DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
In response to the office action from 4/13/2026, the applicant has submitted an amendment, filed 7/29/2026, amending claims 1-4, 6-7, 9-12, 14-15, 17-18, cancelling claims 19-20, adding claims 21-22, while arguing to traverse the prior art and 101 rejections. Applicant’s arguments have been fully considered but are moot with respect to new grounds of rejections further in view of Smith et al. (US Patent 11,562,739) and JIANG et al. (TW470927) mandated by the latest amendments, and for the reasons explained in the response to arguments.
Response to Arguments
In what follows applicant’s arguments will be addressed in the order presented.
Following a broad overview of the latest amendments on page 8 ¶ 1, the remainder of page 8 discusses how it is believed the steps in amended claim 1 map to “example 48” of “2024 AI Examples 47 through 49”. In particular it is concluded that the instant application claim 1 “is eligible” “because the claimed step of “synthesizing speech waveforms” “is not a process that can be practically performed in the human mind” (page 8 last 2 lines). It is further noted on page 9 ¶ 2 lines 5+: “generating a waveform of a synthesized speech voice output cannot practically be performed in the human mind and integrates the abstract idea into a practical application”.
Unfortunately these statement are flawed since other than the use of the word “waveform” in the instant application, it has nothing in common with the “example 48”; i.e., respectfully where in the instant application does one find any limitations teaching: 1) using “waveforms” to “synthesize” “speech”, not to mention how that is done? the only occurrence of the word “waveform” is “a waveform of a synthesized speech”; i.e. a singular “waveform” representing “a synthesized speech”, rather than a plurality of “waveforms” used by a specific process (i.e. “stitching” in Example 48) to generate “synthesiz[ed] speech”. Respectfully, any uttered “speech” is associated with a “waveform”. Using similar words with a patent eligible patent application does not by default result in a subject matter eligible application.
The ”Example 48” not only provides a specific training recipe involving a “plurality” of “waveforms” by a “stitching” mechanism, but also teaches how those “waveforms” are generated via “different” “sources”. In the instant application, the word “waveform” as a singular “waveform” only appears once in the entire claim, without any operations being associated with it, and without any mechanism linking it to generation of “synthesized speech”. As drafted the “waveform” in the instant application serves an alternative definition of the “synthesized speech voice”.
The arguments from the middle of page 9 through the second ¶ of page 10 are directed at why the amended feature of incorporation of “geographical region data” into “neural network” training for “emotion metric” determination and subsequent use in “speech synthesis” is not taught by the primary and secondary references.
Since a new reference (Smith et al.) is used for that feature, therefore the applicant is respectfully directed to the new office action for further details as these arguments are moot with respect to Smith et al. teachings.
Page 10 the last ¶ S2, it is argued: “Applicant further respectfully submits that dependent claims 2-8, 10-16, 18, and 21-22 as amended, are patentable by virtue of their respective direct and ultimate dependencies form allowable independent claims 1, 9, and 17”.
As regards to the new claims 21-22, this argument is moot further in view of JIANG et al. Please visit the new office action.
As regards to the other dependent claims, since applicants have not argued the merits of these dependent claims, but assert patentability solely through their dependence on the allegedly patentable parent claims, they stand or fall with said parent claims and hence no further response to applicant’s arguments is necessary.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18, 21-22 stand rejected:
Claims 1, 9 and 17 correspond to a “method”, “system” and “non-transitory computer-readable medium” respectively associated with a “voice assistant system” which in response to a “voice input” (of “a user”) provides “a synthesized speech voice output” and associated “waveform” with characteristics which mimic the “voice input” at least by sharing two “emotions” and one “prosodic characteristic”, and further based on a “geographical region data” associated with the “user “profile” (e.g., a ¶ 0037 S2: “specific dialect”). They are rejected under 35 U.S.C. because the claimed invention is directed to an abstract idea without significantly more.
More specifically the above function can be mapped to a customer speaking with a store representative where the emotion of the customer is detected and then the representative responds by speaking with an emotion (and/or emotions) and prosody (e.g. pitch) corresponding to that of the customer and further even attempt to mimic the customer’s “dialect” (“geographical region data” associated with the “user profile”); e.g., if the customer is in a hurry and speaks at a high pitched voice, the representative accommodates by providing service (including speech in customer’s dialect) which matches the speed and/or pitch embedded in the customer’s speech and its associated waveform. The claims further only recite the additional limitations of "voice assistant system" (claim 1), "control circuitry" (claim 9), "input/output circuitry" (claim 9), and "computer readable medium" (claim 17), and further based on using “a neural network model”. Therefore, other than recitation of the said elements, nothing in the said claims precludes them from practically being performed in the mind according to the example above. If a claim limitation (and/or limitations), under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components (e.g. the “control circuitry” above), then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the “input/output circuitry” is being used simply as extra solution activity of data gathering and data output. The “voice assistant system” is only being used to receive the voice data and therefore is pre-solution activity. The “computer readable medium” is used as an apply it recitation where the method is stored thereon. Specification ¶ 0093 (last S.) specifically denotes the storage to be “any suitable memory storage” and thus making it a general purpose storage. Similarly, the “control circuit” operates in a similar fashion. As regards to the use of “neural network” in identifying the user’s “geographical region data” (e.g. his “dialect”), the specification simply provides “convolutional”, “recurrent” and “LSTM” as examples used (Sp. ¶ 0023 S1, 0091 S2) which are well known routine examples, and since no specific training procedure via the “neural network” is presented to assess its utility, therefore their introduction amounts to nothing more than a mere use of a routine, well known or generic software without any “unconventional steps that could confine the claim to a particular application” to qualify it as reciting “significantly more”. This conclusion is further justified, since a human can easily determine another person’s dialect based on information or training that has been stored in his memory for identifying a certain dialect in his life. Therefore, these limitations do not tie the claims to a practical application and further are not sufficient to amount to significantly more than the judicial exception.
