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 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.
Claims 3, 5, 7, 15, 17, and 19 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claim 3, the claim recites that “the escalation contact is associated with a portion of the set of documentation that was determined to be relevant to the second inquiry.” The term “relevant” renders the scope of the claim indefinite because the claim does not provide an objective criterion or boundary for determining when a portion of the documentation is relevant to the second inquiry. It is unclear what degree or type of relationship between the second inquiry and the documentation is sufficient to satisfy the limitation. For example, the claim does not specify whether relevance is determined based on keyword matching, semantic similarity, subject matter, a threshold score, or some other criterion. Accordingly, one of ordinary skill in the art would not be reasonably apprised of the scope of the claimed “portion of the set of documentation that was determined to be relevant to the second inquiry.”
Regarding claims 5 and 17, the claims recite identifying “a portion of output from the machine learning model as unhelpful.” The term “unhelpful” renders the scope of the claims indefinite because the claims do not provide an objective standard for determining whether output from the machine learning model is unhelpful. It is unclear what characteristic or degree of deficiency causes an output to qualify as “unhelpful,” such as whether the output must be inaccurate, nonresponsive, incomplete, negatively received by the user, or otherwise deficient. Although claim 5 recites that the portion is identified “using the feedback,” and claim 17 recites identification “using the sentiment,” these limitations specify information used in making the determination but do not establish an objective boundary as to what constitutes “unhelpful” output. Accordingly, the metes and bounds of the claimed limitation cannot be determined with reasonable certainty.
Regarding claims 7, 15, and 19, the claims recite detecting “a sentiment associated with input from the user device.” The scope of the recited “input” is unclear because the respective claims recite multiple communications originating from the user device, including at least a first inquiry and a second inquiry, and the claims do not identify which user input is the input with which the detected sentiment is associated. In claim 7, for example, claim 1 already recites a first inquiry, a second inquiry, and feedback received from the user device before claim 7 recites detecting sentiment associated generally with “input from the user device.” Similarly, claim 15 recites a first inquiry and a second inquiry from the user device before subsequently reciting sentiment associated generally with “input from the user device.” Claim 19 further recites providing “the input from the user device” to an additional machine learning model but does not resolve which previously recited user-originating communication constitutes that input. Accordingly, it is unclear whether the sentiment is associated with the first inquiry, the second inquiry, feedback or another user input, and the scope of the claimed sentiment-detection limitation cannot be determined with reasonable certainty.
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 they recite an abstract idea without significantly more.
Regarding claim 1:
Step 1: is the claim directed to one of the four statutory categories?
Yes, the claim is directed to a machine.
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation: “determine at least one feature, associated with the machine learning model, to eliminate based on the feedback;” is directed to a mental process of judgment under MPEP 2106.04(a)(2)(III).
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitations: “receive, from a user device, a first inquiry; provide the first inquiry to a machine learning model in order to receive a first response; transmit the first response to the user device in response to the first inquiry; receive, from the user device, a second inquiry; provide the second inquiry to the machine learning model in order to receive an identifier of an escalation contact; transmit, in response to the second inquiry, an indication of the escalation contact; receive feedback associated with the first response;… and transmit a command to update the machine learning model by removing the at least one feature” are all directed to mere data gathering under MPEP 2106.05(g).
Further, the limitations: “one or more memories; and one or more processors, communicatively coupled to the one or more memories,” are directed to mere instructions to apply an exception under MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitations: “receive, from a user device, a first inquiry; provide the first inquiry to a machine learning model in order to receive a first response; transmit the first response to the user device in response to the first inquiry; receive, from the user device, a second inquiry; provide the second inquiry to the machine learning model in order to receive an identifier of an escalation contact; transmit, in response to the second inquiry, an indication of the escalation contact; receive feedback associated with the first response;… and transmit a command to update the machine learning model by removing the at least one feature” are directed to the well-understood, routine, and conventional activity of “Receiving and transmitting data over a network” under MPEP 2106.05(d).
Further, the limitations: “one or more memories; and one or more processors, communicatively coupled to the one or more memories,” are directed to generic computing components under MPEP 2106.05(g).
Regarding claim 2:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 1.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “wherein the machine learning model is trained on a set of documentation associated with the onboarding process” is directed to field of use under MPEP 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “wherein the machine learning model is trained on a set of documentation associated with the onboarding process” is directed to field of use under MPEP 2106.05(h).
