DETAILED ACTION
Response to Election of Species Requirement
Applicant’s response dated 4/27/26 has been accepted and entered. In response to an Election requirement, applicant elected, without traverse, Group I comprising claims 1-7 and 15-20. Accordingly, claims 8-14 have been withdrawn from consideration.
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 therefor, subject to the conditions and requirements of this title.
Claims 1-7 and 15-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Revised Guidance Step 2A – Prong 1
Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Here, with respect to independent claims 1, 15 and 18, the claims recite the abstract idea of
obtaining a drivable area model that is configured to detect a drivable area within a road image; and
generating a generic obstacle detection model from the drivable area model, wherein the generic obstacle detection model is configured to detect a plurality of objects on a road surface of the drivable area in the road image
These limitations fall within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, specifically, a mental process, that can be performed in the human mind since each of the above steps could alternatively be performed in the human mind or with the aid of pen and paper. This conclusion follows from CyberSource Corp. v. Retail Decisions, Inc., where our reviewing court held that section 101 did not embrace a process defined simply as using a computer to perform a series of mental steps that people, aware of each step, can and regularly do perform in their heads. 654 F.3d 1366, 1373 (Fed. Cir. 2011); see also In re Grams, 888 F.2d 835, 840–41 (Fed. Cir. 1989); In re Meyer, 688 F.2d 789, 794–95 (CCPA 1982); Elec. Power Group, LLC v. Alstom S.A., 830 F. 3d 1350, 1354–1354 (Fed. Cir. 2016) (“we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category”).
For example, a human could perform the above steps entirely mentally, for example using a mental drivable area and generic obstacle detection model and mentally detecting a drivable area in a road image as well as mentally detecting objects on a road surface of a drivable area in the road image.
Furthermore, mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper. See CyberSource, 654 F.3d at 1375 (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson.”).
Revised Guidance Step 2A – Prong 2
Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which the claim is directed does not include limitations that integrate the abstract idea into a practical application, since the recited features of the abstract idea are being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See, e.g., MPEP §2106.05(f)). This follows conclusion follows from the claim limitations which only broadly recite a generic vehicle, processor, memory (claim 1), outside of the abstract idea in claim 1. Similarly claim 14 additionally recites sensors and a control system in the preamble, which are not further recited in the body of the claim.
In addition, merely “[u]sing a computer to accelerate an ineligible mental process does not make that process patent-eligible.” Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Canada (U.S.), 687 F.3d 1266, 1279 (Fed. Cir. 2012); see also CLS Bank Int’l v. Alice Corp. Pty. Ltd., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) (“simply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.”), aff’d, 573 U.S. 208 (2014). Accordingly, the additional element of a controller does not transform the abstract idea into a practical application of the abstract idea.
In addition, the limitation “obtaining a drivable area model that is configured to detect a drivable area within a road image” constitutes insignificant pre-solution activity that merely gathers data and, therefore, do not integrate the exception into a practical application. See In re Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en banc), aff’d on other grounds, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity); see also CyberSource, 654 F.3d at 1371–72 (noting that even if some physical steps are required to obtain information from a database (e.g., entering a query via a keyboard, clicking a mouse), such data-gathering steps cannot alone confer patentability); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Accord Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05(g)).
Furthermore, the limitation “distributing the generic obstacle detection model to a first vehicle, wherein the first vehicle is configured to autonomously drive the first vehicle using at least the generic obstacle detection model” is insignificant post-solution activity. The Supreme Court guides that the “prohibition against patenting abstract ideas ‘cannot be circumvented by attempting to limit the use of the formula to a particular technological environment’ or [by] adding ‘insignificant postsolution activity.’” Bilski, 561 U.S. at 610–11 (quoting Diehr, 450 U.S. at 191–92). See also MPEP 2106.05(d) II “courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”. In addition, the limitation “the first vehicle is configured to autonomously drive the first vehicle using at least the generic obstacle detection model” is an intended use limitation.
Revised Guidance Step 2B
Under the 2019 PEG step 2B analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the additional elements, such as “computer system”, “processors”, “memory” and “non-transitory computer readable medium” do not amount to an innovative concept since, as stated above in the step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming (See, e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved. See, e.g., MPEP §2106.05 I.A; Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). Thus, these elements, taken individually or together, do not amount to “significantly more” than the abstract ideas themselves.
The additional elements of the dependent claims merely refine and further limit the abstract idea of the independent claims and do not add any feature that is an “inventive concept” which cures the deficiencies of their respective parent claim under the 2019 PEG analysis. None of the dependent claims considered individually, including their respective limitations, include an “inventive concept” of some additional element or combination of elements sufficient to ensure that the claims in practice amount to something “significantly more” than patent-ineligible subject matter to which the claims are directed.