As a result the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as the integration of the “input/output circuitry”, “computer readable medium”, “voice assistant system”, “control circuitry” and “neural network” to carry out the claimed limitations amounts to no more than mere instructions to apply the exception using generic computer components and software. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not therefore patent eligible.
Regarding claims 2, 10, and 18, it is quite possible that a customer’s emotions appear to radically change (“counteract”) while engaging in an interaction with the store representative; e.g., suppose a customer arrives in a hurry (and is nervous and his nervousness is reflected in a pitch of his voice) as he may think the article he is interested may not be in stock, but then calms down as he finds out the article is in stock and his tone accordingly changes.
Regarding claims 3, 11, it is quite reasonable that a “first” and a “second” “emotion” of the customer be “correlated” while dealing with the store representative; e.g., to be in hurry and meanwhile to be joyful when making a request.
Regarding claims 4, 12, for the store representative to detect a dialect of the customer and to tailor his response according to understandings of the dialect. The human mind naturally gets trained from early childhood with language and dialect recognition by memorizing in time their associated sounds and their respective patterns (e.g., dialects and accents). The use of “neural network” (e.g., “recurrent” and “LSTM” (Sp. ¶ 0023 S1, 0091 S2)) to “determine” “dialect” without any specific training process which could also impact the neural network amounts to nothing more than a mere use of a routine, well known or generic software without any “unconventional steps that could confine the claim to a particular application” (MPEP 2106.05(d)) to qualify it as reciting “significantly more”.
Regarding claims 5, and 13, the store representative if being familiar with the customer could interact in a way to make the customer happy by using past experience on the customer personality (profile); e.g., what are the customer preferences in e.g., his favorite products, and/or avoid what triggers the customer getting angry (e.g. if he determines the customer to be a nervous person).
Regarding claims 6, and 14, the store representative if being familiar with the customer could interact in a way to make the customer happy by using past experience on the customer personality and preferences.
Regarding claims 7, and 15, the store representative could tune his vocal response according to certain pitch (prosodic parameters) and/or volume and doing so by e.g. adjusting the volume uniformly and consistently for all words including adjacent words to result in a pleasant experience for the customer.
Regarding claims 8, and 16, the store representative could speak certain words using with certain emotions (“emotion indicator”) so as to emphasize certain points; e.g., in describing smell of a certain perfume to say: boy this smells so gooood (by deliberately stretching on the word “good”).
Regarding claim 21, generation of a transcription from an input voice, is something that that the store representative could generate by simply taking notes or dictations from the customer while the customer is speaking. The use of tags is further made possible by the store representative assigning a number to each item ordered by the customer. The recitation of “inputting” “the one or more tags into the neural network” amounts to nothing more than tasking something to be done “by the neural network” without any details on how the neural network is used; the use of “neural network”, given the broad definition of the said neural network as being e.g., “recurrent” and “LSTM” (Sp. ¶ 0023 S1, 0091 S2), which are well known routine examples, given that no specific training procedure via the “neural network” is presented to assess its utility, amounts to nothing more than a mere use of a routine, well known or generic software without any “unconventional steps that could confine the claim to a particular application” (MPEP 2106.05(d)) to qualify it as reciting “significantly more”.
Regarding claim 22, the store representative behavior in responding to the customer is based on repeated similar (trained ) examples obtained while handling customers with similar voice inputs and providing corresponding responses. A human mind constitutes a natural file with a predetermined format of associating each word in a language to an emotional metric; e.g., the input word excellent is always associated with a positive emotional metric, while the word terrible gets associated with a negative emotional metric. The use of “neural network” to do all these tasks without providing any specific details on the training and use of the said “neural network” amounts to nothing more than a mere use of a routine, well known or generic software without any “unconventional steps that could confine the claim to a particular application” (MPEP 2106.05(d)) to qualify it as reciting “significantly more”.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 21 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Nowhere in the original disclosure one can find any teaching and/or suggestion of inputting any “transcription” with or without “data tags” into a “neural network” (e.g., the “recurrent” and “LSTM” (Sp. ¶ 0023 S1, 0091 S2)).
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.
Claim(s) 1- 6, 8-12, 14, 16-18, 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over LI JIANBIN (CN 108762500) in view of BROMAND et al. (US 2019/0325866), and further in view of Smith et al. (US Patent 11,562,739).
Regarding claim 1, LI JIANBIN does teach a method (Abstract S1: “The invention provides an intelligent robot” which includes according to ¶ 0037 S1: “voice recognition module, an emotions judgment module, and a voice synthesis model” (a voice assistant system and method))
comprising:
receiving a voice input by a voice assistant system (¶ 0039 lines 2+: “The first emotion determination module is used to determine a first emotion feature of the robots 1’s speech” (receiving a voice input by the voice assistant system));
based on the voice input, determine a first emotion of the voice input (¶ 0039 lines 3+: “The first emotion determination module is used to determine a first emotion” (determining a first emotion) “feature of the robot 1’s speech” (based on the voice input));
based on the first emotion of the voice input, determine a second emotion, different from the first emotion, for a response to the voice input (¶ 0039 lines 2+: “The first emotion determination module is used to determine a first emotion” (based on the first emotion) “feature of the robots 1’s speech” (of the voice input) “the second emotion determination module is used to determine a second emotion” (determining a second different emotion) to determine an “output” (a response to the voice input) by the “robot 1”);
generate a synthesized speech voice output as the response to the voice input,
causing an output of the synthesized speech voice output (¶ 0039 line 6: “and the output module is used for the robot1 to output” (causing an output of) “the emotion speech” (of the synthesized voice output)).
