device based on the at least two predicted multi-level client intent classifications”; making a selection based on classification predictions is an evaluation that can be carried out by a human with pen and paper, and is therefore a mental process
“providing the at least two predicted multi-level client intent classifications based on the selection of the agent device”; it is unclear what “providing” means here, Examiner is interpreting this as “outputting”, which amounts to insignificant extra solution activity, mere data gathering and outputting, as per MPEP 2106.05(g) under Step 2A Prong 2; furthermore, this amounts to well-understood, routine, and conventional activity as per MPEP 2106.05(d) (“i. Receiving or transmitting data over a network”) under Step 2B
Claims 5 and 13 recite: “wherein extracting the features corresponding to the client device comprises at least one of extracting text from the communication, extracting user activity data, or extracting user profile data”; this amounts to insignificant extra solution activity, mere data gathering and outputting, as per MPEP 2106.05(g) under Step 2A Prong 2; furthermore, this amounts to well-understood, routine, and conventional activity as per MPEP 2106.05(d) (“i. Receiving or transmitting data over a network”) under Step 2B
Claims 6 and 14 recite: “wherein the machine-learning model comprises a transformer encoder and a classification layer”; a broad recitation of types of machine learning models at a high level of generality amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f)
Claims 7 and 15 recite: “in response to receiving an additional communication transmitted from the client device”; this amounts to insignificant extra solution activity, mere data gathering and outputting, as per MPEP 2106.05(g) under Step 2A Prong 2; furthermore, this amounts to well-understood, routine, and conventional activity as per MPEP 2106.05(d) (“i. Receiving or transmitting data over a network”) under Step 2B
“generating, [utilizing the machine learning model] based on the multi-level client intent selected for the communication, an additional plurality of predicted multi-level client intent classifications for the additional communication”; generating prediction classifications and probabilities based on features is an evaluation that can be carried out by a human in the mind or with pen and paper, and is therefore a mental process
“utilizing the machine learning model”; utilizing a broadly recited machine learning model at a high level of generality amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f)
Claims 8 and 16 recite: “generating the hierarchical intent architecture by generating a first sent of intent classifications at a first level and a second set of client intent classifications at a second level that depend from the first set of intent classifications”; generating a data structure to represent data points can be accomplished by a human with pen and paper, and is thus a mental process
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, 4-5, 8-9, 12-13, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2019/0377794 A1; hereinafter “Li”) in view of Yannam et al. (US 2022/0351058 A1; hereinafter “Yannam”)
As per Claim 1, Li teaches a method comprising:
extracting features corresponding to a client device, in response to receiving a communication from the client device (Li, Para [0020]: “The intelligent customer service agent (105) may determine the user intent based on the received user sessions.” Li, Para [0029]: “Step S410: Obtain a session text of a user to determine a feature vector corresponding to the session text.”
Examiner noes that although Li does not explicitly recite a “device”, Specification [0115] states that “Specifically, the act 702 can include wherein extracting the features corresponding to the client device comprises at least one of extracting text from the communication.”)
generating, utilizing a machine learning model based on the features, a plurality of predicted multi-level client intent classifications for the client device, from a hierarchical intent architecture, and corresponding multi-level client intent classification probabilities (Li, Para [0030]: “Step S420: Input the feature vector into a hierarchical intent classification model, wherein the hierarchical intent classification model is trained based on a pre-built hierarchical intent system, wherein the hierarchical intent system comprises intent labels of a plurality of levels. In one embodiment, the hierarchical intent classification model comprises classifiers, each of the classifiers corresponding to each of the plurality of levels. In one embodiment, step S420 further comprises determining, based on the feature vector by each of the classifiers, probabilities of the session text belonging to each intent label in each level.”)
selecting [at least two] predicted multi-level client intent classifications from the plurality of multi-level client intent classifications utilizing the multi-level client intent classification probabilities (Li, Para [0031]: “Step S430: Determine the user intent based on the probabilities.”)
However, Li does not teach selecting at least two predicted multi-level client intent classifications; providing, via a graphical user interface of an agent device, the at least two predicted multi-level client intent classifications for association with the communication.
