Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220329556 A1 to Daga et al. (“Daga”) in view of US 8271598 B2 to Guy et al. (“Guy”).
Regarding claim 1, Daga taught an information handling system (“communication device”/”server”; consider paragraphs 0197 and 0201-0202), comprising:
a computer readable medium including an artificial intelligence (AI) electronic mail (email) model (“machine learning network” including “data models” for “information” including “emails”; consider paragraphs 0186 and 0193-0196); and a processor operably coupled to the computer readable medium to access the AI email model to:
receive user generated content; train the AI email model using the user generated content; (consider paragraph 0050, specifically “The device may support forward learning based on training data. For example, the device may support forward learning based on past actions of the user (e.g., in response to past notifications provided by the device). In some aspects, the device may improve the accuracy associated with message analysis and/or notifications provided by the device. In some aspects, the device may support a combination of artificial intelligence (e.g., machine learning) and natural language processing for determining whether a message is being sent to the correct recipient”) (consider further paragraph 0068, “In some examples, the communication device 105 (or server 110) may train the machine learning network based on a communication history associated with a user profile, and the machine learning network may provide the output based on the training. In some examples, the communication device 105 (or server 110) may train the machine learning network based on a set of actions associated with a user profile, and the machine learning network may provide the output based on the training. In some aspects, the set of actions may be associated with one or more previous messages provided by the communication device 105 (or the server 110) to the machine learning network, one or more previous outputs received by the communication device 105 (or server 110) from the machine learning network, one or more previously output notifications by the communication device 105 (or another communication device 105), one or more previously transmitted messages by the communication device 105 (or another communication device 105), or a combination thereof”) (consider further paragraphs 0081-0082 regarding wherein the “training data” can include “communication inputs” and that “The content engine 241 may be configured to analyze content, which may be any type of information, including information that is historical or in real-time. The content engine 241 may be configured to receive information from other communication devices 205 and/or the server 210”)
execute the AI email model prior to transmitting an email. (consider paragraph 0046, specifically “In an example with respect to messaging applications (e.g., conference chats, group instant messaging, email), the techniques described herein may be applied to messages, prior to the messages being sent. For example, a device may analyze a message (e.g., using a text analyzer), prior to sending the message, to determine or verify whether the message is for the intended recipient”) (consider further paragraph 0099, “The communication device 205 may provide at least a portion of the message to a machine learning network (e.g., a machine learning network implemented by the content engine 241, the content engine 266, or the content engine 270) to confirm the recipient, for example, prior to sending the message. In an example, the communication device 205 may provide the message (or message portion) to the machine learning network based on receiving a user input (e.g., via the user interface 245) for sending the message. Alternatively, or additionally, the communication device 205 may provide portions of the message in real-time, for example, as the message is input to the communication device 205”)
Daga may be interpreted as not having expressly taught determining whether an email is addressed to a single recipient or multiple recipients; in response to the email being addressed to the single recipient, transmit the email without the AI email model being executed; and in response to the email being address to the multiple recipients, execute the Al email model prior to transmitting the email.
However, Daga taught executing the AI email model prior to transmitting an email (again, consider paragraphs 0046 and 0099).
Daga also further may be interpreted as having expressly taught determining whether an email was intended to be addressed to a single recipient or multiple recipients in order to later execute the AI email model. (again, consider paragraphs 0046 and 0099) (consider further paragraph 0117, specifically “In another example, for a message input via a messaging window associated with a quantity of recipients (e.g., one, multiple), the machine learning network may determine a probability score and/or confidence score based on a correlation between the content (e.g., context information) of the message and the quantity of recipients associated with the messaging window. For example, for cases in which the machine learning network identifies that the contextual information of a message input via a messaging window implies a different quantity of recipients (e.g., a single recipient) compared to the quantity of recipients associated with the messaging window (e.g., multiple recipients), the machine learning network may output a relatively low probability score.”)