LI JIANBIN does not specifically disclose:
training a neural network model based on a plurality of voice input emotion metrics, a plurality of response emotion metrics, wherein determining the first and second emotion is based on accessing the trained neural network model;
wherein the synthesized speech voice output comprises prosodic characteristics corresponding to the second emotion.
BROMAND et al. do teach:
training a neural network model based on a plurality of voice input emotion metrics, a plurality of response emotion metrics, wherein determining the first and second emotion is based on accessing the trained neural network model (¶ 0119 S1-S3: “In some examples the emotion processor 130 also includes one or more machine learning” (using or accessing a neural network model) “models 148 that use training algorithms to” (comprising training) “e.g., improve emotion” (to determine or identify e.g. the “second emotion”) “feature extraction” “and/or map extracted emotion features” (based on a plurality voice input emotion metrics) “to emotions”; ¶ 0149 S1: “In some examples, the response processor 370 also includes one or more machine learning models 314 that use training algorithms” (training the “response” (a plurality of response emotion metrics));
wherein the synthesized speech voice output comprises prosodic characteristics corresponding to the second emotion (¶ 0048 S3-5: “mapping the first emotion feature or the first emotion feature-user pair to a first emotion; executing a first action in response to the first natural utterance and/or generating a synthesized utterance” (generating a synthesized speech voice output in response to a voice input and based on a “first emotion” (e.g. a second emotion)) “and/or a displayed text proposing that the first action be executed or acknowledging that the first action will be executed”; ¶ 0075 S2: “Non-limiting examples of stylistic adjustments adapted to emotions” (e.g. the second emotion on which the synthesized voice depends) “include adjustments in cadence, inflection, tone, volume, pitch” (comprises prosodic characteristics)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of “generating synthesized utterance” based on “emotion” of “user” of BROMAND et al. into the “intelligent robot” “speech synthesis module” of LI JIANBIN would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI JIANBIN to do “stylistic adjustments adapted to emotions” in generating “output” as disclosed in BROMAND et al. ¶ 0075 S1.
LI JIANBIN in view of BROMAND et al. do not specifically disclose:
Training a neural network model based on a geographical region data;
Identifying a geographical region information of a user profile;
Inputting the voice input and the geographical region information into the trained neural network model;
Generate, based at least in part on the geographical region information, a waveform of a synthesized speech voice output.
Smith et al. do teach:
Training a neural network model based on a geographical region data; Identifying a geographical region information of a user profile; Inputting the voice input and the geographical region information into the trained neural network model (Col. 9 lines 13+: “non-audio data” “input to the” (inputting) “speech quality detector 285” (into a trained neural network model) “e.g. GPS location” (an identified geographical region information) of a “user” (of a user profile); note: Col. 7 lines 4+: “The speech quality detector 285 may perform whisper” (receiving a voice input) “detection based on a long-short term memory (LSTM)” (by the “speech quality detector” which uses a neural network) “neural network”; Col. 9 lines 3-4: “speech quality detector” (the neural network) “may receive a user ID” (receives a user profile information));
Generate, based at least in part on the geographical region information, a waveform of a synthesized speech voice output (Col. 2 lines 15-17: “the system may determine weather information for the user’s location” (based on a geographical region information) “output weather information as synthesized speech” (generate a synthesized speech output waveform because according to Col. 20 lines 2-3: “TTS component” (i.e., synthesized speech voice) “to craft a natural sounding audio waveform output” (comprises a waveform output)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “speech quality detector” of Smith et al. into the “generating synthesized utterance” of BROMAND et al. in LI JIANBIN in view of BROMAND et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI JIANBIN in view of BROMAND et al. to benefit from inclusion of “non-audio” data such as “time/date” “location” in generating its speech synthesis output which helps in advancing “speech quality” as disclosed in Smith et al. Column 9 lines 15-22.
Regarding claim 2, LI JIANBIN does not specifically disclose the method of claim 1, wherein the determining the second emotion comprises identifying an emotion that counteracts the first emotion as the second emotion.
BROMAND et al. do teach the method of claim 1, wherein the determining the second emotion comprises identifying an emotion that counteracts the first emotion as the second emotion (¶ 0051 S. before last: “identify a pivot between the first emotion” (the first emotion) “and the second emotion” (and the second emotion) “classify the pivot as one of positive, negative” (counteract each other) “and neutral”).
For obviousness to combine LI JIANBIN and BROMAND et al. see claim 1.
Regarding claim 3, LI JIANBIN does not specifically disclose the method of claim 1, wherein determining the second emotion comprises: determining a correlation between the first emotion and the second emotion; and identifying the second emotion based on the correlation.
BROMAND et al. do teach the method of claim 1, wherein determining the second emotion comprises: determining a correlation between the first emotion and the second emotion; and identifying the second emotion based on the correlation (¶ 0051 S. before last: “identify a pivot between the first emotion” (the first emotion) “and the second emotion” (and the second emotion) “classify the pivot as one of positive” (correlate with each other and help identify each other) “negative” “and neutral”).
For obviousness to combine LI JIANBIN and BROMAND et al. see claim 1.
Regarding claim 4, LI JIANBIN in view of BROMAND et al. do not specifically disclose the method of claim 1, further comprising training the neural network module further based on dialect data, and accessing the trained neural network model to determine a dialect of the voice input; and generating the synthesized speech voice output based on the determined dialect from the trained neural network model.
