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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/19/2026 has been entered.
Response to Amendment
Claims 1-30 are pending in this application.
Applicant’s arguments on claim rejections 35 USC 103, filed 1/19/2026, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Perlov and Mettler May.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-30 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.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” or “configured to” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: an “intelligent flow framework module” in claims 1, 6-7, 9-10, 12, 14-15, 17 and 22. According to current specification in [0021], the intelligent flow framework module comprises network adapters are hardware components which are sufficient structure to perform the recited functions in the claims.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1:
Claim 1 recites “A system”. The claim recites the system comprising an intelligent flow framework module and therefore is a machine.
Step 2A Prong One:
Claim 1 recites the limitation “generate” which specifically recites “wherein the intelligent flow framework module comprises a knowledgebase and a contextual unit and is configured to generate a task flow based on an event and contextual data, wherein the contextual data includes a current state of an actor and environment.” This limitation are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other reciting an “interface”, an “artificial intelligence module” and an “intelligent flow framework module”, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “generate” in the context of this claim encompasses a user mentally, and with the aid of pen and paper generating a task flow based on an event and contextual data which includes a current state of an actor and environment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of using an “interface”, an “artificial intelligence module” and an “intelligent flow framework module” to perform the step amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim also recites the additional element “the event includes a prompt, message, signal, API call, or a combination thereof.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as selecting a particular data source or type of data to be manipulated (See MPEP 2106.05(g)). The claim is not patent eligible.
Claim 3 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow framework module comprises an active knowledgebase, a contextual unit, and a user profiling database.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as selecting a particular data source or type of data to be manipulated (See MPEP 2106.05(g)). The claim is not patent eligible.
Claim 4 is dependent on the claim 3 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim also recites the additional element “the contextual unit includes an emotional module, an artificial conscience module, or any other sub-module required for generating the contextual data.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as selecting a particular data source or type of data to be manipulated (See MPEP 2106.05(g)). The claim is not patent eligible.
Claim 5 is dependent on the claim 4 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim also recites the additional element “the contextual data includes the current state of an actor, environment, actor history, workflow, or a combination thereof.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as selecting a particular data source or type of data to be manipulated (See MPEP 2106.05(g)). The claim is not patent eligible.
Claim 6 is dependent on the claim 3 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow framework module is configured to generate a task based on an event received from the interface, and contextual data retrieved from at least one of the active knowledgebase, the contextual unit, or the user profiling database” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 7 is dependent on the claim 5 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow framework module is configured to monitor the current state of the contextual data.” The limitation amounts to monitoring audit log data relates to transactions or activities (MPEP 2106.05(h)). The claim is not patent eligible.
Claim 8 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow framework module comprises a confidence module and a parameter module.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as selecting a particular data source or type of data to be manipulated (See MPEP 2106.05(g)). The claim is not patent eligible.
Claim 9 is dependent on the claim 4 and includes all the limitations of claim 1. Therefore, claim 9 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow framework module is configured to define a mission based on the event, the contextual data, or a combination thereof” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 10 is dependent on the claim 9 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow framework module is configured to define the at least one task based on the mission, the event, or the contextual data” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 11 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 11 recites the same abstract idea of claim 1. The claim also recites the additional element “the at least one task comprises at least one action, a chain of actions, a graph of actions, a prompt, or a combination thereof.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as selecting a particular data source or type of data to be manipulated (See MPEP 2106.05(g)). The claim is not patent eligible.
Claim 12 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 12 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow framework module is configured to define the at least one task for an intelligent flow agent” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 13 is dependent on the claim 12 and includes all the limitations of claim 1. Therefore, claim 13 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow agent executes the at least one task assigned by the intelligent flow framework module.” The limitation amounts to requiring the use of software to tailor information and provide it to the user on a generic computer (see MPEP 2106.05(f)). The claim is not patent eligible.
Claim 14 is dependent on the claim 12 and includes all the limitations of claim 1. Therefore, claim 14 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow framework module is configured to observe the current state of the task assigned to the intelligent flow agent.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as data gathering (MPEP 2106.05(g)). The claim is not patent eligible.
Claim 15 is dependent on the claim 12 and includes all the limitations of claim 1. Therefore, claim 15 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow framework module is configured to interrupt the execution of the task assigned to the intelligent flow agent based on the event, contextual data, a new task defined by the intelligent flow framework module, or a combination thereof.” The limitation amounts to no more than mere instructions on a general purpose computer ((MPEP 2106.05(f)). The claim is not patent eligible.
Claim 16 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 16 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow framework module comprises network adapters to connect with external devices, sensors, communication devices, agents, machine interfaces, or web services.” The limitation amounts to no more than mere instructions on a general purpose computer ((MPEP 2106.05(f)). The claim is not patent eligible.
