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 .
DETAILED ACTION
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
No
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Yes, if the claims are alternatively construed to be abstract in step 2A1. The claims seek to improve and enable reduction in call center volume supported by the specification, and reflected by the claims e.g. in spec: 0071.
Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
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, 4, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20110060587 A1 Phillips; Michael S. et al. (hereinafter Phillips) in view of US 9319522 B1 Webster; Craig A. (hereinafter Webster).
Re claim 1, Phillips teaches
1. (Original) A system for dynamically adapting natural language dialogue interactions with a customer, comprising: (fig. 1 customer can be any user)
a memory device storing instructions; one or more processors configured to execute the instructions to: (fig. 1)
identify an intent of an incoming customer dialogue message by analyzing linguistic patterns, customer account metadata, and communication channel attributes; (determining intent using language models as linguistic analysis 0010, metadata as context per se for user models, and channel attribute such as 0063)
generate a first event, corresponding to the identified intent, to be placed in an event queue monitored by a dialogue management device; (queue as in cache or data that is freshly processed in real time or live as in fig. 7b)
execute an adaptive response workflow by generating a command for at least one of a natural language processing device, an API server, or a communication interface, wherein the command includes (ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning)
a structured response dialogue message, and a suggestion for an additional follow-up action based on predictive behavior analysis; and (using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning)
transmit the structured response dialogue message and associated follow-up action to the customer via a communication channel. (user can execute the command after follow-up corrective actions, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning)
However, while Phillips teaches a user, and a customer and user can be the same person, it does not specifically define customer models per se, and fails to teach:
Customer as a user (col 4 line 55 to col 5 line 1 customer defined)
process the first event using a customer profile model, wherein the customer profile model dynamically updates with real-time contextual information derived from customer interactions and inferred needs; (Webster col 4 line 55 to col 5 line 1 customer model to profile the customer and fig. 2a-b to provide other follow up services, wherein the model is updateable col 3 lines 48-67, using customer history for predicting intent and the purpose of interaction and channel of communication col 10 lines 4-52)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Phillips to incorporate the above claim limitations as taught by Webster to allow for simple substitution of one known element for another to obtain predictable results such as a user for a customer and a custom model as a customer profile model, both are updatable, wherein the context of customer service adds a context to Phillips in which specific models can now be added, thereby avoiding false negatives for intent extraction.
Re claim 2, Phillips teaches
2. (Currently Amended) The system of claim 1, wherein the dialogue management device integrates real-time customer feedback to adjust the generated command and refine the structured response dialogue message during [[the]]a first customer interaction. (feedback by user, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning)
Re claim 4, Phillips teaches
4. (Original) The system of claim 1, wherein the structured response dialogue message is further tailored based on:
historical communication patterns unique to the customer; (using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
contextual data retrieved from external systems, including third-party integrations; and (external models/ASR 0096-0097, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, metadata as context per se for user models, and channel attribute such as 0063)
a specific communication channel utilized by the customer. (Device ID for instance, external models/ASR 0096-0097, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, metadata as context per se for user models, and channel attribute such as 0063)
Re claim 17, Phillips teaches
17. (New) A system for dynamically adapting natural language dialogue interactions with a customer, comprising: (using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
a memory device storing instructions; one or more processors configured to execute the instructions to: (fig. 1)
identify an intent of an incoming customer dialogue message; (determine what user wants in the command… using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
generate an event, corresponding to the identified intent, to be placed in an event queue; (as in cache per se, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
execute an adaptive response workflow with a series of commands for at least one of a natural language processing device, an API server, or a communication interface to thereby generate a response dialogue message based on the processed event; (ASR device, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
receive a subsequent customer dialogue message; (conversation can continue indefinitely and change topics, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
adjust and refine the response dialogue message based on the subsequent customer dialogue message to generate an updated response dialogue message; (via correction or disambiguation, conversation can continue indefinitely and change topics, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
and transmit the updated response dialogue message to a customer. (via correction or disambiguation, conversation can continue indefinitely and change topics, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
However, while Phillips teaches a user, and a customer and user can be the same person, it does not specifically define customer models per se, and fails to teach:
A user as a customer and…
process the event using a model and a rules-based platform, wherein the model dynamically updates with real-time contextual information derived from customer inferred needs; (Webster col 4 line 55 to col 5 line 1 customer model to profile the customer and fig. 2a-b to provide other follow up services, wherein the model is updateable col 3 lines 48-67, using customer history for predicting intent and the purpose of interaction and channel of communication col 10 lines 4-52)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Phillips to incorporate the above claim limitations as taught by Webster to allow for simple substitution of one known element for another to obtain predictable results such as a user for a customer and a custom model as a customer profile model, both are updatable, wherein the context of customer service adds a context to Phillips in which specific models can now be added, thereby avoiding false negatives for intent extraction.
