DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to claim amendment filed on December 19, 2026 and wherein claims 1, 8, 11, 18, 20 amended and claims 2, 12 cancelled.
In virtue of this communication, claims 1, 3-11, 13-20 are currently pending in this Office Action.
With respect to the objection of claims 8, 18 due to formality issue, as set forth in the previous Office Action, the claim amendment, and argument, see paragraph 2 of page 6 in Remarks filed on December 19, 2026, have been fully considered and the argument is persuasive. Therefore, the objection of claims 8, 18 due to the formality issue, as set forth in the previous Office Action, has been withdrawn.
With respect to (1) the non-statutory obvious-type double patenting rejection of claims 1-2, 4, 7-12, 14-18, 20 as being unpatentable over conflicting claims 1-4, 8-12, 14-17, 19 of U.S. Patent No. 11,545,170 B2, 249,311 B2, (2) the non-statutory obvious-type double patenting rejection of claims 3, 5-6, 13, 15-16, 19 as being unpatentable over conflicting claims 1, 11 of U.S. Patent No. 11,545,170 B2, 249,311 B2 in view of references Grokop et al. (US 20130090926 A1, hereinafter Grokop) and in view of reference Sanghavi et al (US 20150373091 A1, hereinafter Sanghavi). Catanzaro et al (US 20170148431 A1, hereinafter Catanzaro), and Zhou et al. (“Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification”, Proc. Of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, August 7-12, 2016, pp.207-212, hereinafter Zhou, IDS submitted on 11/30/2022), (3) the nonstatutory obviousness-type double patenting rejection of claims 1-20 as being unpatentable over claims 1-4, 6-14, 16-18, 20 of U.S. Patent No. 12,057,136 B2 in view of reference Sanghavi et al (US 20150373091 A1, hereinafter Sanghavi), and (4) the nonstatutory obviousness-type double patenting rejection of claims 1-20 as being unpatentable over claims 1-2, 7, 9-11, 13-14, 17, 19, 25 of U.S. Patent No. 10,878,837 B1 in view of references Grokop et al. (US 20130090926 A1, hereinafter Grokop), Sanghavi et al (US 20150373091 A1, hereinafter Sanghavi), Catanzaro et al (US 20170148431 A1, hereinafter Catanzaro), and Zhou et al. (“Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification”, Proc. Of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, August 7-12, 2016, pp.207-212, hereinafter Zhou, IDS submitted on 11/30/2022), the terminal disclaimer filed on December 19, 2026 has been approved and therefore, the non-statutory double patenting rejection (1), (2), (3), and (4) above, as set forth in the previous Office Action, has been withdrawn.
The Office appreciates the explanation of the amendment and analyses of the prior arts, and however, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993) and MPEP 2145.
Domestic Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under under 35 U.S.C. 120, 121, 365(c), or 386(c) or under 35 U.S.C. 119(e) (if FP 2.10 thereafter or not supported by the previous filed domestic application).
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior provisional application for which benefit is claimed). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed provisional application 62/465,550 failed to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) for one or more claims of this application, about claimed “generating an ephemeral message by overlaying the content item on an image generated by the device; and publishing the ephemeral message on a social media network site” in independent claims 1, 11, 20. Accordingly, dependent claims 3-10, 13-19 are not entitled to the benefit of the prior provisional application above.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 11, 13, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Grokop et al. (US 20130090926 A1, hereinafter Grokop) and in view of references Park (US 20150245185 A1) and Sanghavi et al (US 20150373091 A1, hereinafter Sanghavi).