In an analogous art relating to email message processing based on addressed recipient(s), Guy taught that it may be determined whether an email is addressed to a single recipient or multiple recipients; in response to the email being addressed to the single recipient, transmit the email without an automated email model (“agent”; consider column 2, line 67-column 3, line 18) being executed; and in response to the email being address to the multiple recipients, execute the automated email model prior to transmitting the email which may comprise processing user generated content (consider column 2, lines 32-35, “Groups of recipients may be mined from one or more user's previous email correspondence and ranked by the strength of the group”). (consider column 4, line 61-column 5, line 16, “In the described system, an agent 120 is provided which reviews a list of intended recipients in an email message created by a user to check if the list of intended recipients is likely to include any incorrect recipients. The agent 120 reviews the list of intended recipients using a provided algorithm. A prompt 125 is provided in the form of a dialogue box on the email user interface 102 if the agent 120 determines that the user should review one or more of the intended recipients of the email message. The prompt 125 on the email user interface 102 may include appropriate recommendations, warnings, etc. The user 101 can select the relevant/irrelevant addressees and amend the email message appropriately or the user 101 can choose to disregard the prompt 125. The agent 120 can be provided as part of the email client application 103. Alternatively, the agent 120 can be provided as a service provided over a network from a server. An email user 101 can configure the agent 120 to provide the desired prompts 125 tailored to the user's requirements. This configuration of the agent 120 can include a complete disablement of the agent 120. The agent 120 uses an algorithm to review the list of intended recipients.”) (consider further column 8, lines 1-13, “Referring to FIG. 4, a flow diagram 400 is shown of the method carried out by the agent 120. An email message is created 401 by a user of an email client application and it is determined 402 whether there is more than one intended recipient of the message. If there is a single recipient 403, then no action is taken 404 by the agent. If there is more than one intended recipient 405, the intended recipients are compared 406 with defined correct groups.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the teachings of Daga to include the taught features of Guy such that the modification includes every element as claimed. Given Daga's disclosure of training an AI email model with user generated content, determination of whether an email is addressed to a single or multiple recipients and executing the AI email model prior to transmitting an email, Guy specifically taught that, upon determination that an email is addressed to a single recipient, that transmit the email without an automated email model being executed and, if there are multiple recipients, then the automated email model is executed using user generated content to make further determinations of whether an email should be sent in order to prevent the unintentional sending of emails to unintended recipients and protecting the privacy of users (consider column 1, lines 59-64). Given this specific advantage in Guy, one skilled in the art would have been motivated to modify the teachings of Daga with the teachings of Guy such that the determination of whether an email is addressed to a single or multiple recipients and executing the AI email model prior to transmitting an email as taught in Daga may be further enhanced by the teachings of Guy so that it can be determined whether an email is addressed to a single recipient or multiple recipient and, in response to the email being addressed to the single recipient, transmit the email without the AI email model being executed; and in response to the email being address to the multiple recipients, execute the Al email model prior to transmitting the email as claimed. Therefore, such a modification of the teachings of Daga with the teachings of Guy would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 2, the combined teachings of Daga and Guy taught the information handling system of claim 1.
Daga further taught wherein the user generated content includes previous emails, recipients, company rules, non-disclosure agreements, joint venture agreements, supplier agreements, other business agreements, or a combination thereof. (again, consider further paragraph 0068, “In some examples, the communication device 105 (or server 110) may train the machine learning network based on a communication history associated with a user profile, and the machine learning network may provide the output based on the training. In some examples, the communication device 105 (or server 110) may train the machine learning network based on a set of actions associated with a user profile, and the machine learning network may provide the output based on the training. In some aspects, the set of actions may be associated with one or more previous messages provided by the communication device 105 (or the server 110) to the machine learning network, one or more previous outputs received by the communication device 105 (or server 110) from the machine learning network, one or more previously output notifications by the communication device 105 (or another communication device 105), one or more previously transmitted messages by the communication device 105 (or another communication device 105), or a combination thereof”)
Regarding claim 3, the combined teachings of Daga and Guy taught the information handling system of claim 2.
Daga further taught wherein the processor further accesses the AI email model to:
execute the AI email model when an email with a list of email recipients (“set of recipients”) is created by a user. (consider paragraph 0062, “The set of recipients may include, for example, one or more intended recipients associated with the message, one or more additional recipients different from the one or more intended recipients, or both. In some examples, the communication device 105 may verify the one or more intended recipients based on the output received from the machine learning network. In some aspects, the communication device 105 may select the one or more additional recipients based on the output received from the machine learning network”)
Regarding claim 4, the combined teachings of Daga and Guy taught the information handling system of claim 3.
Daga further taught wherein the processor further accesses the AI email model to:
determine whether any conflicts are present in the list of email recipients. (consider paragraph 0004, specifically “In an example case, the device may determine that the content does not match the profile information associated with the recipient, and the device may perform one or more operations for corrective action (e.g., modifying content of the communication, modifying recipients for the communication, selecting a different messaging window for the communication, selecting a different application for the communication). For example, the device may output a notification to alert a sender that the recipient may be incorrect (e.g., the content is not associated with the recipient, the content is not suitable for the recipient), and the device may refrain from completing the communication. For example, the device may determine that a recipient for a message is incorrect, and the device may refrain from transmitting the message to the recipient and/or output a notification suggesting a different recipient for the message”) (consider further paragraph 0062, “The set of recipients may include, for example, one or more intended recipients associated with the message, one or more additional recipients different from the one or more intended recipients, or both. In some examples, the communication device 105 may verify the one or more intended recipients based on the output received from the machine learning network. In some aspects, the communication device 105 may select the one or more additional recipients based on the output received from the machine learning network”)
Regarding claim 5, the combined teachings of Daga and Guy taught the information handling system of claim 4.
Daga further taught wherein the processor further accesses the AI email model to:
transmit the email to the list of email recipients without modification. (consider paragraph 0046, specifically “In an example with respect to messaging applications (e.g., conference chats, group instant messaging, email), the techniques described herein may be applied to messages, prior to the messages being sent. For example, a device may analyze a message (e.g., using a text analyzer), prior to sending the message, to determine or verify whether the message is for the intended recipient. For example, the device may determine whether the recipient associated with the message is correct.”) (consider further paragraph 0156, specifically “Alternatively, or additionally, at 535, the communication device 105 may transmit the message to a respective communication device 105 associated with one or more recipients of the set of recipients, based on the output received from the machine learning network. In some aspects, outputting the notification, transmitting the message, or both may be based on a comparison of the set of probability scores to a probability threshold, a comparison of the set of confidence scores to a threshold, or both. In some cases, the communication device 105 may output the notification, transmit the message, or both based on the verification of the one or more intended recipients”)
Regarding claim 6, the combined teachings of Daga and Guy taught the information handling system of claim 4.
