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 .
Status of Claims
Claims 1-20 are presented for examination.
Claims 1-20 are rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
On January 7, 2019, the USPTO released new examination guidelines for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if it does not fall within one of the four statutory categories of invention (step 1) or does not meet a test for determining that: the claim recites a judicial exception, e.g. an abstract idea (step 2A prong I), without integration into a practical application (step 2A prong II), and does not recite additional elements that provide significantly more than the recited judicial exception (step 2B).
Step 1: Applicant’s independent claims 1, 9, and 15 are directed toward a “One or more processors…, A system…, A method…” Therefore, it can be seen that it falls within one of the four statutory categories of invention.
With regard to step 2A prong I, does the claim recite a judicial exception, the guidelines provide three groupings of subject matter that are considered abstract ideas:
(a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
(c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Step 2A Prong I Analysis: Applicant’s independent claim 1, 9, and 15 recite the abstract idea, e.g., taking an example of claim 15 here, “…A method, comprising: using one or more neural networks to: for a pixel of an image, calculate probabilities that the pixel belongs to any class of a plurality of different classes; calculate a confidence value that the pixel belongs to a particular class of the plurality of different classes based, at least in part on, one or more differences among two or more calculated probabilities for the pixel; and generate a segmentation of the image that indicates the confidence value of the pixel..…” which comprises “Mental Processes”. For example, a person can generate multiple incidents in terms of driving behaviors of the vehicles or drivers in relation to locations of incidents that they have stored in their memory. They can also generate these incidents in their minds in a zone or area that they have predetermined, or previously thought about. Further, a person can generate profiles/scenarios, and reactions/solutions to incidents in terms of driving behaviors of the vehicles in their minds based on locations of incidents, and based on current information of the vehicle (i.e. a passenger observes the road conditions, incidents or traffic conditions on the road, and drive accordingly based on the previous experience on the road).
Applicant’s independent claims 1, 9, and 15 also recite the abstract idea “…generate a segmentation of the image that indicates the confidence value of the pixel…” which comprises “Mental Processes”. For example, a person can generate multiple incidents in terms of driving behaviors of the vehicles or drivers in relation to locations of incidents that they have stored in their memory.
Applicant’s independent claims 1, 9, and 15 also recite the abstract idea “…calculate probabilities…calculate a confidence value that the pixel belongs to a particular class of the plurality of different classes…”which comprises “Mental Processes”. For example, a person can generate multiple incidents in terms of driving behaviors of the vehicles or drivers in relation to locations of incidents that they have stored in their memory. They can also generate these incidents in their minds in a zone or area that they have predetermined, or previously thought about. Further, a person can generate profiles/scenarios, and reactions/solutions to incidents in terms of driving behaviors of the vehicles in their minds based on locations of incidents, and based on current information of the vehicle (i.e. a passenger observes the road conditions, incidents or traffic conditions on the road, and drive accordingly based on the previous experience on the road).
With regard to Step 2A Prong II, whether the abstract idea is integrated into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Step 2A prong II Analysis: claims 1-20 recite an additional limitation including “One or more processors…, A system…, A method…” which is an example of adding an insignificant extra-solution activity, in this case a pre-solution activity, to the judicial exception. Specifically, this is an example of mere data gathering.
claims 1-20 recite further additional limitations including: “…circuitry to use one or more neural networks…, one or more processors…”. As previously stated, because the written description fails to disclose the corresponding structure for these limitations to perform their functions, the examiner has interpreted these limitations as generic computers or processing devices. Thus, these limitations are examples of generic computer components and are viewed as nothing more than attempts to generally link the use of the judicial exceptions to the technological environment of generic computers.
With regard to Step 2B, under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field.
Step 2B Analysis: Applicant’s claims 1-20 do not recite additional elements that provide significantly more than the recited judicial exception. When considered individually or in combination, the additional limitations of claims 1-20 do not amount to significantly more than the judicial exception for the same reasons discussed above as to why the additional limitations do not integrate the abstract idea into a practical application.
Test for patentability conclusion: Thus, since claims 1-20 are reciting an abstract idea (step 2A prong I), not integrated into a practical application (step 2A prong II), and does not comprise significantly more than the recited abstract idea (step 2B), it is directed toward non-statutory subject matter and is rejected under 35 U.S.C. 101.