Claim 17 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 17 recites the same abstract idea of claim 1. The claim also recites the additional elements “the intelligent flow framework module is configured to transfer the at least one task to a new intelligent flow agent, a network adapter, an external intelligent flow agent, or distribute the at least one task between multiple intelligent flow agents and network adapters depending upon the event, current state of contextual data, a new task defined by the intelligent flow framework module, or a combination thereof.” The limitations amounts to well‐understood, routine, and conventional functions, e.g. receiving or transmitting data over a network (See MPEP 2106.05(d)). The claim is not patent eligible.
Claim 18 is dependent on the claim 12 and includes all the limitations of claim 1. Therefore, claim 18 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow agent relays the at least one task, the event, or the contextual data to an artificial intelligence module.” The limitation amounts to no more than mere instructions on a general purpose computer ((MPEP 2106.05(f)). The claim is not patent eligible.
Claim 19 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 19 recites the same abstract idea of claim 1. The claim also recites the additional element “the artificial intelligence module includes a generative learning model.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as selecting a particular data source or type of data to be manipulated (See MPEP 2106.05(g)). The claim is not patent eligible.
Claim 20 is dependent on the claim 19 and includes all the limitations of claim 1. Therefore, claim 20 recites the same abstract idea of claim 1. The claim also recites the additional element “the generative model is any neural network based on a transformer architecture, pre-trained on large datasets of unlabeled text, and able to generate novel human-like text or speech or visual.” The claim is not patent eligible.
Claim 21 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 21 recites the same abstract idea of claim 1. The claim also recites the additional element “the artificial intelligence module is trained on application-specific workflow or dataset.” The claim is not patent eligible.
Claim 22 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 22 recites the same abstract idea of claim 1. The claim also recites the additional element “the intelligent flow framework module comprises an intelligent flow designer to enable an actor to set at least one workflow, a rule engine, an action, or a combination thereof.” The claim is not patent eligible.
Regarding claim 23:
Step 1:
Claim 23 recites “A method”. The claim recites a series of steps and therefore is a process.
Step 2A Prong One:
Claim 23 recites the limitations “embedding” and “defining” which specifically recite “embedding a contextual data to the event, including a current state of an actor and environment;” and “defining at least one task based on the event and the embedded contextual data;” These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other reciting an “intelligent flow framework module” and an “intelligent flow agent”, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “embedding” and “defining” in the context of this claim encompasses a user mentally, and with the aid of pen and paper adding a contextual data to an event including a current state of an actor and environment, and determining a task or a mission based on the event and the contextual data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim 23 recite the additional elements “receiving an event;” and “monitoring the current state of the contextual data during execution to control the task flow associated with the at least one task.” The limitations amount to adding insignificant extra-solution activity to the judicial exception, such as data gathering (MPEP 2106.05(g)). The claim also recite the additional element “assigning the at least one task to at least one intelligent flow agent;” The limitation amounts to no more than mere instructions on a general purpose computer ((MPEP 2106.05(f)). The limitation also amounts to requiring the use of software to tailor information and provide it to the user on a generic computer (see MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of using an “intelligent flow framework module” and an “intelligent flow agent” to perform the step amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 24 is dependent on the claim 23 and includes all the limitations of claim 23. Therefore, claim 24 recites the same abstract idea of claim 23. The claim also recites the additional element “adding current state of at least one actor, environment, actor history, current workflow, or a combination thereof” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 25 is dependent on the claim 24 and includes all the limitations of claim 23. Therefore, claim 25 recites the same abstract idea of claim 23. The claim also recites the additional element “the at least one actor is user, human, connector, or a non-human logical structure” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 26 is dependent on the claim 24 and includes all the limitations of claim 23. Therefore, claim 26 recites the same abstract idea of claim 23. The claim also recites the additional element “the actor is at least one of a sensor capturing an environmental or physical metric, wherein the captured metric is the event.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as data gathering (MPEP 2106.05(g)). The claim is not patent eligible.
Claim 27 is dependent on the claim 23 and includes all the limitations of claim 23. Therefore, claim 27 recites the same abstract idea of claim 23. The claim also recites the additional element “receiving an event includes generating the event based on at least one prompt, message, signal, API call, or a combination thereof” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Claim 28 is dependent on the claim 23 and includes all the limitations of claim 23. Therefore, claim 28 recites the same abstract idea of claim 23. The claim also recites the additional element “defining at least one task includes generating at least one action, a chain of actions, a graph of actions, a prompt, or a combination thereof” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim is not patent eligible.
Regarding claim 29:
Step 1:
Claim 29 recites “A system”. The claim recites the system comprising a non-transitory storage element coupled to a processor to store the encoded instructions and therefore is a machine.
