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 Arguments
Applicant's arguments filed 11/19/2025 have been fully considered but they are not persuasive. In response to applicant’s argument in the Remarks, first of all, the claimed “first network entity” is defined in current application as, for instance, a source device that may capture image or video data and perform the first set of processing tasks on the image or video data to extract a feature set, then compress the feature set and send the compressed feature set to the destination device, See [08] in specification. Solovyev in Figs.1, 6A, 22; [201]-[204], [595]-[596] teaches the compression framework or encoding included in source device 12 is the first task in a first network entity, and the neural network to support the compression/encoding is the computing graph of which the layers generate feature maps; and the source device 12 sends/streams content to destination device, that the source device 12 is consistent with the definition of claimed first network entity; Liu in Figs.4-6, [50], [76]-[79] teaches a server-based training neural network 520 comprising encoder and feature map generation sends produced content to other network elements (e.g. client device), wherein the server-based training neural network 520 is consistent with the definition of claimed first network entity; and data storage 601 as the claimed buffer in the neural network stores input/out data. Further, Liu in Fig.5, [47], [59]. [87] teaches the neural network of server 520 as the first network entity updates, modifies, compresses and stores additional input into latent space, then combines with the compressed features for image together, then the combination is sent/provided to the display 506 in client device 502, which is the claimed second network entity.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7, 12-25, 29, 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20250126265 A1 SOLOVYEV; Timofey Mikhailovich et al. (hereafter Solovyev) and further in view of US 20220108417 A1 Liu; Ming-Yu et al. (hereafter Liu),
Regarding claim 1, Solovyev discloses A method of processing feature set data formed from media data (Fig.5), the method comprising: performing, by a first network entity, a first part of a computing graph on a set of media data to form a feature map, the first part of the computing graph representing first processing tasks performed by the first network entity (Figs.1, 6A, 22; [201]-[204], [595]-[596], the compression framework or encoding included in source device 12 is the first task in a first network entity, and the neural network to support the compression/encoding is the computing graph of which the layers generate feature maps; and the source device 12 sends/streams content to destination device that is consistent with the definition of claimed first network entity), wherein a second part of the computing graph corresponds to second processing tasks to be performed by a second network entity (Figs.22, 26, [594], [631], the terminal or destination device is the second network entity, and the decoding process is the second task using the neural network that is the computing graph); compressing, by the first network entity, the feature map to form a compressed feature map ([571]); and sending, by the first network entity, the compressed feature map and additional data to enable the second network entity to perform the second part of the computing graph using the feature map and the additional data (Fig.8, [219], [254], encoder in the source device sends compressed data to a decoder that extracts compressed feature from latent code).
Solovyev fails to disclose buffering, by the first network entity, at least a portion of the media data; and sending, by the first entity, the compressed feature map and additional data corresponding to the buffered at least portion of the media data to the second network entity.
However, Liu teaches buffering, by the first network entity, at least a portion of the media data (Figs.4-6, [76]-[79], server-based training neural network 520 comprising encoder and feature map generation sends produced content to other network elements (e.g. client device), wherein the server-based training neural network 520 is consistent with the definition of claimed first network entity); and sending, by the first entity, the compressed feature map and additional data corresponding to the buffered at least portion of the media data to the second network entity (Fig.5, [47], [59], [87], the neural network of server 520 as the first network entity updates, modifies, compresses and stores additional input into latent space, then combines with the compressed, features for image together, then the combination is sent/provided to the display 506 in client device 502, which is the claimed second network entity).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of processing feature set data formed from media data disclosed by Solovyev to include the teaching in the same field of endeavor of Liu, in order to provide techniques are presented to generate images, therewith one or more neural networks are used to generate one or more images based, at least in part, upon speech input received from one or more users, as identified by Liu.
Regarding claim 2, Solovyev discloses The method of claim 1, wherein the first network entity comprises a user equipment (UE) and the second network entity comprises an application server (AS) ([238], [615]).
Regarding claims 3, 19, Liu teaches The method of claim 1, wherein the additional data comprises at least one of buffered images or video frames, a set of features compressed to a lower degree than the compressed feature map, enhancement data of the features of the compressed feature map, or compressed features corresponding to the buffered at least portion of the media data at a different split point ([46]-[47], segmentation mask as the additional has lower degree).
Regarding claims 4, 15, 20, Liu teaches The method of claim 1, wherein sending the compressed feature map comprises sending the compressed feature map at a first time, the method further comprising receiving a request for the additional data at a second time later than the first time, wherein sending the additional data comprises sending the additional data in response to the request for the additional data at a third time later than the second time ([56],[147]).
