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 § 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.
[1] Claims 1, 2, 4-13, 21, 23 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (“Jin”) [NPL titled, “Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs”] in view of Zhang et al. (“Zhang”) [NPL titled, “Ballistocardiogram Based Person Identification and Authentication Using Recurrent Neural Networks”] further in view of Suetsugu [NPL titled, “Softmax function Explained Clearly and in Depth ｜Deep Learning fundamental”]. Note: Suetsugu is being used to explain inherent features of Jin.
Regarding claim 1, Jin discloses the following claim limitations:
A transformer-based content processing apparatus [abstract], comprising: a processor (i.e. inherent in information systems) [abstract]; and a memory (i.e. inherent in information systems) on which are stored processor-readable instructions that when executed by the processor (i.e. inherent in information systems), cause the processor to[abstract]: provide features of (i.e. textual content, social context, visual content) received for verification to a Gated Recurrent Unit (GRU) model (i.e. att-RNN) [fig. 2; page 797, col. 1, para. 5]; execute the GRU model (i.e. att-RNN) to: extract outputs corresponding to (i.e. RTS and RV’ ) as corresponding representations of [fig. 2]; enhance the corresponding representations via a series of operations (i.e. softmax) [page 799, col. 1, para. 4]; generate multiple probabilities (i.e. this is an inherent property of softmax) corresponding to multiple content categories (i.e. rumor or non-rumor) for the received content from the corresponding representations [page 799, col. 1, para. 4; Suetsugu: pages 4-6]; and select one of the multiple content categories (i.e. rumor or non-rumor) with a highest probability as a most probable content category (i.e. this is an inherent property of softmax) for the received content [page 799, col. 1, para. 4; Suetsugu: pages 4-6].
Jin does not explicitly disclose the following claim limitations:
Using an evidence content.
However, in the same field of endeavor Zhang discloses the deficient claim limitations, as follows:
Using an evidence content in a RNN (i.e. subject’s unique feature) [page 3, col. 1, para. 1].
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to modify the teachings of Jin with Zhang to include an evidence content, the reasoning being to enable authentication [Zhang: page 3, col. 1, para. 1].
Regarding claim 2, Zhang discloses the following claim limitations:
The transformer-based content processing apparatus of claim 1, wherein the processor-readable instructions further cause the processor to: receive the content for verification from a requester (i.e. heartbeats in evaluation stage) [page 3, col. 1, para. 1]; and identify the evidence content (i.e. subject’s unique feature) corresponding to the received content from authenticated data sources (i.e. subject’s unique feature added during enrollment) [page 3, col. 1, para. 1].
Regarding claim 4, Jin discloses the following claim limitations:
The transformer-based content processing apparatus of claim 1, wherein the corresponding representations have at least two dimensions (i.e. visual content), a batch (i.e. textual content, social context, visual content), and at least one feature (i.e. textual content, social context, visual content) [fig. 2; page 797, col. 1, para. 5].
Regarding claim 5, Jin discloses the following claim limitations:
The transformer-based content processing apparatus of claim 4, wherein the series of operations comprise a first series of operations including addition and normalization (i.e. concatenation of Rw and Rs’) regularization, and a projection transformation (i.e. output of LSTM) [fig. 2].
Regarding claim 6, Jin discloses the following claim limitations:
The transformer-based content processing apparatus of claim 5, wherein the series of operations further comprise a second series of operations following the first series of operations, wherein the second series of operations include further addition and normalization (i.e. concatenation of RTS and RV’ ) followed by refinement of the features (i.e. softmax) [fig. 2; page 799, col. 1, para. 4].
Regarding claim 7, Jin discloses the following claim limitations:
The transformer-based content processing apparatus of claim 1, wherein the evidence content and the received content include one or more of text data and image data (i.e. textual content, social context, visual content) [fig. 2; page 797, col. 1, para. 5].
Regarding claim 8, Jin discloses the following claim limitations:
The transformer-based content processing apparatus of claim 7, wherein the GRU model includes two GRU models including a text GRU model (i.e. LSTM) that processes the text data and an image GRU (i.e. VGG-19) that processes the image data [fig. 2].
Regarding claim 9, Jin discloses the following claim limitations:
The transformer-based content processing apparatus of claim 8, wherein to extract the corresponding representations, the processor-readable instructions further cause the processor to: extract an output of the last layer of the text GRU (i.e. output of LSTM) as a text representation of the corresponding representations for the evidence content and the content for verification [fig. 2]; and extract an output of the last layer of the image GRU (i.e. output of VGG-19) as an image representation of the corresponding representations for the evidence content and the content for verification [fig. 2].
