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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on June 21, 2024, November 21, 2024 and February 28, 2025 are in compliance with 37 CFR 1.97 and 1.98 and therefore have been considered by the examiner and placed in the file.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) 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 BRI of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
In the following, some of the terms in the claims have been given BRIs in light of the specification. These BRIs are used for purposes of searching for prior art and examining the claims, but cannot be incorporated into the claims. Should Applicant believe that different interpretations are appropriate, Applicant should point to the portions of the specification that clearly support a different interpretation.
The BRI 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), 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):
(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.
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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), 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), 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 first converting unit in claim 15;
a signal processing unit in claim 15;
an image processing unit in claim 15;
an image recognizing unit in claim 15; and
a hazard detection software unit in claim 15.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The BRI for these limitations, based on para. [00119] and Figs. 1, 2 and 10, is that they mean logic of a processor configured to execute instructions stored in a memory device to perform the claimed functions, and equivalents thereof.
Claim Rejections - 35 USC § 112
Claim 10 is 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 10 recites that the analog signal-receiving device of claim 1 is a “block box module”. The present specification mentions “block box module” in multiple locations and states that the analog signal-receiving device can be a block box module, but does not explain what a block box module is or what it does. It is not a term that would be readily understood to those of ordinary skill in the art. For this reason, claim 10 is indefinite.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8, 9, 11-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. No. 11,308,641 B1 to Porta et al. (hereinafter referred to as “Porta”) in view of U.S. Pat. No. 6,975,324 B1 to Valmiki et al. (hereinafter referred to as “Valmiki”).
Regarding claim 1, Porta discloses a method, performed by an electronic device installed in a vehicle (Fig. 1, duplicated below, apparatus 100 constitutes the electronic device; Fig. 2 shows the vehicle that the apparatus 100 is incorporated into; Col. 3, line 40-Col. 4, line 17 discuss incorporation of the apparatus 100 into a vehicle: “In the distributed system embodiment of the apparatus 100, each component may be implemented separately throughout an installation location (e.g., such as a vehicle)”), of outputting a signal of a synthesized image (the BRI for synthesized image, based on Fig. 7 and para. [00102] of the present disclosure, is that it means that the captured image of the target object is modified to include a computer graphic, which in Fig. 7 is shown to be a box 720 that bounds, or marks, the target object; Fig. 1 of Porta, Col. 14, lines 1-16, discloses that the VOUT signals output from the processors 106 to the displays 118 are signals of a synthesized image that includes the view captured by the cameras and a computer graphic such as “highlight detected objects” and/or “distances to detected objects” and/or “provide bounding boxes for detected objects”; Porta does not explicitly disclose that the synthesized image that is output to the displays 118 is an analog signal), the method comprising:
receiving an analog signal for an original image captured using a target camera mounted on a vehicle (Fig. 1, Col. 8, lines 10-27 of Porta disclose that the sensors 140 of the capture devices 102 can be CMOS sensors, which are known in the art to have elements that perform photoelectric conversion of light to produce analog signals representing the original image that are converted by analog-to-digital converters (ADCs) of the CMOS sensors into digital signals; the ADCs of the sensors 140 perform the task of receiving an analog signal for an original image captured using a target camera mounted on a vehicle);
converting the analog signal for the original image into a digital signal based on a resolution of the original image (as indicated above, Col. 8, lines 10-27, discloses that the sensors 140 of the capture devices 102 can be CMOS sensors having elements that perform photoelectric conversion of light to produce analog signals representing the original image that are then converted by ADCs of the sensors into digital signals; the ADCs of the CMOS sensors 140 perform the task of converting the analog signals representing the original image into digital signals comprising a bitstream that is based on the resolution of the original image captured by the elements of the sensors 140);