Claim 1: Grokop teaches a method (title and abstract, ln 1-10 and method steps in figs. 9-10 and implemented by a mobile device such as smartphone, etc., para 21) comprising:
identifying sound recording data on a device (X(1), …, x(fT) from buffer T 870 in fig. 8, as sound recording data, via a microphone 135 of the mobile device 100 to capture acoustic waves of ambience in fig. 1, para 27);
generating, by the device, an acoustic classification of the sound recording data (by classifiers 874, combine decision 876 in fig. 8 and at steps 216, 218 in fig. 2) using an acoustic classification neural network (the classifier 874 are neural network classifiers, para 47);
storing the acoustic classification on the device (DSP with memory and registers, para 25, and thus, and used for speech detection by which user context information is provided);
selecting a content item based on the acoustic classification (user context information is provided based on the acoustic classification through the speech detection, para 47-49);
generating a message based on the content item (contextual reminders based on the provided user context information or alert for the task, etc., based on the classification result, para 49-50).
However, Grokop does not explicitly teach wherein the generated message is an ephemeral message by overlaying the content item on an image generated by the device and publishing the ephemeral message on a social media network site.
Sanghavi teaches an analogous field of endeavor by disclosing a method (title and abstract, ln 1-14 and mobile device in figs. 2A-2B and details as element 400 in fig. 4, para 42, and method steps in fig. 3A-3B, and applied in voice recognition application in the audio subsystem 426 of the mobile device 400, para 49) and wherein generating an ephemeral message by overlaying a content item on an image generated by a device (transient user profile 220, as claimed ephemeral message, by overlaying the content of the transient user profile in the camera view of photo from a scanned room full of people with an embedded camera turned on to capture images of the people in the live video feed with their transient user profiles 220 overlaid and proximate to their images in fig. 2B, para 33, and wherein the transient user profile is a limited time-to-live and destroyed self for a user specified period of time, i.e., ephemeral, para 25) and further teaches publishing the ephemeral message on a social media network site (via a user interface 208, connected to a social network through a server providing map services via module 520, and user profile processing services via module 518 with master user profiles 520, etc., i.e., social media network site, and by broadcasting at step 306) for benefits of clarifying the message in more friendly user-machine interaction in more secured and cost-saving manners (via the text-in-picture in a short time-to-live, para 25 and reducing the change of data breach exposure, and mitigating unauthorized access, etc., inherently and in a cost-saving manner, well-known in the art, e.g., saving storage space and short time-living).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied generating the ephemeral message and wherein generating the ephemeral message by overlaying the content item on the image generated by the device and publishing the ephemeral message on the social media network site, as taught by Sanghavi, to generating the message and the content item selected based on the acoustic classification in the method, as taught by Grokop, for the benefits discussed above.
Claim 11 has been analyzed and rejected according to claim 1 above and the combination of Grokop and Sanghavi further teaches, a system comprising: one or more processors of a machine; and a memory comprising instructions that, when executed by the one or more processors, cause the machine to perform the method of claim 1 (Grokop, DSP 112 and general purpose processor 111, and memory 140 including instructions to be executed by the processors, para 10-11, and Sanghavi, processors including data, image, or CPUs 404, para 42, and memory 450, para 53-55).
Claim 20 has been analyzed and rejected according to claims 1, 11 above.
Claim 3: the combination of Grokop and Sanghavi further teaches, according to claim 1 above, wherein the image includes a frame of a live video feed generated by a camera of the device (Sanghavi, the embedded camera turned on to capture images of the oeople in the live video feed with their transient user profiles 220 overlaid and proximate to their images, para 33, and frame including an area of camera view 218 in fig. 2B).
Claim 13 has been analyzed and rejected according to claims 11, 3 above.
Claims 4, 7, 9, 14, 17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Grokop (above) and in view of references Sanghavi (above) and Catanzaro et al (US 20170148431 A1, hereinafter Catanzaro).
Claim 4: the combination of Grokop and Sanghavi teaches, according to claim 1 above, the acoustic classification neural network (Grokop, the classifier 874 as neural network classifiers for input spectrogram samples, para 47), except explicitly teaching that the acoustic classification neural network comprises a convolutional neural network layer that generates audio feature data that are weighted by an attention layer that updates a recursive neural network layer.