Smith et al. do teach the method of claim 1, further comprising training the neural network module further based on dialect data, and accessing the trained neural network model to determine a dialect of the voice input (Col. 3 lines 34-36: “The server(s) 120 processes (134) the audio data” (for e.g., the voice input) “to determine” (determine) “a speech quality” (using a neural network (Col. 7 lines 4-6)) “indicator representing a quality of the speech” (e.g. how words are “pronounced” (Col. 19 lines 16-18), such as their “accent” (dialect (Col. 23 line 4)); Col. 7 lines 4+: “The speech quality detector 285 may perform” “based on a long-short term memory (LSTM)” “neural network” (uses neural network); Col. 19 lines 16-20: “The TTSFE 616” (training neural network (Col. 19 lines 12-13: “TTSSFE” “synthesize[s] speech using neural network”)) “may also process other data 615” “that indicate how specific words should be pronounced” (based on dialect)));
and generating the synthesized speech voice output based on the determined dialect from the trained neural network model (Col. 19 lines 16-20: “The TTSFE 616 may also process other data 615 that indicate how specific words should be pronounced” (e.g. by taking into account “accent” (dialect (Col. 23 line 4))) “for example by indicating the desired output speech quality in tags formatted according to speech synthesis” (generating a synthesized speech voice output) “markup language (SSML) or in some other form”; Col. 19 lines 12-13: “synthesize speech” (the speech synthesis is done) “using neural network” (by a trained neural network model)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “speech quality detector” of Smith et al. into the “generating synthesized utterance” of BROMAND et al. in LI JIANBIN in view of BROMAND et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI JIANBIN in view of BROMAND et al. to also detect “accent” and provide responses according “to different known pronunciations” as disclosed in Smith et al. Col. 23 lines 1-4.
Regarding claim 5, LI JIANBIN in view of BROMAND et al. do not specifically disclose the method of claim 1, wherein the determining the first emotion for the voice input comprises accessing user profile information associated with a user, and comparing characteristics of the voice input to the user profile information.
Smith et al. do teach
the method of claim 1, wherein the determining the first emotion for the voice input comprises accessing user profile information associated with a user, and comparing characteristics of the voice input to the user profile information (Col. 30 lines 1+: “determining the user profile data” (accessing user profile information) “indicates the first emotion is enabled to be used to perform processing related to the first audio data; and after determining the user profile data indicates the first emotion” (to determine the first emotion) “is enabled to be used to perform processing related to the first audio data” (for e.g. a voice input) “causing the first content to be output”).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the capability of “emotion” determination based on “profile data” of Smith et al. into the “emotion” determination and/or identification of either LI JAINBIN and/or BROMAND et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable to calibrate their “output” and/or “feedback” based on determining when “emotion is enabled” as disclosed in Smith et al. Col. 30 line 3.
Regarding claim 6, LI JIANBIN does not specifically disclose the method of claim 1, wherein the synthesized speech voice output is further based on at least one of: a user voice input history, a user language, a user characteristic, a user location, a user preference, or a metadata tag associated with a user.
BROMAND et al. do teach the method of claim 1, wherein the synthesized speech voice output is further based on at least one of: a user voice input history, a user language, a user characteristic, a user location, a user preference, or a metadata tag associated with a user (¶ 0037 S1: “The adaptation of the synthesized” (synthesized speech output) “utterance acknowledgment or text acknowledgment is in some examples user-specific” (depends on a user characteristic and/or preferences)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of “generating synthesized utterance” based on “emotion” of “user” into the “intelligent robot” “speech synthesis” of LI JIANBIN would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI JIANBIN to tailor its “output” based on a user’s needs.
Regarding claim 8, LI JIANBIN does teach the method of claim 1, wherein determining the first emotion of the voice input includes determining an emotional indicator associated with a word, syllable thereof, or grouping of words (¶ 0039 lines 2+: “The first emotion determination module is used to determine a first emotion feature” (an emotional indicator determined) “of the robots 1’s speech” (using a grouping of words)).
Regarding claim 9, LI JIANBIN does teach a system (Abstract S1: “The invention provides an intelligent robot” which includes according to ¶ 0037 S1: “voice recognition module, an emotions judgment module, and a voice synthesis model” (a voice assistant system))
comprising:
input/output circuitry configured to:
receive a voice input by a voice assistant system (¶ 0039 lines 2+: “The first emotion determination module is used to determine a first emotion feature of the robots 1’s speech” (receiving a voice input by the voice assistant system));
wherein the control circuitry is further configured to:
based on the voice input, determine a first emotion of the voice input (¶ 0039 lines 3+: “The first emotion determination module is used to determine a first emotion” (determining a first emotion) “feature of the robot 1’s speech” (based on the voice input));
based on the first emotion of the voice input, determine a second emotion, different from the first emotion, for a response to the voice input (¶ 0039 lines 2+: “The first emotion determination module is used to determine a first emotion” (based on the first emotion) “feature of the robots 1’s speech” (of the voice input) “the second emotion determination module is used to determine a second emotion” (determining a second different emotion) to determine an “output” (a response to the voice input) by the “robot 1”);
generate a synthesized speech voice output as the response to the voice input,
wherein the input/output circuitry is further configured to:
cause an output of the synthesized speech voice output (¶ 0039 line 6: “and the output module is used for the robot1 to output” (causing an output of) “the emotion speech” (of the synthesized voice output)).
LI JIANBIN does not specifically disclose:
Control circuitry configured to:
train a neural network model based on a plurality of voice input emotion metrics, a plurality of response emotion metrics, wherein determining the first and second emotion is based on accessing the trained neural network model;
wherein the synthesized speech voice output comprises prosodic characteristics corresponding to the second emotion.
