DETAILED ACTION
A. This action is in response to the following communications: Request for Continued Examination filed 08/20/2026.
B. Claims 1-20 remains pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/20/2026 has been entered.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mars, Jason et al. (US Pub. 2020/0151566 A1), herein referred to as “Mars” in view of Friio, Andrea (US Pub. 2022/0070296 A1), herein referred to as “Friio” in further view of Vishnoi, Vishal et al. (US Pub. 2022/0100961 A1), herein referred to as “Vishnoi”.
As for claims 1, 10 and 18, Mars teaches. A system and corresponding method of claim 10 and non-transitory computer-readable storage medium of claim 18 having instructions stored thereon that when executed by at least one processing circuit, cause the at least one processing circuit to perform operations comprising and a processing circuit comprising memory and one or more processors, the processing circuit configured to (par. 21 and 135 implementing an artificial intelligence (AI) virtual assistant platform within hardware environment that consists of processors software and memories):
receive an input from a user device associated with a user during an interaction with a virtual assistant executed by the processing circuit (par. 22 system utilizes a graphical user interface to receive user query and/or user command input in form of text or speech);
determine a classification associated with the input (par. 22 the natural language processing (NLP) components of the system include a competency classification engine to identify competency classification labels for user input data and parse user input into data that is comprehensible which then is converted into program-comprehensible and useable features);
identify a prior user interaction with a virtual assistant associated with the classification (par. 22 using outputs of NLP perform various operations that accesses one or more data sources relevant to the query/command while performing the following data filtering, data aggregation and the like to the data accessed from one or more data sources; par. 24 data sources can be external; par. 26 classification engine 120 classifies user input data (query/command) wherein training input used in training learning algorithm of classification engine may include crowdsourced data obtained from one or more disparate user query and/or command data sources and/or platforms (e.g. messaging platform etc.), it is to be noted that the system can utilize training data from any suitable external data sources wherein the learning algorithm may be continually trained using user quires and commands);
determine a prior resolution based on the prior user interaction, wherein the prior resolution comprises an action available to the user (par. 27 using data derived on processing prior quires data, which consists of one or more prior query per se (e.g. text data or the like), data derived based upon processing the prior query (according to method 200 or the like, query response data, metadata about the query and the like; par. 91-92 storing past queries for identifying supplemental classification labels based upon user input data, performing identification of successive cognate user input to generate response to successive cognate user query; a successive, cognate user query relates to query that is posed by user and relates to the prior query thereby identifying a successful relation between prior query and a successive cognate query based upon classification; this can be refine or redefined based upon prior query posed by user and is an extension or continuation of the prior query where the system can chain or link/associate together a prior query and the successive cognate query; thereby resolutions are used to form seamless and/or consistent responses that relate to queries);
determine, based on current financial data associated with the user and the prior resolution, if the action available to the user satisfies a threshold (par. 96 the trained data used to classify user input data according to a pool of competency classification labels as described in S220; par. 97 an arbitrary threshold can be met with user posed question “how about last year?” in which system uses current financial data “income” to reply to the user in “Successive, Cognate” or “Follow-on” query since a structure of the text or language of the query suggests a positive likelihood or high probability that the query is related to and intended to refine a prior query of the user); and
generate and provide, based on the action available to the user satisfying the threshold, an output via the user device, the output corresponding to the prior resolution (par. 97 the competency classification label of the successive, cognate user query may function to define a universe of functions and operations applicable to the query when generating a response and the supplemental classification label may function to trigger an additional query handling process that involves identifying a prior, related query and updating the response to the prior, related query with slot values derived for the successive, cognate query; this is but one example of an output that can be displayed on the user interface the user is interacting with by inputting queries into the system).