Daga further taught wherein the processor further accesses the AI email model to:
provide notification to the user that a potential conflict exists in the list of email recipients. (again, consider paragraph 0004, specifically “In an example case, the device may determine that the content does not match the profile information associated with the recipient, and the device may perform one or more operations for corrective action (e.g., modifying content of the communication, modifying recipients for the communication, selecting a different messaging window for the communication, selecting a different application for the communication). For example, the device may output a notification to alert a sender that the recipient may be incorrect (e.g., the content is not associated with the recipient, the content is not suitable for the recipient), and the device may refrain from completing the communication. For example, the device may determine that a recipient for a message is incorrect, and the device may refrain from transmitting the message to the recipient and/or output a notification suggesting a different recipient for the message”) (consider paragraph 0046, “In an example with respect to messaging applications (e.g., conference chats, group instant messaging, email), the techniques described herein may be applied to messages, prior to the messages being sent. For example, a device may analyze a message (e.g., using a text analyzer), prior to sending the message, to determine or verify whether the message is for the intended recipient. For example, the device may determine whether the recipient associated with the message is correct. If the device detects a possibility of an incorrect recipient (e.g., based on a probability score and/or a confidence score), the device may output a notification to alert the user of the same”) (consider further paragraph 0048, “The device may refer to various criteria when determining if a message is being sent to the correct recipient. In an example, if multiple messaging windows are open and the device determines (e.g., based on message content) that an analyzed message is for a discussion on another message window, the device may alert the user of the same. In some examples, the device may check or verify if the text in a message (e.g., intended message) implies that the message is being sent to a single user or multiple users. For example, a message including text such as “This looks good to me, what do you think” or “Are you getting where is he leading to” may imply that the message is being sent to a single user. If the device analyzes the text and detects that the message is addressed to (e.g., being sent to) multiple users, the device may notify the user of the discrepancy.”)
Regarding claim 7, the combined teachings of Daga and Guy taught the information handling system of claim 6.
Daga further taught wherein the processor further accesses the AI email model to:
provide notification to the user that a potential conflict exists in the list of email recipients. (again, consider paragraph 0004, specifically “In an example case, the device may determine that the content does not match the profile information associated with the recipient, and the device may perform one or more operations for corrective action (e.g., modifying content of the communication, modifying recipients for the communication, selecting a different messaging window for the communication, selecting a different application for the communication). For example, the device may output a notification to alert a sender that the recipient may be incorrect (e.g., the content is not associated with the recipient, the content is not suitable for the recipient), and the device may refrain from completing the communication. For example, the device may determine that a recipient for a message is incorrect, and the device may refrain from transmitting the message to the recipient and/or output a notification suggesting a different recipient for the message”) (consider paragraph 0046, “In an example with respect to messaging applications (e.g., conference chats, group instant messaging, email), the techniques described herein may be applied to messages, prior to the messages being sent. For example, a device may analyze a message (e.g., using a text analyzer), prior to sending the message, to determine or verify whether the message is for the intended recipient. For example, the device may determine whether the recipient associated with the message is correct. If the device detects a possibility of an incorrect recipient (e.g., based on a probability score and/or a confidence score), the device may output a notification to alert the user of the same”) (consider further paragraph 0048, “The device may refer to various criteria when determining if a message is being sent to the correct recipient. In an example, if multiple messaging windows are open and the device determines (e.g., based on message content) that an analyzed message is for a discussion on another message window, the device may alert the user of the same. In some examples, the device may check or verify if the text in a message (e.g., intended message) implies that the message is being sent to a single user or multiple users. For example, a message including text such as “This looks good to me, what do you think” or “Are you getting where is he leading to” may imply that the message is being sent to a single user. If the device analyzes the text and detects that the message is addressed to (e.g., being sent to) multiple users, the device may notify the user of the discrepancy.”)
Regarding claim 8, the combined teachings of Daga and Guy taught the information handling system of claim 7.
Daga further taught wherein the processor further accesses the AI email model to:
receiving user revisions to the list of email recipients. (consider paragraph 0128, specifically “The communication device 205 may transmit the message based on a user input. For example, based on the output notification provided by the communication device 205, the user input may confirm any of a recipient for a message, content of the message, a messaging window for sending the message, and an application for sending the message. The communication device 205 may transmit the message based on the user input.”) (consider further paragraph 0129, specifically “Alternatively, or additionally, the communication device 205 may perform one or more operations for modifying the recipient for the message, modifying content of the message, selecting a different messaging window for sending the message, and/or selecting a different application for sending the message. For example, based on the output notification (e.g., probability scores, confidence scores) provided by the communication device 205, the user input may invalidate (e.g., remove or modify) a recipient for the message, modify content of the message, select a different messaging window for sending the message, and/or select a different application for sending the message.”) (consider also paragraph 0183 regarding transmitting “the message” “with a different quantity of recipients” based on “a user input (e.g., confirming the message and/or different quantity of recipients)”)
Regarding claim 9, the combined teachings of Daga and Guy taught the information handling system of claim 8.
Daga further taught wherein the processor further accesses the AI email model to:
provide feedback to the AI email model with the user revisions to the list of email recipients. (consider paragraph 0129, specifically “In some aspects, the communication device 205 may update data models (e.g., data model 242, data model 267, data model 286) and/or training data (e.g., training data 243, training data 268, training data 287) based on user decisions.”)
Regarding claim 10, the combined teachings of Daga and Guy taught the information handling system of claim 9.