Yannam teaches selecting at least two predicted multi-level client intent classifications (Yannam, Para [0042]: “The method may include selecting a top predetermined number of intents from the ranked intents.”)
providing, via a graphical user interface of an agent device, the at least two predicted multi-level client intent classifications for association with the communication (Yannam, Para [0042]: “The method may include displaying the predetermined number of intents on the user interface. The method may include enabling the user to select an intent from the displayed intents. The method may include receiving a selection of an intent from a user.”)
Yannam is analogous art because it is in the field of endeavor of machine learning. It would have been obvious before the effective filing date of the claimed invention to combine the hierarchical intent classification of Li with the ranked intent and display to the user of Yannam. One of ordinary skill in the art would have been motivated to do so in order to assist a user in efficiently selecting the option for the assistance they need (Yannam [0035]: “The user may select which result is most appropriate. Upon selection, the user may be directed to the selected bot. The challenge in this approach may include presenting the listing to the user in a user interface that does not overwhelm the user. As such, only a minimal number of intents may be presented to the user.”)
As per Claim 4, the combination of Li and Yannam teaches the method of claim 1. Yannam teaches selecting the agent device based on the at least two predicted multi-level client intent classifications (Yannam, Para [0042]: “The method may include directing the user to the chatbot associated with the selected intent.”)
providing the at least two predicted multi-level client intent classifications based on the selection of the agent device (See 112(b) rejection for circular reasoning above.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li and Yannam for at least the reasons recited in the rejection to Claim 1.
As per Claim 5, the combination of Li and Yannam teaches the method of claim 1. Li teaches wherein extracting the features corresponding to the client device comprises at least one of extracting text from the communication, extracting user activity data, or extracting user profile data. (Li recites extracting text from the communication in [0020]: “The intelligent customer service agent (105) may determine the user intent based on the received user sessions.” Li, Para [0029]: “Step S410: Obtain a session text of a user to determine a feature vector corresponding to the session text.”)
As per Claim 8, the combination of Li and Yannam teaches the method of claim 1. Li teaches further comprising generating the hierarchical intent architecture by generating a first sent of intent classifications at a first level and a second set of client intent classifications at a second level that depend from the first set of intent classifications. (Li recites extracting text from the communication in [0020]: “The intelligent customer service agent (105) may determine the user intent based on the received user sessions.” Li, Para [0029]: “Step S410: Obtain a session text of a user to determine a feature vector corresponding to the session text.”)
As per Claim 9, this is a non-transitory computer-readable medium claim corresponding to method claim 1. Li [0081] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer.” Claim 9 is rejected for similar reasons as Claim 1.
As per Claim 12, this is a non-transitory computer-readable medium claim corresponding to method claim 4. Li [0081] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer.” Claim 12 is rejected for similar reasons as Claim 4.
As per Claim 13, this is a non-transitory computer-readable medium claim corresponding to method claim 5. Li [0081] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer.” Claim 13 is rejected for similar reasons as Claim 5.
As per Claim 16, this is a non-transitory computer-readable medium claim corresponding to method claim 8. Li [0081] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer.” Claim 16 is rejected for similar reasons as Claim 8.
As per Claim 17, this is a system claim comprising a processor and a non-transitory computer-readable storage medium corresponding to method claim 1. Li [0081-0082] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer. According to still another aspect, a computing device comprising a memory and a processor is further provided, wherein the memory having executable codes stored therein, and the processor implementing the method described in conjunction with FIG. 4 when executing the executable codes.” Claim 17 is rejected for similar reasons as Claim 1.
As per Claim 20, this is a system claim comprising a processor and a non-transitory computer-readable storage medium corresponding to method claim 4. Li [0081-0082] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer. According to still another aspect, a computing device comprising a memory and a processor is further provided, wherein the memory having executable codes stored therein, and the processor implementing the method described in conjunction with FIG. 4 when executing the executable codes.” Claim 20 is rejected for similar reasons as Claim 4.