BROMAND et al. do teach:
Control circuitry configured to:
train a neural network model based on a plurality of voice input emotion metrics, a plurality of response emotion metrics, wherein determining the first and second emotion is based on access the trained neural network model (¶ 0119 S1-S3: “In some examples the emotion processor 130 also includes one or more machine learning” (using or accessing a neural network model) “models 148 that use training algorithms to” (comprising training) “e.g., improve emotion” (to determine or identify e.g. the “second emotion”) “feature extraction” “and/or map extracted emotion features” (based on a plurality voice input emotion metrics) “to emotions”; ¶ 0149 S1: “In some examples, the response processor 370 also includes one or more machine learning models 314 that use training algorithms” (training the “response” (a plurality of response emotion metrics));
wherein the synthesized speech voice output comprises prosodic characteristics corresponding to the second emotion (¶ 0048 S3-5: “mapping the first emotion feature or the first emotion feature-user pair to a first emotion; executing a first action in response to the first natural utterance and/or generating a synthesized utterance” (generating a synthesized speech voice output in response to a voice input and based on a “first emotion” (e.g. a second emotion)) “and/or a displayed text proposing that the first action be executed or acknowledging that the first action will be executed”; ¶ 0075 S2: “Non-limiting examples of stylistic adjustments adapted to emotions” (e.g. the second emotion on which the synthesized voice depends) “include adjustments in cadence, inflection, tone, volume, pitch” (comprises prosodic characteristics)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of “generating synthesized utterance” based on “emotion” of “user” of BROMAND et al. into the “intelligent robot” “speech synthesis module” of LI JIANBIN would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI JIANBIN to do “stylistic adjustments adapted to emotions” in generating “output” as disclosed in BROMAND et al. ¶ 0075 S1.
LI JIANBIN in view of BROMAND et al. do not specifically disclose:
Train a neural network model based on a geographical region data;
Identify a geographical region information of a user profile;
Input the voice input and the geographical region information into the trained neural network model;
Generate, based at least in part on the geographical region information, a waveform of a synthesized speech voice output.
Smith et al. do teach:
Training a neural network model based on a geographical region data; Identify a geographical region information of a user profile; Input the voice input and the geographical region information into the trained neural network model (Col. 9 lines 13+: “non-audio data” “input to the” (inputting) “speech quality detector 285” (into a trained neural network model) “e.g. GPS location” (an identified geographical region information) of a “user” (of a user profile); note: Col. 7 lines 4+: “The speech quality detector 285 may perform whisper” (receiving a voice input) “detection based on a long-short term memory (LSTM)” (by the “speech quality detector” which uses a neural network) “neural network”; Col. 9 lines 3-4: “speech quality detector” (the neural network) “may receive a user ID” (receives a user profile information));
Generate, based at least in part on the geographical region information, a waveform of a synthesized speech voice output (Col. 2 lines 15-17: “the system may determine weather information for the user’s location” (based on a geographical region information) “output weather information as synthesized speech” (generate a synthesized speech output waveform because according to Col. 20 lines 2-3: “TTS component” (i.e., synthesized speech voice) “to craft a natural sounding audio waveform output” (comprises a waveform output)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “speech quality detector” of Smith et al. into the “generating synthesized utterance” of BROMAND et al. in LI JIANBIN in view of BROMAND et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI JIANBIN in view of BROMAND et al. to benefit from inclusion of “non-audio” data such as “time/date” “location” in generating its speech synthesis output which helps in advancing “speech quality” as disclosed in Smith et al. Column 9 lines 15-22.
Regarding claim 10, LI JIANBIN does not specifically disclose the system of claim 9, wherein the control circuitry configured to determine the second emotion is further configured to determine an emotion that counteracts the first emotion as the second emotion.
BROMAND et al. do teach the system of claim 9, wherein the control circuitry configured to determine the second emotion is further configured to determine an emotion that counteracts the first emotion as the second emotion (¶ 0051 S. before last: “identify a pivot between the first emotion” (the first emotion) “and the second emotion” (and the second emotion) “classify the pivot as one of positive, negative” (counteract each other) “and neutral”).
For obviousness to combine LI JIANBIN and BROMAND et al. see claim 9.
Regarding claim 11, LI JIANBIN does not specifically disclose the system of claim 9, wherein the control circuitry configured to determine the second emotion is further configured to: determine a correlation between the first emotion and the second emotion; and identify the second emotion based on the correlation.
BROMAND et al. do teach the system of claim 9, wherein the control circuitry configured to determine the second emotion is further configured to: determine a correlation between the first emotion and the second emotion; and identify the second emotion based on the correlation (¶ 0051 S. before last: “identify a pivot between the first emotion” (the first emotion) “and the second emotion” (and the second emotion) “classify the pivot as one of positive” (correlate with each other and help identify each other) “negative” “and neutral”).
For obviousness to combine LI JIANBIN and BROMAND et al. see claim 9.
Regarding claim 12, LI JIANBIN in view of BROMAND et al. do not specifically disclose the system of claim 9, wherein the control circuitry is further configured to: train the neutral network model further based on dialect data; and access the trained neural network model to determine a dialect of the voice input; and generate the synthesized speech voice output based on the determined dialect from the trained neural network model.
Smith et al. do teach the system of claim 9, wherein the control circuitry is further configured to: train the neutral network model further based on dialect data; and access the trained neural network model to determine a dialect of the voice input (Col. 3 lines 34-36: “The server(s) 120 processes (134) the audio data” (for e.g., the voice input) “to determine” (determine) “a speech quality” (using a neural network (Col. 7 lines 4-6)) “indicator representing a quality of the speech” (e.g. how words are “pronounced” (Col. 19 lines 16-18), such as their “accent” (dialect (Col. 23 line 4)); Col. 7 lines 4+: “The speech quality detector 285 may perform” “based on a long-short term memory (LSTM)” “neural network” (uses neural network); Col. 19 lines 16-20: “The TTSFE 616” (training neural network (Col. 19 lines 12-13: “TTSSFE” “synthesize[s] speech using neural network”)) “may also process other data 615” “that indicate how specific words should be pronounced” (based on dialect)));
and generate the synthesized speech voice output based on the determined dialect from the trained neural network model (Col. 19 lines 16-20: “The TTSFE 616 may also process other data 615 that indicate how specific words should be pronounced” (e.g. by taking into account “accent” (dialect (Col. 23 line 4))) “for example by indicating the desired output speech quality in tags formatted according to speech synthesis” (generating a synthesized speech voice output) “markup language (SSML) or in some other form”; Col. 19 lines 12-13: “synthesize speech” (the speech synthesis is done) “using neural network” (by a trained neural network model)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “speech quality detector” of Smith et al. into the “generating synthesized utterance” of BROMAND et al. in LI JIANBIN in view of BROMAND et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI JIANBIN in view of BROMAND et al. to also detect “accent” and provide responses according “to different known pronunciations” as disclosed in Smith et al. Col. 23 lines 1-4.