Daga further taught wherein the processor further accesses the AI email model to:
transmit the email to a revised list of email recipients. (again, consider paragraph 0128, specifically “The communication device 205 may transmit the message based on a user input. For example, based on the output notification provided by the communication device 205, the user input may confirm any of a recipient for a message, content of the message, a messaging window for sending the message, and an application for sending the message. The communication device 205 may transmit the message based on the user input.”) (again, consider also paragraph 0183 regarding transmitting “the message” “with a different quantity of recipients” based on “a user input (e.g., confirming the message and/or different quantity of recipients)”)
Regarding claim 11, Daga taught a method of transmitting emails at an information handling system using an artificial intelligence (AI) electronic mail (email) model (“machine learning network” including “data models” for “information” including “emails”; consider paragraphs 0186 and 0193-0196), the method comprising:
receiving user generated content including previous emails, recipients, company rules, non-disclosure agreements, joint venture agreements, supplier agreements, other business agreements, or a combination thereof; training the AI email model using the user generated content; (consider paragraph 0050, specifically “The device may support forward learning based on training data. For example, the device may support forward learning based on past actions of the user (e.g., in response to past notifications provided by the device). In some aspects, the device may improve the accuracy associated with message analysis and/or notifications provided by the device. In some aspects, the device may support a combination of artificial intelligence (e.g., machine learning) and natural language processing for determining whether a message is being sent to the correct recipient”) (consider further paragraph 0068, “In some examples, the communication device 105 (or server 110) may train the machine learning network based on a communication history associated with a user profile, and the machine learning network may provide the output based on the training. In some examples, the communication device 105 (or server 110) may train the machine learning network based on a set of actions associated with a user profile, and the machine learning network may provide the output based on the training. In some aspects, the set of actions may be associated with one or more previous messages provided by the communication device 105 (or the server 110) to the machine learning network, one or more previous outputs received by the communication device 105 (or server 110) from the machine learning network, one or more previously output notifications by the communication device 105 (or another communication device 105), one or more previously transmitted messages by the communication device 105 (or another communication device 105), or a combination thereof”) (consider further paragraphs 0081-0082 regarding wherein the “training data” can include “communication inputs” and that “The content engine 241 may be configured to analyze content, which may be any type of information, including information that is historical or in real-time. The content engine 241 may be configured to receive information from other communication devices 205 and/or the server 210”) and
executing the AI email model when an email with a list of email recipients is created prior to transmitting the email. (consider paragraph 0046, specifically “In an example with respect to messaging applications (e.g., conference chats, group instant messaging, email), the techniques described herein may be applied to messages, prior to the messages being sent. For example, a device may analyze a message (e.g., using a text analyzer), prior to sending the message, to determine or verify whether the message is for the intended recipient”) (consider paragraph 0062, “The set of recipients may include, for example, one or more intended recipients associated with the message, one or more additional recipients different from the one or more intended recipients, or both. In some examples, the communication device 105 may verify the one or more intended recipients based on the output received from the machine learning network. In some aspects, the communication device 105 may select the one or more additional recipients based on the output received from the machine learning network”) (consider further paragraph 0099, “The communication device 205 may provide at least a portion of the message to a machine learning network (e.g., a machine learning network implemented by the content engine 241, the content engine 266, or the content engine 270) to confirm the recipient, for example, prior to sending the message. In an example, the communication device 205 may provide the message (or message portion) to the machine learning network based on receiving a user input (e.g., via the user interface 245) for sending the message. Alternatively, or additionally, the communication device 205 may provide portions of the message in real-time, for example, as the message is input to the communication device 205”)
Daga may be interpreted as not having expressly taught determining whether an email is addressed to a single recipient or multiple recipients; in response to the email being addressed to the single recipient, transmit the email without the AI email model being executed; and in response to the email being address to the multiple recipients, execute the Al email model prior to transmitting the email.
However, Daga taught executing the AI email model prior to transmitting an email (again, consider paragraphs 0046 and 0099).
Daga also further may be interpreted as having expressly taught determining whether an email was intended to be addressed to a single recipient or multiple recipients in order to later execute the AI email model. (again, consider paragraphs 0046 and 0099) (consider further paragraph 0117, specifically “In another example, for a message input via a messaging window associated with a quantity of recipients (e.g., one, multiple), the machine learning network may determine a probability score and/or confidence score based on a correlation between the content (e.g., context information) of the message and the quantity of recipients associated with the messaging window. For example, for cases in which the machine learning network identifies that the contextual information of a message input via a messaging window implies a different quantity of recipients (e.g., a single recipient) compared to the quantity of recipients associated with the messaging window (e.g., multiple recipients), the machine learning network may output a relatively low probability score.”)