Re claim 18, Phillips teaches
18. (New) The system of claim 17, wherein adjusting and refining the response dialogue message comprises:
identifying supplemental context-specific information based on the subsequent customer dialogue message; and (user can alter context any time e.g. GPS then play a song, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
generating a predictive follow-up recommendation comprising a proactive action, (as in fig. 7b, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
wherein the updated response dialogue message comprises the predictive follow-up recommendation, and(as in fig. 7b, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
wherein the proactive action relates to checking an account balance, submitting a payment, closing an account, performing a routine account action, or combinations thereof. (0062 under BRI, a routine account as in a user profile account e.g. music playlist, and as in fig. 7b, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
Re claim 19, Phillips teaches
19. (New) The system of claim 18, wherein the proactive action is generated using customer information, wherein the customer information comprises stored data associated with a customer account, and wherein the stored data comprises one or more of customer identification information, bank accounts, mortgage loan accounts, car loan accounts, account numbers, authorized users associated the customer accounts, account balances, account payment history, typical account information, or combinations thereof. (“customer account” 0062 under BRI, a routine account as in a user profile account e.g. music playlist, and as in fig. 7b, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
Re claim 20, Phillips teaches
20. (New) The system of claim 18, wherein the one or more processors are further configured to execute the instructions to:
generate and transmit, via an automated dialogue interface, a request for additional information to the customer; (any user request or command at any point, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
receive, via the automated dialogue interface, the additional information; and (using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
adjust the updated response dialogue message based on the additional information. (corrective action e.g. fig. 7b, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
Claims 3 and 5-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20110060587 A1 Phillips; Michael S. et al. (hereinafter Phillips) in view of US 9319522 B1 Webster; Craig A. (hereinafter Webster) and further in view of US 20180211260 A1 Zhang; Yongzheng et al. (hereinafter Zhang).
Re claim 3, Phillips teaches
3. (Currently Amended) The system of claim 1, wherein the adaptive response workflow includes a multi-step processing pipeline configured to:
retrieve supplementary information to augment the structured response dialogue message. (using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
However, the combination while relative to user interactions, fails to teach:
analyze sentiments of the incoming customer dialogue message; (Zhang sentiment plays a role in ranking urgency or priority 0024 0027 fig. 4)
prioritize tasks based on inferred urgency levels; and (Zhang sentiment plays a role in ranking urgency or priority 0024 0027 fig. 4)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Phillips in view of Webster to incorporate the above claim limitations as taught by Zhang to allow for simple substitution of one known element for another to obtain predictable results such as using emotion or sentiment to determine another context layer to avoid fixation that a user not frustrated based on plainly spoken language, thereby reducing false negatives or positives in-context and re-ranking inquiries or tickets for customer resolution related events.
Re claim 5, Phillips teaches
5. (Currently Amended) A method for providing dynamically adaptive natural language dialogue with a customer, comprising: (fig. 1)
receiving a customer dialogue message through an automated dialogue interface in a customer interaction; (conversation, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
generating a first event in response to the inferred objective, the first event capturing contextual parameters of the customer interaction; (back and forth disambiguation, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
executing a processing workflow for the first event by:
retrieving customer account data; (“customer account” 0062 under BRI, a routine account as in a user profile account e.g. music playlist, and as in fig. 7b, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
identifying supplemental context-specific information; and (context and using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
generating a predictive follow-up recommendation; (fig. 7b and fc, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
composing a response dialogue message, the response dialogue message comprising: (fig. 7b and fc, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
content aligned with the inferred objective, and (can be anything like GPS or play a song, fig. 7b and fc, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
supplemental information enhancing [[the]] a relevance of the response; and transmitting the response dialogue message and the predictive follow-up recommendation to the customer. (“did you mean to say”… fig. 7b and fc, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
However, while Phillips teaches a user, and a customer and user can be the same person, it does not specifically define customer models per se, and fails to teach:
Customers per se
determining an inferred objective of the customer dialogue message based on: historical interaction patterns… (Webster col 4 line 55 to col 5 line 1 customer model to profile the customer and fig. 2a-b to provide other follow up services, wherein the model is updateable col 3 lines 48-67, using customer history for predicting intent and the purpose of interaction and channel of communication col 10 lines 4-52)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Phillips to incorporate the above claim limitations as taught by Webster to allow for simple substitution of one known element for another to obtain predictable results such as a user for a customer and a custom model as a customer profile model, both are updatable, wherein the context of customer service adds a context to Phillips in which specific models can now be added, thereby avoiding false negatives for intent extraction.
