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 Amendment
A Preliminary Amendment was entered 01/03/2025. Claims 17-32 are pending and are new. Claims 1-16 are cancelled. A substitute specification and abstract are also entered.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/03/2025 and 04/09/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are considered by examiner.
Claim Interpretation
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.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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 limitations are:
Claims 31 and 32:
“vehicle camera unit” is identified as element 1 of Fig 1 and described in prose in at least ¶ [0006]-[0009], with a series of stages of using the camera unit data described in ¶ [0058]-[0060] and associated Fig 2, 3.
“vehicle computing unit” is identified as element 5 of Fig 1 and described in prose in at least ¶ [0011]-[0047], describing the series of stages of using the camera unit data as outlined in ¶ [0058]-[0064] and associated Fig 2, 3.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, each are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
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(b)
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 17-30 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 17 recites the limitation “wherein, the personal information is reduced, prior to the processing of the camera image by image analysis, the method comprises degrading original image data from the camera unit in stages,” and it is unclear if “the method” of this limitation is meant to refer to a method for reducing personal information or to refer back to the preamble method. Thus, Applicant has failed to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
For purposes of examination “the method” is interpreted as a method specific to reducing the personal information specific of the current wherein limitation and not “the method” of the preamble. Appropriate clarification is necessary.
Claims 18-30 are rejected based on their dependency to claim 17.
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.
Claims 17, 19-22, 24-29 are rejected under 35 U.S.C. 103 as being unpatentable over Dimiccoli et al (Mitigating Bystander Privacy Concerns in Egocentric Activity Recognition with Deep Learning and Intentional Image Degradation) in view of Ariyoshi et al (JP 2024/006034, with US 2025/0272961 used as the English translation with claiming foreign priority to associated application JP 2022/106573).
Regarding Claim 17, Dimiccoli et al teach a method (method of performing image degradation to preserve privacy of a bystander; Fig 1, 7 and 3.1 Survey ¶ 1, 4, 4.2 Image Degradation by Image Processing) comprising:
reducing personal information in a camera image from a camera unit of a vehicle by degrading the camera image (images are taken with a body-mounted camera and the camera has a blurring filter to filter the photo of the persons identified in the image to provide privacy protection; Fig 1 and 3.1 Survey ¶ 1),
wherein, the personal information is reduced, prior to the processing of the camera image by image analysis (the camera has a blurring filter to filter the photo of the persons identified in the image to provide privacy protection prior to further analysis, such as activity labeling and annotation; Fig 1, 7 and 3.1 Survey ¶ 1, 4, 4.2 Image Degradation by Image Processing), the method (interpreted as a method specific to reducing the personal information specific of the current wherein limitation) comprises degrading original image data from the camera unit in stages (degradation of personal information by filtering is performed at three different filters based on criteria; Fig 7, table 2 and 4.2 Image Degradation by Image Processing),
wherein each of the stages involve performing a type of degradation characteristic (degradation filtering is performed at three different levels for each filter based on criteria; Fig 7and 4.2 Image Degradation by Image Processing), wherein each of the stages has at least one changeable parameter for determining an intensity of the degradation in the respective stage (the filter levels of intensity can be modified (example k=10, k=20, k=40 for mean filter) based on criteria for degradation; Fig 7and 4.2 Image Degradation by Image Processing),
wherein, in to determine distribution of the degradation over the stages, values of parameters for each of the stages are established by a computing unit by an optimization algorithm using i) minimum information to be provided for the vehicle function, and ii) a desired level of restriction of personal information (the filter levels of intensity can be modified (example k=10, k=20, k=40 for mean filter) based on criteria for degradation including the criteria of preserving the privacy of the bystander in the image and maximizing the classifier performance using square kernels for the filter; Fig 7and 4.2 Image Degradation by Image Processing), wherein i) and ii) are used as target values (criteria for degradation including the criteria of preserving the privacy of the bystander in the image and maximizing the classifier performance using square kernels for the filter; Fig 7and 4.2 Image Degradation by Image Processing), and
wherein the parameters to achieve the target values are determined (the filtering level is determined based the filter parameters to preserve bystander privacy and maximize classifier performance; Fig 7and 4.2 Image Degradation by Image Processing).
