DETAILED ACTION
This is responsive to the amendment filed 10 November 2025.
Claims 1-20 remain pending and are considered below.
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 .
Response to Arguments
The rejections under 35 USC 101 have been withdrawn.
Applicant's arguments filed 10 November 2025 regarding the prior art rejections have been fully considered but they are not persuasive.
Applicant argues:
More specifically, Li fails to teach at least, "upon an analysis condition being satisfied while the message is being drafted, analyze the message by applying at least one of a message- analysis model or heuristic to generate a feedback score for the message, wherein the feedback score indicates a likelihood that the message would benefit from advanced feedback," as recited in claim 1. The Office Action points to the tone detection features of Li as being the claimed "feedback score." In turn, the Office Action asserts that the comparison of a detected tone to another tone is the claimed threshold comparison. (Office Action, at 21.) For instance, the Office Action cites to the following sentence of Li: "When an improper tone for a text segment within the content is detected, one or more notification mechanisms may be employed to notify the user of the improper tone." (Li, 60.) There is no comparison in Li of a feedback score to a feedback threshold, and there is no feedback score generated that indicates a likelihood that the message would benefit from advanced feedback.
The Examiner respectfully disagrees. Li discloses, upon an analysis condition being satisfied while the message is being drafted (“a text segment is complete”) (“In some implementations, automatic tone detection may be done by first determining when a text segment is complete (e.g., when a sentence is complete) and then submitting the completed text segment for tone detection upon its completion. Alternatively and/or additionally, automatic tone detection may be performed once a determination is made that content creation is complete (e.g., the user's name at the end of the email message)”, [0037]), analyze the message by applying at least one of a message-analysis model or heuristic to generate a feedback classification for the message (“The tone modification service may include an improper tone detection model 154 for determining if any of the detected tones are improper. In some implementations, the tone detection model 154 may include a classifier that classifies certain tones as improper”, [0043]);
based on the feedback classification, trigger generation of advanced feedback for the message (“When an improper tone for a text segment within the content is detected, one or more notification mechanisms may be employed to notify the user of the improper tone”, [0060], see also “Additionally, the UI element 340 may contain one or more suggested rephrases such as the suggested rephrase 350 for modifying the tone from the improper tone to a more proper tone for the content being created”, [0061]).
Li does not explicitly disclose that the feedback classification is performed based on a generated feedback score for the message crossing a feedback threshold.
However, in another embodiment, Li discloses performing classification based on a generated score for the message crossing a feedback threshold (“each tone detection model may include one or more classifiers that classify the segment as either being associated or not associated with a specific tone. In some implementations, the classifier may provide a score identifying the level of association of each segment with the tone. If the score meets a threshold requirement, the tone detection model may determine that the segment conveys the tone. When the score does not meet the threshold requirement, the model may determine that the segment does not convey the tone”, [0040]).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the different embodiments in Li to yield the predictable result of performing Li’s improper tone classification based on a generated score for the message crossing a feedback threshold as performed in Li’s tone classification because such is a standard practice in binary classification (e.g. improper tone or not).
Applicant further argues:
In addition, Li does not teach the claimed prompt generation. This deficiency of Li makes sense as Li does not appear to be using a language model, such as a large language model (LLM). For instance, claim 1 recites "generate an artificial intelligence (AI) prompt, for a language model, including dynamic and static segments, wherein the dynamic segments are populated with text from the message and the static segments include request instructions." Li does not teach such features. Rather, in Li, different types of models must be selected.
However, Li discloses generate an artificial intelligence (AI) prompt (an input), for a language model, including dynamic and static segments, wherein the dynamic segments are populated with text from the message (a segment having an identified tone) and the static segments include request instructions (requested by the user) (“each rephasing model may receive as an input a segment having an identified tone as well as additional data and provide as an output one or more suggested rephrases for the segment that modify, where the rephrases convey a desired tone”, [0046], see also “the desired tone is requested by the user”, [0047]). Note that a machine learning model configured to understand and generate natural language is an AI (machine learning) language model.
Applicant then argues:
Further, Li does not teach "wherein execution of the message-analysis model or heuristic is configured to utilize fewer computing resources than execution of the language model." The Office Action cites to paragraphs 29 and 49 of Li as teaching such features. (Office Action, at 23.) Applicant respectfully disagrees. These sections of Li simply state that models may generally be hosted locally or remotely.