Step 2A Prong One:
Claim 29 recites the limitations “embed”, “define” and “determine” which specifically recite “embed a contextual data to the event;” “define a mission based on the event and embedded contextual data;” and “determine all available actions to complete the mission;” These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other reciting a “processor” and a “non-transitory storage element”, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “embed”, “define” and “determine” in the context of this claim encompasses a user mentally, and with the aid of pen and paper adding a contextual data to an event, determining a task or a mission based on the event and the contextual data, and determining all available actions to complete the task or the mission. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim 29 recite the additional element “receive an event;” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as data gathering (MPEP 2106.05(g)). The claim also recite the additional elements “a processor hosting an intelligent flow framework module comprising an intelligent flow agent, an active knowledgebase, and a contextual unit;” and “reconfigure at least one action from all the available actions during execution based on a current state of an actor and environment the contextual data.” The limitations also amounts to requiring the use of software to tailor information and provide it to the user on a generic computer (see MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of using a “processor” and a “non-transitory storage element” to perform the step amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 30:
Step 1:
Claim 30 recites “A method”. The claim recites a series of steps and therefore is a process.
Step 2A Prong One:
Claim 30 recites the limitation “generating” which specifically recite “generating an event based on the at least one contextual data;” This limitation is processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other reciting an “intelligent flow framework module” and an “intelligent flow agent”, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, ““generating” in the context of this claim encompasses a user mentally, and with the aid of pen and paper generating an event based on a contextual data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim 30 recite the additional element “receiving at least one threshold-grade contextual data including a current state of an actor and environment;” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as data gathering (MPEP 2106.05(g)). The claim also recite the additional element “relaying the event and the contextual data to a generative learning model for determining at least one task; wherein relaying of the event and the contextual data is routed through an intelligent flow agent.” The limitation amounts to no more than mere instructions on a general purpose computer ((MPEP 2106.05(f)). The limitation also amounts to requiring the use of software to tailor information and provide it to the user on a generic computer (see MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of using an “intelligent flow framework module” and an “intelligent flow agent” to perform the step amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
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.
Claims 1-7, 9-14 and 16-28 are rejected under 35 U.S.C. 103 as being unpatentable over D'agostino et al. (US 2018/0097910, hereinafter “D'agostino”) in view of Perlov (US 2022/0180258), and further in view of Mettler May et al. (US 2017/0232324, hereinafter “Mettler May”).
Regarding claim 1, D'agostino teaches A system comprising ([0062]: In certain embodiments, computer system 300 may include one or more processors 302, a communication interface 304, and memory device 306.):
an interface ([0125]: FIG. 12 illustrates an example graphical user interface 1200 that can be displayed on the agent device 180 for use by the agent 182.);
an artificial intelligence module ([0136]: Since the artificial intelligence engine 1304 may learn to modify its behavior, information describing relationships for a universe of all combinations of interaction responses, interaction requests, and context data of interaction sessions may not need to be maintained by the artificial intelligence server 190.); and
an intelligent flow framework module communicatively coupled to the interface and the artificial intelligence module, wherein the intelligent flow framework module comprises a knowledgebase and a contextual unit ([0063]: Context server 170 includes a context aggregator module 400, a context analyzer module 402, an interaction detector module 410, an identifier assignment module 412, a task determination module 414, a context selection module 416, and an interaction facilitator module 418. [0130]: The artificial intelligence server 190 receives input 1302 and generates output 138, and includes an artificial intelligence engine 1304 and a knowledge data storage 1306. [0137]: At block 1410, the artificial intelligence engine 1304 of the artificial intelligence server 190 can use the subset of the context data to generate at least one interaction response in response to the task associated with the interaction request.).
D'agostino does not explicitly teach [a module] is configured to generate a task flow based on an event and contextual data, wherein the contextual data includes a current state of an actor and environment.
Perlov teaches [a module] is configured to generate a task flow based on an event and contextual data ([0064]: In block 910, workflow system 320 may generate an actor comprising an input and output. In some embodiments, the actor may accept a variety of inputs and provide a variety of outputs. For example, an actor may accept strings, Booleans, arrays, constant values, or other types of data as inputs, and provide similar outputs. [0066]: In block 930, the system generates a workflow comprising a plurality of stages and actors. It will be appreciated that any number of stages and actors may be used to represent a dynamic workflow.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the artificial intelligence engine of D'agostino with the teaching about the dynamic workflow of Perlov because it dynamically identifies parameters in real time to enhance the visibility of data flows and to diagnose and correct errors in real time (Perlov, [0003]).
Mettler May teaches wherein the contextual data includes a current state of an actor and environment ([0111]: (ii) An algorithm estimates the actor's own state for example detects and extracts relevant stroke timing features and their attributes (for example the time and strength of the impact). In parallel another) by applying an algorithm may be used to estimate the environment state data collected by one or more sensors. (iii) The actor's state and environment state are combined to determine the interactions and outcomes relevant to the activity.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the artificial intelligence engine of D'agostino and Perlov with the teaching about the actor's state and environment state of Mettler May because it would determine the interactions and outcomes relevant to the activity (Mettler May, [0111]).