In an analogous art relating to email message processing based on addressed recipient(s), Guy taught that it may be determined whether an email is addressed to a single recipient or multiple recipients; in response to the email being addressed to the single recipient, transmit the email without an automated email model (“agent”; consider column 2, line 67-column 3, line 18) being executed; and in response to the email being address to the multiple recipients, execute the automated email model prior to transmitting the email which may comprise processing user generated content (consider column 2, lines 32-35, “Groups of recipients may be mined from one or more user's previous email correspondence and ranked by the strength of the group”). (consider column 4, line 61-column 5, line 16, “In the described system, an agent 120 is provided which reviews a list of intended recipients in an email message created by a user to check if the list of intended recipients is likely to include any incorrect recipients. The agent 120 reviews the list of intended recipients using a provided algorithm. A prompt 125 is provided in the form of a dialogue box on the email user interface 102 if the agent 120 determines that the user should review one or more of the intended recipients of the email message. The prompt 125 on the email user interface 102 may include appropriate recommendations, warnings, etc. The user 101 can select the relevant/irrelevant addressees and amend the email message appropriately or the user 101 can choose to disregard the prompt 125. The agent 120 can be provided as part of the email client application 103. Alternatively, the agent 120 can be provided as a service provided over a network from a server. An email user 101 can configure the agent 120 to provide the desired prompts 125 tailored to the user's requirements. This configuration of the agent 120 can include a complete disablement of the agent 120. The agent 120 uses an algorithm to review the list of intended recipients.”) (consider further column 8, lines 1-13, “Referring to FIG. 4, a flow diagram 400 is shown of the method carried out by the agent 120. An email message is created 401 by a user of an email client application and it is determined 402 whether there is more than one intended recipient of the message. If there is a single recipient 403, then no action is taken 404 by the agent. If there is more than one intended recipient 405, the intended recipients are compared 406 with defined correct groups.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the teachings of Daga to include the taught features of Guy such that the modification includes every element as claimed. Given Daga' s disclosure of training an AI email model with user generated content, determination of whether an email is addressed to a single or multiple recipients and executing the AI email model prior to transmitting an email, Guy specifically taught that, upon determination that an email is addressed to a single recipient, that transmit the email without an automated email model being executed and, if there are multiple recipients, then the automated email model is executed using user generated content to make further determinations of whether an email should be sent in order to prevent the unintentional sending of emails to unintended recipients and protecting the privacy of users (consider column 1, lines 59-64). Given this specific advantage in Guy, one skilled in the art would have been motivated to modify the teachings of Daga with the teachings of Guy such that the determination of whether an email is addressed to a single or multiple recipients and executing the AI email model prior to transmitting an email as taught in Daga may be further enhanced by the teachings of Guy so that it can be determined whether an email is addressed to a single recipient or multiple recipient and, in response to the email being addressed to the single recipient, transmit the email without the AI email model being executed; and in response to the email being address to the multiple recipients, execute the Al email model prior to transmitting the email as claimed. Therefore, such a modification of the teachings of Daga with the teachings of Guy would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 12, the combined teachings of Daga and Guy taught the information handling system of claim 11.
Daga further taught the method further comprising:
determining whether any conflicts are present in the list of email recipients. (consider paragraph 0004, specifically “In an example case, the device may determine that the content does not match the profile information associated with the recipient, and the device may perform one or more operations for corrective action (e.g., modifying content of the communication, modifying recipients for the communication, selecting a different messaging window for the communication, selecting a different application for the communication). For example, the device may output a notification to alert a sender that the recipient may be incorrect (e.g., the content is not associated with the recipient, the content is not suitable for the recipient), and the device may refrain from completing the communication. For example, the device may determine that a recipient for a message is incorrect, and the device may refrain from transmitting the message to the recipient and/or output a notification suggesting a different recipient for the message”) (consider further paragraph 0062, “The set of recipients may include, for example, one or more intended recipients associated with the message, one or more additional recipients different from the one or more intended recipients, or both. In some examples, the communication device 105 may verify the one or more intended recipients based on the output received from the machine learning network. In some aspects, the communication device 105 may select the one or more additional recipients based on the output received from the machine learning network”)
Regarding claim 13, the combined teachings of Daga and Guy taught the information handling system of claim 12.
Daga further taught the method further comprising:
transmitting the email to the list of email recipients without modification when no conflicts are present. (consider paragraph 0046, specifically “In an example with respect to messaging applications (e.g., conference chats, group instant messaging, email), the techniques described herein may be applied to messages, prior to the messages being sent. For example, a device may analyze a message (e.g., using a text analyzer), prior to sending the message, to determine or verify whether the message is for the intended recipient. For example, the device may determine whether the recipient associated with the message is correct.”) (consider further paragraph 0156, specifically “Alternatively, or additionally, at 535, the communication device 105 may transmit the message to a respective communication device 105 associated with one or more recipients of the set of recipients, based on the output received from the machine learning network. In some aspects, outputting the notification, transmitting the message, or both may be based on a comparison of the set of probability scores to a probability threshold, a comparison of the set of confidence scores to a threshold, or both. In some cases, the communication device 105 may output the notification, transmit the message, or both based on the verification of the one or more intended recipients”)
Regarding claim 14, the combined teachings of Daga and Guy taught the information handling system of claim 12.
Daga further taught the method further comprising:
providing notification to a user that a potential conflict exists in the list of email recipients. (again, consider paragraph 0004, specifically “In an example case, the device may determine that the content does not match the profile information associated with the recipient, and the device may perform one or more operations for corrective action (e.g., modifying content of the communication, modifying recipients for the communication, selecting a different messaging window for the communication, selecting a different application for the communication). For example, the device may output a notification to alert a sender that the recipient may be incorrect (e.g., the content is not associated with the recipient, the content is not suitable for the recipient), and the device may refrain from completing the communication. For example, the device may determine that a recipient for a message is incorrect, and the device may refrain from transmitting the message to the recipient and/or output a notification suggesting a different recipient for the message”) (consider paragraph 0046, “In an example with respect to messaging applications (e.g., conference chats, group instant messaging, email), the techniques described herein may be applied to messages, prior to the messages being sent. For example, a device may analyze a message (e.g., using a text analyzer), prior to sending the message, to determine or verify whether the message is for the intended recipient. For example, the device may determine whether the recipient associated with the message is correct. If the device detects a possibility of an incorrect recipient (e.g., based on a probability score and/or a confidence score), the device may output a notification to alert the user of the same”) (consider further paragraph 0048, “The device may refer to various criteria when determining if a message is being sent to the correct recipient. In an example, if multiple messaging windows are open and the device determines (e.g., based on message content) that an analyzed message is for a discussion on another message window, the device may alert the user of the same. In some examples, the device may check or verify if the text in a message (e.g., intended message) implies that the message is being sent to a single user or multiple users. For example, a message including text such as “This looks good to me, what do you think” or “Are you getting where is he leading to” may imply that the message is being sent to a single user. If the device analyzes the text and detects that the message is addressed to (e.g., being sent to) multiple users, the device may notify the user of the discrepancy.”)
