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 .
DETAILED ACTION
Response to Amendment
This is in response to applicant’s amendment/response filed on 04/14/2026, which has been entered and made of record. Claims 1, 4, 9, 10 have been amended. Claim 3 has been canceled. No claim has been added. Claims 1-2, 4-10 are pending in the application.
Response to Arguments
Applicant's arguments filed on 04/14/2026 have been fully considered but they are not persuasive.
Applicant submits “The basis for this rejection is that the functions for the claimed backbone conversion network and sampling module are not described with "any specific hardware computer structures." This is not correct. Each feature and function is addressed separately below.” (Remarks, Page 5-7).
The examiner disagrees with Applicant’s premises and conclusion. Applicant spent three pages explaining various network, model, sequence, voxel. However, applicant did not address the issue which is “failure to disclose sufficient corresponding structure (e.g., the computer and the algorithm) in the specification that performs the entire claimed function”. For example, if applicant wants to say a computer or a processor is the structure that execute the code, then applicant need to point out which section of specification recites “a computer” or “a processor”. Examiner cannot imply the structure is there merely because it maybe something to implement the code. Applicant needs to specifically point out the structure in the specification that is sufficient to performs the entire claimed function.
Applicant submits “Zhang is not prior art to the present application. The present application was filed on January 9, 2024. The paper was not published on September 16, 2023 as alleged by the Examiner. That was the date a first draft was submitted to the IEEE. The paper was published after the application was filed. It was first presented at the conference that was conducted on May 13-17, 2024. The paper was then added to IEEE explore on August 8, 2024. See, https://ieeexplore.ieee.org/document/10609864. The website identified by the Examiner includes the submission history and there is no evidence to support a 2023 publication. The website indicates a first draft submitted on September 16, 2023 and then a final revised version was submitted prior to the May 2024 conference on April 26, 2024. This rejection should be withdrawn because the paper was not "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." (Remarks, Page 8)
The examiner disagrees with Applicant’s premises and conclusion. The prior art Zhang clearly labeled V1 and dated September 16, 2023. Applicant merely argued it was a draft and not published but without providing any support evidence. However, the Zhang document did not indicate it was a confidential or private use only. On the other hand, the examiner found the document on a website that is accessible by the public. It seems the general public could obtain a copy of the paper by downloading from the website below.
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Also, another website indicated the publication date is September 2023:
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Preponderance of evidence on hand, the prior art Zhang was available to the public on September 2023. Therefore, the 102 rejection is maintained. If applicant believe Zhang is not available to the general public, applicant may need to provide further evidence to support this declaration.
35 USC § 112 (f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: "a backbone conversion network", “an event sampling module” and " video to event prediction pipeline” in claim claims 1-10.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. However, the examiner cannot find any specific hardware or computer structures in the specification to perform the recited claim functions. According to MPEP section 2181.II, a rejection under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph is appropriate if the specification discloses no corresponding algorithm associated with a computer or microprocessor.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 4-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1, 2, 4-10 is determined to invoke 35 U.S.C. 112(f). there is no disclosure of structure, material or acts for performing the recited function, the claim fails to satisfy the requirements of 35 U.S.C. 112(b). See MPEP 2181.III.
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.
Claims 1-2, 4-10 are 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
As to claim 1-2, 4-10, when a claim containing a computer-implemented 35 U.S.C. 112(f) claim limitation is found to be indefinite under 35 U.S.C. 112(b) for failure to disclose sufficient corresponding structure (e.g., the computer and the algorithm) in the specification that performs the entire claimed function, it will also lack written description under section 112(a). See MPEP 2181.IV.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-10 are rejected under 35 U.S.C. 102(a)(1) as being anticiapted by Zhang et al. (“V2CE: Video to Continuous Events Simulator.” 2024 IEEE International Conference on Robotics and Automation (ICRA) (2023): 12455-12461., 09/16/2023, https://doi.org/10.48550/arXiv.2309.08891 ).
As to claim 1, Zhang discloses a video to event prediction pipeline system, comprising:
a backbone conversion network having a model that is configured to receive a raw active pixel sensor video sequence and convert it into 3D predicted voxels (Zhang, Abstract, Page 2, Fig. 2, Motion-Aware Event Voxel Prediction Pipeline” Page 2, “Motion-Aware Event Voxel Prediction”);
an event sampling module configured to receive the 3D predicted voxels and create event timestamps in a continuous scale by leveraging nonlinear dynamics of event firing trends in each voxel of the 3D predicted voxels (Page 4, “Voxels to Continuous Events Sampling” “This second task aims to recover the exact event timestamps in a continuous scale from Stage1’s output event voxel.”);
wherein the backbone conversion network comprises a series of training loss function modules, the training loss function modules teaching the backbone conversion network to account for variations in the active pixel sensor video sequence caused by adjustable camera parameters of the active pixel sensor video sequence (Page 3, “The first loss to introduce is the Spatial-Temporal-Pyramid Loss” “Temporal-Pyramid Loss” “Adversarial Loss (ADV Loss”), wherein the training loss function module comprises a loss module that encourages the model to extract multi-scale information from adjacent voxels by applying coarse supra- voxel matching (Page 3, “The STP Loss encourages the model to extract multi-scale information from adjacent voxels, enhancing its robustness against noise by applying coarse supra-voxel matching.”).
As to claim 2, claim 1 is incorporated and the Zhang discloses the adjustable camera parameters comprise one or more of exposure, ISO, and aperture (Page 2, “Additionally, both camera types have adjustable parameters such as exposure, ISO, and aperture”).