However, Li teaches wherein execution of the message-analysis model or heuristic (“the client device 120 may also include a local tone detection service 124 for providing some intelligent tone detection of content”, [0035]) is configured to utilize fewer computing resources than execution of the language model (the tone modification service 116) (“Although shown as one server, the server 110 may represent multiple servers for performing various different operations. For example, the server 110 may include one or more processing servers for performing the operations of the tone detection service 114 and the tone modification service 116”, [0025], note that the tone detection performed on the client device while tone modification may be performed on a plurality of servers i.e. more computing resources).
Applicant finally argues:
With respect to claim 11, Li fails to teach at least "prior to delivering the message to the recipient, holding the message to analyze the message by applying at least one of a message- analysis model or heuristic to generate a feedback score for the message" and based on the feedback score crossing a feedback threshold, instead of delivering the message to the recipient, transmit a feedback alert message for surfacing in the messaging application." Li fails to teach an architecture where a server intercepts and holds a message (e.g., after the user clicks "send") for further analysis prior to delivery to the intended recipient. As such, claim 11 and the claims depending therefrom are also allowable over Li.
However, Li discloses, based on the feedback score crossing a feedback threshold, transmit a feedback alert message for surfacing in the messaging application (“Once the detected tone(s) are identified, the detected tone(s) and if identified, the overall tone of the document may be transmitted back as an output to the applications 126/112, where they are used to provide display data to the user to notify the user of the detected tones”, [0042]).
In this embodiment, Li does not explicitly disclose prior to delivering the message to the recipient, holding the message to perform the message analysis and transmitting the feedback alert instead of delivering the message to the recipient.
However, Li discloses invention is directed to performing revision/rephrasing of messages (“Technical solutions and implementations provided herein optimize the process of detecting improper tone and providing suggestions for modifying the tone by notifying the user of one or more tones detected in content and by providing easily accessible UI element(s) which contain intelligently suggested rephrases for modifying the improper tone to a desired tone”, [0021]). To take advantage of Li’s revision/rephrasing, some messages sent to recipients, such as emails and instant messages, would be revised before sharing them with their intended recipients (“review the content carefully before it is shared with others”, [0040], see also for example [0044] where email sent to a user’s manager may be revised).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the different embodiments in Li to yield the predictable result of, prior to delivering the message to the recipient, holding the message to perform the message analysis (note the message has to be stored, at least temporarily so it can be processed) and transmitting the feedback alert (overall tone of the document) instead/before of delivering the message to the recipient in order to fix messages such as emails before sending them to the recipients.
All of Applicant’s arguments regarding the prior art rejections have been considered and they are not persuasive.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2021/0397793).
Claim 1:
Li discloses a system for generating advanced feedback for a draft message using a language model, comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor ([0005]), cause the system to perform operations comprising:
receive text for a message being drafted in a messaging application (“The UI element includes a content pane 320 of a draft email message being created. In some implementations, for content such as email messages, instant messages, web postings and the like, where the content the user is creating relates to communications with one or more other individuals, the content creation application and/or web browser plugin may function to automatically perform tone detection”, [0058]);
upon an analysis condition being satisfied while the message is being drafted (“a text segment is complete”) (“In some implementations, automatic tone detection may be done by first determining when a text segment is complete (e.g., when a sentence is complete) and then submitting the completed text segment for tone detection upon its completion. Alternatively and/or additionally, automatic tone detection may be performed once a determination is made that content creation is complete (e.g., the user's name at the end of the email message)”, [0037]), analyze the message by applying at least one of a message-analysis model or heuristic to generate a feedback classification for the message (“The tone modification service may include an improper tone detection model 154 for determining if any of the detected tones are improper. In some implementations, the tone detection model 154 may include a classifier that classifies certain tones as improper”, [0043]);
based on the feedback classification, trigger generation of advanced feedback for the message (“When an improper tone for a text segment within the content is detected, one or more notification mechanisms may be employed to notify the user of the improper tone”, [0060], see also “Additionally, the UI element 340 may contain one or more suggested rephrases such as the suggested rephrase 350 for modifying the tone from the improper tone to a more proper tone for the content being created”, [0061]).
Li does not explicitly disclose that the feedback classification is performed based on a generated feedback score for the message crossing a feedback threshold.