Dependent claims
Step 1: claims 2-8, 10-14, 16-20 are dependent of claims 1, 9, 15, which are “circuitry to use one or more neural networks…, one or more processors…”, (Therefore, it can be seen that it falls within one of the four statutory categories of invention)(Step 1: yes)).
Step 2A Prong One: claims 2-8, 10-14, 16-20 recite the limitation of “…comparing the two or more probabilities calculated for the pixel…, a comparison of a likelihood that one or more objects belong to at least two different semantic classes…, generate a visualization map comprising the segmentation of the image and the confidence value of the pixel…” steps; in (claims 2-8, 10-14, 16-20). These claims recite an abstract idea which is directed to mental process.
Step 2A Prong Two: This judicial exception is not integrated into a practical application; the claims do not include any additional elements that integrate the abstract idea into a practical application. The “generate a segmentation of the image that indicates the confidence value of the pixel…calculate the confidence value for the pixel belonging to the particular class based, at least in part, on a highest probability value calculated for the pixel and a second highest probability value calculated for the pixel.” steps are recited at a high-level of generality (i.e., as a generic outputting/data gathering means/display means) and amount to mere solution activities to apply the recited abstract idea(s) and/or solution activities in the field of navigation.
Step 2B: The claims 2-8, 10-14, 16-20 do not include any additional elements that are sufficient to amount to significantly more than the judicial exception for similar reasons as that discussed in Step 2A Prong Two.
As such, 1-20 are rejected under 35 USC 101 as being drawn to an abstract idea without significantly more, and thus are ineligible.
Claim Rejections - 35 USC § 102
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)(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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nowicka et al. (US Pub. No.: 2023/0023434 A1: hereinafter “Nowicka”).
Consider claims 1, 9, and 15:
Nowicka teaches one or more processors, a system, and a method (Fig. 1B element 84, the processor) comprising: using one or more neural networks (See Nowicka, e.g., ¶ [0060], Fig. 1B elements 52-130, 84, 182, Fig. 7 element 700, the CNN, Fig. 12) to: for a pixel of an image, calculate probabilities that the pixel belongs to any class of a plurality of different classes (See Nowicka, e.g., “…the system performed object detection and/or classification for each identified region of interest using a trained CNN…the image stream is provided to a MVA…If the MVA detects a desired object (e.g., boat), then the image or a portion of the image including the detected object (e.g., a region of interest) and associated object detection information are input to the trained CNN to classify each object. The trained CNN may output an object classification along with a confidence value indicating a likelihood that the desired object has been detected. If a desired object is identified with a confidence value that exceeds a threshold, then a notification may be transmitted to the vessel…” of Abstract, ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312, when the sailing vessel in FIG. 11A (or other classified object of the different object classifications) has the confidence value above the threshold in paragraph [0108], so that it is displayed); calculate a confidence value that the pixel belongs to a particular class of the plurality of different classes (See Nowicka, e.g., “…output an object classification along with a confidence value indicating a likelihood that the desired object has been detected…” of ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312) based, at least in part on, one or more differences among two or more calculated probabilities for the pixel (See Nowicka, e.g., “…The trained CNN may output an object classification along with a confidence value indicating a likelihood that the desired object has been detected. If a desired object is identified with a confidence value that exceeds a threshold, then a notification may be transmitted to the vessel…” of ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312); and generate a segmentation of the image that indicates the confidence value of the pixel (See Nowicka, e.g., “…an MVA component on a vessel to identify objects appearing in captured images. In step 602, the MVA receives a stream of 360-degree images (such as image 612) captured over time by one or more image capture devices…the MVA detects outliers in the image frame, indicating a region of interest for further object analysis…an object is detected in the water region 616 by detecting image pixels having properties… The bounds of the potential objects are then identified above and below the horizon and bounding boxes 620 are defined for each object. Next, in step 608, identified objects are tracked across frames in the 360-degree image stream…” of ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312).
