DETAILED ACTION
Notice of 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/02/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
The Independent claims 1, 20 recite “a system comprising: one or more processors”; “and a computer-readable medium having encoded thereon computer-executable instructions to cause the one or more processors to perform operations comprising: rendering a representation of an email application on a user interface (UI), the representation including at least one email message or a message view comprising a plurality of email messages”; “receiving an indication that a new email message has been received”; “in response to receiving the indication, dynamically generating a prompt for input to a large language model (LLM), wherein the prompt is usable to cause the LLM to analyze content of the new email message and determine a priority of the new email message”; “wherein the priority is localized to a context based at least in part by a recipient and sender of the new email message”; “inputting the prompt to the LLM”; “receiving an output from the LLM indicating the determined priority”; “and rendering, on the representation, the determined priority of the new email message”. The limitations above as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process, as this could be performed in the human mind or with the aid of pen and paper.
The limitation of " rendering ... ", "receiving ... ", "generating ... ", “inputting…”, as drafted covers mental activities. More specifically, a person can have an email app open in any device such as phone or laptop, where plurality of email messages are displayed, can receive an indication that he got a new email, can determine the priority of the email based on the sender and having a quick look at the content. The person can write down the priority of the email. The above steps, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind. There is, nothing in the claim element precludes the step from practically being performed in the human mind. Additionally, the mere nominal recitation of a generic computer appliance does not take the claim limitation out of the mental processes grouping. Thus, the claims recite a mental process.
The claims recite the additional limitations of “large language model”, “user interface”, “processor”, “computer readable storage medium” for performing the method, which are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Throughout the specification large language model or LLM is specified in a generic manner, which is not sufficient to amount to significantly more than the judicial exception. “ User interface”, “processor”, “computer readable storage medium” are also recited as a generic component, which are not sufficient to amount to significantly more than the judicial exception. All those are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. This is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. There is no indication
that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claims 1 and 20 are therefore not drawn to eligible subject matter as this is directed to an abstract idea without
significantly more than the abstract idea.
The Independent claim 11 recites “a method to be performed by a data processing system, the method comprising: rendering a representation of an email application on a user interface (UI), the representation including at least one email message or a message view comprising a plurality of email messages”; “receiving an indication that a new email message has been received”; “in response to receiving the indication, dynamically generating a prompt for input to a large language model (LLM), wherein the prompt is usable to cause the LLM to analyze content of the new email message and determine a priority of the new email message”; “wherein the priority is determined at least in part by a context of the new email message”; “inputting the prompt to the LLM”; “and rendering, on the representation, the priority of the new email message”. The limitations above as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process, as this could be performed in the human mind or with the aid of pen and paper.
The limitation of " rendering ... ", "receiving ... ", "generating ... ", “inputting…”, as drafted covers mental activities. More specifically, a person can have an email app open in any device such as phone or laptop, where plurality of email messages are displayed, can receive an indication that he got a new email, can determine the priority of the email based on the sender and having a quick look at the content. The person can write down the priority of the email. The above steps, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind. There is, nothing in the claim element precludes the step from practically being performed in the human mind. Additionally, the mere nominal recitation of a generic computer appliance does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
The claims recite the additional limitations of “large language model”, “user interface”, for performing the method, which are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Throughout the specification large language model or LLM is specified in a generic manner, which is not sufficient to amount to significantly more than the judicial exception. “ User interface” is also recited as a generic component, which is not sufficient to amount to significantly more than the judicial exception. All those are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. This is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. There is no indication
that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claim 11 is therefore not drawn to eligible subject matter as this is directed to an abstract idea without
significantly more than the abstract idea.
Claims 2, 12 recite “wherein an event-based assistant (EBA) is invoked to cause the prompt to be generated”, where giving an input to perform a task can be performed with the aid of pen and paper. The claim recites additional limitation of “event-based assistant”, which is specified in specification in a generic way and is not sufficient to amount to significantly more than the judicial exception. The claims 2 , 12 as drafted, are not patent eligible.
Claims 3, 13 recite “wherein the prompt is further usable to cause the LLM to analyze content of the new email message and generate a summary of contents of the new email message”, where generating a summary of an email by analyzing it’s content could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of “LLM”, which is specified in specification in a generic way and is not sufficient to amount to significantly more than the judicial exception. The claims 3 , 13 as drafted, are not patent eligible.