Catanzaro teaches an analogous field of endeavor by disclosing a method (title and abstract, ln 1-12 and fig. 1) and wherein an acoustic classification neural network is disclosed (including RNN model by inputting speech spectrograms 105, para 51; generating classification result, para 191) to comprise a convolutional neural network layer (including a row convolution layer following the recurrent layer in fig. 7, para 101; including 1D or 2D invariant convolution 110 by taking speech spectrogram 105 in fig. 1) that generates audio feature data (output from the row convolution layer in fig. 7 or output to the recurrent or GRU bidirectiona layers 115 in fig. 1) that are weighted by an attention layer (one or more fully connected layers 120 as the claimed attention layer are applied to the previous layer L-1 with
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, e.g., Wl as weight to the previous layer htl-1, para 60, i.e., an attention layer added to the decoder, para 44) that updates a recursive neural network layer (hl-1t is the row convolution layer as one of the recursive neural network layer, which is weighted through the equation hlt above, and further network parameters through the backpropagation through time algorithm, para 61-63, i.e., a recursive neural network layer is updated by
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and backpropagation through time algorithm, para 61-63) for benefits of improving performance of the system (particularly with long inputs or outputs, para 44), enhancing a practicability in variety environment and situations (support multiple language applications when the involved languages are quite different, para 8), and accurately predicting with small portion of the future information (para 101).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the acoustic classification neural network comprising the convolutional neural network layer that generates the audio feature data that are weighted by the attention layer that updates the recursive neural network layer, as taught by Catanzaro, to the acoustic classification neural network in the method, as taught by the combination of Grokop and Sanghavi, for the benefits discussed above.
Claim 7: the combination of Grokop, Sanghavi, and Catanzaro teaches, according to claim 4 above, wherein the attention layer is a fully connected neural network layer (Catanzaro, fully connected neural network layer 120 in fig. 1, para 51), wherein the recursive neural network layer processes the audio feature data from the convolutional neural network layer over time steps of the recursive neural network layer (Catanzaro, the output of the invariant convolution layer 110 to the recurrent or GRU bidirectional layers in fig. 1; time step l in convolution layer by equation 1 and further processed by the recursive neural network layers of equation 2, and wherein l is the time step).
Claim 9: the combination of Grokop, Sanghavi, and Catanzaro teaches, according to claim 4 above, wherein the attention layer is trained to generate the acoustic classification using backpropagation (Catanzaro, training the deep recurrent networks by back propagation, para 144 and wherein the deep recurrent networks inherently include the convolution layer, attention layer, row convolution layer, etc., and the discussion in claims 1, 4 above),
wherein the acoustic classification neural network is trained on training audio from one or more different environments including at least an outdoor environment and an indoor environment (Catanzaro, including the environment such as a café, a bus, i.e., indoor, and a street, a pedestrian area, i.e., outdoor, para 170).
Claim 14 has been analyzed and rejected according to claims 11, 4 above.
Claim 17 has been analyzed and rejected according to claims 14, 7 above.
Claim 19 has been analyzed and rejected according to claims 14, 9 above.
Claims 5-6, 8, 15-16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Grokop (above) and in view of references Sanghavi (above), Catanzaro (above), and Zhou et al. (“Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification”, Proc. Of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, August 7-12, 2016, pp.207-212, hereinafter Zhou, IDS submitted on 11/30/2022).
Claim 5: the combination of Grokop, Sanghavi, and Catanzaro further teaches, according to claim 4 above, wherein the convolutional neural network layer outputs to a bi-directional long short-term memory LSTM neural network layer (Catanzaro, the layer 110 as convolution layer and output to LSTMs included in a bidirectional recurrent or GRU in fig. 1, para 43), except explicitly teaching wherein the convolutional neural network layer outputs to the attention layer.