Consider claims 2, 10, and 16:
Nowicka teaches everything claimed as implemented above in the rejection of claims 1, 9, and 15. In addition, Nowicka teaches further comprising using the one or more neural networks to calculate the confidence value based, at least in part, on comparing the two or more probabilities calculated for the pixel (See Nowicka, e.g., “…The trained CNN may output an object classification along with a confidence value indicating a likelihood that the desired object has been detected. If a desired object is identified with a confidence value that exceeds a threshold, then a notification may be transmitted to the vessel…” of ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312).
Consider claims 3, 11, and 17:
Nowicka teaches everything claimed as implemented above in the rejection of claims 1, 9, and 15. In addition, Nowicka teaches further comprising using the one or more neural networks to calculate the confidence value for the pixel belonging to the particular class (See Nowicka, e.g., “…The trained CNN may output an object classification along with a confidence value indicating a likelihood that the desired object has been detected. If a desired object is identified with a confidence value that exceeds a threshold, then a notification may be transmitted to the vessel…” of ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312) based, at least in part, on a highest probability value calculated for the pixel and a second highest probability value calculated for the pixel (See Nowicka, e.g., “…the system performed object detection and/or classification for each identified region of interest using a trained CNN…the image stream is provided to a MVA…If the MVA detects a desired object (e.g., boat), then the image or a portion of the image including the detected object (e.g., a region of interest) and associated object detection information are input to the trained CNN to classify each object. The trained CNN may output an object classification along with a confidence value indicating a likelihood that the desired object has been detected. If a desired object is identified with a confidence value that exceeds a threshold, then a notification may be transmitted to the vessel…” of ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312, when the sailing vessel in FIG. 11A (or other classified object of the different object classifications) has the confidence value above the threshold in paragraph [0108], so that it is displayed).
Consider claims 4, 12, and 18:
Nowicka teaches everything claimed as implemented above in the rejection of claims 1, 9, and 15. In addition, Nowicka teaches further comprising using the one or more neural networks to calculate the confidence value for the pixel belonging to the particular class (See Nowicka, e.g., “…The trained CNN may output an object classification along with a confidence value indicating a likelihood that the desired object has been detected. If a desired object is identified with a confidence value that exceeds a threshold, then a notification may be transmitted to the vessel…” of ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312) based, at least in part, on a comparison of a likelihood that one or more objects belong to at least two different semantic classes (See Nowicka, e.g., “…the system performed object detection and/or classification for each identified region of interest using a trained CNN…the image stream is provided to a MVA…If the MVA detects a desired object (e.g., boat), then the image or a portion of the image including the detected object (e.g., a region of interest) and associated object detection information are input to the trained CNN to classify each object. The trained CNN may output an object classification along with a confidence value indicating a likelihood that the desired object has been detected. If a desired object is identified with a confidence value that exceeds a threshold, then a notification may be transmitted to the vessel…” of ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312, when the sailing vessel in FIG. 11A (or other classified object of the different object classifications) has the confidence value above the threshold in paragraph [0108], so that it is displayed).
Consider claims 6, 13, and 19:
Nowicka teaches everything claimed as implemented above in the rejection of claims 1, 9, and 15. In addition, Nowicka teaches wherein the calculated confidence value indicates predication accuracy of the one or more neural networks performing one or more classification tasks (See Nowicka, e.g., “…the system performed object detection and/or classification for each identified region of interest using a trained CNN…the image stream is provided to a MVA…If the MVA detects a desired object (e.g., boat), then the image or a portion of the image including the detected object (e.g., a region of interest) and associated object detection information are input to the trained CNN to classify each object. The trained CNN may output an object classification along with a confidence value indicating a likelihood that the desired object has been detected. If a desired object is identified with a confidence value that exceeds a threshold, then a notification may be transmitted to the vessel…” of ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312, when the sailing vessel in FIG. 11A (or other classified object of the different object classifications) has the confidence value above the threshold in paragraph [0108], so that it is displayed).