Regarding claim 3:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 1.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “wherein the escalation contact is associated with a portion of the set of documentation that was determined to be relevant to the second inquiry” is directed to field of use under MPEP 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “wherein the escalation contact is associated with a portion of the set of documentation that was determined to be relevant to the second inquiry” is directed to field of use under MPEP 2106.05(h).
Regarding claim 4:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 1.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “wherein the escalation contact is selected from a plurality of possible escalation contacts” is directed to field of use under MPEP 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “wherein the escalation contact is selected from a plurality of possible escalation contacts” is directed to field of use under MPEP 2106.05(h).
Regarding claim 5:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitations: “identify, using the feedback, a portion of output from the machine learning model as unhelpful; and determine the at least one feature based on the at least one feature being mapped to the portion of the output” are directed to a mental process of evaluation and judgment under MPEP 2106.04(a)(2)(III).
Regarding claim 6:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 1.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitations: “transmit, to the user device, a prompt; and receive, from the user device, the feedback in response to the prompt” is directed to mere data gathering under MPEP 2106.04(a)(2)(III).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitations: “transmit, to the user device, a prompt; and receive, from the user device, the feedback in response to the prompt” are directed to the well-understood, routine, and conventional activity of “Receiving and transmitting data over a network” under MPEP 2106.05(d).
Regarding claim 7:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation: “detect a sentiment associated with input from the user device, wherein the feedback comprises the sentiment” is directed to a mental process of judgment under MPEP 2106.04(a)(2)(III).
Claim 8 is rejected with the same rationale as claim 1.
Regarding claim 9:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 8.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “transmitting, from the user device and to the onboarding system, a confirmation of the escalation contact, wherein the confirmation triggers a message to be sent to the escalation contact” is directed to mere data gathering under MPEP 2106.04(a)(2)(III).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “transmitting, from the user device and to the onboarding system, a confirmation of the escalation contact, wherein the confirmation triggers a message to be sent to the escalation contact” is directed to the well-understood, routine, and conventional activity of “Receiving and transmitting data over a network” under MPEP 2106.05(d).
Regarding claim 10:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 8.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “wherein the indication of the escalation contact comprises a hyperlink to an email address or a chat identifier associated with the escalation contact” is directed to field of use under MPEP 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “wherein the indication of the escalation contact comprises a hyperlink to an email address or a chat identifier associated with the escalation contact” is directed to field of use under MPEP 2106.05(h).
Regarding claim 11:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation: “initiating a chat group including the escalation contact” is directed to methods of organizing human activity under MPEP 2106.04(a)(2)(II).
Regarding claim 12:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 8.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “wherein the feedback comprises a rating” is directed to field of use under MPEP 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “wherein the feedback comprises a rating” is directed to field of use under MPEP 2106.05(h).
Regarding claim 13:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 8.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “wherein the feedback comprises a text description” is directed to field of use under MPEP 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “wherein the feedback comprises a text description” is directed to field of use under MPEP 2106.05(h).
Regarding claim 14:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 8.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “receiving, from the onboarding system and at the user device, an indication of an assignment” is directed to mere data gathering under MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “receiving, from the onboarding system and at the user device, an indication of an assignment” is directed to the well-understood, routine, and conventional activity of “Receiving and transmitting data over a network” under MPEP 2106.05(d).
Claim 15 is rejected with the same rationale as claim 1.
Regarding claim 16:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 15.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “transmit the command to a machine learning host associated with the machine learning model” is directed to mere data gathering under MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “transmit the command to a machine learning host associated with the machine learning model” is directed to the well-understood, routine, and conventional activity of “Receiving and transmitting data over a network” under MPEP 2106.05(d).
Regarding claim 17:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitations: “identify, using the sentiment, a portion of output from the machine learning model as unhelpful; and determine the at least one feature based on the at least one feature being mapped to the portion of the output” are directed to a mental process of judgment under MPEP 2106.04(a)(2)(III).
Regarding claim 18:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 15.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitations: “transmit a request including the first inquiry to a machine learning host associated with the machine learning model; and receive the first response from the machine learning host in response to the request” are directed to mere data gathering under MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitations: “transmit a request including the first inquiry to a machine learning host associated with the machine learning model; and receive the first response from the machine learning host in response to the request” are directed to the well-understood, routine, and conventional activity of “Receiving and transmitting data over a network” under MPEP 2106.05(d).