Regarding claim 2, D'agostino in view of Perlov and Mettler May teaches wherein the event includes a prompt, message, signal, API call, or a combination thereof (D'agostino, [0095]: In processing the interaction response, interaction facilitator module 418 may determine from the context data of the mobile application session that user 110 has since left the location of the workstation (e.g., using GPS data of the mobile device), such that the interaction facilitator module 418 translates the interaction response sent from web server 148 as an online web portal session message (for transmission to the workstation) to a mobile application session message for transmission to the mobile device, in order to effectively and timely reach user 110. [0101]: In an example embodiment, the user 110 may be prompted at the beginning of the virtual chat session 600A to answer various security questions to authenticate the user 110 as part of the interaction response authentication procedures. [0137] and Fig. 14: At block 1400, the interaction detector module 410 detects an interaction request in an interaction session using an interaction channel.).
Regarding claim 3, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow framework module comprises an active knowledgebase, a contextual unit, and a user profiling database (D'agostino, [0053]: Data storage 152 may be provided using one or more data storage devices configured to store information consistent with the disclosed embodiments. In the example embodiment shown in FIG. 2, data storage 152 may include customer data 200, account data 202, and transaction data 204. In one aspect, customer data 200 may include one or more data records uniquely identifying one or more users 110 of business entity 190 associated with system 140. [0130]: In FIG. 13, an example configuration of an artificial intelligence server 190 is shown. The artificial intelligence server 190 receives input 1302 and generates output 138, and includes an artificial intelligence engine 1304 and a knowledge data storage 1306.).
Regarding claim 4, D’agostino in view of Perlov and Mettler May teaches wherein the contextual unit includes an emotional module, an artificial conscience module, or any other sub-module required for generating the contextual data (D'agostino, [0063]: In FIG. 4, an example configuration of context server 170 is shown. Context server 170 includes a context aggregator module 400, a context analyzer module 402, an interaction detector module 410, an identifier assignment module 412, a task determination module 414, a context selection module 416, and an interaction facilitator module 418.).
Regarding claim 5, D’agostino in view of Perlov and Mettler May teaches wherein the contextual data includes the current state of an actor, environment, actor history, workflow, or a combination thereof (D'agostino, [0074]: The context selection module 416 selects a subset of the context data from the context data available to the context server 170 to associate with the interaction request or response. The subset of the context data can then be used in the requested interaction session after that session is established. In this way, activity history of user 110 relevant to the interaction request or response may be identified for use while irrelevant context data can be ignored. [0129]: In an example embodiment, the artificial intelligence server 190 may be used to perform various operations discussed herein using artificial intelligence to automate operations, make selections and generate messages such as interaction responses. “Artificial intelligence” is used herein to broadly describe any computationally intelligent systems that combine knowledge, techniques, and methodologies. An artificial intelligence server 190 may be any system configured to apply knowledge data (e.g., dynamic decision making models and techniques) and that can adapt itself in changing environments and update or generate new knowledge data based on such environments.).
Regarding claim 6, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow framework module is configured to generate a task based on an event received from the interface, and contextual data retrieved from at least one of the active knowledgebase, the contextual unit, or the user profiling database (D'agostino, [0063]: In FIG. 4, an example configuration of context server 170 is shown. Context server 170 includes a context aggregator module 400, a context analyzer module 402, an interaction detector module 410, an identifier assignment module 412, a task determination module 414, a context selection module 416, and an interaction facilitator module 418. [0137] and Fig. 14: At block 1404, the task determination module 414 determines a task associated with the interaction request.);
Regarding claim 7, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow framework module is configured to monitor the current state of the contextual data (D'agostino, [0071]: The interaction detector module 410 can detect an interaction request for an interaction session between a request initiator and a request service provider. The interaction detector module 410 can also detect an interaction response of the request service provider during an interaction session with the request initiator. In an example embodiment, each interaction to and from the system 140 is sent to interaction detector module 410 for processing and/or the interaction detector module 410 monitors communications being sent or received by, or within, system 140 to identify interaction requests and responses.).
Regarding claim 9, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow framework module is configured to define a mission based on the event, the contextual data, or a combination thereof (D'agostino, [0063]: In FIG. 4, an example configuration of context server 170 is shown. Context server 170 includes a context aggregator module 400, a context analyzer module 402, an interaction detector module 410, an identifier assignment module 412, a task determination module 414, a context selection module 416, and an interaction facilitator module 418.).
Regarding claim 10, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow framework module is configured to define the at least one task based on the mission, the event, or the contextual data (D'agostino, [0063]: In FIG. 4, an example configuration of context server 170 is shown. Context server 170 includes a context aggregator module 400, a context analyzer module 402, an interaction detector module 410, an identifier assignment module 412, a task determination module 414, a context selection module 416, and an interaction facilitator module 418.).