Claims 4, 14 recite “wherein the instructions further cause the one or more processors to perform operations comprising: rendering the summary within a message list view of the UI”, where summary can be represented on a paper, which could be performed with the aid of pen and paper. The claim recites additional limitation of UI, which is specified in specification in a generic way and is not sufficient to amount to significantly more than the judicial exception. The claims 4 , 14 as drafted, are not patent eligible.
Claims 5, 15 recite “wherein the summary is rendered in a subject line for the new email message as a micro-summary.”, where summary can be presented in the subject line, which could be an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 5, 15 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claims 6, 16 recite “wherein the instructions further cause the one or more processors to perform operations comprising: providing an option to expand the micro-summary to a detailed summary”, where determining that summary can be expanded, could be an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 6, 16 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claims 7, 17 recite “wherein the prompt is further usable to cause the LLM to provide a reason for the priority; wherein the instructions further cause the one or more processors to perform operations comprising: rendering the reason on the UI”, where determining the reason for prioritizing an email and making a note of the reason, could be an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of LLM, which is specified in specification in a generic way and is not sufficient to amount to significantly more than the judicial exception. The claims 7 , 17 as drafted, are not patent eligible.
Claims 8, 18 recite “wherein the instructions further cause the one or more processors to perform operations comprising: rendering, on the user interface, a window for providing user feedback pertaining to the determined priority”, where providing a feedback regarding the priority, could be an evaluation, observation and could be performed with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 8, 18 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claims 9, 19 recite “wherein the instructions further cause the one or more processors to perform operations comprising: updating the determined priority based on contextual information for the user”, where updating the priority of the email based on information from user, could be performed with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 9, 19 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claim 10 recites “wherein the instructions further cause the one or more processors to perform operations comprising: in response to a user input, sorting messages in the message view based on the determined priority”, where sorting the emails based on user’s input, could be performed with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 10 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 20 was evaluated under 35 U.S.C. 101 as being directed to non-statutory subject matter for “computer-readable storage medium”, but due to the fact that the specification in para.[0117], specifies that “the phrase "computer storage medium," "computer-readable storage medium" and variations thereof, does not include waves, signals, and/or other transitory and/or intangible communication media, per se.”, no 35 USC 101 rejection has been given.
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 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 of this title, 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, 3-6, 10, 11, 13-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shirwadkar et al. ( US 20260073121 A1), hereinafter referenced as Shirwadkar, in view of Bar-on et al. (US 20210406836 A1), hereinafter referenced as Bar-on, further in view of Osi et al. (US 20250322152 A1), hereinafter referenced as Osi.
Regarding Claim 1, Shirwadkar teaches a system comprising: one or more processors; and a computer-readable medium having encoded thereon computer-executable instructions to cause the one or more processors ( Shirwadkar: Para.[0022],[0081], Fig.6, client device 600 includes a processing unit (CPU) 622 in communication with a mass memory 630 ( Computer readable storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, optical storage, cloud storage, magnetic storage devices, or any other physical or material medium) via a bus 624, which can be used to tangibly store the desired information or data or instructions and which can be accessed by a computer or processor ), to perform operations comprising:
rendering a representation of an email application on a user interface (UI), the representation including at least one email message or a message view comprising a plurality of email messages ( Shirwadkar: Para.[0034], [0041],[0045], Figs. 1, 3, system 106 may be a service provider and/or network provider from where services and/or applications may be accessed, sourced or executed from. For example, system 106 can represent the cloud-based architecture associated with a network and/or electronic mail platform (e.g., Yahoo! Mail®). Process 300 begins with Step 302 where an electronic message addressed to an inbox of a user is identified, where triaging engine 200 may function as an application installed and/or executing on UE 102 ( user’s device). Such application may be a web-based application accessed by UE 102 over network 104 from cloud system 106);
wherein the priority is localized to a context based at least in part by a recipient and sender of the new email message ( Shirwadkar: Para.[0067], Fig. 3, In Step 308, based on the analysis in Step 306, engine 200 can determine whether the electronic message is a triaging candidate based on sender ID, sender domain, content, category of content, type of content, recipient ID, message type, message format, a time, date, a location, and the like, or some combination thereof);
Shirwadkar while teaching the system of claim 1, fails to explicitly teach the claimed, receiving an indication that a new email message has been received; in response to receiving the indication, dynamically generating a prompt for input to a large language model (LLM), wherein the prompt is usable to cause the LLM to analyze content of the new email message and determine a priority of the new email message; inputting the prompt to the LLM; receiving an output from the LLM indicating the determined priority; and rendering, on the representation, the determined priority of the new email message.