Regarding claim 19:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation is dependent on claim 15.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “provide the input from the user device to an additional machine learning model in order to receive an indication of the sentiment” is directed to mere data gathering under MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “provide the input from the user device to an additional machine learning model in order to receive an indication of the sentiment” is directed to the well-understood, routine, and conventional activity of “Receiving and transmitting data over a network” under MPEP 2106.05(d).
Regarding claim 20:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitations: “map the second inquiry to at least one documentation file; and map the at least one documentation file to the escalation contact” are directed to a mental process of evaluation and judgment under MPEP 2106.04(a)(2)(III).
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, 4-10, 12-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Pre-Grant Patent 2023/0188480 (Menon et al; Menon) in view of US Patent 11,811,729 (Maney Jr et al; Maney Jr).
Regarding claim 1 and analogous claims 8 and 15:
Menon teaches:
1. one or more memories;
(Menon, ¶0038)
“These additional components may include, but are not limited to, computer processing units (CPUs), graphical processing units (GPUs), memory banks, graphic adaptors, external ports and connections, peripherals, power supplies, etc., required for the operation of server 320 [i.e. one or more memories;].”
2. and one or more processors, communicatively coupled to the one or more memories, configured to:
(Menon, ¶0035)
“By way of example and not limitation, mobile device 304 can be a smartphone device, a tablet, a tablet personal computer (PC), or a laptop PC. In some embodiments, mobile device 304 can be any suitable electronic device connected to a network 308 via a wired or wireless connection and capable of running software applications like software application 302 [i.e. and one or more processors, communicatively coupled to the one or more memories, configured to:].”
3. receive, from a user device, a first inquiry;
(Menon, ¶0030, Fig. 1)
“At the first operation 105 of FIG. 1, user details/data are collected. The user details may include any data that a user enters in a chatbot conversation with a virtual agent [i.e. receive, from a user device, a first inquiry;].”
4. provide the first inquiry to a machine learning model in order to receive a first response;
(Menon, ¶0075, Fig. 10)
“FIG. 10 illustrates an exemplary process 1000 for generating and correcting chatbot responses based on reinforcement learning (RL), according to some embodiments. In some embodiments, self-correcting chatbot application 322 of server 320 as depicted in FIG. 4 in communication with other components of system 300 to implement process 1000 [i.e. provide the first inquiry to a machine learning model in order to receive a first response;].”
5. transmit the first response to the user device in response to the first inquiry;
(Menon, ¶0050, Fig. 6B)
“FIG. 6B is an example illustration of the basic conversation flow depicted in FIG. 6A. FIG. 6B includes a table 650 showing a chatbot conversation about resort booking between an agent and a user. Other examples can be a user asking the chatbot which route is the best transportation route to arrive at a destination, a user inquiring about a procedure to get a certificate, etc [i.e. transmit the first response to the user device in response to the first inquiry;].”
6. receive, from the user device, a second inquiry;
(Menon, ¶0050, Fig. 6B)
“FIG. 6B is an example illustration of the basic conversation flow depicted in FIG. 6A. FIG. 6B includes a table 650 showing a chatbot conversation about resort booking between an agent and a user. Other examples can be a user asking the chatbot which route is the best transportation route to arrive at a destination, a user inquiring about a procedure to get a certificate, etc [i.e. receive, from the user device, a second inquiry;].”
7. receive feedback associated with the first response;
(Menon, ¶0031)
“At operation 120, the chatbot receives a user reaction to the recommendation. The reaction or feedback can be positive or negative.”
8. determine at least one feature, associated with the machine learning model, to eliminate based on the feedback;
(Menon, ¶0061)
“In some embodiments, a conversation includes a direct indication regarding whether a given option or choice is accepted. If an option is accepted, feedback analyzer 404 can calculate a reward score to represent a positive reward. If an option is rejected, feedback analyzer 404 can calculate a reward score to represent a negative reward.”
(Menon, ¶0064)
“If a negative reward score is received, RL engine 406 can lower down the weight of the corresponding recommendation/option [i.e. determine at least one feature, associated with the machine learning model,]. When a particular recommendation/option is repeatedly assigned negative rewards, its weight will be repeatedly decreased. Eventually, when the weight turns to zero or is lower than a threshold number, RL engine 406 can notify recommendation module 408 to stop or remove this recommendation [i.e. to eliminate based on the feedback;].”
9. and transmit a command to update the machine learning model by removing the at least one feature.
(Menon, ¶0064)
“Eventually, when the weight turns to zero or is lower than a threshold number, RL engine 406 can notify recommendation module 408 to stop or remove this recommendation [i.e. and transmit a command to update the machine learning model by removing the at least one feature].”