Mars does not specifically teach details about interaction to be conversation; however in the same field of endeavor Friio teaches a conversation with a virtual assistant (par. 3 a personal bot assistant and an asynchronous resolution facilitator; receiving a customer request from a first customer, the first customer request being received in a first conversation between the first customer and the personal assistant bot via a personal device corresponding to the first customer); identify based on the classification, a prior conversation between the virtual assistant and the user, prior conversation associated with classification (par. 46 history of chats between assistant and user are stored on Universal Contact Server (USC) which are tagged and used in future conversations to meet user needs, to do this, the UCS 246 may be configured to identify data pertinent to the interaction history for each customer such as, for example, data related to comments from agents, customer communication history, and the like. Each of these data types then may be stored in the customer database 222 or on other modules and retrieved as functionality described herein requires.); determine a prior resolution based on the prior conversation wherein the prior resolution is associated with an action available ( par. 46 use history to complete an action in current conversation); respond based on the action available to the user satisfying the threshold to the input by providing an output response into the conservation via the user device the output response corresponding to at least one of the prior resolution or the action available to the user (par. 46 and 65 The dialog manager 272 receives the syntactic and semantic representation from the text analytics module 270 and manages the general flow of the conversation based on a set of decision rules. In this regard, the dialog manager 272 maintains a history and state of the conversation and, based on those, generates an outbound communication. As described in further detail below, the conversation path may be selected based on an understanding of a particular purpose or topic of the conversation. ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Friio into Mars because Friio suggests automating aspects of contact center operations and customer experience, including customer services offered through an application executed on a mobile computing device.
Even if it is found that the teachings in par. 92-96 of Mars do not imply “determine, based on current financial data associated with the user and the prior resolution, if the action available to the user would cause, upon performance of the action, the current financial data to satisfy a threshold and respond, based on the action available to the user being determined to cause the current financial data to satisfy the threshold, to the input by providing an output response into the conversation via the user device, the output response corresponding to at least one of the prior resolution or the action available to the user; then it is also found in an alternative that Vishnoi teaches this limitation in paragraphs 5, 71, 82, 116 and 130; wherein Vishnoi discusses a virtual assistant that interacts with a user via chats (e.g. chatbot) that uses different skills to determine user intent, one of which skills is a banking skill, upon a users utterance (user input) into a banking skill agent the user is able to ask financial data about a users checking and saving accounts; one such question can be asking if the user has enough money to make a purchase, this functions across domains (banking and retail); to enable a satisfaction of a threshold being met to complete a purchase or in other case determine if the action of buying an item is available to a user upon checking to see if the user has enough money and then responding positively when the threshold is satisfied, yes the user has enough money for purchase, continue with the retail sale of the item.
Par. 82 “ …For example, if a user is engaged in a conversation with a shopping skill (e.g., the user has made some selections for purchase), the user may want to jump to a banking skill (e.g., the user may want to ensure that he/she has enough money for the purchase), and then return to the shopping skill to complete the user's order. To address this, the states section in the dialog flow definition of the first skill can be configured to initiate an interaction with the second different skill in the same digital assistant and then return to the original dialog flow…”
Par. 130 “…For example, consider a digital assistant has a banking skill and a skill for an online retail shop. If a user inputs the question “What's my balance?”, this could apply to both the user's bank account balance and the balance remaining on a gift card that is registered with the online retailer. If the user such as a customer enters this question before entering the context of either skill, the digital assistant should give them a choice of which “balance” flow to enter (either in the banking skill or the retailer skill). However, if the user enters this question from within the banking skill, the digital assistant should automatically pick the “balance” flow that corresponds to the banking skill (and disregard intents from other skills, even if they meet the standard Confidence Threshold routing parameter)…”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vishnoi into Mars as modified by Friio, because Vishnoi suggests in paragraph 3 that instead of the end user learning a fixed set of keywords or commands that the bot knows how to respond to, an intelligent bot may be able to understand the end user's intention based upon user utterances in natural language and respond accordingly.