Regarding claim 13, LI JIANBIN in view of BROMAND et al. do not specifically disclose the system of claim 9, wherein the control circuitry configured to determine the first emotion for the voice input is further configured to access user profile information associated with a user, and compare characteristics of the voice input to the user profile information.
Smith et al. do teach
the system of claim 9, wherein the control circuitry configured to determine the first emotion for the voice input is further configured to access user profile information associated with a user, and compare characteristics of the voice input to the user profile information (Col. 30 lines 1+: “determining the user profile data” (accessing user profile information) “indicates the first emotion is enabled to be used to perform processing related to the first audio data; and after determining the user profile data indicates the first emotion” (to determine the first emotion) “is enabled to be used to perform processing related to the first audio data” (for e.g. a voice input) “causing the first content to be output”).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the capability of “emotion” determination based on “profile data” of Smith et al. into the “emotion” determination and/or identification of either LI JIANBIN and/or BROMAND et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable to calibrate their “output” and/or “feedback” based on determining when “emotion is enabled” as disclosed in Smith et al. Col. 30 line 3.
Regarding claim 14, LI JIANBIN does not specifically disclose the system of claim 9, wherein the synthesized speech voice output is further based on at least one of a user voice input history, a user language, a user characteristic, a user location, a user preference, or a metadata tag associated with a user.
BROMAND et al. do teach the system of claim 9, wherein the synthesized speech voice output is further based on at least one of a user voice input history, a user language, a user characteristic, a user location, a user preference, or a metadata tag associated with a user (¶ 0037 S1: “The adaptation of the synthesized” (synthesized speech output) “utterance acknowledgment or text acknowledgment is in some examples user-specific” (depends on user characteristics and/or preferences)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of “generating synthesized utterance” based on “emotion” of “user” into the “emotion determination module” of LI JIANBIN would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI JIANBIN to tailor its “feedback” based on a user’s needs.
Regarding claim 16, LI JIANBIN does teach the system of claim 9, wherein the control circuitry is configured to determine the first emotion of the voice input includes determining an emotional indicator associated with a word, syllable thereof, or grouping of words (¶ 0039 lines 2+: “The first emotion determination module is used to determine a first emotion feature” (an emotional indicator determined) “of the robots 1’s speech” (using a grouping of words)).
Regarding claim 17, LI JIANBIN does teach [a method] (Abstract S1: “The invention provides an intelligent robot” which includes according to ¶ 0037 S1: “voice recognition module, an emotions judgment module, and a voice synthesis model” (a voice assistant system and method))
receive a voice input by a voice assistant system (¶ 0039 lines 2+: “The first emotion determination module is used to determine a first emotion feature of the robots 1’s speech” (receiving a voice input by the voice assistant system));
based on the voice input, determine a first emotion of the voice input (¶ 0039 lines 3+: “The first emotion determination module is used to determine a first emotion” (determining a first emotion) “feature of the robot 1’s speech” (based on the voice input));
based on the first emotion of the voice input, determine a second emotion, different from the first emotion, for a response to the voice input (¶ 0039 lines 2+: “The first emotion determination module is used to determine a first emotion” (based on the first emotion) “feature of the robots 1’s speech” (of the voice input) “the second emotion determination module is used to determine a second emotion” (determining a second different emotion) to determine an “output” (a response to the voice input) by the “robot 1”);
generate a synthesized speech voice output as the response to the voice input,
cause an output of the synthesized speech voice output (¶ 0039 line 6: “and the output module is used for the robot1 to output” (causing an output of) “the emotion speech” (of the synthesized voice output)).
LI JIANBIN does not specifically disclose:
A non-transitory computer-readable medium having instructions encoded thereon that, when executed by control circuitry, cause the control circuitry to:
train a neural network model based on a plurality of voice input emotion metrics, a plurality of response emotion metrics, wherein determining the first and second emotion is based on access the trained neural network model
wherein the synthesized speech voice output comprises prosodic characteristics corresponding to the second emotion.