generating a digital image based on the digital signal (Fig. 1, Col. 8, lines 10-27, these digital bit streams generated by the sensors 140 are transformed by logic 142 of the sensors 140 into video frames, which constitutes generating a digital image based on the digital signal);
determining a target object in the digital image using one or more image recognition models (Col. 8, lines 38-44, the processors 106 include convolutional neural networks (CNNs) 150 that use deep learning and computer vision techniques to perform image recognition in order to detect target objects in the images: “[t]he CNN module 150 may be configured to implement convolutional neural network capabilities. The CNN module 150 may be configured to implement computer vision using deep learning techniques. The CNN module 150 may be configured to implement pattern and/or image recognition using a training process through multiple layers of feature-detection”; Col. 8, line 61-Col. 9, line 11, discloses the CNNs 150 determining target objects in the digital images using the CNN recognition models: “[t]he CNN module 150 may determine a likelihood that pixels in the video frames belong to a particular object and/or objects in response to the descriptors. For example, using the descriptors, the CNN module 150 may determine a likelihood that pixels correspond to a particular object (e.g., a person, a vehicle, a car seat, a tree, etc.) and/or characteristics of the object (e.g., a mouth of a person, a hand of a person, headlights of a vehicle, a branch of a tree, a seatbelt of a seat, etc.).”);
generating a synthesized image by synthesizing a computer graphic for the target object with the digital image (as indicated above, Fig. 1 of Porta, Col. 14, lines 1-16, discloses that the VOUT signals output from the processors 106 to the displays 118 are signals comprising a synthesized image that includes the view captured by the cameras and a computer graphic such as a graphic to “highlight detected objects” and/or indicate “distances to detected objects” and/or provide “bounding boxes for detected objects”, etc.; therefore, the processors 106 generate a synthesized image by synthesizing a computer graphic for the target object with the digital image);
converting the synthesized image into an analog signal (Porta does not explicitly disclose converting the synthesized image into a analog signal); and
transmitting the analog signal for the synthesized image to an analog signal-receiving device installed in the vehicle (Col. 11, lines 18-32, discloses that the decision making modules 158 of the processors 106 transmit the VOUT signals comprising the synthesized digital image to the display devices 118, which constitutes transmitting the signal for the synthesized image to a signal-receiving device installed in the vehicle; Porta does not explicitly disclose that the transmitted signal is an analog signal).
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As indicated above, Porta does not explicitly disclose converting the synthesized image into an analog signal and transmitting the analog signal comprising the synthesized image to the displays 118. The BRI for these claim limitations, based on Fig. 2 and para. [0064] of the present disclosure, is that the analog signal can be, for example, a CVBS or TVI signal that is transmitted to a device that receives the analog signal.
Valmiki, in the same field of endeavor, discloses converting a digital image signal into a CVBS signal (Col. 65, lines 33-46). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the processors 106 of Porta to include circuitry configured to convert the synthesized image into a CVBS signal as taught by Valmiki and to use at least one display device capable of receiving the CVBS signal, such as circuitry of a display device that receives and processes the signal to cause it to be displayed. One of ordinary skill in the art would have been motivated to make the modification to improve the versatility of the distributed camera system of Porta to display signals in various formats, including those formatted as CVBS signals. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (modifying the software and/or hardware of the processors 106 and displays 118 to be able to generate and display CVBS signals, respectively).
Regarding claim 2, Porta discloses that generating of the digital image based on the digital signal comprises generating the digital image having a raw data or YUV data format based on the digital signal (Col. 8, lines 10-21: “[t]he sensor 140a (e.g., a camera imaging sensor such as a CMOS sensor) of the capture device 102a may receive light from the lens 112a (e.g., the signal IM_A). The camera sensor 140a may perform a photoelectric conversion of the light from the lens 112a. The logic 142a may transform the bitstream into a human-legible content (e.g., video data and/or video frames). For example, the logic 142a may receive pure (e.g., raw) data from the camera sensor 140a and generate video data based on the raw data (e.g., the bitstream)”).