Regarding claim 11, D’agostino in view of Perlov and Mettler May teaches wherein the at least one task comprises at least one action, a chain of actions, a graph of actions, a prompt, or a combination thereof (D'agostino, [0026]: The context data is related to activity of the request initiator. The method also includes associating a subset of the context data with the interaction request. The subset of the context data is the context data of the plurality of other interaction sessions that are related to the task. [0073]: The task determination module 414 determines a task associated with the interaction request or response. [0083]: At block 506, the context server 170 obtains context data (e.g., from the context data storage 420) of other interaction sessions associated with the identifier that is related to the activity of user 110.).
Regarding claim 12, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow framework module is configured to define the at least one task for an intelligent flow agent (D'agostino, [0073]: The task determination module 414 determines a task associated with the interaction request or response.).
Regarding claim 13, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow agent executes the at least one task assigned by the intelligent flow framework module (D'agostino, [0034]: The memory also stores computer executable instructions that when executed by the processor cause the processor to generate at least one interaction response in response to the task using the subset of the context data. [0137]: At block 1410, the artificial intelligence engine 1304 of the artificial intelligence server 190 can use the subset of the context data to generate at least one interaction response in response to the task associated with the interaction request.).
Regarding claim 14, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow framework module is configured to observe the current state of the task assigned to the intelligent flow agent (D'agostino, [0038]: A subsequent interaction response may be generated by detecting that the task and the subset of the context data are associated with an interaction response, and generating the subsequent interaction response based on the associated interaction response. [0071]: The interaction detector module 410 can detect an interaction request for an interaction session between a request initiator and a request service provider. The interaction detector module 410 can also detect an interaction response of the request service provider during an interaction session with the request initiator. In an example embodiment, each interaction to and from the system 140 is sent to interaction detector module 410 for processing and/or the interaction detector module 410 monitors communications being sent or received by, or within, system 140 to identify interaction requests and responses.).
Regarding claim 16, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow framework module comprises network adapters to connect with external devices, sensors, communication devices, agents, machine interfaces, or web services (D'agostino, [0047]: The system 140 may include one or more servers to facilitate or carry out a service requested by user 110 via the client device 104…System 140 may also include one or more agent devices 180 for use by one or more agents 182 (e.g., workstations operated by live agents at a contact centre). System 140 may also include an artificial intelligence server 190 to automatically perform operations, make selections, and generate interaction messages as described herein.).
Regarding claim 17, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow framework module is configured to transfer the at least one task to a new intelligent flow agent, a network adapter, an external intelligent flow agent, or distribute the at least one task between multiple intelligent flow agents and network adapters depending upon the event, current state of contextual data, a new task defined by the intelligent flow framework module, or a combination thereof (D'agostino, [0073]: The task determination module 414 determines a task associated with the interaction request or response. It will be appreciated that a task can represent a specific transaction to be carried out by or for user 110 (e.g., transfer $500 from savings account of user 110 to chequing account of user 110), or it can represent a general subject area among many possible subjects for which the interaction request or response could relate to (e.g., mortgage product of user 110). [0143]: It will be appreciated that the artificial intelligence server 190 may be incorporated into a single computer or a single server or service, or alternatively, may be distributed among one or more computing systems 300. In an example embodiment, one or more servers or devices of system 140 may have its own respective artificial intelligence server 190 or implement its own respective artificial intelligence engine 1304, such as agent device 180 to implement an automated bot agent 182.).
Regarding claim 18, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow agent relays the at least one task, the event, or the contextual data to an artificial intelligence module (D'agostino, [0140]: In this example, at block 1410, the artificial intelligence server 190 may generate an interaction response in the form of a list of suggested responses (e.g., a first suggested response being the balance of the chequing account, and a second suggested response being the balance of the credit card account), and send such suggested responses to an agent device 180 for selection by an agent 182 (e.g., bank employee) servicing the virtual chat session as the bank representative.).
Regarding claim 19, D’agostino in view of Perlov and Mettler May teaches wherein the artificial intelligence module includes a generative learning model (D'agostino, [0129]: An artificial intelligence server 190 may be any system configured to apply knowledge data (e.g., dynamic decision making models and techniques) and that can adapt itself in changing environments and update or generate new knowledge data based on such environments.).
Regarding claim 20, D’agostino in view of Perlov and Mettler May teaches wherein the generative model is any neural network based on a transformer architecture, pre-trained on large datasets of unlabeled text, and able to generate novel human-like text or speech or visual (D'agostino, [0133]: In an example embodiment, the artificial intelligence engine 1304 may be trained based on any data accessible in system 140, such as previous interaction requests, interaction responses and context data associated with one or more interaction sessions, to generate the knowledge data. The training set of data can be limited to data associated with a particular user 110 or client device 104 participating in a specific interaction session. In another example embodiment, the training set of data can include other data accessible to the artificial intelligence engine 1304, such as context data of interaction sessions involving other users 110 and client devices 104 not participating in the specific interaction session to which the input 1302 may relate. [0134]: In an example embodiment, the training process of the artificial intelligence engine 1304 to generate the knowledge data can be iterative. Training may be based on a wide variety of learning rules or training algorithms.).