As for claims 2 and 11, Mars teaches. The system of claim 1 and corresponding method of claim 10, wherein the output comprises an interface via a graphical user interface (GUI) of the user device, the interface comprising an actionable item associated with the prior resolution; and receiving, by the processing circuit, a selection of the actionable item from the user device; wherein the processing circuit is further configured to autonomously perform the action available to the user based on a selection of the actionable item (par. 90-91 for a given user input data in which the slots have been identified and slot labels assigned thereto, the response template may function to automatically pull slot data of the user input data into its one or more slot sections based on the slot label associated with the slot data. The method 700 for implementing an artificially intelligent assistant for conversational interactions includes storing one or more prior queries S705, identifying a supplemental classification label based on user input data S710, performing slot identification and identifying slot classification labels of the successive S715, configuring and executing one or more computer-executable operations for generating a response to the successive, cognate user query S720. Par. 38 The user interface system 105 may include any type of device or combination of devices capable of receiving user input data and presenting a response to the user input data from the artificially intelligent virtual assistant.).
As for claims 3, 12 and 19, Mars teaches. The system of claim 1 and corresponding method of claim 10 and non-transitory computer-readable storage medium of claim 18, wherein the processing circuit is further configured to:
generate a recommended resolution based on the current financial data and the input, wherein the recommended resolution comprises a recommended action available to the user; and generate and provide a recommendation output via the user device, the recommendation output corresponding to the recommended resolution (par. 51 For example, if the artificially intelligent virtual assistant is configured by a system implementing method 200 to be competent in three areas of competency including, Income competency, Balance competency, and Spending competency in the context of a user's banking, then the deep classification machine learning algorithm may generate a classification label for each of Income, Balance, and Spending; par. 80 financial data can be used in the data fetching step to enable the system to response to query related to user finances (e.g. “income”); “Income competency-specific functions may include functions that enable fetching of financial data of the user and operations that enable summation or aggregation of portions of the financial data of the user.”; also note par. 28 and 30).
Mars does not specifically teach details about interaction to be conversation; however in the same field of endeavor Friio teaches a conversation with a virtual assistant (par. 3 a personal bot assistant and an asynchronous resolution facilitator; receiving a customer request from a first customer, the first customer request being received in a first conversation between the first customer and the personal assistant bot via a personal device corresponding to the first customer);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Friio into Mars because Friio suggests automating aspects of contact center operations and customer experience, including customer services offered through an application executed on a mobile computing device.
As for claims 4, 13 and 20, Mars teaches. The system of claim 3 and corresponding method of claim 12 and non-transitory computer-readable storage medium of claim 19, wherein the processing circuit is further configured to: determine prior decision components used to generate the prior resolution; determine current decision components used to generate the recommended resolution; compare at least one of the prior decision components and at least one of the current decision components; and generate and provide a comparison output via the user device, the comparison output corresponding to the comparison of the at least one of the prior decision components and the at least one of the current decision components (par. 91-92 using prior query outcomes to create seamless and or consistent responses to the related queries).
Mars does not specifically teach details about interaction to be conversation; however in the same field of endeavor Friio teaches a conversation with a virtual assistant (par. 3 a personal bot assistant and an asynchronous resolution facilitator; receiving a customer request from a first customer, the first customer request being received in a first conversation between the first customer and the personal assistant bot via a personal device corresponding to the first customer);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Friio into Mars because Friio suggests automating aspects of contact center operations and customer experience, including customer services offered through an application executed on a mobile computing device.
As for claim 5 and 14, Mars teaches. The system of claim 3 and corresponding method of claim 12, wherein the recommendation response comprises an interface via a graphical user interface (GUI) of the user device, the interface comprises an actionable item associated with the recommended resolution; receiving, by the processing circuit, a selection of the actionable item from the user device; and wherein the processing circuit is further configured to initiate the recommended action of the recommended resolution based on a selection of the actionable item (par. 38 The user interface system 105 may include any type of device or combination of devices capable of receiving user input data and presenting a response to the user input data from the artificially intelligent virtual assistant; par. 40 perform slot value identification of the user input data that includes identifying details in the query or command that enables the system to service the query or command. In slot value identification, the system may function to segment or parse the query or command to identify operative terms that trigger one or more actions or operations by the system required for servicing the query or command. Accordingly, the method 200 may initially function to decompose a query or command into intelligent segments and convert each of those segments into machine-useable objects or operations. The method 200 may then function to use the slot value identifications and slot value extractions to generate one or more handlers (e.g., computer-executable tasks) for the user input data that indicate all the computer tasks that should be performed by the artificially intelligent virtual assistant to provide a response to the user query or user command.).