Consider claims 5, 7, 14, and 20:
Nowicka teaches everything claimed as implemented above in the rejection of claims 1, 9, and 15. In addition, Nowicka teaches further comprising using the one or more neural networks to: generate a vector of scores for the pixel (See Nowicka, e.g., “…The trained CNN may output an object classification along with a confidence value indicating a likelihood that the desired object has been detected. If a desired object is identified with a confidence value that exceeds a threshold, then a notification may be transmitted to the vessel…” of ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312), wherein the vector of scores comprises a score for each different class for the pixel; and compare two or more scores from the vector of scores to calculate the confidence value (See Nowicka, e.g., “…the system performed object detection and/or classification for each identified region of interest using a trained CNN…the image stream is provided to a MVA…If the MVA detects a desired object (e.g., boat), then the image or a portion of the image including the detected object (e.g., a region of interest) and associated object detection information are input to the trained CNN to classify each object. The trained CNN may output an object classification along with a confidence value indicating a likelihood that the desired object has been detected. If a desired object is identified with a confidence value that exceeds a threshold, then a notification may be transmitted to the vessel…” of ¶ [0089]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312, when the sailing vessel in FIG. 11A (or other classified object of the different object classifications) has the confidence value above the threshold in paragraph [0108], so that it is displayed).
Consider claim 8:
Nowicka teaches everything claimed as implemented above in the rejection of claim 9. In addition, Nowicka teaches (See Nowicka, e.g., “…display a time series of various sensor information and/or other parameters as part of or overlaid on a graph or map, which may be referenced to a position and/or orientation of mobile structure 130, location and classification of detected objects, and/or other elements of system 100. For example, user interface 132 may be adapted to display a time series of positions, headings, and/or orientations of mobile structure 130 and/or other elements of system 100 overlaid on a geographical map, which may include one or more graphs indicating a corresponding time series of actuator control signals, sensor information, and/or other sensor and/or control signals…” of Abstract, ¶ [0066], ¶ [0087]-¶ [0100], ¶ [0106]-¶ [0111], and Figs. 1-2 elements 52-216, Figs. 7-1C elements 700-1160, and Fig. 13 steps 1300-1312, when the sailing vessel in FIG. 11A (or other classified object of the different object classifications) has the confidence value above the threshold in paragraph [0108], so that it is displayed).
Obviousness Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US Patent No. 12,430,402 B2. Although the claims at issue are not identical, they are not patentably distinct from each other, take an example of claims 1, 9, and 15 of the instant application and claims 1, 10, 18, 24, and 30 of the US Patent No. 12,466,430 B2 (Please see the Table below):
Claims of US Pat. No.: 12,430,402 B2 (hereinafter ‘402)
Claims of Pending Application No.: 19/316,955
Reasoning
1.One or more processors, comprising circuitry to use one or more neural networks to: identify one or more objects in one or more images based, at least in part, on segmentation of the one or more images; identify, based on the segmentation, one or more probabilities indicating that one or more pixels of the one or more objects is part of a corresponding object; generate one or more confidence values indicating predicted classifications to be associated with the one or more objects based, at least in part, on two or more probabilities generated for a same pixel; and in response to the one or more confidence values, cause one or more actions to be performed, wherein the one or more actions includes at least activating one or more steering systems of a vehicle.
10. A system, comprising one or more processors to use one or more neural networks to: identify one or more objects in one or more images based, at least in part, on segmentation of the one or more images; identify, based on the segmentation, one or more probabilities indicating that one or more pixels of the one or more objects is part of a corresponding object; generate one or more confidence values indicating predicted classifications to be associated with the one or more objects based, at least in part, on two or more probabilities generated for a same pixel; and in response to the one or more confidence values, cause one or more actions to be performed, wherein the one or more actions includes at least activating one or more steering systems of a vehicle.
18. A non-transitory machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to use one or more neural networks to: identify one or more objects in one or more images based, at least in part, on segmentation of the one or more images; identify, based on the segmentation, one or more probabilities indicating that one or more pixels of the one or more objects is part of a corresponding object; generate one or more confidence values indicating predicted classifications to be associated with the one or more objects based, at least in part, on two or more probabilities generated for a same pixel; and in response to the one or more confidence values, cause one or more actions to be performed, wherein the one or more actions include activating one or more medical warning systems.