Regarding claim 21, D’agostino in view of Perlov and Mettler May teaches wherein the artificial intelligence module is trained on application-specific workflow or dataset (D'agostino, [0133]: In an example embodiment, the artificial intelligence engine 1304 may be trained based on any data accessible in system 140, such as previous interaction requests, interaction responses and context data associated with one or more interaction sessions, to generate the knowledge data. The training set of data can be limited to data associated with a particular user 110 or client device 104 participating in a specific interaction session. In another example embodiment, the training set of data can include other data accessible to the artificial intelligence engine 1304, such as context data of interaction sessions involving other users 110 and client devices 104 not participating in the specific interaction session to which the input 1302 may relate. [0134]: In an example embodiment, the training process of the artificial intelligence engine 1304 to generate the knowledge data can be iterative. Training may be based on a wide variety of learning rules or training algorithms.).
Regarding claim 22, D’agostino in view of Perlov and Mettler May teaches wherein the intelligent flow framework module comprises an intelligent flow designer to enable an actor to set at least one workflow, a rule engine, an action, or a combination thereof (D'agostino, [0047]: The system 140 may include one or more servers to facilitate or carry out a service requested by user 110 via the client device 104. [0073]: The task determination module 414 determines a task associated with the interaction request or response. It will be appreciated that a task can represent a specific transaction to be carried out by or for user 110 (e.g., transfer $500 from savings account of user 110 to chequing account of user 110), or it can represent a general subject area among many possible subjects for which the interaction request or response could relate to (e.g., mortgage product of user 110). [0098]: The interaction facilitator module 418 can adjust the interaction response data based on the offers already provided to user 110, as set out in the subset of the context data associated with the interaction response.).
Regarding claim 23, D'agostino teaches A method implemented by an intelligent flow framework module comprising ([0026]: discussing about a method of processing an interaction request):
receiving an event ([0137] and Fig. 14: At block 1400, the interaction detector module 410 detects an interaction request in an interaction session using an interaction channel.);
embedding a contextual data to the event ([0137] and Fig. 14: At block 1402, the identifier assignment module 412 assigns an identifier to the interaction request… At block 1408 the context selection module 416 selects a subset of the context data from the context data available to the context server 170 (e.g., stored in context data storage 420) and associates such context data with the interaction request.); and
defining at least one task based on the event and the embedded contextual data ([0137] and Fig. 14: At block 1404, the task determination module 414 determines a task associated with the interaction request.); and
assigning the at least one task to at least one intelligent flow agent ([0137] and Fig. 14: At block 1410, the artificial intelligence engine 1304 of the artificial intelligence server 190 can use the subset of the context data to generate at least one interaction response in response to the task associated with the interaction request.).
D'agostino does not explicitly teach embedding a contextual data to the event, including a current state of an actor and environment; and monitoring the current state of the contextual data during execution to control the task flow associated with the at least one task.
Perlov teaches monitoring the current state of the contextual data during execution to control the task flow associated with the at least one task ([0066]: In block 930, the system generates a workflow comprising a plurality of stages and actors. It will be appreciated that any number of stages and actors may be used to represent a dynamic workflow. In block 940, the system executes the generated workflow. The workflow system may execute the workflow by executing stages in a specific order by default.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the artificial intelligence engine of D'agostino with the teaching about the dynamic workflow of Perlov because it dynamically identifies parameters in real time to enhance the visibility of data flows and to diagnose and correct errors in real time (Perlov, [0003]).
Mettler May teaches embedding a contextual data to the event, including a current state of an actor and environment ([0111]: (ii) An algorithm estimates the actor's own state for example detects and extracts relevant stroke timing features and their attributes (for example the time and strength of the impact). In parallel another) by applying an algorithm may be used to estimate the environment state data collected by one or more sensors. (iii) The actor's state and environment state are combined to determine the interactions and outcomes relevant to the activity.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the artificial intelligence engine of D'agostino and Perlov with the teaching about the actor's state and environment state of Mettler May because it would determine the interactions and outcomes relevant to the activity (Mettler May, [0111]).
Regarding claim 24, D’agostino in view of Perlov and Mettler May teaches wherein embedding the contextual data includes adding current state of at least one actor, environment, actor history, current workflow, or a combination thereof (D'agostino, [0074]: The context selection module 416 selects a subset of the context data from the context data available to the context server 170 to associate with the interaction request or response. The subset of the context data can then be used in the requested interaction session after that session is established. In this way, activity history of user 110 relevant to the interaction request or response may be identified for use while irrelevant context data can be ignored.).