However, Bar-on does teach the claimed, receiving an indication that a new email message has been received ( Bar-on: Para.[0111], [0122], the email host server can, notify the client device that a new email message is received and is ready for processing. Notification can be done by accessing one or more client-level notification devices, such as speakers, haptic elements, and/or other service);
and rendering, on the representation, the determined priority of the new email message ( Bar-on: Para.[0210], Fig. 3A includes the priority message view selector 318. This view may be configured to display information extracted from one or more emails that relates only to messages that are categorized as high priority and/or personal or professional messages that should not be missed or otherwise overlooked).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bar-on’s teaching of systems and methods for locally classifying email messages received by an email client and extracting information, organizing, displaying based on the classification, into the system and method of a decision intelligence (DI)-based, computerized framework for determining and implementing the mechanisms for compiling and/or displaying a summary of an electronic message within an inbox , taught by Shirwadkar, because, this would improve the functionality of email communications protocols for both senders and recipients
of email by enhancing privacy and improving efficiency of displaying, digesting, and acting upon information contained in, or referenced by, an email message directed to particular recipient. (Bar-on, Para.[0037],[0038]).
Shirwadkar in view of Bar-on, while teaching the system of claim 1, fails to explicitly teach the claimed, in response to receiving the indication, dynamically generating a prompt for input to a large language model (LLM), wherein the prompt is usable to cause the LLM to analyze content of the new email message and determine a priority of the new email message; inputting the prompt to the LLM; receiving an output from the LLM indicating the determined priority.
However, Osi does teach the claimed, in response to receiving the indication, dynamically generating a prompt for input to a large language model (LLM), wherein the prompt is usable to cause the LLM to analyze content of the new email message and determine a priority of the new email message ( Osi: Para.[0013], [0024], LLM analysis can be performed using a suitable prompt, to generate an Urgency label (e.g., "urgent message" or "nonurgent message) for each and every incoming email messages);
inputting the prompt to the LLM ( Osi: Para.[0013], [0024], inputting the LLM with prompt to generate an Urgency label);
receiving an output from the LLM indicating the determined priority( Osi: Para.[0013], [0023], LLM can generate output label or tag with the urgency level/score, such as in a scale of 0 to 100, where 0 indicates an entirely non-urgent message, for which no damage would happen and no adverse results would happen if the message is not read and/or not responded and/or not acted upon; and where 100 indicates an extremely urgent message, in which significant and irreparable damage is expected to happen if the message is not immediately read and/or not responded and/or not acted upon) ;
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Osi’s teaching of systems and methods for utilizing a Large Language Model (LLM) for automatically labeling non-labeled textual data-items for the purpose of creating a training dataset for training a Machine Learning (ML) model to be used to classify new/incoming documents or messages or other data-items, into the system and method , taught by Shirwadkar in view of Bar-on, because, by updating Machine Learning (ML) model periodically with new data items would improve classification accuracy over time. (Osi, Para.[0004],[0049]).
Regarding claim 11, Shirwadkar teaches a method to be performed by a data processing system, the method comprising : rendering a representation of an email application on a user interface (UI), the representation including at least one email message or a message view comprising a plurality of email messages ( Shirwadkar: Para.[0034], [0041],[0045], Figs. 1, 3, system 106 may be a service provider and/or network provider from where services and/or applications may be accessed, sourced or executed from. For example, system 106 can represent the cloud-based architecture associated with a network and/or electronic mail platform (e.g., Yahoo! Mail®). Process 300 begins with Step 302 where an electronic message addressed to an inbox of a user is identified, where triaging engine 200 may function as an application installed and/or executing on UE 102 ( user’s device). Such application may be a web-based application accessed by UE 102 over network 104 from cloud system 106);
wherein the priority is determined at least in part by a context of the new email message ( Shirwadkar: Para.[0067], Fig. 3, In Step 308, based on the analysis in Step 306, engine 200 can determine whether the electronic message is a triaging candidate based on sender ID, sender domain, content, category of content, type of content, recipient ID, message type, message format, a time, date, a location, and the like, or some combination thereof);
Shirwadkar while teaching the method of claim 1, fails to explicitly teach the claimed, receiving an indication that a new email message has been received; in response to receiving the indication, dynamically generating a prompt for input to a large language model (LLM), wherein the prompt is usable to cause the LLM to analyze content of the new email message and determine a priority of the new email message; inputting the prompt to the LLM; and rendering, on the representation, the priority of the new email message.