Regarding claim 15, the combined teachings of Daga and Guy taught the information handling system of claim 11.
Daga further taught the method further comprising:
receiving user revisions to the list of email recipients; (consider paragraph 0128, specifically “The communication device 205 may transmit the message based on a user input. For example, based on the output notification provided by the communication device 205, the user input may confirm any of a recipient for a message, content of the message, a messaging window for sending the message, and an application for sending the message. The communication device 205 may transmit the message based on the user input.”) (consider further paragraph 0129, specifically “Alternatively, or additionally, the communication device 205 may perform one or more operations for modifying the recipient for the message, modifying content of the message, selecting a different messaging window for sending the message, and/or selecting a different application for sending the message. For example, based on the output notification (e.g., probability scores, confidence scores) provided by the communication device 205, the user input may invalidate (e.g., remove or modify) a recipient for the message, modify content of the message, select a different messaging window for sending the message, and/or select a different application for sending the message.”) (consider also paragraph 0183 regarding transmitting “the message” “with a different quantity of recipients” based on “a user input (e.g., confirming the message and/or different quantity of recipients)”)
providing feedback to the AI email model with the user revisions to the list of email recipients; (consider paragraph 0129, specifically “In some aspects, the communication device 205 may update data models (e.g., data model 242, data model 267, data model 286) and/or training data (e.g., training data 243, training data 268, training data 287) based on user decisions.”) and
transmitting the email to a revised list of email recipients. (again, consider paragraph 0128, specifically “The communication device 205 may transmit the message based on a user input. For example, based on the output notification provided by the communication device 205, the user input may confirm any of a recipient for a message, content of the message, a messaging window for sending the message, and an application for sending the message. The communication device 205 may transmit the message based on the user input.”) (again, consider also paragraph 0183 regarding transmitting “the message” “with a different quantity of recipients” based on “a user input (e.g., confirming the message and/or different quantity of recipients)”)
Regarding claim 16, Daga taught an information handling system (“communication device”/”server”); consider paragraphs 0197 and 0201-0202), comprising:
a computer readable medium including an artificial intelligence (AI) electronic mail (email) model (“machine learning network” including “data models” for “information” including “emails”; consider paragraphs 0186 and 0193-0196); and a processor operably coupled to the computer readable medium to access the AI email model to:
receive user generated content including previous emails, recipients, company rules, non-disclosure agreements, joint venture agreements, supplier agreements, other business agreements, or a combination thereof; train the AI email model using the user generated content; (consider paragraph 0050, specifically “The device may support forward learning based on training data. For example, the device may support forward learning based on past actions of the user (e.g., in response to past notifications provided by the device). In some aspects, the device may improve the accuracy associated with message analysis and/or notifications provided by the device. In some aspects, the device may support a combination of artificial intelligence (e.g., machine learning) and natural language processing for determining whether a message is being sent to the correct recipient”) (consider further paragraph 0068, “In some examples, the communication device 105 (or server 110) may train the machine learning network based on a communication history associated with a user profile, and the machine learning network may provide the output based on the training. In some examples, the communication device 105 (or server 110) may train the machine learning network based on a set of actions associated with a user profile, and the machine learning network may provide the output based on the training. In some aspects, the set of actions may be associated with one or more previous messages provided by the communication device 105 (or the server 110) to the machine learning network, one or more previous outputs received by the communication device 105 (or server 110) from the machine learning network, one or more previously output notifications by the communication device 105 (or another communication device 105), one or more previously transmitted messages by the communication device 105 (or another communication device 105), or a combination thereof”) (consider further paragraphs 0081-0082 regarding wherein the “training data” can include “communication inputs” and that “The content engine 241 may be configured to analyze content, which may be any type of information, including information that is historical or in real-time. The content engine 241 may be configured to receive information from other communication devices 205 and/or the server 210”) and
execute the AI email model when an email with a list of email recipients is created prior to transmitting the email. (consider paragraph 0046, specifically “In an example with respect to messaging applications (e.g., conference chats, group instant messaging, email), the techniques described herein may be applied to messages, prior to the messages being sent. For example, a device may analyze a message (e.g., using a text analyzer), prior to sending the message, to determine or verify whether the message is for the intended recipient”) (consider paragraph 0062, “The set of recipients may include, for example, one or more intended recipients associated with the message, one or more additional recipients different from the one or more intended recipients, or both. In some examples, the communication device 105 may verify the one or more intended recipients based on the output received from the machine learning network. In some aspects, the communication device 105 may select the one or more additional recipients based on the output received from the machine learning network”) (consider further paragraph 0099, “The communication device 205 may provide at least a portion of the message to a machine learning network (e.g., a machine learning network implemented by the content engine 241, the content engine 266, or the content engine 270) to confirm the recipient, for example, prior to sending the message. In an example, the communication device 205 may provide the message (or message portion) to the machine learning network based on receiving a user input (e.g., via the user interface 245) for sending the message. Alternatively, or additionally, the communication device 205 may provide portions of the message in real-time, for example, as the message is input to the communication device 205”)
Daga may be interpreted as not having expressly taught determining whether an email is addressed to a single recipient or multiple recipients; in response to the email being addressed to the single recipient, transmit the email without the AI email model being executed; and in response to the email being address to the multiple recipients, execute the Al email model prior to transmitting the email.