However, the combination while relative to user interactions, fails to teach:
…and real-time sentiment analysis (Zhang sentiment plays a role in ranking urgency or priority 0024 0027 fig. 4)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Phillips in view of Webster to incorporate the above claim limitations as taught by Zhang to allow for simple substitution of one known element for another to obtain predictable results such as using emotion or sentiment to determine another context layer to avoid fixation that a user not frustrated based on plainly spoken language, thereby reducing false negatives or positives in-context and re-ranking inquiries or tickets for customer resolution related events.
Re claim 6, while Phillips teaches a user, and a customer and user can be the same person, it does not specifically define customer models per se, and fails to teach:
6. (Currently Amended) The method of claim 5, wherein the predictive follow-up recommendation includes a proactive action, such as suggesting a future appointment or offering additional services relevant to [[the]] a customer context. (Webster col 4 line 55 to col 5 line 1 customer model to profile the customer and fig. 2a-b to provide other follow up services, wherein the model is updateable col 3 lines 48-67, using customer history for predicting intent and the purpose of interaction and channel of communication col 10 lines 4-52)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Phillips to incorporate the above claim limitations as taught by Webster to allow for simple substitution of one known element for another to obtain predictable results such as a user for a customer and a custom model as a customer profile model to provide advertising or marketing purposes, both are updatable, wherein the context of customer service adds a context to Phillips in which specific models can now be added, thereby avoiding false negatives for intent extraction.
Re claim 7, Phillips teaches
7. (Original) The method of claim 5, further comprising dynamically updating a customer sentiment model based on successive iterations of dialogue during a single interaction session. (using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
Re claim 8, Phillips teaches
8. (Original) The method of claim 5, wherein the response dialogue message is composed using a machine learning model trained on multimodal inputs, including text, audio, and prior user behavioral data. (using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
Re claims 9 and 14, Phillips teaches
9. (New) The system of claim 1, wherein the one or more processors are further configured to execute the instructions to:
initiate a subsequent interaction by generating a natural language phrase in response to receiving the incoming customer dialogue message; (using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
transmit the natural language phrase to the customer via a communication channel; (send back to customer, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
receive a subsequent incoming customer dialogue message; (resolution until user satisfied, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
adjust the command; and (resolution until user satisfied, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
refine the structured response dialogue message during the subsequent interaction. (structure as in fig. 7b-c, resolution until user satisfied, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
Re claim 10, Phillips teaches
10. (New) The system of claim 9, wherein the one or more processors are further configured to execute the instructions to:
identify a subsequent intent of the subsequent incoming customer dialogue message by analyzing linguistic patterns, customer account metadata, and communication channel attributes; and (determining intent using language models as linguistic analysis 0010, metadata as context per se for user models, and channel attribute such as 0063)
generate a subsequent event, corresponding to the identified subsequent intent, to be placed in the event queue monitored by the dialogue management device. (cache as a queue under BRI, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
Re claims 11 and 16, Phillips teaches
11. (New) The system of claim 1, wherein the additional follow-up action and the structured response dialogue message are further generated using a rules-based platform. (grammar, vocabulary, models, etc. using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
Re claim 12, Phillips teaches
12. (New) The system of claim 1, wherein generating the additional follow-up action further comprises determining a series of commands for the execution of the additional follow-up action. (e.g. fig. 7b-c, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
Re claim 13, Phillips teaches
13. (New) The system of claim 1, wherein the intent of the incoming customer dialogue message and the additional follow-up action relate to an account action or an opt-in/opt-out action. (“customer account” and action thereof 0062 under BRI, a routine account as in a user profile account e.g. music playlist, and as in fig. 7b, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
Re claim 15, Phillips teaches
15. (New) The method of claim 14, further comprising:
identifying a subsequent inferred objective of the subsequent customer dialogue message; and (e.g. fig. 7b-7c, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
executing a subsequent processing workflow for a subsequent event based on the subsequent inferred objective. (e.g. fig. 7b-7c, using user behavior e.g. user history 0089 with ASR device as in fig. 1 to determine user intent of a command/request as in fig. 7b-c with an updateable model array using text and speech as a medium 0060 0105 and 0010 updatable models i.e. learning, external models/ASR 0096-0097, and channel attribute such as 0063)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20180183737 A1 Subbarayan; Anand et al.
Financial based accounts and chat interactions
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
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