However, Bar-on does teach the claimed, receiving an indication that a new email message has been received ( Bar-on: Para.[0111], [0122], the email host server can, notify the client device that a new email message is received and is ready for processing. Notification can be done by accessing one or more client-level notification devices, such as speakers, haptic elements, and/or other service);
and rendering, on the representation, the priority of the new email message ( Bar-on: Para.[0210], Fig. 3A includes the priority message view selector 318. This view may be configured to display information extracted from one or more emails that relates only to messages that are categorized as high priority and/or personal or professional messages that should not be missed or otherwise overlooked).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bar-on’s teaching of systems and methods for locally classifying email messages received by an email client and extracting information, organizing, displaying based on the classification, into the system and method of a decision intelligence (DI)-based, computerized framework for determining and implementing the mechanisms for compiling and/or displaying a summary of an electronic message within an inbox , taught by Shirwadkar, because, this would improve the functionality of email communications protocols for both senders and recipients
of email by enhancing privacy and improving efficiency of displaying, digesting, and acting upon information contained in, or referenced by, an email message directed to particular recipient. (Bar-on, Para.[0037],[0038]).
Shirwadkar in view of Bar-on, while teaching the system of claim 1, fails to explicitly teach the claimed, in response to receiving the indication, dynamically generating a prompt for input to a large language model (LLM), wherein the prompt is usable to cause the LLM to analyze content of the new email message and determine a priority of the new email message; inputting the prompt to the LLM.
However, Osi does teach the claimed, in response to receiving the indication, dynamically generating a prompt for input to a large language model (LLM), wherein the prompt is usable to cause the LLM to analyze content of the new email message and determine a priority of the new email message ( Osi: Para.[0013], [0024], LLM analysis can be performed using a suitable prompt, to generate an Urgency label (e.g., "urgent message" or "nonurgent message) for each and every incoming email messages);
inputting the prompt to the LLM ( Osi: Para.[0013], [0024], inputting the LLM with prompt to generate an Urgency label);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Osi’s teaching of systems and methods for utilizing a Large Language Model (LLM) for automatically labeling non-labeled textual data-items for the purpose of creating a training dataset for training a Machine Learning (ML) model to be used to classify new/incoming documents or messages or other data-items, into the system and method , taught by Shirwadkar in view of Bar-on, because, by updating Machine Learning (ML) model periodically with new data items would improve classification accuracy over time. (Osi, Para.[0004],[0049]).
Claim 20 is computer-readable storage medium claim having computer-executable instructions stored thereupon which, when executed by one or more processors of a computing device, cause the computing device ( Shirwadkar: Para.[0022],[0081], Fig.6, client device 600 includes a processing unit (CPU) 622 in communication with a mass memory 630 ( Computer readable storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, optical storage, cloud storage, magnetic storage devices, or any other physical or material medium) via a bus 624, which can be used to tangibly store the desired information or data or instructions and which can be accessed by a computer or processor ), to perform the steps in system claim 1 above and as such, claim 20 is similar in scope and content to claim 1 and therefore, claim 20 is rejected under similar rationale as presented against claim 1 above.
Regarding Claim 3, Shirwadkar in view of Bar-on, further in view of Osi, teach the system of claim 1. Shirwadkar further teaches, wherein the prompt is further usable to cause the LLM to analyze content of the new email message and generate a summary of contents of the new email message ( Shirwadkar: Para.[0051],[0073], Fig. 3, LLMs can generate, comprehend, analyze and output human-like outputs based on a given input, prompt or context. In step 316, engine 200 can call a message summarization model, an LLM which can perform operations to generate an NLP summary of the content associated with the electronic message).
Claim 13 is a method claim performing the steps in system claim 3 above and as such, claim 13 is similar in scope and content to claim 3 and therefore, claim 13 is rejected under similar rationale as presented against claim 3 above.
Regarding Claim 4, Shirwadkar in view of Bar-on, further in view of Osi, teach the system of claim 3. Shirwadkar further teaches, wherein the instructions further cause the one or more processors to perform operations comprising: rendering the summary within a message list view of the UI ( Shirwadkar: Para.[0073], the summary may be constricted to a number of characters proportionate to an amount of space available within a message inbox item that is capable of being displayed within an inbox of a user's messaging account display page).