BROMAND et al. do teach:
A non-transitory computer-readable medium having instructions encoded thereon that, when executed by control circuitry (page 14 second column lines 5+: “A non-transitory computer readable medium comprising: a command processor having one or more first sequences of instructions which, when executed by the command processor”),
cause the control circuitry to:
train a neural network model based on a plurality of voice input emotion metrics, a plurality of response emotion metrics, wherein determining the first and second emotion is based on access the trained neural network model (¶ 0119 S1-S3: “In some examples the emotion processor 130 also includes one or more machine learning” (using or accessing a neural network model) “models 148 that use training algorithms to” (comprising training) “e.g., improve emotion” (to determine or identify e.g. the “second emotion”) “feature extraction” “and/or map extracted emotion features” (based on a plurality voice input emotion metrics) “to emotions”; ¶ 0149 S1: “In some examples, the response processor 370 also includes one or more machine learning models 314 that use training algorithms” (training the “response” (a plurality of response emotion metrics));
wherein the synthesized speech voice output comprises prosodic characteristics corresponding to the second emotion (¶ 0048 S3-5: “mapping the first emotion feature or the first emotion feature-user pair to a first emotion; executing a first action in response to the first natural utterance and/or generating a synthesized utterance” (generating a synthesized speech voice output in response to a voice input and based on a “first emotion” (e.g. a second emotion)) “and/or a displayed text proposing that the first action be executed or acknowledging that the first action will be executed”; ¶ 0075 S2: “Non-limiting examples of stylistic adjustments adapted to emotions” (e.g. the second emotion on which the synthesized voice depends) “include adjustments in cadence, inflection, tone, volume, pitch” (comprises prosodic characteristics)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of “generating synthesized utterance” based on “emotion” of “user” of BROMAND et al. into the “intelligent robot” “speech synthesis module” of LI JIANBIN would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI JIANBIN to do “stylistic adjustments adapted to emotions” in generating “output” as disclosed in BROMAND et al. ¶ 0075 S1.
LI JIANBIN in view of BROMAND et al. do not specifically disclose:
Train a neural network model based on a geographical region data;
Identify a geographical region information of a user profile;
Input the voice input and the geographical region information into the trained neural network model;
Generate, based at least in part on the geographical region information, a waveform of a synthesized speech voice output.
Smith et al. do teach:
Train a neural network model based on a geographical region data; Identify a geographical region information of a user profile; Input the voice input and the geographical region information into the trained neural network model (Col. 9 lines 13+: “non-audio data” “input to the” (inputting) “speech quality detector 285” (into a trained neural network model) “e.g. GPS location” (an identified geographical region information) of a “user” (of a user profile); note: Col. 7 lines 4+: “The speech quality detector 285 may perform whisper” (receiving a voice input) “detection based on a long-short term memory (LSTM)” (by the “speech quality detector” which uses a neural network) “neural network”; Col. 9 lines 3-4: “speech quality detector” (the neural network) “may receive a user ID” (receives a user profile information));
Generate, based at least in part on the geographical region information, a waveform of a synthesized speech voice output (Col. 2 lines 15-17: “the system may determine weather information for the user’s location” (based on a geographical region information) “output weather information as synthesized speech” (generate a synthesized speech output waveform because according to Col. 20 lines 2-3: “TTS component” (i.e., synthesized speech voice) “to craft a natural sounding audio waveform output” (comprises a waveform output)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “speech quality detector” of Smith et al. into the “generating synthesized utterance” of BROMAND et al. in LI JIANBIN in view of BROMAND et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI JIANBIN in view of BROMAND et al. to benefit from inclusion of “non-audio” data such as “time/date” “location” in generating its speech synthesis output which helps in advancing “speech quality” as disclosed in Smith et al. Column 9 lines 15-22.
Regarding claim 18, LI JIANBIN do not specifically disclose the non-transitory computer-readable medium of claim 17, wherein the control circuitry is further caused to determine the second emotion is further configured to determine an emotion that counteracts the first emotion as the second emotion.
BROMAND et al. do teach the non-transitory computer-readable medium of claim 17, wherein the control circuitry is further caused to determine the second emotion is further configured to determine an emotion that counteracts the first emotion as the second emotion (¶ 0051 S. before last: “identify a pivot between the first emotion” (the first emotion) “and the second emotion” (and the second emotion) “classify the pivot as one of positive, negative” (counteract each other) “and neutral”).
For obviousness to combine LI JIANBIN and BROMAND et al. see claim 17.
Regarding claim 21, LI JIANBIN in view of BROMAND et al. do teach the method of claim 1, where inputting the voice input into the trained neural network model comprises:
Generating a transcription corresponding to the voice input (BROMAND et al.: ¶ 0080: “Optionally, the user device 102 includes a speech-to-text (STT) engine 124 for converting an analog signal of the natural utterance” (converting a voice input) “into a digitalized textual” (into text or transcription) “component and/or digitalized non-textual component of the natural utterance which can be fed to a command processor 150”);
Generating one or more data tags corresponding to the voice input, wherein the one or more tags comprise one or more of an entity, a type, a part of speech, a sequence indicator, a pattern indicator, a keyword, a metric, user information, or a prosodic characteristic (BROMAND: ¶ 0075 2nd column lines 4-5: “the textual component including one or more stylistic tags” (generating one or more tags corresponding to e.g., the voice input) “e.g., “quick” and “loud.”; ¶ 0076 last S: “the text including one or more stylistic tags, e.g., “excited”. Using the verbal stylizing engine 122, text of the acknowledgement displayed via the display 110, the text having been stylized (e.g., ALL CAPS and 3 exclamation points !!!) according to the tags” (the generated tags) “and thereby adapted to the user's emotion(s)” (correspond to a prosodic characteristic and/or pattern indicator)).
Inputting the transcription and the one or more data tags into the trained neural network model (BROMAND: ¶ 0119 S1: “the emotion processor 130 also includes one or more machine learning models” (a trained neural network) “148 that use training algorithms to, e.g., improve emotion feature extraction from textual” (is inputted a transcription which comprises “stylistic tags” (tags (¶ 0075 2nd column line 5)) “and non-textual components of natural utterances”).