Regarding claim 25, D’agostino in view of Perlov and Mettler May teaches wherein the at least one actor is user, human, connector, or a non-human logical structure (D'agostino, [0043]: Client devices 104 may be associated with one or more users 110. Users 110 can include both real and/or virtual/automated entities. The computing environment 100 may include multiple client devices 104, each associated with a separate user 110 or with one or more users 110. [0074]: The context selection module 416 selects a subset of the context data from the context data available to the context server 170 to associate with the interaction request or response. The subset of the context data can then be used in the requested interaction session after that session is established. In this way, activity history of user 110 relevant to the interaction request or response may be identified for use while irrelevant context data can be ignored.).
Regarding claim 26, D’agostino in view of Perlov and Mettler May teaches wherein the actor is at least one of a sensor capturing an environmental or physical metric, wherein the captured metric is the event (D'agostino, [0056]: In an example embodiment, customer data 200 may include geographic position data associated with user 110 and/or at least one of client devices 104 registered to user 110. For instance, the geographic position data may identify a current geographic position of user 110 and/or client devices 104, and additionally or alternatively, one or more prior geographic positions of user 110 and/or client devices 104. In certain aspects, system 140 may obtain a portion of the geographic position data from client device 104 across communication network 120.).
Regarding claim 27, D’agostino in view of Perlov and Mettler May teaches wherein receiving an event includes generating the event based on at least one prompt, message, signal, API call, or a combination thereof (D'agostino, [0095]: In processing the interaction response, interaction facilitator module 418 may determine from the context data of the mobile application session that user 110 has since left the location of the workstation (e.g., using GPS data of the mobile device), such that the interaction facilitator module 418 translates the interaction response sent from web server 148 as an online web portal session message (for transmission to the workstation) to a mobile application session message for transmission to the mobile device, in order to effectively and timely reach user 110. [0101]: In an example embodiment, the user 110 may be prompted at the beginning of the virtual chat session 600A to answer various security questions to authenticate the user 110 as part of the interaction response authentication procedures. [0137] and Fig. 14: At block 1400, the interaction detector module 410 detects an interaction request in an interaction session using an interaction channel.).
Claim 28 is rejected under the same rationale as claim 11.
Claims 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over D'agostino in view of Mettler May.
Regarding claim 29, D'agostino teaches A system comprising ([0062]: In certain embodiments, computer system 300 may include one or more processors 302, a communication interface 304, and memory device 306.):
a processor hosting an intelligent flow framework module comprising an intelligent flow agent, an active knowledgebase ([0130]: In FIG. 13, an example configuration of an artificial intelligence server 190 is shown. The artificial intelligence server 190 receives input 1302 and generates output 138, and includes an artificial intelligence engine 1304 and a knowledge data storage 1306.), and contextual unit ([0063]: In FIG. 4, an example configuration of context server 170 is shown. Context server 170 includes a context aggregator module 400, a context analyzer module 402, an interaction detector module 410, an identifier assignment module 412, a task determination module 414, a context selection module 416, and an interaction facilitator module 418.); and
a non-transitory storage element coupled to the processor to store the encoded instructions, wherein the encoded instructions, when implemented by the processor, configure the system to ([0062]: Memory device 306 can include tangible and non-transitory computer-readable medium having stored therein computer programs, sets of instructions, code, or data to be executed by processor 302.):
receive an event ([0137] and Fig. 14: At block 1400, the interaction detector module 410 detects an interaction request in an interaction session using an interaction channel.);
embed a contextual data to the event ([0137] and Fig. 14: At block 1402, the identifier assignment module 412 assigns an identifier to the interaction request… At block 1408 the context selection module 416 selects a subset of the context data from the context data available to the context server 170 (e.g., stored in context data storage 420) and associates such context data with the interaction request.);
define a mission based on the event and embedded contextual data ([0137] and Fig. 14: At block 1404, the task determination module 414 determines a task associated with the interaction request.); and
determine all available actions to complete the mission ([0137] and Fig. 14: At block 1410, the artificial intelligence engine 1304 of the artificial intelligence server 190 can use the subset of the context data to generate at least one interaction response in response to the task associated with the interaction request. It will be appreciated that the description of blocks 502, 504, 506 and 508 may apply in a similar manner to blocks 1402, 1404, 1406 and 1408, respectively.).
D'agostino does not explicitly teach reconfigure at least one action from all the available actions during execution based on a current state of an actor and environment the contextual data.
Mettler May teaches reconfigure at least one action from all the available actions during execution based on a current state of an actor and environment the contextual data ([0111]: (ii) An algorithm estimates the actor's own state for example detects and extracts relevant stroke timing features and their attributes (for example the time and strength of the impact). In parallel another) by applying an algorithm may be used to estimate the environment state data collected by one or more sensors. (iii) The actor's state and environment state are combined to determine the interactions and outcomes relevant to the activity.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the artificial intelligence engine of D'agostino with the teaching about the actor's state and environment state of Mettler May because it would determine the interactions and outcomes relevant to the activity (Mettler May, [0111]).