The elements of the instant process steps when taken in combination do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself because the claims do not effect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of an electronic device itself which implements the abstract idea (e.g., the general purpose computer and/or the computer system which implements the process are not made more efficient or technologically improved); the claims do not perform a transformation or reduction of a particular article to a different state or thing (i.e., the claims do not use the abstract idea in the claimed process to bring about a physical change. See, e.g., Diamond v. Diehr, 450 U.S. 175 (1981), where a physical change, and thus patentability, was imparted by the claimed process; contrast, Parker v. Flook, 437 U.S. 584 (1978), where a physical change, and thus patentability, was not imparted by the claimed process); and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment (e.g., “the first vehicle is configured to autonomously drive the first vehicle using at least the generic obstacle detection model” claim 1).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2-4 and 16-17 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.
With respect to claims 2, 4 and 17, the limitations “the drivable area model is a binary segmentation model . . . drivable area . . . one or more background areas complementary to the drivable area” (claim 2) and “drivable area . . . one or more background areas . . . generic obstacle . . . objects on a road surface . . . mutually exclusive categories” (claim 4) is indefinite in view of the specification. The specification indicates the generic obstacle detection pertains to a generic obstacle segmented area (¶ 112). The Spec. further indicates “one or more background areas 814 include remaining areas in the first road image 810 that are not part of the drivable area 812”. Spec ¶ 103. Accordingly, under a BRI, the metes and bounds of what is and is not required in the terms “binary segmentation model”, “background area” and “mutually exclusive”, i.e., how can a binary model produce three mutually exclusive areas? How can everything not segmented as a road be considered background if there is a generic obstacle area? One possible solution to improve clarity would be to remove “binary” and “mutually exclusive” from the claim set.
With respect to claims 3 and 16, the limitation “generating the generic obstacle detection model from the drivable area model includes adding an extra model output to the drivable area model” is unclear and indefinite in view of the remaining claim language, drawings and specification. It is unclear what is meant by “adding an extra model output”, i.e., is the obstacle detection model an output of the DAM? Is it a part of the DAM such that both combined are one model? How is an “extra model” added, i.e., what is the technical process for adding an extra model to a currently existing model? It is recommended to clarify in the claim language more precisely what is occurring to improve clarity.
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 1-5, 15-17 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 20220057806 to Guo et al. (Guo) in view of US 20190187720 to Fowe et al. (Fowe)
With respect to claims 1, 15 and 18, Guo discloses a method for obstacle detection, comprising:
at a computer system including one or more processors and memory:
obtaining a drivable area model that is configured to detect a drivable area within a road image;
(FIG. 5, 510 generate segmentation map, i.e., FIG. 6 including detection of drivable area 630 within road image 600; Fig. 3 depicting model overview in one example; ¶¶ 28 neural network model semantic segmentation . . . segmentation map 270; 49 “road area 630”)
generating a generic obstacle1 detection model from the drivable area model, wherein the generic obstacle detection model is configured to detect a plurality of objects on a road surface of the drivable area in the road image; and
(520, FIG. 5 detecting an unknown object by optical model using depth map and segmentation map; ¶¶ 17, 28-30, 39-42, 46, 50, claims 1 and 13; ¶¶ 31, 32 network architecture 300 may also perform independent obstacle detection; 66 detection of various objects in environment, signs, lights, lane lines, curbs; 25 detection module 220 . . . detect vehicles; 18 classify unknown object; 38 38 “network architecture 300 may use both the segmentation map 360 and the depth map 380 in grayscale and images from other views to determine an object type . . . red pixels in the segmentation map 360 may represent objects in the vehicle category”)
autonomously driving a vehicle using at least the generic obstacle detection model
(¶¶ 5 “An automated driving system may adapt a motion plan or maneuver according to the detected obstacle”; 6 “detect that the unknown object is an obstacle when the unknown object satisfies criteria . . . adapt a vehicle plan of the automated driving according to the height”)
(¶¶ 48 “the vehicle 100 may subsequently maneuver, adapt a motion plan, adapt a vehicle plan, or the like during automated driving according to the height and distance to the obstacle. Furthermore, the detection system 170 may register the obstacle's location, the object parameters 295”; 50 by estimating the height and the distance to the unknown object 650. The vehicle 100 may safely maneuver, adapt a vehicle plan, or adapt a motion plan during automated driving using the characteristics or features of the obstacle” 51 when the unknown object 650 is detected as an obstacle, the operator may takeover or disengage the automated driving according to a perceived collision or danger; 78; claim 1 “detect that the unknown object is an obstacle . . . determine a height of the obstacle and a distance to the obstacle according to the optical model and the criteria . . . adapt a vehicle plan of the automated driving according to the height”).