As for claims 6 and 15, Mars teaches. The system of claim 1 and corresponding method of claim 10, wherein the processing circuit is further configured to: determine an interaction frequency associated with interactions between the user and the virtual assistant executed by the processing circuit and being associated with the classification; determine, based on the input and the interaction frequency, an interaction period associated with a future predicted interaction between the user and the virtual assistant executed by the processing circuit associated with the classification; and provide a notification output via the user device, the notification output corresponding to a notification associated with the future predicted interaction (par. 56 and 59 S220 may provide the user input data, either synchronously (i.e., in parallel) or asynchronously, to each of the specific-competency trained deep machine learning algorithms to generate a suggestion and/or prediction of a competency classification label and associated probability of intent match, according to the training of the specific-competency algorithm that matches at least one of the multiple areas of competency for the user input data and predetermined competency threshold may be based on a statistical analysis of historical user input data).
As for claim 7, Mars teaches. The system of claim 6, wherein the notification output comprises an interface via a graphical user interface (GUI) of the user device, the interface comprises an actionable item associated with the prior resolution; and wherein the processing circuit is further configured to initiate the action corresponding to the prior resolution within the interaction period based on a selection of the actionable item (par. 59 and 105 pulling/retrieving historical queries and/or prior query data based upon user input; these prior data would include all resolutions/results of said query).
As for claims 8 and 16, Mars teaches. The system of claim 1 and corresponding method of claim 10, wherein the processing circuit is further configured to: determine prior decision components used to generate the prior resolution; and generate and provide a logic output via the user device, the logic output corresponding to the prior resolution and at least one of the prior decision components (par. 105 using historical queries and prior query data of user to create new outputs as noted in par. 92 as well).
Mars does not specifically teach details about interaction to be conversation; however in the same field of endeavor Friio teaches a conversation with a virtual assistant (par. 3 a personal bot assistant and an asynchronous resolution facilitator; receiving a customer request from a first customer, the first customer request being received in a first conversation between the first customer and the personal assistant bot via a personal device corresponding to the first customer);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Friio into Mars because Friio suggests automating aspects of contact center operations and customer experience, including customer services offered through an application executed on a mobile computing device.
As for claims 9 and 17, Mars teaches. The system of claim 8 and corresponding method of claim 16, wherein the logic response comprises an actionable item associated with the at least one of the prior decision components; and wherein in response to receiving an indication of an adjustment of the actionable item, the processing circuit is further configured to: determine a decision adjustment of the at least one of the prior decision components corresponding to the adjustment of the actionable item; generate, based on the decision adjustment, an updated resolution, wherein the updated resolution comprises an updated action available to the user; generate and provide an updated resolution output via the user device, the updated resolution output corresponding to the updated action of the updated resolution (par. 92 and 97 The successive, cognate user query may typically function to refine or redefine a prior query posed by the user).
Mars does not specifically teach details about interaction to be conversation; however in the same field of endeavor Friio teaches a conversation with a virtual assistant (par. 3 a personal bot assistant and an asynchronous resolution facilitator; receiving a customer request from a first customer, the first customer request being received in a first conversation between the first customer and the personal assistant bot via a personal device corresponding to the first customer);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Friio into Mars because Friio suggests automating aspects of contact center operations and customer experience, including customer services offered through an application executed on a mobile computing device.
(Note:) It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Inquires
Any inquiry concerning this communication should be directed to NICHOLAS AUGUSTINE at telephone number (571)270-1056.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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/NICHOLAS AUGUSTINE/Primary Examiner, Art Unit 2178 September 14, 2026
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