Menon does not explicitly teach:
1. provide the second inquiry to the machine learning model in order to receive an identifier of an escalation contact;
2. transmit, in response to the second inquiry, an indication of the escalation contact;
Maney Jr teaches:
1. provide the second inquiry to the machine learning model in order to receive an identifier of an escalation contact;
(Maney Jr., col. 9: 21-29)
“The AI function 704 may process the free-form input data to determine one or more intent classifiers and one or more emotion classifiers based on the free-form input data [i.e. provide the second inquiry to the machine learning model]. At 708, one of a plurality of service department identifiers may be selected based on at least one of the one or more intent classifiers or the one or more emotion classifiers [i.e. in order to receive an identifier of an escalation contact;]. “
2. transmit, in response to the second inquiry, an indication of the escalation contact;
(Maney Jr., col. 9: 29-31)
“At 710, one of a plurality of service representative identifiers associated with the selected service department identifier may be selected [i.e. transmit, in response to the second inquiry, an indication of the escalation contact;].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. Menon’s background explicitly identifies “Enterprises today have developed conversational interfaces such as chatbots to adapt to fast-grown business needs, but many customers/users still prefer to talk to a human agent (e.g., a sales representative) rather than a virtual agent with bot/machine intelligence at a customer service portal (Menon, ¶0003). Maney Jr supplies the routing to a human agent through service representative identifiers based on the intentions or emotions of the user.
Regarding claim 4:
Menon and Maney Jr teach the machine of claim 1.
Maney Jr teaches:
1. wherein the escalation contact is selected from a plurality of possible escalation contacts.
(Maney Jr, col. 2: 64-67; col. 3: 1-2)
“FIG. 2 is a schematic block diagram of communication system 100 of FIG. 1, further revealing that the service provider may involve a plurality of representatives 208 (e.g., representatives 220, 222, 224, and 226) associated with a plurality of different service departments (e.g., including groups, sub-groups, etc.) [i.e. wherein the escalation contact is selected from a plurality of possible escalation contacts].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Regarding claim 5 and analogous claim 17:
Menon and Maney Jr teach the machine of claim 1.
Menon teaches:
1. identify, using the feedback, a portion of output from the machine learning model as unhelpful;
(Menon, ¶0060)
“At operation 512, feedback analyzer 404 performs sentiment analysis on the conversation lines in the tail end of the conversation to identify negative and positive sentiments or parts [i.e. identify, using the feedback, a portion of output from the machine learning model as unhelpful;].”
2. and determine the at least one feature based on the at least one feature being mapped to the portion of the output.
(Menon, ¶0060)
“However, if feedback analyzer 404 determines that the conversation lines in the tail end contain negative sentiment(s), this feedback will be used through reinforcement learning to change (i.e., self-correct) the response/recommendation provided by the chatbot agent [i.e. and determine the at least one feature based on the at least one feature being mapped to the portion of the output].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Regarding claim 6:
Menon and Maney Jr teach the machine of claim 1.
Menon teaches:
1. transmit, to the user device, a prompt;
(Menon, ¶0054)
“Responsive to receiving the recommendation provided by the agent at operation 735 based on user details collected at operation 705, the user is asked whether he/she likes the recommendation at operation 740 [i.e. transmit, to the user device, a prompt;].”
2. and receive, from the user device, the feedback in response to the prompt.
(Menon, ¶0055)
“If the user is not interested in the recommendation (e.g., no reaction) or rejects the recommendation, the sentiment analysis on user feedback at operation 745 can identify this negative feedback 775 and move flow 770 to operation 780 [i.e. and receive, from the user device, the feedback in response to the prompt].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Regarding claim 7:
Menon and Maney Jr teach the machine of claim 1.
Menon teaches:
1. detect a sentiment associated with input from the user device, wherein the feedback comprises the sentiment.
(Menon, ¶0055)
“If the user is not interested in the recommendation (e.g., no reaction) or rejects the recommendation, the sentiment analysis on user feedback at operation 745 can identify this negative feedback 775 and move flow 770 to operation 780 [i.e. detect a sentiment associated with input from the user device, wherein the feedback comprises the sentiment].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Regarding claim 9:
Menon and Maney Jr teach the machine of claim 1.
Maney Jr teaches:
1. transmitting, from the user device and to the onboarding system, a confirmation of the escalation contact, wherein the confirmation triggers a message to be sent to the escalation contact.