Regarding claim 10, Jin discloses the following claim limitations:
The transformer-based content processing apparatus of claim 9, wherein to generate the multiple probabilities, the processor-readable instructions further cause the processor to: generate a concatenated representation of the evidence content and the content for verification by concatenating the text representation and the image representation (i.e. concatenation of RTS and RV’ ) [fig. 2].
Regarding claim 11, Jin discloses the following claim limitations:
The transformer-based content processing apparatus of claim 1, wherein the multiple content categories include an authentic content category (i.e. non-rumor), an inauthentic content category (i.e. rumor) and an indeterminate content category (i.e. rumor includes those that cannot be determined to be non-rumor) [page 799, col. 1, para. 4].
Regarding claim 12, Jin discloses the following claim limitations:
The transformer-based content processing apparatus of claim 1, wherein the content for verification is a social media post (i.e. microblogs) and the content for verification includes only image data (i.e. visual content) and the evidence content includes only text data (i.e. textual content) [page 797, col. 1, paras. 4 and 5; page 798, col. 2, para. 4].
Regarding claim 13, Jin and Zhang disclose the following claim limitations:
The transformer-based content processing apparatus of claim 1, wherein the content for verification comprises user authentication data (i.e. Zhang teaches user authentication, so can be extended to include text or images to authenticate an user) including textual user authentication data and image user authentication data (i.e. textual content, social context, visual content) [Jin: fig. 2; page 797, col. 1, para. 5 and Zhang: page 3, col. 1, para. 1].
Regarding claims 21, 23 and 24, all limitations are rejected on similar basis to claims 1, 5, 6 and 8.
[2] Claims 3 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (“Jin”) [NPL titled, “Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs”] in view of Zhang et al. (“Zhang”) [NPL titled, “Ballistocardiogram Based Person Identification and Authentication Using Recurrent Neural Networks”] further in view of Suetsugu [NPL titled, “Softmax function Explained Clearly and in Depth ｜Deep Learning fundamental”] further in view of Zhou et al. (“Zhou”) [NPL titled, “Multimodal Fake News Detection via CLIP-Guided Learning”]. Note: Suetsugu is being used to explain inherent features of Jin.
Regarding claim 3, Jin and Zhang disclose the claim limitations as set forth in claim 1.
Jin and Zhang do not explicitly disclose the following claim limitations:
The transformer-based content processing apparatus of claim 1, wherein to provide the features, the processor-readable instructions further cause the processor to: extract, using a Contrastive Language-Image Pre-training (CLIP) model, features of the evidence content and the received content.
However, in the same field of endeavor Zhou discloses the deficient claim limitations, as follows:
extract, using a Contrastive Language-Image Pre-training (CLIP) model, features of the content (i.e. text encoder (CLIP) and image encoder (CLIP)) [fig. 2].
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to modify the teachings of Jin and Zhang with Zhou to use a CLIP model the reasoning being to find similarity between paired images and texts [Zhou: page 2826, col. 2, para. 4].
Regarding claim 22, all limitations are rejected on similar basis to claim 3.
[3] Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (“Jin”) [NPL titled, “Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs”] in view of Zhang et al. (“Zhang”) [NPL titled, “Ballistocardiogram Based Person Identification and Authentication Using Recurrent Neural Networks”] further in view of Suetsugu [NPL titled, “Softmax function Explained Clearly and in Depth ｜Deep Learning fundamental”] further in view of Choi et al. (“Choi”) [US 2022/0229435]. Note: Suetsugu is being used to explain inherent features of Jin.
Regarding claim 14, Jin and Zhang disclose the claim limitations as set forth in claim 1.
Jin and Zhang do not explicitly disclose the following claim limitations:
The transformer-based content processing apparatus of claim 1, wherein the processor-readable instructions further cause the processor to: receive user feedback on the generated multiple probabilities; and train the GRU model on the user feedback.
However, in the same field of endeavor Choi discloses the deficient claim limitations, as follows:
The transformer-based content processing apparatus of claim 1, wherein the processor-readable instructions further cause the processor to: receive user feedback on the generated multiple probabilities (i.e. user feedback to softmax calculations) [para. 0080]; and train the GRU model on the user feedback (i.e. softmax learns based on user feedback) [para. 0080].
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to modify the teachings of Jin and Zhang with Choi to use user feedback the reasoning being to adapt the model according to user feedback [Choi: para. 0080].
Allowable Subject Matter
[4] Claims 19 and 20 are allowed.
Conclusion
[5] Any inquiry concerning this communication or earlier communications from the examiner should be directed to SATH V PERUNGAVOOR whose telephone number is (571)272-7455. The examiner can normally be reached M-F, 8 am-5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, COLLEEN FAUZ can be reached at (571) 272-1667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SATH V PERUNGAVOOR/Supervisory Patent Examiner, Art Unit 2488
Prosecution Insights