However, in another embodiment, Li discloses performing classification based on a generated score for the message crossing a feedback threshold (“each tone detection model may include one or more classifiers that classify the segment as either being associated or not associated with a specific tone. In some implementations, the classifier may provide a score identifying the level of association of each segment with the tone. If the score meets a threshold requirement, the tone detection model may determine that the segment conveys the tone. When the score does not meet the threshold requirement, the model may determine that the segment does not convey the tone”, [0040]).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the different embodiments in Li to yield the predictable result of performing Li’s improper tone classification based on a generated score for the message crossing a feedback threshold as performed in Li’s tone classification because such is a standard practice in binary classification (e.g. improper tone or not).
Li further discloses wherein generation of the advanced feedback comprises:
generate an artificial intelligence (AI) prompt (an input), for a language model, including dynamic and static segments, wherein the dynamic segments are populated with text from the message (a segment having an identified tone) and the static segments include request instructions (requested by the user) (“each rephasing model may receive as an input a segment having an identified tone as well as additional data and provide as an output one or more suggested rephrases for the segment that modify, where the rephrases convey a desired tone”, [0046], see also “the desired tone is requested by the user”, [0047]); wherein execution of the message-analysis model or heuristic (“the client device 120 may also include a local tone detection service 124 for providing some intelligent tone detection of content”, [0035]) is configured to utilize fewer computing resources than execution of the language model (the tone modification service 116) (“Although shown as one server, the server 110 may represent multiple servers for performing various different operations. For example, the server 110 may include one or more processing servers for performing the operations of the tone detection service 114 and the tone modification service 116”, [0025], note that the tone detection performed on the client device while tone modification may be performed on a plurality of servers i.e. more computing resources);
provide the AI prompt as input to the language model; in response to the AI prompt, receive an output payload from the language model (“each rephasing model may receive as an input a segment having an identified tone as well as additional data and provide as an output one or more suggested rephrases for the segment that modify, where the rephrases convey a desired tone”, [0046]); post-process (approve) the received output payload to extract advanced feedback for the draft message (“The suggestions may then be displayed on the same UI screen as the document contents to enable the user to quickly and efficiently identify improper tone and/or approve the most appropriate suggested rephrased text segment”, [0087]); and cause a display of the extracted advanced feedback within a user interface of the messaging application (“The suggestions may then be displayed on the same UI screen as the document contents to enable the user to quickly and efficiently identify improper tone and/or approve the most appropriate suggested rephrased text segment”, [0087]).
Claim 2:
Li discloses the system of claim 1, wherein the analysis condition includes at least one of expiration of a time period, entry of a threshold amount of text, entry of a particular character, or a pause (“some applications may automatically submit a request for tone detection when a user begins creating content (e.g., when the user finishes writing a sentence)”, [0037]).
Claim 3:
Li discloses the system of claim 1, wherein the operations further comprise, in response to the feedback score crossing the feedback threshold, surface a feedback notification ([0040]).
Claim 4:
Li discloses the system of claim 3, wherein the feedback notification further includes data generated from applying the at least one of the message-analysis model or the heuristic ([0040]).
Claim 5:
Li discloses the system of claim 1, wherein the message-analysis model is applied (the local tone detection service 124 of the client device 120) ([0049]).
Claim 6:
Li discloses the system of claim 1, wherein the at least one of the message-analysis model or the heuristic is based on at least one of a length of the message or a number of recipients ([0071]).
Claim 7:
Li discloses the system of claim 1, wherein the at least one of the message-analysis model or the heuristic is based on at least one of a relationship of a sender of the message to one or more recipients of the message, a frequency of communication between the sender of the message and the one or more recipients, or whether the one or more recipients are internal or external to a domain of the sender ([0044], see also [0075]).
Claim 9:
Li discloses the system of claim 1, wherein the at least one of the message-analysis model or the heuristic is based on at least one of profanity, dangerous language, emotive terms, or sensitive topics within the message ([0043]).
Claim 10:
Li discloses the system of claim 1, wherein the AI prompt further includes data generated (tone) from application of the at least one of the message-analysis model or the heuristic (tone detection) ([0046], note that the tone is determined by applying tone detection).