Regarding claim 3, Porta discloses that the determining of the target object in the digital image using the one or more image recognition models comprises:
generating first processing data for a deep learning image recognition scheme and second processing data for a computer vision image recognition scheme, respectively, based on the digital image (the first processing data is the data generated by the processors 106 for processing by the CNNs 150 of the processors 106 and the second processing data is the data generated by the processors 106 for processing by the sensor fusion (SF) modules 152 of the processors 106; Col. 8, lines 38-44, discloses that the CNNs perform deep learning image recognition: “[t]he CNN module 150 may be configured to implement computer vision using deep learning techniques. The CNN module 150 may be configured to implement pattern and/or image recognition using a training process through multiple layers of feature-detection”; Col. 11, lines 40-67 and Col. 15, 37-55, the SF modules 152 constitute computer vision image recognition models because they aggregate the data from the computer vision portions 162 of the video processing pipelines 156, which perform image recognition through computer vision techniques, and from the CNNs 150 to perform computer vision image recognition: “[t]he sensor fusion module 152 may aggregate data from the sensors 114, the CNN module 150 and/or the video pipeline 156 to build a model and/or abstraction of the environment around the ego vehicle 50”);
generating deep learning recognition data for the first processing data using a deep learning image recognition model (Col. 8, lines 38-44, as indicated above, the CNNs 150 generate deep learning object recognition data);
generating computer vision recognition data for the second processing data using a computer vision image recognition model (Col. 15, lines 37-55; as indicated above, the SF modules 152 generate computer vision recognition data: “[t]he sensor fusion module 152 may aggregate data from the sensors 114, the CNN module 150 and/or the video pipeline 156 to build a model and/or abstraction of the environment around the ego vehicle 50. The computer vision operations may enable the processors 106a-106n to understand the environment, a state of objects, relative positions of objects and/or a meaning of objects to derive inferences (e.g., detect that the state of a streetlight is red, detect that a street sign indicates the ego vehicle 50 should stop, understand that a pedestrian is walking across the street from right to left, understand that brake lights of a vehicle ahead indicate that the vehicle is slowing down, etc.). The sensor fusion module 152 may enable a comparison and/or cross-reference of the data received from the vehicle sensors 114 at a particular time to the video data captured at another particular time in order to adjust a confidence level of an inference”); and
determining the target object in the digital image based on the deep learning recognition data and the computer vision recognition data (as indicated above, the SF module 152 uses the recognition data generated by the CNNs 150 and the recognition data obtained by fusing the data generated by the computer vision tasks performed in the computer vision portion 162 of the pipeline 156 to determine the target object and the confidence level with which target objects are recognized).
Regarding claim 4, Porta discloses that the generating of the first processing data and the second processing data, respectively, based on the digital image comprises generating the first processing data and the second processing data, respectively, by applying at least one type of processing among color format conversion, filtering, noise reduction, cropping, or scaling, to the digital image (Col. 10, lines 38-50, discloses that the video processing pipelines 156 of the processors 106 can apply different types of processing including color format conversion, filtering and noise reduction to the digital images generated by the logic 142 of the capture devices 102: “[t]he video pipeline module 156 may be further configured to support or provide a sensor RGB to YUV raw image pipeline to improve image quality, perform bad pixel detection and correction, demosaicing, white balance, color and tone correction, gamma correction, adjustment of hue, saturation, brightness and contrast adjustment, chrominance and luminance noise filtering”).