Regarding claim 5, 21, Liu teaches The method of claim 4, wherein the request identifies images or video frames by at least one of sequence numbers or a time interval ([58], [72], [637]).
Regarding claim 6, 22, Solovyev discloses The method of claim 4, wherein the request specifies a compression ratio or target bit rate for the additional data ([31], [492]).
Regarding claims 7, 16, Liu teaches The method of claim 4, wherein the request includes data specifying at least one of a cause for the request or a priority for the request ([56]).
Regarding claims 12, 29, Solovyev discloses The method of claim 1, wherein the first and second processing tasks include one or more of object detection, object tracking, or instance segmentation ([222]).
Regarding claim 13, Solovyev discloses The method of claim 1, wherein the at least portion of the media data spans a round-trip time between the first network entity and the second network entity and a time for the second network entity to perform the second part of the computing graph ([160]).
Regarding claim 14, see the rejection for claim 1.
Regarding claim 17, Solovyev discloses A method of processing feature set data formed from media data, the method comprising: receiving, by a first network entity, a compressed feature map including features extracted from media data (fig.5, [239]); decompressing, by the first network entity, the compressed feature map ([219]); and performing, by the first network entity, a second part of the computing graph on the feature map using the additional data ([282]), the second part of the computing graph corresponding to processing tasks to be performed by the first network entity ([571]).
Solovyev fails to disclose processed according to a first part of a computing graph and additional data from a second network entity, the additional data corresponding to at least a portion of the media data that was buffered by the second network entity;
However, Liu teaches and processed according to a first part of a computing graph and additional data from a second network entity, the additional data corresponding to at least a portion of the media data that was buffered by the second network entity ([47], [72]);
Regarding claim 18, Solovyev discloses The method of claim 17, wherein the first network entity comprises an application server (AS) and the second network entity comprises a user equipment (UE) ([238], [615]).
Regarding claim 23, Liu teaches The method of claim 20, wherein sending the request comprises, after processing the compressed feature map alone, determining that the additional data is needed ([58]).
Regarding claim 24, Liu teaches The method of claim 23, wherein processing the compressed feature map alone includes at least one of detecting an object using the compressed feature map or determining that a confidence value for the compressed feature map is below a threshold ([52]).
Regarding claim 25, Liu teaches The method of claim 20, wherein sending the request comprises sending the request based on at least one of an estimated communication network quality, a predicted communication network quality, a computing capability of the second network entity, or a latency requirement of the one or more processing tasks ([80]).
Regarding claim 30, see the rejection for claim 17.
Claim(s)8, 9, 26, 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Solovyev, in view of Liu, and further in view of US 11275987 B1 Gottin; Vinicius Michel et al. (hereafter Gottin).
Regarding claims 8, 26, Gottin teaches The method of claim 1, further comprising receiving a request to increase or decrease a size of a buffer used to buffer the at least portion of the media data (col.4 lines 67-col.5 line 15).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention having all the references Solovyev, Liu and Gottin before him/her, to modify the method of processing feature set data formed from media data disclosed by Solovyev to include the teaching in the same field of endeavor of Liu and Gottin, in order to provide techniques are presented to generate images, therewith one or more neural networks are used to generate one or more images based, at least in part, upon speech input received from one or more users, as identified by Liu, and a method and apparatus for optimizing performance of a storage system, as identified by Gottin.
Regarding claims 9, 27, Liu teaches The method of claim 8, wherein the request specifies the size according to a number of images or video frames or a number of features ([56]).
Claim(s) 10, 11, 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Solovyev, in view of Liu, and further in view of US 20190109926 A1 Hotchkies; Blair Livingstone et al. (hereafter Hotchkies).
Regarding claims 10, 28, Hotchkies teaches The method of claim 8, wherein the request specifies the size according to a period of time to be covered by the buffered at least portion of the media data ([88], [112]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention having all the references Solovyev, Liu and Hotchkies before him/her, to modify the method of processing feature set data formed from media data disclosed by Solovyev to include the teaching in the same field of endeavor of Liu and Hotchkies, in order to provide techniques are presented to generate images, therewith one or more neural networks are used to generate one or more images based, at least in part, upon speech input received from one or more users, as identified by Liu, and reduce latency associated with content delivery to different extents, as identified by Hotchkies.
Regarding claim 11, Hotchkies teaches The method of claim 8, further comprising increasing or decreasing the buffered at least portion of the media data according to the request ([19]).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRACY Y. LI whose telephone number is (571)270-3671. The examiner can normally be reached Monday Friday (8:30 AM- 4:30 PM) EST.
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/TRACY Y. LI/ Primary Examiner, Art Unit 2487
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