Regarding claim 30, D'agostino teaches A method implemented by an intelligent flow framework module comprising ([0026]: discussing about a method of processing an interaction request):
receiving at least one threshold-grade contextual data ([0039]: In certain example embodiments, the context data of the plurality of other interaction sessions may be assigned weighting values based on interaction channel type and the subset of the context data includes the context data of the plurality of other interaction sessions satisfying a weighting threshold value. [0137] and Fig. 14: At block 1400, the interaction detector module 410 detects an interaction request in an interaction session using an interaction channel.);
generating an event based on the at least one contextual data ([0137] and Fig. 14: At block 1402, the identifier assignment module 412 assigns an identifier to the interaction request.); and
relaying the event and the contextual data to a generative learning model for determining an execution of at least one task, wherein relaying of the event and the contextual data is routed through an intelligent flow agent ([0137] and Fig. 14: At block 1404, the task determination module 414 determines a task associated with the interaction request…At block 1410, the artificial intelligence engine 1304 of the artificial intelligence server 190 can use the subset of the context data to generate at least one interaction response in response to the task associated with the interaction request.).
D'agostino does not explicitly teach [data] including a current state of an actor and environment.
Mettler May teaches [data] including a current state of an actor and environment ([0111]: (ii) An algorithm estimates the actor's own state for example detects and extracts relevant stroke timing features and their attributes (for example the time and strength of the impact). In parallel another) by applying an algorithm may be used to estimate the environment state data collected by one or more sensors. (iii) The actor's state and environment state are combined to determine the interactions and outcomes relevant to the activity.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the artificial intelligence engine of D'agostino with the teaching about the actor's state and environment state of Mettler May because it would determine the interactions and outcomes relevant to the activity (Mettler May, [0111]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over D'agostino in view of Perlov, in view of Mettler May and further in view of Karins et al. (US 2012/0143808, hereinafter “Karins”).
Regarding claim 8, D'agostino in view of Perlov and Mettler May teaches the system of claim 1 as discussed above. D'agostino also teaches wherein the intelligent flow framework module comprises a parameter module ([0063]: In FIG. 4, an example configuration of context server 170 is shown. Context server 170 includes a context aggregator module 400, a context analyzer module 402, an interaction detector module 410, an identifier assignment module 412, a task determination module 414, a context selection module 416, and an interaction facilitator module 418.).
D'agostino in view of Perlov and Mettler May does not explicitly teach wherein the intelligent flow framework module comprises a confidence module.
Karins teaches wherein the intelligent flow framework module comprises a confidence module ([0115]: The artificial intelligence module 208, in the depicted embodiment, includes an artificial intelligence definition module 334, an object probability module 336, a confidence module 338, and an action module 340.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the artificial intelligence engine of D'agostino, Perlov and Mettler May with the teaching about the confidence module of Karins because it determines a confidence metric corresponding to each estimated probability. Each confidence metric, in a further embodiment, represents a strength of an estimated probability (Karins, [0014]).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over D'agostino in view of Perlov, in view of Mettler May and further in view of Nair (US 2024/0296142).
Regarding claim 15, D'agostino in view of Perlov and Mettler May teaches the system of claim 12 as discussed above. D'agostino in view of Perlov and Mettler May does not explicitly teach wherein the intelligent flow framework module is configured to interrupt the execution of the task assigned to the intelligent flow agent based on the event, contextual data, a new task defined by the intelligent flow framework module, or a combination thereof.
Nair teaches wherein the intelligent flow framework module is configured to interrupt the execution of the task assigned to the intelligent flow agent based on the event, contextual data, a new task defined by the intelligent flow framework module, or a combination thereof ([0050]: Examples of interrupt-driven tasks can include (but are not limited to) image segmentation, speech translation, and object detection and identification. Processed always-on tasks can monitor a continuous stream of input (e.g., sensor) data to detect a trigger event and provide a trigger signal (e.g., an interrupt) in response to the trigger event for an interrupt-driven task processed by a separate hardware or software system.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the artificial intelligence engine of D'agostino, Perlov and Mettler May with the teaching about the interrupt-driven tasks of Nair because it would provide efficient and timely processing with reduced power and hardware requirements (Nair, [0050]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Coles et al. (US 20240104457) discloses that a method performed by a provider computing system includes training one or more artificial intelligence (AI) models to generate task prompts.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHONG H NGUYEN whose telephone number is (571)270-1766. The examiner can normally be reached Monday-Friday, 8:30am-5pm EST.
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/PHONG H NGUYEN/ Primary Examiner, Art Unit 2156
March 9, 2026