Regarding claim 5, Porta discloses that the generating of the deep learning recognition data for the first processing data using the deep learning image recognition model comprises:
determining target object information for the first processing data using the deep learning image recognition model (as indicated above in the rejections of claims 1 and 3, the CNNs 150 of the processors 106 are deep learning image recognition models that determine target object information; Col. 8, lines 38-44, the processors 106 include a convolutional neural networks (CNNs) 150 that use deep learning and computer vision techniques to perform image recognition in order to detect target objects in the images: “[t]he CNN module 150 may be configured to implement convolutional neural network capabilities. The CNN module 150 may be configured to implement computer vision using deep learning techniques. The CNN module 150 may be configured to implement pattern and/or image recognition using a training process through multiple layers of feature-detection”; Col. 8, line 61-Col. 9, line 11, discloses the CNNs 150 determining target objects in the digital images using the CNN recognition models: “[t]he CNN module 150 may determine a likelihood that pixels in the video frames belong to a particular object and/or objects in response to the descriptors. For example, using the descriptors, the CNN module 150 may determine a likelihood that pixels correspond to a particular object (e.g., a person, a vehicle, a car seat, a tree, etc.) and/or characteristics of the object (e.g., a mouth of a person, a hand of a person, headlights of a vehicle, a branch of a tree, a seatbelt of a seat, etc.).”); and
generating the deep learning recognition data by determining whether to detect the target object in the digital image corresponding to a current frame, based on previous object information for previous frames within a predetermined frame range and the target object information (the BRI for this limitation, based on paras. [0022] and [0092]-[0096] of the present disclosure, is that it means that when a detection result is generated for an object in a current frame, a determination is made as to whether the target object has been detected with sufficient confidence in the current frame based on the detection results for the current and one or more previous frames. In Porta, the CNN 150 performs object recognition to obtain detection results from at least one earlier-in-time frame and the current frame and determines whether the target object has been detected in the current frame based on the level of the confidence that the extracted features of the object from an earlier frame and the extracted feature of the object in the current frame match stored features for the target object. If so, the width of the stored target object is used to determine the distance and velocity of the target object from the ego vehicle (Col. 32, lines 25-Col. 33, line 41)).
Regarding claim 6, Porta discloses that the target object information comprises at least one of a type, coordinates, a shape, or a score indicating recognition accuracy of the target object (Col. 17, lines 23-49: “[t]he processors 106a-106n may determine a type of the detected objects based on a classification. The classification may be based on information extracted from the video data and/or information from the sensors 114 (e.g., environmental factors)… The processors 106a-106n may rule out and/or increase a likelihood of certain types of objects. For example, the classification may comprise a confidence level for a particular hypothesis (or diagnosis) about the condition (e.g., capability) of the detected objects. When the confidence level is above a pre-determined threshold value, the classification may be considered to be confirmed by the processors 106a-106n. A high confidence level for a particular type of object may indicate that evidence is consistent with the particular type of object. A low confidence level for a particular type of object may indicate that evidence is inconsistent with the particular type of object and/or not enough evidence is available yet. Various checks may be performed to determine the confidence level. The implementation of the classification and/or confidence level to determine the type of object may be varied based on the design criteria of a particular implementation”).
Regarding claim 8, Porta discloses that the generating of the computer vision recognition data for the second processing data using the computer vision image recognition model comprises generating the computer vision recognition data comprising calibration information based on a camera parameter for the camera for the second processing data using the computer vision image recognition model (Col. 38, line 63-Col. 39, line 25 discloses that the processors 106 generate calibration information that is output to the cameras 102 and that is based on the objects recognized by the computer vision image recognition models 152).
Regarding claim 9, Porta discloses that the generating of the synthesized image by synthesizing the computer graphic for the target object with the digital image comprises:
generating, as the computer graphic, a marker image layer displaying a location of the target object based on target object information comprised in the deep learning recognition data and calibration information comprised in the computer recognition data (the BRI for the term “marker image layer”, based on para. [00103] and Fig. 7 of the present disclosure, is that it means some type of computer graphic that is displayed over or on the captured image of the recognized object; as indicated above in the rejection of claim 1, Col. 14, lines -16 of Porta discloses generating various types of computer graphics, such as “highlight detected objects”, “distances to detected objects”, “bounding boxes for detected objects”, etc. , that are displayed together with the images captured by the cameras 102 as synthesized images; the markers of Porta such as the bounding boxes around objects that are recognized and the distances to them that are determined via the deep learning CNNs 150 provide the locations of the target objects that are displayed along with the images captured by the cameras 102; the displayed marker image layer that is displayed with the image is also based on calibration information because, as indicated above in the rejection of claim 8, the outputs of the processors 106 can include calibration information based on recognized objects that causes the cameras 102 to be calibrated); and
generating the synthesized image by synthesizing the digital image with the computer graphic (as indicated above in the rejection of claim 1, the processors 106 of Porta generate the synthesized images by synthesizing the digital images with the computer graphics before causing the synthesized images to be displayed on display devices 118).