Claim 14 is a method claim performing the steps in system claim 4 above and as such, claim 14 is similar in scope and content to claim 4 and therefore, claim 14 is rejected under similar rationale as presented against claim 4 above.
Regarding Claim 5, Shirwadkar in view of Bar-on, further in view of Osi, teach the system of claim 4. Shirwadkar further teaches, wherein the summary is rendered in a subject line for the new email message as a micro-summary ( Shirwadkar: Para.[0077], Fig. 3, in step 318, a concise summary of the email content can be generated and can be placed within the inbox as a message item, placed at the top of the email or in the subject line preview, and the like, thereby allowing the recipient to receive a quick overview at a glance, which can be provided without a need for the user to open the message in some embodiments).
Claim 15 is a method claim performing the steps in system claim 5 above and as such, claim 15 is similar in scope and content to claim 5 and therefore, claim 15 is rejected under similar rationale as presented against claim 5 above.
Regarding Claim 6, Shirwadkar in view of Bar-on, further in view of Osi, teach the system of claim 5. Shirwadkar further teaches, wherein the instructions further cause the one or more processors to perform operations comprising: providing an option to expand the micro-summary to a detailed summary ( Shirwadkar: Para.[0078], Fig. 3, in Step 320, engine 200 can cause the summary to be displayed ( detail summary) as a new mail item in the inbox listing of the recipient user's inbox account, the summary can be displayed as a mail item within another tab of the inbox interface (UI) and/or within a separate folder of the inbox; where, in some embodiments, the original message may be displayed).
Claim 16 is a method claim performing the steps in system claim 6 above and as such, claim 16 is similar in scope and content to claim 6 and therefore, claim 16 is rejected under similar rationale as presented against claim 6 above.
Regarding Claim 10, Shirwadkar in view of Bar-on, further in view of Osi, teach the system of claim 1. Bar-on further teaches, wherein the instructions further cause the one or more processors to perform operations comprising: in response to a user input, sorting messages in the message view based on the determined priority ( Bar-on: Para.[0210],Fig. 3A, priority message view selector 318 may be configured to display information extracted from one or more emails that relates only to messages that are categorized as high priority and/or personal or professional messages that should not be missed or otherwise overlooked. An incoming message may be categorized, may be passed to one or more parsers, and one or more items of data can be extracted can be output from those parsers. That data, in tum, can be used to populate one or more portions of the priority message view).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bar-on’s teaching of systems and methods for locally classifying email messages received by an email client and extracting information, organizing, displaying based on the classification, into the system and method, taught by Shirwadkar in view of Osi, because, this would improve the functionality of email communications protocols for both senders and recipients
of email by enhancing privacy and improving efficiency of displaying, digesting, and acting upon information contained in, or referenced by, an email message directed to particular recipient. (Bar-on, Para.[0037],[0038]).
Claims 2, 7, 8, 12, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Shirwadkar et al. ( US 20260073121 A1), hereinafter referenced as Shirwadkar, in view of Bar-on et al. (US 20210406836 A1), hereinafter referenced as Bar-on, further in view of Osi et al. (US 20250322152 A1), hereinafter referenced as Osi, further in view of Chattopadhyay et al. (US 20250315835 A1), hereinafter referenced as Chattopadhyay.
Regarding Claim 2, Shirwadkar in view of Bar-on, further in view of Osi, teach the system of claim 1. Shirwadkar in view of Bar-on, further in view of Osi fail to teach the claimed, wherein an event-based assistant (EBA) is invoked to cause the prompt to be generated.
However, Chattopadhyay does teach the claimed, wherein an event-based assistant (EBA) is invoked to cause the prompt to be generated ( Chattopadhyay: Para.[0128], Fig. 3, The fraud analysis system 300 includes a generative AI (GenAI) assistant module or copilot module 320, which serves as an interface between the user 305, the host application 310, and a large language model (LLM) 330. The GenAI assistant 320 draws information from data sources 340, including but not limited to databases 350, Internet and web pages 360, and documents 370).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Chattopadhyay’s teaching of systems and methods for analyzing potentially suspicious transactions, risk factors, entities, or events, into the system and method, taught by Shirwadkar in view of Bar-on, further in view of Osi, because, this would improve fraud analysis process by using large language models, and improves the speed, throughput of individual analysts by reducing the amount of time, memory, and user input required to achieve a given level of analysis and reporting for potentially suspicious transactions, entities, or events. (Chattopadhyay, Para.[0100]).