Regarding claim 22, LI JIANBIN in view of BROMAND et al. do teach the method of claim 1, where training the neural network model comprises:
Receiving a plurality of voice inputs and a plurality of corresponding responses (BROMAND: ¶ 0119 lines 1-5: “In some examples the emotion processor 130 also includes one or more machine learning models 148 that use training” (receiving training data) “algorithms to, e.g., improve emotion feature extraction from textual and non-textual components of natural utterances” (comprising a plurality of voice inputs); ¶ 0149 S1: “In some examples, the response processor 370 also includes one or more machine learning models 314 that use training” (receiving training data) “algorithms to, e.g., improve” “pivot-adapted responses” (comprising corresponding responses));
Extracting, from the plurality of voice inputs, the plurality of voice input emotion metrics (BROMAND: ¶ 0119: “In some examples the emotion processor 130 also includes one or more machine learning models 148 that use training” (receiving training data) “algorithms to, e.g., improve emotion feature extraction” (for extracting emotion metrics) “from” “natural utterances” (from the plurality of voice inputs));
Extracting, from the plurality of corresponding responses, the plurality of response emotion metrics (BROMAND: ¶ 0121: “The response processor” (from the plurality of corresponding responses) “170 receives the tagged acknowledgement from the emotion” (extract response
emotion metrics) “processor 130”);
Generating an input file having a predetermined format, wherein the input file comprises the plurality of voice input emotion metrics and the plurality of response emotion metrics (BROMAND: ¶ 0120: “In some examples, the user is able to directly train the emotion processor 130, e.g., by inputting preferences via the interface 104 of the user device to program how particular natural utterances” (using voice inputs) “textual components of natural utterances, or non-textual components of natural utterances should be mapped to emotions” (and their associated emotion metrics) “emotion adaptations, acknowledgements” (to generate an input file) “and/or acknowledgement formats” (having a predetermined format) “and how emotions should be mapped to inferred commands”);
And inputting the input file into the neural network model to generate the trained neural network model (BROMAND: ¶ 0119: “the emotion processor” “includes one or more machine learning models” (the trained neural network model) “map textual and non-textual components of natural utterances to acknowledgements” (comprises the input file)).
Claim(s) 7, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over LI JIANBIN in view of BROMAND et al. and smith et al., and further in view of JIANG et al. (TW470927).
Regarding claim 7, LI KIANBIN in view of BROMAND et al. do not specifically disclose the method of claim 1, wherein the generating the synthesized speech voice output further comprises:
Comparing a first value of a first word and a second value of a second word that is adjacent to the first word; and
Based at least in part on the comparing, performing an interpolation operation to insert a transition between the first word and the second word.
JIANG et al. do teach
Comparing a first value of a first word and a second value of a second word that is adjacent to the first word (“Description” S4 before last: “The first conversion function proposed” “is based on” “the first pitch” (a first value) “of the first character” (of a first word) “of the adjacent two characters” (and a second adjacent word’s) “and the first pitch” (a second value) “of the next character” (are compared)); and
Based at least in part on the comparing, performing an interpolation operation to inset a transition between the first word and the second word (“Description” S3-S2 before last: “The pitch of the two-character pitch, the pitch of the previous pitch and the pitch of the next pitch” “are used to generate the pitch length of the mixed pitch” (an interpolation operation involving “pitch” (first and second values) of the “two-character” (of the first word and its adjacent second word is performed), “Then, the audio of the above mixed syllable is inserted” (inserting the interpolation (a transition value)) “into the phonetic amplitude signal of the previous” (between the first word) “and the next two-tone sounds” (and the second word) “so that when the adjacent two-word sound is emitted in a synthesized voice” (as part of generating a synthesized speech) “the pronunciation between the previous and the next sound can be pronounced Smooth”).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the methods associated with generating “synthesized voice” of JAING et al. into the “speech synthesis module” of LI KIANBIN in LI KIANBIN in view of BROMAND et al. and Smith et al., would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI KIANBIN in view of BROMAND et al. and Smith et al. to accomplish “smoothening synthesized voice speech” which can significantly help with speech quality by “smoothen[ing]” “speech” between “adjacent” “words” as disclosed in JAING et al. Abstract and the “Description” S. before last.
Regarding claim 15, LI KIANBIN in view of BROMAND et al. do not specifically disclose the system of claim 9, wherein the control circuitry configured to generate the synthesized speech voice output is further configured to:
Compare a first value of a first word and a second value of a second word that is adjacent to the first word; and
Based at least in part on the comparing, perform an interpolation operation to insert a transition between the first word and the second word.
JIANG et al. do teach
Compare a first value of a first word and a second value of a second word that is adjacent to the first word (“Description” S4 before last: “The first conversion function proposed” “is based on” “the first pitch” (a first value) “of the first character” (of a first word) “of the adjacent two characters” (and a second adjacent word’s) “and the first pitch” (a second value) “of the next character” (are compared)); and
Based at least in part on the comparing, perform an interpolation operation to inset a transition between the first word and the second word (“Description” S3-S2 before last: “The pitch of the two-character pitch, the pitch of the previous pitch and the pitch of the next pitch” “are used to generate the pitch length of the mixed pitch” (an interpolation operation involving “pitch” (first and second values) of the “two-character” (of the first word and its adjacent second word is performed), “Then, the audio of the above mixed syllable is inserted” (inserting the interpolation (a transition value)) “into the phonetic amplitude signal of the previous” (between the first word) “and the next two-tone sounds” (and the second word) “so that when the adjacent two-word sound is emitted in a synthesized voice” (as part of generating a synthesized speech) “the pronunciation between the previous and the next sound can be pronounced Smooth”).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the methods associated with generating “synthesized voice” of JAING et al. into the “speech synthesis module” of LI KIANBIN in LI KIANBIN in view of BROMAND et al. and Smith et al., would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable LI KIANBIN in view of BROMAND et al. and Smith et al. to accomplish “smoothening synthesized voice speech” which can significantly help with speech quality by “smoothen[ing]” “speech” between “adjacent” “words” as disclosed in JAING et al. Abstract and the “Description” S. before last.
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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/Farzad Kazeminezhad/
Art Unit 2653
October 7th 2026.