Claim 11:
Li discloses a messaging-server system for generating advanced feedback for a message using a language model, comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor ([0005]), cause the system to perform operations comprising:
receive an initial sent message, from a messaging application, intended for a recipient (“The UI element includes a content pane 320 of a draft email message being created. In some implementations, for content such as email messages, instant messages, web postings and the like, where the content the user is creating relates to communications with one or more other individuals, the content creation application and/or web browser plugin may function to automatically perform tone detection. This may be done to warn the user of tone that may be disrespectful or otherwise improper when the user is communicating with others. In some implementations, automatic tone detection may be done by first determining when a text segment is complete (e.g., when a sentence is complete) and then submitting the completed text segment for tone detection upon its completion. Alternatively and/or additionally, automatic tone detection may be performed once a determination is made that content creation is complete (e.g., the user's name at the end of the email message)”, [0058]);
analyze the message by applying at least one of a message-analysis model or heuristic to generate a feedback score for the message (“In some implementations, automatic tone detection may be done by first determining when a text segment is complete (e.g., when a sentence is complete) and then submitting the completed text segment for tone detection upon its completion. Alternatively and/or additionally, automatic tone detection may be performed once a determination is made that content creation is complete (e.g., the user's name at the end of the email message)”, [0037], see also “each tone detection model may include one or more classifiers that classify the segment as either being associated or not associated with a specific tone. In some implementations, the classifier may provide a score identifying the level of association of each segment with the tone. If the score meets a threshold requirement, the tone detection model may determine that the segment conveys the tone. When the score does not meet the threshold requirement, the model may determine that the segment does not convey the tone”, [0040]);
based on the feedback score crossing a feedback threshold, transmit a feedback alert message for surfacing in the messaging application (“Once the detected tone(s) are identified, the detected tone(s) and if identified, the overall tone of the document may be transmitted back as an output to the applications 126/112, where they are used to provide display data to the user to notify the user of the detected tones”, [0042]).
In this embodiment, Li does not explicitly disclose prior to delivering the message to the recipient, holding the message to perform the message analysis and transmitting the feedback alert instead of delivering the message to the recipient.
However, Li discloses invention is directed to performing revision/rephrasing of messages (“Technical solutions and implementations provided herein optimize the process of detecting improper tone and providing suggestions for modifying the tone by notifying the user of one or more tones detected in content and by providing easily accessible UI element(s) which contain intelligently suggested rephrases for modifying the improper tone to a desired tone”, [0021]). To take advantage of Li’s revision/rephrasing, some messages sent to recipients, such as emails and instant messages, would be revised before sharing them with their intended recipients (“review the content carefully before it is shared with others”, [0040], see also for example [0044] where email sent to a user’s manager may be revised).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the different embodiments in Li to yield the predictable result of, prior to delivering the message to the recipient, holding the message to perform the message analysis (note the message has to be stored, at least temporarily so it can be processed) and transmitting the feedback alert (overall tone of the document) instead/before of delivering the message to the recipient in order to fix messages such as emails before sending them to the recipients.
Li further discloses:
receive an indication of an interaction with at least one of the feedback alert message or a feedback activation user interface element; based on receiving the indication of the interaction, trigger generation of advanced feedback for the message (“the user may utilize a UI element of the applications 126/112 to set the desired tone of the content to a specific tone (e.g., a menu option is used to set the tone of the document to neutral). In another example, the user may utilize a UI element to request that specific detected tones be converted to specific desired tones (e.g., modify impolite tones to polite tones)”, [0047]), wherein generation of the advanced feedback comprises:
generate an artificial intelligence (AI) prompt (an input), for a language model, including dynamic and static segments, wherein the dynamic segments are populated with text from the message (a segment having an identified tone) and the static segments include request instructions (requested by the user) (“each rephasing model may receive as an input a segment having an identified tone as well as additional data and provide as an output one or more suggested rephrases for the segment that modify, where the rephrases convey a desired tone”, [0046], see also “the desired tone is requested by the user”, [0047]); provide the AI prompt to the language model, wherein execution of the message-analysis model or heuristic (“the client device 120 may also include a local tone detection service 124 for providing some intelligent tone detection of content”, [0035]) is configured to utilize fewer computing resources than execution of the language model (the tone modification service 116) (“Although shown as one server, the server 110 may represent multiple servers for performing various different operations. For example, the server 110 may include one or more processing servers for performing the operations of the tone detection service 114 and the tone modification service 116”, [0025], note that the tone detection performed on the client device while tone modification may be performed on a plurality of servers i.e. more computing resources); in response to the AI prompt, receive an output payload from the language model including the advanced feedback (“each rephasing model may receive as an input a segment having an identified tone as well as additional data and provide as an output one or more suggested rephrases for the segment that modify, where the rephrases convey a desired tone”, [0046]); and cause a display of the advanced feedback within a user interface of the messaging application (“The suggestions may then be displayed on the same UI screen as the document contents to enable the user to quickly and efficiently identify improper tone and/or approve the most appropriate suggested rephrased text segment”, [0087]).