24. One or more processors, comprising circuitry to train one or more neural networks to: identify one or more objects in one or more images based, at least in part, on segmentation of the one or more images; identify, based on the segmentation, one or more probabilities indicating that one or more pixels of the one or more objects is part of a corresponding object; generate one or more confidence values indicating predicted classifications to be associated with the one or more objects based, at least in part, on two or more probabilities generated for a same pixel; and in response to the one or more confidence values, cause one or more actions to be performed, wherein the one or more actions include activating one or more warning systems.
30. A system, comprising one or more processors to train one or more neural networks to: identify one or more objects in one or more images based, at least in part, on segmentation of the one or more images; identify, based on the segmentation, one or more probabilities indicating that one or more pixels of the one or more objects is part of a corresponding object; generate one or more confidence values indicating predicted classifications to be associated with the one or more objects based, at least in part, on two or more probabilities generated for a same pixel; and in response to the one or more confidence values, cause one or more actions to be performed, wherein the one or more actions include causing to at least activate one of: one or more fertilization systems; one or more watering systems; or one or more weeding systems.
1. One or more processors, comprising: circuitry to use one or more neural networks to: for a pixel of an image, calculate probabilities that the pixel belongs to any class of a plurality of different classes; calculate a confidence value that the pixel belongs to a particular class of the plurality of different classes based, at least in part on, one or more differences among two or more calculated probabilities for the pixel; and generate a segmentation of the image that indicates the confidence value of the pixel.
9. A system, comprising: one or more processors to use one or more neural networks to: for a pixel of an image, calculate probabilities that the pixel belongs to any class of a plurality of different classes; calculate a confidence value that the pixel belongs to a particular class of the plurality of different classes based, at least in part on, one or more differences among two or more calculated probabilities for the pixel; and generate a segmentation of the image that indicates the confidence value of the pixel.
15. A method, comprising: using one or more neural networks to: for a pixel of an image, calculate probabilities that the pixel belongs to any class of a plurality of different classes; calculate a confidence value that the pixel belongs to a particular class of the plurality of different classes based, at least in part on, one or more differences among two or more calculated probabilities for the pixel; and generate a segmentation of the image that indicates the confidence value of the pixel.
Claims 1, 10, 18, 24, and 30 of ‘402 only differ from the instant application, in that the claims 1, 10, 18, 24, and 30 of ‘402 specify “generate one or more confidence values indicating predicted classifications to be associated with the one or more objects based, at least in part, on two or more probabilities generated for a same pixel; and in response to the one or more confidence values, cause one or more actions to be performed, wherein the one or more actions includes at least activating one or more steering systems of a vehicle.”. Nonetheless, the removal of said limitations from claims 1, 9, and 15 of the instant application made claims 1, 9, and 15 a broader version of claims 1, 10, 18, 24, and 30 of ‘402. Therefore, since omission of an element and its function in combination is an obvious expedient if the remaining elements perform the same function as before (In re Karlson (CCPA) 136 USPQ 184 (1963)), claims 1, 9, and 15 are not patentably distinct from claims 1, 10, 18, 24, and 30 of '402.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Smolyanskiy et al. (US Pub. No.: 2022/0269271 A1) teaches “A method, computer readable medium, and system are disclosed for performing autonomous path navigation using deep neural networks. The method includes the steps of receiving image data at a deep neural network (DNN), determining, by the DNN, both an orientation of a vehicle with respect to a path and a lateral position of the vehicle with respect to the path, utilizing the image data, and controlling a location of the vehicle, utilizing the orientation of the vehicle with respect to the path and the lateral position of the vehicle with respect to the path.”
Kim et al. (US Pat. No.: 10,726,303 B1) teaches “A learning method for generating parameters capable of representing a degree of credibility of an object detection during a process of the object detection is provided. And the method includes steps of: (a) a learning device instructing a convolutional layer to generate a convolutional feature map by applying a convolutional operation to a training image; (b) the learning device instructing an anchor layer to generate an RPN confidence map including RPN confidence scores; (c) the learning device instructing an FC layer to generate CNN confidence scores, to thereby generate a CNN confidence map; and (d) the learning device instructing a loss layer to learn parameters in the CNN and the RPN by performing backpropagation using an RPN loss and a CNN loss, generated by referring to the RPN confidence map, the CNN confidence map, an estimated object detection result and a GT object detection result.”
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/BABAR SARWAR/Primary Examiner, Art Unit 3667