Dimiccoli et al does not teach wherein the camera image processed by image analysis is used to perform a function of the vehicle.
Ariyoshi et al is analogous art pertinent to the technological application claimed in the current application and teaches wherein the camera image processed by image analysis is used to perform a function of the vehicle (the image processing device 100 receives camera images from outside of the vehicle and the image is converted to protect the privacy of persons depicted in the image, where the image data is used for pedestrian behavior prediction and utilized for operating the vehicle; Fig 1, 9 and ¶ [0032]-[0034], [0039], [0067]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Dimiccoli et al with Ariyoshi et al including wherein the camera image processed by image analysis is used to perform a function of the vehicle. By using the image data outside of the vehicle, the privacy of people may be maintained while still advancing technological advancement of driving assistance in a behavior prediction model while maintaining traffic safety, as recognized by Ariyoshi et al (¶ [0002]-[0004]).
Regarding Claim 19, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 17 (as described above), wherein a measure for a preference between the minimum information to be provided for (interpreted as the measure is based on one of the three following measurements:) the vehicle function, a desired level of restriction of personal information, or a prioritization of one of the target values is predefined by a user (Dimiccoli et al, the filtering level is predetermined based on three different setting filter levels to preserve bystander privacy with the training based on user defined values; Fig 7and 4.2 Image Degradation by Image Processing).
Regarding Claim 20, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 17 (as described above), wherein a measure for a preference between the minimum information to be provided for the vehicle function, a desired level of restriction of personal information, or a prioritization of one of the target values is predefined by the computing unit (Dimiccoli et al, the filtering level is predetermined based on three different setting filter levels to preserve bystander privacy with the inference based on CNN (computer) defined values; Fig 7and 4.2 Image Degradation by Image Processing).
Regarding Claim 21, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 17 (as described above), wherein the stages include: an image sensor of the camera unit, hardware settings in a control unit of the camera unit, and software processing in the control unit of the camera unit for algorithmic image editing (Dimiccoli et al, the system includes the camera on a phone with hardware and software to process the image data; Fig 5 and 4.1 Computational Approach, 4.1 Data Collection ¶ 1).
Regarding Claim 22, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 17 (as described above), wherein the desired level of restriction of personal information is defined by a structural similarity index with respect to the camera image (Ariyoshi et al, the mosaic region of the face is determined by the trained machine learning model based on a contour of a face (structural similarity index) using facial attributes to determine face contour region of interest; Fig 2, 3 and ¶ [0045]-[0048]; examiner notes the applicant states prior art teaches on the limitation as described in the specification ¶ [0032]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Dimiccoli et al with Ariyoshi et al including wherein the desired level of restriction of personal information is defined by a structural similarity index with respect to the camera image. By using a similarity analysis by the machine learning model to determine the region of interest to perform the mosaic processing the model performs specific privacy restriction while allowing for visual clarity in the surrounding view of the image, resulting in less computation and maximizing the environmental data for control of the vehicle, as recognized by Ariyoshi et al (¶ [0004]-[0006]).
Regarding Claim 24, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 17 (as described above), wherein the computing unit is arranged in the vehicle (Ariyoshi et al, a camera is mounted for in-vehicle images and a camera is mounted for out-vehicle images; Fig 1 and ¶ [0033]), wherein continuously updated values of the parameters for each of the stages are established by the computing unit for a continuously updated determination of the distribution of the degradation over the stages (Ariyoshi et al, the image determination unit performs a continuity of the image data privacy in a time series including responding to malfunctions of camera or software to not store the data; Fig 6, 7 and ¶ [0053], [0056]-[0057], [0060]-[0062]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Dimiccoli et al with Ariyoshi et al including wherein the computing unit is arranged in the vehicle, wherein continuously updated values of the parameters for each of the stages are established by the computing unit for a continuously updated determination of the distribution of the degradation over the stages. By updating the data analysis, the model performs specific privacy restriction while allowing for visual clarity in the surrounding view of the image, resulting in less computation and maximizing the environmental data for control of the vehicle, as recognized by Ariyoshi et al (¶ [0004]-[0006]).