Regarding claim 11, Porta discloses receiving driving information of the vehicle and determining whether there is a hazardous element in a current state of the vehicle based on driving information of the vehicle and the deep learning recognition data (Col. 9, line 62-Col. 10, line 6, Fig. 1 shows the CNN 150 being used together with a driving policy (DP) module that receives driving information and determines by using deep learning and computer vision techniques whether there is a hazardous element based on the driving information: “[t]he driving policy module 154 may be configured to enable human-like intuition. The driving policy module 154 may allow the vehicle to share the road with human drivers. For example, sensing, mapping, and powerful computer vision may provide a model of the environment and/or reaction time of a vehicle to be better than that of a human driver....”; Col. 25, lines 5-16, discloses that the processors 106 can cause warning icons to be displayed on the displays 118 and that the processors 106 can determine whether a “collision threat” exists based on objects recognized by the CNNs 150 and/or by the SF model 152 and cause an appropriate action to be taken and/or provide appropriate warnings).
Regarding claim 12, Porta discloses that the receiving of the driving information of the vehicle comprises at least one of: receiving operation information of the vehicle from a driving information relay module; or receiving location information of the vehicle from a global positioning system (GPS) module (the SF model 152 processes the information obtained by the cameras 102 and other sensors 112 of the vehicles and processes it in the processors 106, which constitutes receiving operation information of the vehicle from a driving information relay module; the cameras 102 and the other sensors together constitute the driving information relay module).
Regarding claim 13, Porta discloses outputting a hazard alert, such as a warning icon or audible warning when there is the hazardous element, namely, a detected collision alert (Col. 34, lines 14-41).
Regarding claim 14, Porta discloses a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1 (Col. 34, line 65-Col. 35, line 20; Fig. 1, memory 108 stores instructions for execution by the processor 106 to cause the processor 106 to perform the operations described above in the rejection of claim 1).
Regarding claim 15, the rejection of claim 1 applies mutatis mutandis to claim 15. As indicated above, the BRIs for the terms recited in claim 1 are that each of these terms corresponds to logic of a processor configured to perform the recited functions by executing software stored in memory. As indicated above in the rejection of claim 14, Porta discloses that the processors 106 perform the functions discussed above by executing software stored in memory 108. Likewise, Valmiki discloses processors and memory for performing the functions discussed above in the rejection of claim 1 (Fig. 1 and Col. 5, lines 11-49)).
Regarding claim 16, the rejection of claim 3 applies mutatis mutandis to claim 16.
Regarding claim 17, the rejection of claim 5 applies mutatis mutandis to claim 17.
Regarding claim 18, the rejection of claim 6 applies mutatis mutandis to claim 18.
Regarding claim 20, the rejection of claim 9 applies mutatis mutandis to claim 20.
Allowable Subject Matter
Claims 7 and 19 are 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.
None of the prior art teaches or suggests the following steps recited in claims 7 and 19: generating of the deep learning recognition data by determining whether to detect the target object in the digital image comprises determining to detect the target object in the digital image when the score for the target object of the current frame is less than a predetermined first threshold and an average value of previous scores for the target object of the previous frames is greater than or equal to a second threshold.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Publ. Appl. No. 2023/0273033 A1 discloses systems and methods for measuring an inter-vehicle distance by acquiring a driving image photographed by a photographing device of a first vehicle which is being driven, detecting first feature points of a second vehicle region in a first frame corresponding to a frame in which the second frame is detected before a frame in which the second vehicle is not detected among a plurality of frames constituting the driving image, when the second vehicle is not detected from the driving image, detecting second feature points in a second frame corresponding to a current frame by tracking the detected first feature points, calculating a feature point change value between the first feature points and the second feature points, and calculating an inter-vehicle distance from the photographing device of the first vehicle to the second vehicle based on the calculated feature point change value. A forward collision warning is generated when the system detects a front vehicle located in front of the vehicle's driving route that is within a particular distance that informs the driver that there is a risk of collision.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL J SANTOS whose telephone number is (571)272-2867. The examiner can normally be reached M-F 9-5.
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, Matt Bella can be reached at (571)272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL JOSEPH SANTOS/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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