Claim 12:
Li discloses the messaging-server system of claim 11, wherein the operations further comprise: receiving a revised draft of the message that has been revised according to the advanced feedback; and delivering the revised draft of the message to one or more recipients indicated in the message, without applying the at least one of the message-analysis model or heuristic against the revised draft of the message (“The suggestions may then be displayed on the same UI screen as the document contents to enable the user to quickly and efficiently identify improper tone and/or approve the most appropriate suggested rephrased text segment”, [0087], note the approval i.e. selection is performed directly by the user without any applying the at least one of the message-analysis model or heuristic).
Claim 13:
Li discloses the messaging-server system of claim 11, wherein the feedback alert message further includes data generated from applying the at least one of the message-analysis model or the heuristic ([0042]).
Claim 14:
Li discloses the messaging-server system of claim 11, wherein execution of the message-analysis model or heuristic (the local tone detection service 124 of the client device 120) utilizes fewer computing resources than execution of the language model (the tone modification service 116) ([0049], see also [0029] where it is suggested that server based models are more powerful than local ones).
Claim 15:
Li discloses the messaging-server system of claim 11, wherein the AI prompt further includes data generated from application of the at least one of the message-analysis model or the heuristic ([0046]).
Claim 16:
Li discloses a computer-implemented method for generating advanced feedback for a draft message using a language model, comprising:
receiving text for a draft message being drafted in a messaging application (“The UI element includes a content pane 320 of a draft email message being created. In some implementations, for content such as email messages, instant messages, web postings and the like, where the content the user is creating relates to communications with one or more other individuals, the content creation application and/or web browser plugin may function to automatically perform tone detection”, [0058]); upon an analysis condition being satisfied a first time while the message is being drafted (“a text segment is complete”), analyzing the draft message by applying at least one of a message-analysis model or heuristic to generate a first feedback score for the message, wherein the first feedback score is below a feedback threshold (“In some implementations, automatic tone detection may be done by first determining when a text segment is complete (e.g., when a sentence is complete) and then submitting the completed text segment for tone detection upon its completion”, [0037], see also “each tone detection model may include one or more classifiers that classify the segment as either being associated or not associated with a specific tone. In some implementations, the classifier may provide a score identifying the level of association of each segment with the tone. If the score meets a threshold requirement, the tone detection model may determine that the segment conveys the tone. When the score does not meet the threshold requirement, the model may determine that the segment does not convey the tone”, [0040]).
Li does not explicitly disclose that the feedback classification is performed based on a generated feedback score for the message crossing a feedback threshold.
However, in another embodiment, Li discloses performing classification based on a generated score for the message crossing a feedback threshold (“each tone detection model may include one or more classifiers that classify the segment as either being associated or not associated with a specific tone. In some implementations, the classifier may provide a score identifying the level of association of each segment with the tone. If the score meets a threshold requirement, the tone detection model may determine that the segment conveys the tone. When the score does not meet the threshold requirement, the model may determine that the segment does not convey the tone”, [0040]).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the different embodiments in Li to yield the predictable result of performing Li’s improper tone classification based on a generated score for the message crossing a feedback threshold as performed in Li’s tone classification because such is a standard practice in binary classification (e.g. improper tone or not).