Regarding Claim 25, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 24 (as described above), wherein the parameters for each of the stages are established by the computing unit depending on established situational parameters (Ariyoshi et al, the image determination unit performs a continuity of analyzing the data including consistency of the face direction data; Fig 7, 8 and ¶ [0061], [0064]), wherein the established situational parameters include: distance of a face of a person from the camera unit, facial movements of a person relative to the camera unit, ambient conditions, or driving situation (Ariyoshi et al, the face direction and gaze direction (interpreted as facial movements based on face and eye gaze direction) are determined relative to the camera; Fig 6, 7 and ¶ [0064]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Dimiccoli et al with Ariyoshi et al including wherein the parameters for each of the stages are established by the computing unit depending on established situational parameters, wherein the established situational parameters include: distance of a face of a person from the camera unit, facial movements of a person relative to the camera unit, ambient conditions, or driving situation. By updating the data analysis, the model performs specific privacy restriction while allowing for visual clarity in the surrounding view of the image, resulting in less computation and maximizing the environmental data for control of the vehicle, as recognized by Ariyoshi et al (¶ [0004]-[0006]).
Regarding Claim 26, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 17 (as described above), wherein values of the parameters of the stages for predefined camera images or for camera images from predefined scenes are specified by the computing unit and stored in a control unit of the vehicle (Ariyoshi et al, the face direction and gaze direction are analyzed with the image processing unit 130 based on the trained machine learning model 180 (thereby predefined in training and applied during inference) with the privacy of the person protected with mosaic processing performed by image conversion unit 140), with data stored in storage unit 170; Fig 2-4 and ¶ [0043]-[0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Dimiccoli et al with Ariyoshi et al including wherein values of the parameters of the stages for predefined camera images or for camera images from predefined scenes are specified by the computing unit and stored in a control unit of the vehicle. By storing data from training the model, the model can efficiently perform specific privacy restriction while allowing for visual clarity in the surrounding view of the image, resulting in less computation and maximizing the environmental data for control of the vehicle, as recognized by Ariyoshi et al (¶ [0004]-[0006]).
Regarding Claim 27, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 26 (as described above), wherein the values of the parameters stored in the control unit of the vehicle are used for the degradation instead of the values of the parameters continuously updated by the computing unit when a predefined camera image or a camera image from a predefined scene is present during operation (Ariyoshi et al, the trained model that acquires a face area to determine the contour for mosaic processing used for privacy protection is based on stored image parameters associated with the face and gaze direction; F Fig 2-4 and ¶ [0043]-[0052]).
Regarding Claim 28, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 17 (as described above), wherein the computing unit determines the values of the parameters by a numerical method (Dimiccoli et al, the filter levels for degradation of bystander privacy are determined based on a formulated criteria; Fig 7and 4.2 Image Degradation by Image Processing).
Regarding Claim 29, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 17 (as described above), wherein the computing unit determines the values of the parameters using a pretrained artificial neural network (Dimiccoli et al, a CNN is trained with filtered images of different degradation values and used during inference; 5. Results ¶ 1-3).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Dimiccoli et al (Mitigating Bystander Privacy Concerns in Egocentric Activity Recognition with Deep Learning and Intentional Image Degradation) in view of Ariyoshi et al (JP 2024/006034, with US 2025/0272961 used as the English translation with claiming foreign priority to associated application JP 2022/106573) and Tang (US 2022/0058394).
Regarding Claim 23, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 17 (as described above).