Zhou teaches an analogous field of endeavor by disclosing a method (title and abstract, ln 1-18 and fig. 1) and wherein a convolutional neural network layer is disclosed (LSTM layer in fig. 1) and configured to output to an attention layer (attention layer from the LSTM layer in fig. 1) for benefits of improving an operation performance (by capturing important semantic information in the input sentence by using a combination of LSTM and attention layer in fig. 1, abstract).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied wherein the convolutional neural network layer outputs to the attention layer, as taught by Zhou, to the bi-directiona LSTM neural network layer, as taught by the combination of Grokop, Sanghavi, and Catanzaro, for the benefits discussed above.
Claim 6: the combination of Grokop, Sanghavi, Catanzaro, and Zhou further teaches, according to claim 5 above, wherein the bi-directional LSTM neural network layer and the attention layer are configured to output to a deep neural network layer to generate the acoustic classification of the sound recording data (Grokop, the neural network classification and the discussed in claim 1 above, and Catanzaro, optimizing deep RNNs, para 50 and para 65, para 44, and Zhou, output layer for output of the relation classification result in fig. 1, and deep neural networks by using CNNs for relation classification, session 2 Related Work, p.208).
Claim 8: he combination of Grokop, Sanghavi, Catanzaro, and Zhou further teaches, according to claim 7 above, wherein the audio feature data generated by the convolutional neural network layer (Catanzaro, the row convolutional layer as an output layer of the recurrent layer in fig. 7 and Zhou, output from LSTM layer to the attention layer in fig. 1) is weighted by the attention layer for each of the time steps (Catanzaro, one or more fully connected layers 120 as the claimed attention layer are applied to the previous layer L-1 with
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, para 60, i.e., an attention layer added to the decoder, para 44; the row convolution layer as the output layer of the recurrent layer in fig. 7, and the discussion in claim 1 above and Zhou, a weight vector is produced and merging word-level features from each time step into a sentence-level feature vector for a final output by multiplying the weight vector, session 3 Mode, p.208), and wherein the audio feature data processed by the recursive neural network layer is the weighted audio feature data (Catanzaro, fig. 7, recursive neural network layer represented by equation 3, where Wl is the weight to the previous feature of the hidden layer hl-1t and U is the recurrent weight matrix, para 59 and further weighted by the fully-connected layer in fig. 1 and Zhou, weighted sum of the output vectors is final representation r of the sentence, session 3.3 Attention).
Claim 15 has been analyzed and rejected according to claims 14, 5 above.
Claim 16 has been analyzed and rejected according to claims 15, 6 above.
Claim 18 has been analyzed and rejected according to claims 17, 8 above.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Grokop (above) and in view of references Sanghavi (above), and Kates (US 20100027820 A1).
Claim 10: the combination of Grokop and Sanghavi further teaches, according to claim 1 above, wherein the acoustic classification neural network further comprises a classification layer that generates the acoustic classification (Grokop, classification performed by hidden Markov model or Gausian mixture model, para 47), except explicitly teaching a plurality of scene categories that includes one or more of a group comprising: a bus, a cafe, a car, a city center, a forest, a grocery store, a home, a lakeside beach, a library, a railway station, an office, a residential area, a train, a tram, and an urban park and wherein the classification layer outputs a numerical value for each scene category of the plurality of scene categories, the numerical value indicating a likelihood that the acoustic classification is of a given scene category from the plurality of scene categories.
Kates teaches an analogous field of endeavor by disclosing a method (title and abstract, ln 1-5 and a system in fig. 1 and a method, abstract) and wherein a plurality of scene categories is disclosed to include one or more of a group comprising: a bus, a cafe, a car, a city center, a forest, a grocery store, a home, a lakeside beach, a library, a railway station, an office, a residential area, a train, a tram, and an urban park (restaurant, traffic noise, etc., para 19) and further teaches wherein the classification layer outputs a numerical value for each scene category of a plurality of scene categories, the numerical value indicating a likelihood that the acoustic classification is of a given scene category from the plurality of scene categories (environment classifier 32 with numbers describing classification accuracies in tables 2-4 in figs. 8-10, para 88) for benefits of improving the accuracy of classification in multiple categories (by fine classification based on categories, e.g., type of background is identified by the classification, para 9).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied wherein the plurality of scene categories that includes one or more of a group comprising: a bus, a cafe, a car, a city center, a forest, a grocery store, a home, a lakeside beach, a library, a railway station, an office, a residential area, a train, a tram, and an urban park and wherein the classification layer outputs the numerical value for each scene category of the plurality of scene categories, the numerical value indicating the likelihood that the acoustic classification is of the given scene category from the plurality of scene categories, as taught by Kates, to the classification layer that generates the acoustic classification in the method, as taught by the combination of Grokop and Sanghavi, for the benefits discussed above.