(Maney Jr, col. 8: 6-9)
“A message may then be generated and sent to the selected service representative identifier, where the message includes an identifier for identifying information associated with the incoming service message (step 512 of FIG. 5) [i.e. transmitting, from the user device and to the onboarding system, a confirmation of the escalation contact, wherein the confirmation triggers a message to be sent to the escalation contact].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Regarding claim 10:
Menon and Maney Jr teach the method of claim 8.
Maney Jr teaches:
1. wherein the indication of the escalation contact comprises a hyperlink to an email address or a chat identifier associated with the escalation contact.
(Maney Jr, col. 7: 51-54)
“The plurality of service representative identifiers may correspond to a plurality of different service representatives or service representative addresses or numbers for communicating with the service representatives [i.e. wherein the indication of the escalation contact comprises… a chat identifier associated with the escalation contact].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Regarding claim 12:
Menon and Maney Jr teach the method of claim 8.
Menon teaches:
1. wherein the feedback comprises a rating.
(Menon, ¶0069)
“When a chatbot response or recommendation is presented to a user, a survey collecting user feedback about the recommendation is sent to the user.”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Regarding claim 13:
Menon and Maney Jr teach the method of claim 8..
Menon teaches:
1. wherein the feedback comprises a text description.
(Menon, ¶0074, Fig. 9C)
“FIGS. 9C and 9D show exemplary user interfaces of an enhanced response generation process based on RL learning. As shown in user interface 940 of FIG. 9C, based on user data collected for planning a resort for a user, the chatbot agent cannot find a predefined option for the user [i.e. wherein the feedback comprises a text description.]. “
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Regarding claim 16:
Menon and Maney Jr teach the machine of claim 15.
Menon teaches:
1. transmit the command to a machine learning host associated with the machine learning model.
(Menon, ¶0040)
“In some embodiments, self-correcting chatbot application 322 of server 320 includes a data collection module 402, a feedback analyzer 404, a reinforcement learning (RL) engine 406, a recommendation module 408, and a model monitoring module 410…For example, recommendation module 408 and a model monitoring module 410 may be deployed on separate servers (including server 320) that are communicatively coupled to each other [i.e. transmit the command to a machine learning host associated with the machine learning model].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Regarding claim 18:
Menon and Maney Jr teach the machine of claim 15.
Menon teaches:
1. transmit the command to a machine learning host associated with the machine learning model and receive the first response from the machine learning host in response to the request.
(Menon, ¶0036)
“Network 308 can be an intranet network, an extranet network, a public network, or combinations thereof used by software application 302 to exchange information with one or more remote or local servers, such as server 320 [i.e. transmit the command to a machine learning host associated with the machine learning model].”
(Menon, ¶0037)
“In some embodiments, server 320 is configured to store, process and analyze the information received from user 306, via software application 302, and subsequently transmit in real time processed data back to software application 302 [i.e. and receive the first response from the machine learning host in response to the request].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Regarding claim 19:
Menon and Maney Jr teach the machine of claim 15.
Menon teaches:
1. provide the input from the user device to an additional machine learning model in order to receive an indication of the sentiment.
(Menon, ¶0061)
“An RL model associates each option or choice of a response with a reward score. Feedback analyzer 404 calculates the reward score for each option/choice based on sentiment analysis. Different mechanisms can be used to calculate a reward score can be calculated. In some embodiments, a conversation includes a direct indication regarding whether a given option or choice is accepted. If an option is accepted, feedback analyzer 404 can calculate a reward score to represent a positive reward. If an option is rejected, feedback analyzer 404 can calculate a reward score to represent a negative reward [i.e. provide the input from the user device to an additional machine learning model in order to receive an indication of the sentiment].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Regarding claim 20:
Menon and Maney Jr teach the machine of claim 15.
Maney Jr teaches:
1. map the second inquiry to at least one documentation file;
(Maney Jr, col. 8: 6-9)
“A message may then be generated and sent to the selected service representative identifier, where the message includes an identifier for identifying information associated with the incoming service message (step 512 of FIG. 5) [i.e. map the second inquiry to at least one documentation file;].”
2. and map the at least one documentation file to the escalation contact.
(Maney Jr, col. 8: 9-13)
“In some embodiments, the identifier may be a point or link for identifying or locating (e.g. in a database) the information associated with the incoming service message. The identifier may be referred to as a knowledge delivery article [i.e. map the second inquiry to at least one documentation file;].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. The motivation is the same as claim 1.