However, Daga taught executing the AI email model prior to transmitting an email (again, consider paragraphs 0046 and 0099).
Daga also further may be interpreted as having expressly taught determining whether an email was intended to be addressed to a single recipient or multiple recipients in order to later execute the AI email model. (again, consider paragraphs 0046 and 0099) (consider further paragraph 0117, specifically “In another example, for a message input via a messaging window associated with a quantity of recipients (e.g., one, multiple), the machine learning network may determine a probability score and/or confidence score based on a correlation between the content (e.g., context information) of the message and the quantity of recipients associated with the messaging window. For example, for cases in which the machine learning network identifies that the contextual information of a message input via a messaging window implies a different quantity of recipients (e.g., a single recipient) compared to the quantity of recipients associated with the messaging window (e.g., multiple recipients), the machine learning network may output a relatively low probability score.”)
In an analogous art relating to email message processing based on addressed recipient(s), Guy taught that it may be determined whether an email is addressed to a single recipient or multiple recipients; in response to the email being addressed to the single recipient, transmit the email without an automated email model (“agent”; consider column 2, line 67-column 3, line 18) being executed; and in response to the email being address to the multiple recipients, execute the automated email model prior to transmitting the email which may comprise processing user generated content (consider column 2, lines 32-35, “Groups of recipients may be mined from one or more user's previous email correspondence and ranked by the strength of the group”). (consider column 4, line 61-column 5, line 16, “In the described system, an agent 120 is provided which reviews a list of intended recipients in an email message created by a user to check if the list of intended recipients is likely to include any incorrect recipients. The agent 120 reviews the list of intended recipients using a provided algorithm. A prompt 125 is provided in the form of a dialogue box on the email user interface 102 if the agent 120 determines that the user should review one or more of the intended recipients of the email message. The prompt 125 on the email user interface 102 may include appropriate recommendations, warnings, etc. The user 101 can select the relevant/irrelevant addressees and amend the email message appropriately or the user 101 can choose to disregard the prompt 125. The agent 120 can be provided as part of the email client application 103. Alternatively, the agent 120 can be provided as a service provided over a network from a server. An email user 101 can configure the agent 120 to provide the desired prompts 125 tailored to the user's requirements. This configuration of the agent 120 can include a complete disablement of the agent 120. The agent 120 uses an algorithm to review the list of intended recipients.”) (consider further column 8, lines 1-13, “Referring to FIG. 4, a flow diagram 400 is shown of the method carried out by the agent 120. An email message is created 401 by a user of an email client application and it is determined 402 whether there is more than one intended recipient of the message. If there is a single recipient 403, then no action is taken 404 by the agent. If there is more than one intended recipient 405, the intended recipients are compared 406 with defined correct groups.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the teachings of Daga to include the taught features of Guy such that the modification includes every element as claimed. Given Daga' s disclosure of training an AI email model with user generated content, determination of whether an email is addressed to a single or multiple recipients and executing the AI email model prior to transmitting an email, Guy specifically taught that, upon determination that an email is addressed to a single recipient, that transmit the email without an automated email model being executed and, if there are multiple recipients, then the automated email model is executed using user generated content to make further determinations of whether an email should be sent in order to prevent the unintentional sending of emails to unintended recipients and protecting the privacy of users (consider column 1, lines 59-64). Given this specific advantage in Guy, one skilled in the art would have been motivated to modify the teachings of Daga with the teachings of Guy such that the determination of whether an email is addressed to a single or multiple recipients and executing the AI email model prior to transmitting an email as taught in Daga may be further enhanced by the teachings of Guy so that it can be determined whether an email is addressed to a single recipient or multiple recipient and, in response to the email being addressed to the single recipient, transmit the email without the AI email model being executed; and in response to the email being address to the multiple recipients, execute the Al email model prior to transmitting the email as claimed. Therefore, such a modification of the teachings of Daga with the teachings of Guy would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 17, the combined teachings of Daga and Guy taught the information handling system of claim 16.
Daga further taught wherein the processor further accesses the AI email model to:
determine whether any conflicts are present in the list of email recipients. (consider paragraph 0004, specifically “In an example case, the device may determine that the content does not match the profile information associated with the recipient, and the device may perform one or more operations for corrective action (e.g., modifying content of the communication, modifying recipients for the communication, selecting a different messaging window for the communication, selecting a different application for the communication). For example, the device may output a notification to alert a sender that the recipient may be incorrect (e.g., the content is not associated with the recipient, the content is not suitable for the recipient), and the device may refrain from completing the communication. For example, the device may determine that a recipient for a message is incorrect, and the device may refrain from transmitting the message to the recipient and/or output a notification suggesting a different recipient for the message”) (consider further paragraph 0062, “The set of recipients may include, for example, one or more intended recipients associated with the message, one or more additional recipients different from the one or more intended recipients, or both. In some examples, the communication device 105 may verify the one or more intended recipients based on the output received from the machine learning network. In some aspects, the communication device 105 may select the one or more additional recipients based on the output received from the machine learning network”)
Regarding claim 18, the combined teachings of Daga and Guy taught the information handling system of claim 17.
