Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-9, 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ding et al. (Privacy-Preserving Feature Extraction via Adversarial Training, hereinafter referred to as “Ding”).
Regarding claim 1, Ding teaches a method comprising: receiving media content; providing the received media content to a first portion of layers of a trained machine-learning model, wherein the first portion of layers of the trained machine-learning model is configured to run within a trusted execution environment of a computing system; receiving, from the first portion of layers of the trained machine-learning model, extracted feature data of the provided media content, wherein the extracted feature data was generated by the first portion of layers of the trained machine-learning model based at least in part on the provided media content; providing the extracted feature data to a second portion of layers of the machine-learning model, wherein the second portion of layers of the machine-learning model is configured to run outside the trusted execution environment of the computing system; receiving, from the second portion of layers of the machine-learning model, output data, wherein the output data was generated by the second portion of layers of the trained machine-learning model based at least in part on the provided extracted feature data; and using the received output data to perform an action (met by leveraging the characteristics of layered connections in deep neural networks; further met by separate the deep neural network into two parts by an intermediate layer where the first part is considered as an encoder of the raw data, and the second part is a prediction model based on encoded features). This is read in (Page 1968, Paragraph 3).
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See also (Fig 1.).
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Additionally, see (Page 1969, Paragraph 1).
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Regarding claim 2, Ding as read in (Page 1969, Paragraph 1) in the rejection of claim 1, incorporated herein, meets wherein the media content is protected using a copy-protection measure (met by require the transformed data not to contain privacy information).
Regarding claim 3, Ding as read in (Page 1968, Paragraph 3) in the rejection of claim 1, wherein the first portion of layers of the trained machine-learning model comprises a neural network (met by separate the deep neural network into two parts by an intermediate layer where the first part is considered as an encoder of the raw data, and the second part is a prediction model based on encoded features).
Regarding claim 4, Ding teaches wherein the neural network comprises a convolutional neural network (met by deep learning architectures such as convolutional neural networks have been applied to many fields). This is read in (Page 1969, Paragraph 4).
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Regarding claim 5, Ding as read in (Page 1969, Paragraph 1) in the rejection of claim 1, incorporated herein, meets wherein the trusted execution environment of the computing system comprises a portion of a processor of the computing system, and wherein the trusted execution environment is configured to limit access to data inside the trusted execution environment (met by require the transformed data not to contain privacy information).
Regarding claim 6, Ding as read in (Fig. 1) in the rejection of claim 1, incorporated herein, meets wherein the trusted execution environment of the computing system comprises a wired network to which the computing system is connected (met by demonstrating a client side neural network which can be interpreted as a client server or local network, thus the claimed limitation is within the scope of the prior art).
Regarding claim 7, Ding teaches wherein generating the extracted feature data comprises at least one of a vectorization process or a hashing process (met by al is a vector containing the activation values of the lth layer neurons). This is read in (Page 1969, Paragraph 5).
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Regarding claim 8, Ding teaches wherein the extracted feature data is configured to be unintelligible to a human observer (met by CryptoNets, which is an encrypted deep learning model using homomorphic encryption wherein the user can directly encrypt the data, and then upload the ciphertext to the cloud for computation). This is read in (Page 1968, Paragraph 2).
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Regarding claim 9, Ding as read in (Page 1968, Paragraph 3) in the rejection of claim 1, incorporated herein, meets wherein the second portion of layers is configured to perform automatic content recognition on the extracted feature data, and wherein the output data comprises automatic content recognition data (met by second part is a prediction model based on encoded features).
Regarding claim 19, the claim is substantially identical to claim 1, the analysis of which is incorporated herein.
Regarding claim 20, the claim is substantially identical to claim 1, the analysis of which is incorporated herein.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 10-14, 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ding in view of Bhasker et al. (US012489952B1, hereinafter referred to as “Bhasker”).
Regarding claim 10, Ding fails to teach wherein the media content comprises video content, and wherein a first subset of layers of the second portion of layers is configured to identify individuals within the video content based on the extracted feature data. However, Bhasker amends this deficiency.
Bhasker teaches when an object is a person, identifying the object in video is performed). This is read in (Column 6, Line 65-67).
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Therefore, it would have been prima facie obvious to one of ordinary skill in the art to have modified Ding to incorporate the teachings of Bhasker in order to provide Devices, systems, and methods for generating and presenting jumplinks for objects in video (Abstract).
Regarding claim 11, Bhasker as read in the rejection of claim 10, incorporated herein, meets wherein the output data comprises identification data associated with the identified individuals within the video content (met by when an object is a person, identifying the object in video is performed).
Regarding claim 12, Bhasker teaches wherein a second subset of layers of the second portion of layers is configured to detect objects based on the extracted feature data (met by object identifier techniques; further met by analyze video frames; further met by identify closest matching objects presented in video frames). This is read in (Column 3, Line 30-40).
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Regarding claim 13, Bhasker teaches wherein a third subset of layers of the second portion of layers is configured to recognize text based on the extracted feature data (met by machine learning analysis of text; further met by identify text displayed in the video). This is read in (Column 3, Line 6-9).
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Regarding claim 14, Bhasker teaches transmitting, via a copy-protected link, the media content to a device linked to the computing system (met by the device may cause the video and an indication that the jumplink corresponds to the video frame where the object is represented to be presented). This is read in (Column 15, Line 15-17).
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Regarding claim 16, Bhasker as read in the rejection of claim 14, incorporated herein, meets wherein the action comprises transmitting the received output data to a content-presentation device (met by presentation of the video and indication by showing images of the objects or people identified). This is read in (Column 15, Line 18-24).
Regarding claim 17, Bhasker teaches wherein the output data comprises person identification data, and wherein the action comprises generating an overlay comprising the person identification data and displaying the overlay upon the media content (met by Object (106), Object (108), and Person (112). This is read in (FIG. 8).
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Regarding claim 18, Bhasker as read in the rejection of claim 17, incorporated herein, meets wherein the output data comprises object recognition data, and wherein the action comprises inserting an object into the media content based upon the object recognition data (met by Object (106) and Object (108)).
Allowable Subject Matter
Claim 15 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 15 recites updating, by the computing system, the second portion of layers of the machine-learning model, wherein during the updating, the first portion of layers remain unmodified. This limitation is not met by the prior art.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW JAMES BODNARK whose telephone number is (703)756-5378. The examiner can normally be reached 8a-5p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at (571) 272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW JAMES BODNARK/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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