Claims 2-3, 7, 10-11, 15, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Li and Yannam, further in view of Miranda et al. (US 2022/0351058 A1; hereinafter “Miranda”).
As per Claim 2, the combination of Li and Yannam teaches the method of claim 1. Yannam teaches further comprising: receiving, via the graphical user interface of the agent device, a selection of a multi-level client intent classification of the at least two predicted multi-level client intent classifications (Yannam, Para [0042]: “The method may include displaying the predetermined number of intents on the user interface. The method may include enabling the user to select an intent from the displayed intents. The method may include receiving a selection of an intent from a user.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li and Yannam for at least the reasons recited in the rejection to Claim 1.
However, the combination does not teach based on the received selection, generating an association between the multi-level client intent classification and the communication
However, the combination does not teach transformer encoder.
Miranda teaches wherein the machine-learning model comprises a transformer encoder and a classification layer (Miranda [0051]: “In some embodiments, where the user selects a presented dialogue item corresponding to a predicted intent (e.g., obtained from a prediction model), feedback subsystem 116 may determine a feedback score associated with the predicted intent (e.g., based on the user selecting the corresponding dialogue item) and use the feedback score to update one or more configurations of the prediction model (e.g., one or more weights, biases, or other parameters of the prediction model).”)
Miranda is analogous art because it is in the field of endeavor of machine learning. It would have been obvious before the effective filing date of the claimed invention to combine the chatbot intent classification of Li and Yannam with the model updating based on user intent selection of Miranda. One of ordinary skill in the art would have been motivated to do so in order to improve the accuracy of the intent prediction (Miranda [0006]: “In some embodiments, based on the user selection and the first question matching a first intent of the predicted intents, the first intent may be provided as reference feedback for the prediction model. As an example, the first intent may be used to update one or more configurations of the prediction model (e.g., weights, biases, or other parameters of the prediction model). In this way, for example, the prediction model may be trained or configured to generate more accurate predictions.”)
As per Claim 3, the combination of Li, Yannam, and Miranda teaches the method of claim 2. Miranda teaches further comprising updating the machine learning model utilizing the association between the multi-level client intent classification and the communication (Miranda [0051]: “In some embodiments, where the user selects a presented dialogue item corresponding to a predicted intent (e.g., obtained from a prediction model), feedback subsystem 116 may determine a feedback score associated with the predicted intent (e.g., based on the user selecting the corresponding dialogue item) and use the feedback score to update one or more configurations of the prediction model (e.g., one or more weights, biases, or other parameters of the prediction model).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li and Yannam with Miranda for at least the reasons recited in the rejection to Claim 2.
As per Claim 7, the combination of Li, Yannam, and Miranda teaches the method of claim 2. Yannam teaches further comprising generating the hierarchical intent architecture by generating a first set of intent classifications at a first level and a second set of client intent classifications at a second level that depend from the first set of intent classifications. (Li [0042]: “The method may include directing the user to the chatbot associated with the selected intent.” Li [0040]: “Each chatbot included in the network of chatbots is operable to identify a domain of intents. Each domain of intents may correspond to a logical grouping of user queries. The method may include transmitting the query to each chatbot included in the network of chatbots. The method may include identifying an intent for the query at each chatbot included in the network of chatbots. The method may include, for each identified intent, identifying at each chatbot, an accuracy level of a correspondence between the identified intent and the query. The method may include receiving each identified intent and the accuracy level at the user interface.” Li [0089]: “Section 306 shows option three for processing a query at a chatbot network. Option three may include a pointer approach for on-demand invocation. Such a system may include intercommunications between chatbots. Each chatbot in the system may be able to invoke another chatbot in the network. A descendent chatbot may be invoked by a predecessor chatbot. A predecessor chatbot may also be invoked by a descendent chatbot.”
Examiner notes that the above limitations by Yannam indicate that when a user selects an intent, they are directed to another chatbot in an “ecosystem” of chatbots, and that each chatbot has the capability of identifying an intent, and each chatbot can invoke other chatbots. Therefore, the user can again be presented with a new set of intents when directed to the next chatbot.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li and Yannam for at least the reasons recited in the rejection to Claim 1.