Li further discloses wherein generation of the advanced feedback comprises:
receiving additional text for the draft message; upon an analysis condition being satisfied a second time (“a text segment is complete”), after receiving the additional text, analyzing the draft message by applying the at least one of the message-analysis model or heuristic to generate a second feedback score for the message, wherein the second feedback score exceeds the feedback threshold (“In some implementations, automatic tone detection may be done by first determining when a text segment is complete (e.g., when a sentence is complete) and then submitting the completed text segment for tone detection upon its completion”, [0037], note that the user may enter multiple sentences, see [0039] and tone detection will be performed at the end of each sentence, see also “each tone detection model may include one or more classifiers that classify the segment as either being associated or not associated with a specific tone. In some implementations, the classifier may provide a score identifying the level of association of each segment with the tone. If the score meets a threshold requirement, the tone detection model may determine that the segment conveys the tone. When the score does not meet the threshold requirement, the model may determine that the segment does not convey the tone”, [0040]);
based on the second feedback score crossing a feedback threshold, surfacing a feedback notification in the messaging application (“Once the detected tone(s) are identified, the detected tone(s) and if identified, the overall tone of the document may be transmitted back as an output to the applications 126/112, where they are used to provide display data to the user to notify the user of the detected tones”, [0042]);
receiving an indication of an interaction with the feedback notification or a feedback activation user interface element; and triggering generation of the advanced feedback for the message (“the user may utilize a UI element of the applications 126/112 to set the desired tone of the content to a specific tone (e.g., a menu option is used to set the tone of the document to neutral). In another example, the user may utilize a UI element to request that specific detected tones be converted to specific desired tones (e.g., modify impolite tones to polite tones)”, [0047]), wherein generation of the advanced feedback comprises:
generating an artificial intelligence (AI) prompt (an input), for a language model, including dynamic and static segments, wherein the dynamic segments are populated with text from the message (a segment having an identified tone) and the static segments include request instructions (requested by the user) (“each rephasing model may receive as an input a segment having an identified tone as well as additional data and provide as an output one or more suggested rephrases for the segment that modify, where the rephrases convey a desired tone”, [0046], see also “the desired tone is requested by the user”, [0047]); providing the AI prompt to the language model; in response to the AI prompt, receiving an output payload from the language model including the advanced feedback (“each rephasing model may receive as an input a segment having an identified tone as well as additional data and provide as an output one or more suggested rephrases for the segment that modify, where the rephrases convey a desired tone”, [0046]); and causing a display of the advanced feedback within a user interface of the messaging application (“The suggestions may then be displayed on the same UI screen as the document contents to enable the user to quickly and efficiently identify improper tone and/or approve the most appropriate suggested rephrased text segment”, [0087]).
Claim 17:
Li discloses the computer-implemented method of claim 16, wherein the feedback notification is surfaced in the messaging application that does not occlude the additional text of the draft message (see Fig. 3A where the feedback notification “This sentence seems to have an angry tone” does not occlude (i.e. obstruct) any of the additional sentences).
Claim 18:
Li discloses the computer-implemented method of claim 17, wherein the feedback notification includes data generated from applying the at least one of the message-analysis model or the heuristic ([0040]).
Claim 19:
Li discloses the computer-implemented method of claim 16, wherein triggering the generation of the advanced feedback is based on receiving the interaction ([0047]).
Claim 20:
Li discloses the computer-implemented method of claim 16, wherein: triggering the generation of the advanced feedback is based on the feedback score crossing a feedback threshold ([0040]); and causing the display of the advanced feedback is based on receiving the interaction ([0047]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2021/0397793) in view of Ramakrishnan (US 2021/0118546).
Claim 8:
Li discloses the system of claim 1, but does not explicitly disclose wherein the at least one of the message-analysis model or the heuristic is based on at least one of an amount of time spent drafting the message or an amount of the message that has been retyped.
In an analogous art similarly analyzing a message by applying at least one of the message-analysis model or the heuristic, Ramakrishnan discloses wherein the at least one of the message-analysis model or the heuristic is based on at least one of an amount of time spent drafting the message or an amount of the message that has been retyped (“Examples of information that may be sent by computing device 102A and/or the email application (or related applications) include: active duration of time spent on email authoring; active duration of time spent on email viewing/reading; number of times an email application/client has been manually refreshed; number of scrolls/highlights made to received emails; number of emails composed and/or sent; number of emails opened and/or read; recipient list for email; number of re-writes or corrections made during authoring of email”, [0031]).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of basing Li’s at least one of the message-analysis model or the heuristic is on at least one of an amount of time spent drafting the message or an amount of the message that has been retyped because those are indicative of possible anxiety which may affect the tone detected by Li (see Ramakrishnan [0023]).
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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/SAMUEL G NEWAY/Primary Examiner, Art Unit 2657