Claims 2-3, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over US Pre-Grant Patent 2023/0188480 (Menon et al; Menon) in view of US Patent 11,811,729 (Maney Jr et al; Maney Jr), further in view of US Pre-Grant Patent 2021/0264372 (Asseer et al; Asseer).
Regarding claim 2:
Menon and Maney Jr teach the machine of claim 1.
Menon teaches:
1. wherein the machine learning model is trained on a set of documentation [associated with the onboarding process.]
(Menon, ¶0023)
“Conventionally, a chatbot is created by training a model on question & answer datasets to provide answers to certain generic questions. The chatbot may include a natural language understanding (NLU) unit that is trained using supervised ML techniques. A supervised ML model may be trained over conversational datasets using sequence-to-sequence (Seq2Seq) based techniques [i.e. wherein the machine learning model is trained on a set of documentation].”
Asseer teaches:
1. [wherein the machine learning model is trained on a set of documentation] associated with the onboarding process.
(Asseer, ¶0065)
“Sends new employees all departmental specific information such as code of ethics, policies, health and safety, et [i.e. associated with the onboarding process].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon and Maney Jr with the predictable improvement of Asseer as the data given would train Menon’s chatbot model using Asseer’s onboarding documentation because Menon and Maney Jr teach training chatbot models on question-and-answer and conversational datasets to provide answers, while Asseer teaches automatically answering onboarding questions using onboarding information and documents, including policies and health-and-safety materials.
Regarding claim 3:
Menon, Maney Jr, and Asseer teach dependent claim 2.
Maney Jr teaches:
1. wherein the escalation contact is associated with a portion of the set of documentation that was determined to be relevant to the second inquiry.
(Maney, col. 8: 9-11)
“In some embodiments, the identifier may be a point or link for identifying or locating (e.g. in a database) the information associated with the incoming service message [i.e. wherein the escalation contact is associated with a portion of the set of documentation that was determined to be relevant to the second inquiry].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Menon and Maney Jr with Asseer. The motivation is the same as claim 2.
Regarding claim 14:
Menon and Maney Jr teach the machine of claim 1.
Asseer teaches:
1. receiving, from the onboarding system and at the user device, an indication of an assignment.
(Asseer, ¶0062)
“Optionally, the system can poll all of the new employees and students to determine their satisfaction after a pre-determined amount of time (such as after their first week). The results of the poll may be stored in the database for action by onboarding staff and/or the system takes action in response to the results [i.e. receiving, from the onboarding system and at the user device, an indication of an assignment].”
One of ordinary skill would have been motivated to modify Menon and Maney Jr with Asseer’s onboarding communications so the automated chatbot would deliver required training assignments directly to new employees, thereby centralizing onboarding communications and reducing manual HR follow-up, because Asseer teaches its system “Informs new employees and students of all mandatory training (Asseer, ¶0066).”
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over US Pre-Grant Patent 2023/0188480 (Menon et al; Menon) in view of US Patent 11,811,729 (Maney Jr et al; Maney Jr), further in view of US Patent 9,654,640 (Brydon et al; Brydon).
Regarding claim 11:
Menon and Maney Jr teach the method of claim 8.
Menon teaches:
1. wherein the indication of the escalation contact is received using a chat application executed by the user device, and the method further comprises:
(Menon, ¶0034, Fig. 2)
“FIG. 2 shows an exemplary user interface 200 of an RL-based chatbot conversation, according to some embodiments [i.e. wherein the indication of the escalation contact is received using a chat application executed by the user device, and the method further comprises:].”
Neither Menon nor Maney Jr explicitly teaches:
1. initiating a chat group including the escalation contact.
(Brydon, col. 13: 57-64)
“In some embodiments Service Interface Logic 175 includes features configured to a requester to communicate with several experts at once in response to a single customer service inquiry. For example, Service Interface Logic 175 may be configured to display a multi-party chat window on Client Devices 110, the chat window being configured to receive text, audio and/or video from more than one of Expert Devices 115 in parallel [i.e. initiating a chat group including the escalation contact].”
One of ordinary skill, at the time the invention was filed, would have been motivated to modify Menon with Maney Jr. with Brydon’s multi-party chat interface so that, after USAA identifies the selected representative, the user could initiate an application-based group chat including that representative and additional experts, thereby enabling collaborative and timely resolution of the inquiry. Brydon expressly teaches allowing a requester to “communicate with several experts at once in response to a single customer service inquiry (Brydon, col. 13: 57-58).”
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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