Response to Arguments
Applicant's arguments filed on December 19, 2026 have been fully considered and but are moot in view of the new ground(s) of rejection necessitated by the applicant amendment. Although a new ground of rejection has been used to address additional limitations that have been added to claims 1, 11, 20, a response is considered necessary for several of applicant’s arguments since references Grokop, Sanghavi will continue to be used to meet several claimed limitations.
With respect to the prior art rejection of independent claim 1, similar to claims 11, 20, under 35 USC §103(a), as set forth in the Office Action, applicant argued: “Grokop does not teach generating content based on acoustic classification” because “Grokop is directed to speech and speaker recognition, not acoustic scene classification” and Grokop “analyzing speech data and generating user context information related to recognized speech or speakers, para 49-50” and claimed “selecting a content item based on the acoustic classification” is not supported by “cited portions of Grokop” and specifically, “at paragraph 49, Grokop teaches that user context information is provided based on speech recognition results – but this contextual information about speech content or speaker identity, not content items selected based on acoustic scene classification … At paragraph 50, Grokop teaches using this speech-related context for contextual reminders or alert for the task … this relates to speech content, not acoustic scene-based content selection as claimed” and “The claimed invention specifically requires (1) acoustic classification of environmental sounds, (2) selecting content items based on that classification, and (3) overlaying on images. Grokop fails to teach this combination”, as asserted in paragraph 7 of page 7 and paragraphs 1-3 of page 8 in Remarks filed on December 19, 2025.
In response to the argument above, the Office respectfully disagrees because
(1) claim broadly recited “sound recording data”, “generating … an acoustic classification of the sound recording data using an acoustic classification neural network” which neither reciting the argued “acoustic classification of environmental sounds”, or “acoustic scene classification”, nor what “content item” is, nor “acoustic scene classification” such as “restaurant vs. park vs. office”, nor “acoustic scene-based content”, etc., and
(2) Grokop teaches “sound recording data” (spectrogram of ambient sound collected by microphones, para 27), Grokop further teaches that the sound recording data is generated by using neural network classifier (874, 876 applied to the spectrogram X(i,j) in fig. 8, para 40, and neural network classifier, para 47) and “a content item” is further disclosed (context inform, such as identified person, type of environment, a dialogue in the data, when the speech happened, fraction of time of the user, etc., para 49) which is provided by the acoustic classifier (through speech detection of the ambient sound, para 49 and speech detection is performed through classifier in fig. 8, para 40), and thus, Grokop’s disclosure above essentially anticipated the broadly claimed and argued feature “generating, content based on acoustic classification” and “selecting a content time based on the acoustic classification” according to their BRIs of “content” or “content item”, but applicant is in silence, and thus, the argument above is moot.
Applicant further argued “Sanghavi does not cure the deficiencies of Grokop”, see paragraph 4-6 of page 8 in Remarks filed on December 19, 2025.
In response to the argument above, it is notified that the second prior art Sanghavi does not have to disclosed the claimed features the first prior art Grokop has taught (see the discussion above) and thus, the argument above is moot.
Applicant further challenged combination of Grokop and Sanghavi and argued “no motivation to combine because “cited benefits are generic and do not establish a motivation specific to combining these particular references”, as asserted in paragraph 7 of page 8 and paragraphs 1-2 of page 9 in Remarks filed on December 19, 2025.