Daga further taught wherein the processor further accesses the AI email model to:
transmit the email to the list of email recipients without modification when no conflicts are present. (consider paragraph 0046, specifically “In an example with respect to messaging applications (e.g., conference chats, group instant messaging, email), the techniques described herein may be applied to messages, prior to the messages being sent. For example, a device may analyze a message (e.g., using a text analyzer), prior to sending the message, to determine or verify whether the message is for the intended recipient. For example, the device may determine whether the recipient associated with the message is correct.”) (consider further paragraph 0156, specifically “Alternatively, or additionally, at 535, the communication device 105 may transmit the message to a respective communication device 105 associated with one or more recipients of the set of recipients, based on the output received from the machine learning network. In some aspects, outputting the notification, transmitting the message, or both may be based on a comparison of the set of probability scores to a probability threshold, a comparison of the set of confidence scores to a threshold, or both. In some cases, the communication device 105 may output the notification, transmit the message, or both based on the verification of the one or more intended recipients”)
Regarding claim 19, the combined teachings of Daga and Guy taught the information handling system of claim 17.
Daga further taught wherein the processor further accesses the AI email model to:
provide notification to a user that a potential conflict exists in the list of email recipients. (again, consider paragraph 0004, specifically “In an example case, the device may determine that the content does not match the profile information associated with the recipient, and the device may perform one or more operations for corrective action (e.g., modifying content of the communication, modifying recipients for the communication, selecting a different messaging window for the communication, selecting a different application for the communication). For example, the device may output a notification to alert a sender that the recipient may be incorrect (e.g., the content is not associated with the recipient, the content is not suitable for the recipient), and the device may refrain from completing the communication. For example, the device may determine that a recipient for a message is incorrect, and the device may refrain from transmitting the message to the recipient and/or output a notification suggesting a different recipient for the message”) (consider paragraph 0046, “In an example with respect to messaging applications (e.g., conference chats, group instant messaging, email), the techniques described herein may be applied to messages, prior to the messages being sent. For example, a device may analyze a message (e.g., using a text analyzer), prior to sending the message, to determine or verify whether the message is for the intended recipient. For example, the device may determine whether the recipient associated with the message is correct. If the device detects a possibility of an incorrect recipient (e.g., based on a probability score and/or a confidence score), the device may output a notification to alert the user of the same”) (consider further paragraph 0048, “The device may refer to various criteria when determining if a message is being sent to the correct recipient. In an example, if multiple messaging windows are open and the device determines (e.g., based on message content) that an analyzed message is for a discussion on another message window, the device may alert the user of the same. In some examples, the device may check or verify if the text in a message (e.g., intended message) implies that the message is being sent to a single user or multiple users. For example, a message including text such as “This looks good to me, what do you think” or “Are you getting where is he leading to” may imply that the message is being sent to a single user. If the device analyzes the text and detects that the message is addressed to (e.g., being sent to) multiple users, the device may notify the user of the discrepancy.”)
Regarding claim 20, the combined teachings of Daga and Guy taught the information handling system of claim 19.
Daga further taught wherein the processor further accesses the AI email model to:
receive user revisions to the list of email recipients; (consider paragraph 0128, specifically “The communication device 205 may transmit the message based on a user input. For example, based on the output notification provided by the communication device 205, the user input may confirm any of a recipient for a message, content of the message, a messaging window for sending the message, and an application for sending the message. The communication device 205 may transmit the message based on the user input.”) (consider further paragraph 0129, specifically “Alternatively, or additionally, the communication device 205 may perform one or more operations for modifying the recipient for the message, modifying content of the message, selecting a different messaging window for sending the message, and/or selecting a different application for sending the message. For example, based on the output notification (e.g., probability scores, confidence scores) provided by the communication device 205, the user input may invalidate (e.g., remove or modify) a recipient for the message, modify content of the message, select a different messaging window for sending the message, and/or select a different application for sending the message.”) (consider also paragraph 0183 regarding transmitting “the message” “with a different quantity of recipients” based on “a user input (e.g., confirming the message and/or different quantity of recipients)”)
provide feedback to the AI email model with the user revisions to the list of email recipients; (consider paragraph 0129, specifically “In some aspects, the communication device 205 may update data models (e.g., data model 242, data model 267, data model 286) and/or training data (e.g., training data 243, training data 268, training data 287) based on user decisions.”) and
transmit the email to a revised list of email recipients. (again, consider paragraph 0128, specifically “The communication device 205 may transmit the message based on a user input. For example, based on the output notification provided by the communication device 205, the user input may confirm any of a recipient for a message, content of the message, a messaging window for sending the message, and an application for sending the message. The communication device 205 may transmit the message based on the user input.”) (again, consider also paragraph 0183 regarding transmitting “the message” “with a different quantity of recipients” based on “a user input (e.g., confirming the message and/or different quantity of recipients)”)
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/G. C. Neurauter, Jr./Primary Examiner, Art Unit 2459
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