As per Claim 10, this is a non-transitory computer-readable medium claim corresponding to method claim 2. Li [0081] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer.” Claim 10 is rejected for similar reasons as Claim 2.
As per Claim 11, this is a non-transitory computer-readable medium claim corresponding to method claim 3. Li [0081] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer.” Claim 11 is rejected for similar reasons as Claim 3.
As per Claim 15, this is a non-transitory computer-readable medium claim corresponding to method claim 7. Li [0081] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer.” Claim 15 is rejected for similar reasons as Claim 7.
As per Claim 18, this is a system claim comprising a processor and a non-transitory computer-readable storage medium corresponding to method claim 2. Li [0081-0082] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer. According to still another aspect, a computing device comprising a memory and a processor is further provided, wherein the memory having executable codes stored therein, and the processor implementing the method described in conjunction with FIG. 4 when executing the executable codes.” Claim 18 is rejected for similar reasons as Claim 2.
As per Claim 19, this is a system claim comprising a processor and a non-transitory computer-readable storage medium corresponding to method claim 3. Li [0081-0082] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer. According to still another aspect, a computing device comprising a memory and a processor is further provided, wherein the memory having executable codes stored therein, and the processor implementing the method described in conjunction with FIG. 4 when executing the executable codes.” Claim 19 is rejected for similar reasons as Claim 3.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Li and Yannam, further in view of Mohan (US 2022/0351058 A1).
As per Claim 6, the combination of Li and Yannam teaches the method of claim 1. Li teaches wherein the machine-learning model comprises [a transformer encoder and] a classification layer (Li [0022]: “Based on the above observations and statistics, a simplified hierarchical intent classification model is employed in the disclosed embodiments. The number of classifiers included is the same as the number of levels.”)
However, the combination does not teach transformer encoder.
Mohan teaches wherein the machine-learning model comprises a transformer encoder and a classification layer (Mohan [0022] discloses the classification layer: “Textual data, such as textual data received from a voice assistant or through a chatbot interface, may be provided to the natural language model and the dependency embedding generation model, each of which may generate output data. The output data from each of the natural language model and the dependency embedding generation model is concatenated, and the concatenated data is provided to an intent and entity classifier model to identify and tag (e.g., label) entities of the textual data, and to determine an intent of the received textual data”. Mohan states that the model also includes embedding generation. Mohan states that this comprises a transformer encoder in [0055]: “In some examples, the dependency-based word embeddings are passed through the linear neural network layer 314, which may include a single layer transformer encoder followed by a linear neural network layer, to generate word embeddings.”)
Mohan is analogous art because it is in the field of endeavor of machine learning. It would have been obvious before the effective filing date of the claimed invention to combine the intent classification for a chatbot of Li and Yannam with the embedding with transformer encoder of Mohan. One of ordinary skill in the art would have been motivated to do so in order to better understand natural language queries from chatbot users by using BERT models that are trained in the appropriate field (Mohan [0002-0004]: “Moreover, while customers have other options to communicate with a retailer, such as through the use of virtual assistants and chatbots, received queries may incur grammatical inconsistencies and other idiosyncrasies, causing discrepancies in any response received. As such, there are opportunities to improve natural language understanding in conversational systems … The machine learning processes may employ a natural language model, such as a Bidirectional Encoder Representation from Transformers (BERT) model, which is trained on retail data.”)
As per Claim 14, this is a non-transitory computer-readable medium claim corresponding to method claim 6. Li [0081] discloses: “As above, according to another aspect, a computer-readable storage medium is further provided; the computer-readable storage medium having stored thereon a computer program for enabling a computer to perform the method described in conjunction with FIG. 4 when the computer program is executed in the computer.” Claim 14 is rejected for similar reasons as Claim 6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD A SIEGER whose telephone number is (571)272-9710. The examiner can normally be reached M-F 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LEONARD A SIEGER/Examiner, Art Unit 2126
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