Claim 12 is a method claim performing the steps in system claim 2 above and as such, claim 12 is similar in scope and content to claim 2 and therefore, claim 12 is rejected under similar rationale as presented against claim 2 above.
Regarding Claim 7, Shirwadkar in view of Bar-on, further in view of Osi, teach the system of claim 1. Shirwadkar in view of Bar-on, further in view of Osi fail to teach the claimed, wherein the prompt is further usable to cause the LLM to provide a reason for the priority; wherein the instructions further cause the one or more processors to perform operations comprising: rendering the reason on the UI.
However, Chattopadhyay does teach the claimed, wherein the prompt is further usable to cause the LLM to provide a reason for the priority; wherein the instructions further cause the one or more processors to perform operations comprising: rendering the reason on the UI ( Chattopadhyay : Para.[0137], Fig. 8, The prompt composer construct a prompt for LLM to generate the desired natural language output to the analysts. Para. [0161]- [0163], Fig. 11, In step 1150, the method 1100 includes determining whether the alert is a high-risk alert. If yes, execution proceeds to step 1160. If no, execution proceeds to step 1170. In step 1160, the method 1100 includes calling the API to raise the priority of the alert and using the LLM to provide a natural-language output explaining the reasons for the raised priority. In step 1170, the method 1100 includes calling the API to lower the priority of the alert and using the LLM to provide a natural-language output explaining the reasons for the lowered priority).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Chattopadhyay’s teaching of systems and methods for analyzing potentially suspicious transactions, risk factors, entities, or events, into the system and method, taught by Shirwadkar in view of Bar-on, further in view of Osi, because, this would improve fraud analysis process by using large language models, and improves the speed, throughput of individual analysts by reducing the amount of time, memory, and user input required to achieve a given level of analysis and reporting for potentially suspicious transactions, entities, or events. (Chattopadhyay, Para.[0100]).
Claim 17 is a method claim performing the steps in system claim 7 above and as such, claim 17 is similar in scope and content to claim 7 and therefore, claim 17 is rejected under similar rationale as presented against claim 7 above.
Regarding Claim 8, Shirwadkar in view of Bar-on, further in view of Osi, teach the system of claim 1. Shirwadkar in view of Bar-on, further in view of Osi fail to teach the claimed, wherein the instructions further cause the one or more processors to perform operations comprising: rendering, on the user interface, a window for providing user feedback pertaining to the determined priority.
However, Chattopadhyay does teach the claimed, wherein the instructions further cause the one or more processors to perform operations comprising: rendering, on the user interface, a window for providing user feedback pertaining to the determined priority ( Chattopadhyay : Para.[0180]-[0182], Fig. 20, In step 2060, the method 2000 obtains feedback from end users (e.g., fraud analysts) on the functioning of the model. Execution then proceeds to step 2070, where the method 2000 includes tuning the model based on the testing and feedback, until the outputs of the model are deemed to be of acceptable quality and usefulness. Execution then proceeds to step 2080, where the method 2000 includes interfacing the model with the user interface of the host application. This may for example involve adding a chat window to the user interface that permits the user/analyst to interact with the LLM to analyze incoming alerts).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Chattopadhyay’s teaching of systems and methods for analyzing potentially suspicious transactions, risk factors, entities, or events, into the system and method, taught by Shirwadkar in view of Bar-on, further in view of Osi, because, this would improve fraud analysis process by using large language models, and improves the speed, throughput of individual analysts by reducing the amount of time, memory, and user input required to achieve a given level of analysis and reporting for potentially suspicious transactions, entities, or events. (Chattopadhyay, Para.[0100]).
Claim 18 is a method claim performing the steps in system claim 8 above and as such, claim 18 is similar in scope and content to claim 8 and therefore, claim 18 is rejected under similar rationale as presented against claim 8 above.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Shirwadkar et al. ( US 20260073121 A1), hereinafter referenced as Shirwadkar, in view of Bar-on et al. (US 20210406836 A1), hereinafter referenced as Bar-on, further in view of Osi et al. (US 20250322152 A1), hereinafter referenced as Osi, further in view of Ghafourifar et al. (US 20200004877 A1), hereinafter referenced as Ghafourifar.
Regarding Claim 9, Shirwadkar in view of Bar-on, further in view of Osi, teach the system of claim 1. Shirwadkar in view of Bar-on, further in view of Osi fail to teach the claimed, wherein the instructions further cause the one or more processors to perform operations comprising: updating the determined priority based on contextual information for the user.