As to claim 4, Zhang discloses a video to event prediction pipeline system, comprising:
a backbone conversion network having a model that is configured to receive a raw active pixel sensor video sequence and convert it into 3D predicted voxels (Zhang, Abstract, Page 2, Fig. 2, Motion-Aware Event Voxel Prediction Pipeline” Page 2, “Motion-Aware Event Voxel Prediction”);
an event sampling module configured to receive the 3D predicted voxels and create event timestamps in a continuous scale by leveraging nonlinear dynamics of event firing trends in each voxel of the 3D predicted voxels (Page 4, “Voxels to Continuous Events Sampling” “This second task aims to recover the exact event timestamps in a continuous scale from Stage1’s output event voxel.”);
wherein the backbone conversion network comprises a series of training loss function modules, the training loss function modules teaching the backbone conversion network to account for variations in the active pixel sensor video sequence caused by adjustable camera parameters of the active pixel sensor video sequence (Page 3, “The first loss to introduce is the Spatial-Temporal-Pyramid Loss” “Temporal-Pyramid Loss” “Adversarial Loss (ADV Loss”), wherein the training loss function module comprises a loss module that encourages the model to prioritize neighboring events (Page 3, “Temporal-Pyramid Loss (TP Loss, LTP ) is designed to prioritize neighboring events, which are crucial for voxel level event reconstruction.”).
As to claim 5, claim 4 is incorporated and the Zhang discloses the training loss function module comprises a loss module that encourages the model to align information flow between the predicted event frames and the active pixel sensor video sequence (Page 3, “This addresses the issue of sparsity in voxels and ensures better and aligned information flow between generated event frames and the input frame sequence.”).
As to claim 6, claim 5 is incorporated and the Zhang discloses the training loss function module comprises a loss module that encourages the model to enhance realness of the predicted 3D event based voxels by training a discriminator using ground truth and predicted voxels and real and fake samples (Page 3, “Adversarial Loss (ADV Loss, LADV) aims to enhance the realness of our generated event voxels.”).
As to claim 7, claim 6 is incorporated and the Zhang discloses the training loss function module comprises a loss module that encourages the model to compute average brightness of voxels exceeding a threshold and align with brightness of ground truth voxels (Page 3, “compute the average brightness Ia of voxels exceeding a threshold β, and align this Ia with that of the ground truth voxels.”).
As to claim 8, claim 7 is incorporated and the Zhang discloses the event sampling module ensures that each event influences a voxel series only for a predetermined duration (Page 4, “Each event influences the voxel series for a short and finite duration which can be characterized by a continuous-time unit step signal (with an on-time duration same as δ).”).
As to claim 9, Zhang discloses a video to event prediction pipeline system, comprising:
a backbone conversion network having a model that is configured to receive a raw active pixel sensor video sequence and convert it into 3D predicted voxels (Zhang, Abstract, Page 2, Fig. 2, Motion-Aware Event Voxel Prediction Pipeline” Page 2, “Motion-Aware Event Voxel Prediction”);
an event sampling module configured to receive the 3D predicted voxels and create event timestamps in a continuous scale by leveraging nonlinear dynamics of event firing trends in each voxel of the 3D predicted voxels (Page 4, “Voxels to Continuous Events Sampling” “This second task aims to recover the exact event timestamps in a continuous scale from Stage1’s output event voxel.”);
wherein the backbone conversion network comprises a series of training loss function modules, the training loss function modules teaching the backbone conversion network to account for variations in the active pixel sensor video sequence caused by adjustable camera parameters of the active pixel sensor video sequence (Page 3, “The first loss to introduce is the Spatial-Temporal-Pyramid Loss” “Temporal-Pyramid Loss” “Adversarial Loss (ADV Loss”), the event sampling module ensures that each event influences a voxel series only for a predetermined duration (Page 4, “Each event influences the voxel series for a short and finite duration which can be characterized by a continuous-time unit step signal (with an on-time duration same as δ).”).
As to claim 10, Zhang discloses a video to event prediction pipeline system, comprising:
a backbone conversion network having a model that is configured to receive a raw active pixel sensor video sequence and convert it into 3D predicted voxels (Zhang, Abstract, Page 2, Fig. 2, Motion-Aware Event Voxel Prediction Pipeline” Page 2, “Motion-Aware Event Voxel Prediction”);
an event sampling module configured to receive the 3D predicted voxels and create event timestamps in a continuous scale by leveraging nonlinear dynamics of event firing trends in each voxel of the 3D predicted voxels (Page 4, “Voxels to Continuous Events Sampling” “This second task aims to recover the exact event timestamps in a continuous scale from Stage1’s output event voxel.”);
wherein the backbone conversion network comprises a series of training loss function modules, the training loss function modules teaching the backbone conversion network to account for variations in the active pixel sensor video sequence caused by adjustable camera parameters of the active pixel sensor video sequence (Page 3, “The first loss to introduce is the Spatial-Temporal-Pyramid Loss” “Temporal-Pyramid Loss” “Adversarial Loss (ADV Loss”), the event sampling module assumes that each voxel of the 3D predicted voxels conforms to a slope distribution described by a probability density function (Page 4, “To accurately model this phenomenon, we assume that each voxel and its neighboring voxels conform to a slope distribution described by the Probability Density Function”).
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 YU CHEN whose telephone number is (571)270-7951. The examiner can normally be reached on M-F 8-5 PST Mid-day flex.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xiao Wu can be reached on 571-272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YU CHEN/
Primary Examiner, Art Unit 2613
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