In response to the argument above, the Office further disagrees because generating an ephemeral message by overlaying a content or content item on an image and then publishing such message on a social media network site is notoriously well-known in the art, e.g., reading a book and playing a movie with captions of lyric/speech, classification “R”, “PG-13”, etc., overlayed on the played movie or background images as ephemeral message due to short living time, etc., supported by social media applications such as NetflixTM, AppleTVTM, AmazonTM, DisneyTM or Disney+TM, by which, the associated benefits are also well-known in the art, e.g., enhanced security and lower storage and e-discovery costs, etc., other than argued “hindside” and no need to alleged “replacement” as argued, and thus, modifying Grokop’s selected “content item” and generated “message” by using Sanghavi’s the generated “ephemeral message” and selected “content” overlayed with images for the well-known benefits above would be obvious for one having ordinary skill in the art and thus, the argument above is also moot.
With respect to the amended feature “publishing the ephemeral message on a social media network site” (original claim 2), applicant further argued that Sanghavi does not teaches this feature because Sanghavi’s “transient user profiles” as claimed “ephemeral message” is “user identity/profile data”, “pre-existing data bout people”, “not generated from environmental acoustic analysis” and because Sanghavi’s “broadcasting or sharing transient user profiles to nearby devices or authorized recipients” including “for proximity-based social networking”, “relates to privacy control”, “user-initiated for social connection”, etc., and “the amended claims require publications of an ephemeral message … content is automatically selected based on acoustic environment”, “the message documents or enhances a moment/location” and “an automated content augmentation workflow”, etc., and “Sanghavi does not teach or suggest the claimed technical integration: No connection between acoustic classification and content selection”, “no overlay of acoustically-determined content”, and no teaching of acoustic-to-visual content mapping”, etc., as asserted in paragraphs 3-7 of page 9, paragraphs 1-5 of page 10 in Remarks filed on December 19, 2025.
In response to the argument above, the Office further disagrees because
(1) claim 1, recite neither argued “acoustically-determined content” (not same as claimed “selecting a content item based on the acoustic classification”), nor “automatically selected” or “automatically” “publishing”, nor “acoustic environment” by which “content item” is selected, nor “the message documents or enhances a moment/location”, nor “an automated content augmentation workflow”, etc. and claim also is in silence whether “ephemeral message“ would be mapped to Sanghavi’s “transient user profiles” including “user identity/profile data”, “pre-existing data about people” or not, etc.,
(2) as discussed above, “publishing an ephemeral message on a social media network site” is common in the art (example of reading the books and playing movie with caption, classification labels in a short-living form, etc.), and
(3) again, Sanghavi does not have to teach the features such as “generated from environmental acoustic analysis”, etc., and Sanghavi teaches “user profile” is “transient”, i.e., ephemeral, and Sanghavi teaches shares the “user profile” to authorized people via “Internet” network with servers, (fig. 1, para 20), i.e., conditionally “publishing”, via a social networks (para 26) including “analytics module (518), map services (520), transient user profile module (518) for processing user’s profiles, etc., (para 58), i.e., “social media network site” according to its BRI, and thus, essentially anticipated the broadly claimed and argued “publishing the ephemeral message on a social media network site” and therefore, the argument above is not persuasive.
With respect to other prior arts Catanzaro, Zhou, Kates, applied in the dependent claim rejections, applicant further argued that these prior arts failed to recited claimed feature “acoustic scene classification leading to content overlay” or “for content selection”, etc., as asserted in paragraphs 3, 5, 7 of page 11 in Remarks filed on December 19, 2025.
In response to the argument above, the Office further disagrees because, again prior arts Catanzaro, Zhou, Kates do not have to disclose the features the combination of Grokop and Sanghavi have taught and thus, the argument above is not persuasive.
In the response to this office action, the Office respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Office in prosecuting this application.
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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/LESHUI ZHANG/
Primary Examiner,
Art Unit 2695
Prosecution Insights