However, Ghafourifar does teach the claimed, wherein the instructions further cause the one or more processors to perform operations comprising: updating the determined priority based on contextual information for the user (Ghafourifar: Para.[0031], Fig. 2, Message processing service 240 includes one or more computer devices 241A through 241N, configured to perform the functions for maintaining contexts, processing messages, creating automated augmented summarization of messages, prioritizing messages, estimating response times including implied actions, and generating auto-response “quick actions” that are based on the content and context of received messages).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ghafourifar’s teaching of systems and methods for intelligent automated task assessment and list generation, taught by Shirwadkar in view of Bar-on, further in view of Osi, because, this would improve resource planning and task scheduling by dynamically maintaining a “to-do” list that is aware of activities of other people, e.g., people within an organization, and their task progress . (Ghafourifar, Para.[0006]).
Claim 19 is a method claim performing the steps in system claim 9 above and as such, claim 19 is similar in scope and content to claim 9 and therefore, claim 19 is rejected under similar rationale as presented against claim 9 above.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant's disclosure.
Schyma et al. (US 20250371278 A1) teaches an intelligent assistant incorporating a Large Language Model (LLM) can transform a transcript containing communications regarding tasks into structured data. The transcript can be a meeting transcript or a transcript of a chatbot chat that was autonomously initiated by the intelligent assistant to obtain additional information regarding a task. With LLM assistance, the intelligent assistant transforms unstructured communication data from the transcript into structured data which is then assigned to relevant task objects stored in a database. Prior to processing a transcript, personal data therein can be sanitized, and the intelligent assistant can divide the transcript into smaller segments which each encapsulate a discussion of a different topic.
Seck et al. (US 20250284881 A1) teaches a processing system which may receive a plurality of email messages from an email service. The processing system may provide the plurality of email messages to a classification model in order to receive an indication of at least one relevant email message in the plurality of email messages. The processing system may extract, from the at least one relevant email message, a snippet corresponding to a field in the form. The processing system may generate a draft copy of the form that includes the snippet in the field and may output, to a user device, an indication that the snippet was added to the form.
Ailem et al. (US 20260004135 A1) teaches Methods, systems, and computer storage media for providing a data analysis pipeline using a data analysis pipeline engine in a data intelligence system are described. A data analysis pipeline refers to a structured sequence of data processing steps that support transforming raw data into meaningful insights or actionable outcomes. The data analysis pipeline engine is an unsupervised learning pipeline based on clustering, topic modeling, and Large Language Models (LLMs). For example, the data analysis pipeline can use advanced machine learning techniques to automatically categorize emails into semantically similar clusters, enabling the data intelligence system to quickly identify and prioritize potentially high-risk emails for further investigation. The data analysis pipeline employs AI agents for context-aware graph induction relevance assessment. The AI agents employ induction and deduction loops to build and refine a data feature hypergraph (e.g., vulnerability hypergraph) that encompasses identified relevant data providing a holistic view of a contextual landscape.
Ramesh et al. (The LLM Revolution: How Large Language Models Are Reshaping Salesforce Development, International Journal of Science, Engineering and Technology,2020 ) teaches the rapid evolution of artificial intelligence which has fundamentally altered the way technology integrates into business ecosystems, and nowhere is this more evident than in the domain of Salesforce development. The advent of Large Language Models (LLMs), trained on massive datasets and designed to understand human language with unprecedented complexity, has reshaped the processes of application development, customer interaction, system integration, and workflow automation within Salesforce platforms. LLMs are not merely tools for generating text; they represent a paradigm shift in augmenting developer capabilities, reducing time to-market, enhancing personalization, and enabling a deeper level of business intelligence for Salesforce users. By leveraging their ability to interpret natural language, optimize code generation, and augment decision making, LLMs have enabled developers to build more adaptable, scalable, and innovative solutions that align with the dynamic needs of businesses. As organizations embrace Salesforce as a leading customer relationship management (CRM) tool, the incorporation of LLMs adds an extra layer of intelligence and predictive capabilities to the development cycle. They offer developers the means to automate repetitive tasks, generate business logic from prompts, optimize customer journeys using real-time conversational data, and even assist non-technical users in configuring Salesforce by bridging the gap between technical coding and natural language communication.
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/NADIRA SULTANA/Examiner, Art Unit 2653
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