Dimiccoli et al in view of Ariyoshi et al do not teach wherein the minimum information for the vehicle function comprises a mean standard deviation or a signal-to-noise ratio with respect to the camera image.
Tang is analogous art pertinent to the technological application claimed in the current application and teaches wherein the minimum information for the vehicle function comprises a mean standard deviation or a signal-to-noise ratio with respect to the camera image (the processor 102 receives the vehicle images from sensors 182 and may perform noise filtering (thereby determining a signal to noise ratio); Fig 1 and ¶ [0080], [0090]; examiner notes the applicant states prior art teaches on the limitation as described in the specification ¶ [0034]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Dimiccoli et al in view of Ariyoshi et al with Tang including wherein the minimum information for the vehicle function comprises a mean standard deviation or a signal-to-noise ratio with respect to the camera image. By identifying image noise, the noise may be filtered, thereby improving the image used for assessing the environment while protecting privacy of people detected in the image data, as recognized by Tang (¶ [0025]).
Claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Dimiccoli et al (Mitigating Bystander Privacy Concerns in Egocentric Activity Recognition with Deep Learning and Intentional Image Degradation) in view of Ariyoshi et al (JP 2024/006034, with US 2025/0272961 used as the English translation with claiming foreign priority to associated application JP 2022/106573) and Hutchings et al (US 2020/0198644).
Regarding Claim 30, Dimiccoli et al in view of Ariyoshi et al teach the method of claim 29 (as described above).
Dimiccoli et al in view of Ariyoshi et al do not teach wherein the pretrained artificial neural network is continuously trained further on a server based on data from camera units of other vehicles, wherein updates to the artificial neural network are transmitted back to the other vehicles in a fleet.
Hutchings et al is analogous art pertinent to the technological application claimed in the current application and teaches wherein the pretrained artificial neural network is continuously trained further on a server based on data from camera units of other vehicles, wherein updates to the artificial neural network are transmitted back to the other vehicles in a fleet (statistical properties of the image data are gathered from autonomous vehicles and used for training of machine learning models to detect the environmental data with the modeled data used for operating a fleet of autonomous vehicles; ¶ [0031]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Dimiccoli et al in view of Ariyoshi et al with Hutchings et al including wherein the pretrained artificial neural network is continuously trained further on a server based on data from camera units of other vehicles, wherein updates to the artificial neural network are transmitted back to the other vehicles in a fleet. By utilizing the image data for training the machine learning model, driving for multiple vehicles of a fleet may be collectively improved for detecting, monitoring and managing the environment data and control the vehicle, as recognized by Hutchings et al (¶ [0030]-[0031]).
Allowable Subject Matter
Claim 18 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Regarding Claim 18, the prior art was not readily identified to teach, suggest or provide motivation to combine the plurality of prior art references to teach the following limitations in combination with the base claim:
The method of claim 17, wherein, when two of the target values cannot be achieved at a same time by changing the parameters, the optimization algorithm optimizes the parameters to an extent that, depending on use case, either the target value i) is achieved for the minimum information provided for the vehicle function and the level of restriction of personal information is as close to the target value ii) as possible, or the target value ii) is achieved for the level of restriction of personal information and the minimum information provided for the vehicle function is as close to the target value i) as possible.
Claims 31, 32 are allowed.
Regarding Claims 31, 32, the prior art was not readily identified to teach, suggest or provide motivation to combine the plurality of prior art references to teach the entirety of the applicant’s claim limitations, as interpreted under 35 U.S.C. § 112(f). The applicant is advised to carefully consider the claim language and the associated limitations regarding the identified interpretation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hassan et al (Automatically Detecting Bystanders in Photos to Reduce Privacy Risks) teach a computer vision and machine learning model and system to automatically identify bystanders in images and perform labeling for the detection followed by refining the image for privacy protection.
Meng et al (US 2019/0370577) teach a method and system for collecting video stream of a vehicle driver and use the visual data to perform image recognition to allow for the vehicle to engage for operation.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661