Guo fails to explicitly disclose the generic obstacle detection model is distributed to the vehicle that uses it for autonomous driving although Guo at least suggests this arrangement since Guo discloses detection system 170 can be onboard the vehicle and/or remotely located (170, 220, FIG. 1-2; ¶ 22 in one approach, functionality associated with at least one module of the detection system 170 is implemented within the vehicle 100 while further functionality is implemented within a server, an edge server, a cloud-based computing system, or the like).
Fowe, from the same field of endeavor, also discloses detection of objects, including unknown object (¶¶ 28-30, 34, 43, 46) using a remote computing platform that generates, trains and updates models (FIG. 1, training database 125, uknown object platform 113) and distributes the models to vehicles (via communication network 115, to vehicles, i.e., 103 a-n; ¶¶ 61-64 unknown object platform can use unknown object detection events to train a machine learning object classifier to provide a continuous and up to date stream of training data to constantly improve the object detection machine learning classifiers of the computer vision systems 101 . . . re-train object detection machine learning models or classifiers in the cloud by the machine learning module 307. In step 415, the update module 309 transmits the trained machine learning object classifier to the computer vision systems 101 and/or the vehicles 103 or devices equipped with the computer vision systems 101. In one embodiment, the self-driving vehicles 103 can get a batch update of a newly trained vision algorithms, and this update process can also be automated as an over-the-air (OTA) update via the communication network 115).
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of effective filing date for the models of Guo to be generated and updated at a remote computer system for transmission to vehicles for use in autonomous driving, as taught by Fowe, since processing capability is limited onboard vehicles and model generation is more suited to an offboard source. In addition, distribution of the obstacle detection model to vehicles from a remote location allows for crowdsourcing updates at a central location for real time updates to the model that can be distributed over the air to many vehicles improving the reliability of the model and reducing object detection failures (Fowe, ¶¶ 61-64; 2; 33).
With respect to claim 2, Guo in view of Fowe disclose the drivable area model is a binary2 segmentation model configured to predict, from the road image a drivable area category corresponding to a drivable area in the road image and a background category corresponding to one or more background areas complementary to the drivable area in the road image
(Guo, drivable area 630, and background areas including remaining areas in image 600 that are not part of the drivable area 630, FIG. 6 and corresponding description)
With respect to claims 3 and 16, as best understood in view of the 112(b) rejection above, Guo in view of Fowe disclose generating the generic obstacle detection model from the drivable area model includes adding an extra model output to the drivable area model to generate the generic obstacle detection model including the extra model output, the extra model output of the generic obstacle detection model indicating a generic obstacle category
(i.e., Guo, depth estimation model output, i.e., 370, 380 FIG. 3 and corresponding description; 540, 520, FIG. 5 detecting an unknown object by optical model using depth map and segmentation map; ¶¶ 17, 28-30, 39-42, 46, 50)
(i.e., Guo ¶¶ 32 “network architecture 300 may determine the obstacles parameters 295 more accurately by combining reconstruction, adversarial, and segmentation losses of RGB data to train the neural network model 320. Once the neural network model 320 reaches a steady-state, the obstacle parameters 295 may be estimated. In one approach, the network architecture 300 may also perform more independent obstacle detection . . .obstacle location, map data . . . In this way, the network architecture 300 improves detecting unknown objects or obstacles in unmapped areas”; 44 detection system 170 may determine the obstacle parameters 295 more accurately by combining reconstruction, adversarial, and segmentation losses of image data to train the neural network model 320”; 35 neural network model 320 may use a skip 330 connection to provide a cross-layer connection by feeding a previous layer's information to concatenate with another layer”)
(Fowe ¶¶ 27-28 machine learning have enabled real-time mapping and sensing of a vehicle 103's environment, particularly with respect to autonomous or semi-autonomous vehicles (e.g., self-driving cars) . . . an understanding of the environment provides increased safety and situational awareness while driving in a vehicle 103 by, for instance, providing information about potential obstacles, the behavior of others on the road, and safe, drivable areas . . . avoid both static obstacles (e.g., guard rails, medians, signs, lamp posts, etc.) and dynamic obstacles (e.g., other vehicles, pedestrians, animals, road debris, etc.). By recognizing what those obstacles are, the vehicles 103 can take appropriate action to safely navigate around the obstacles or objects . . .use of convolutional neural networks (e.g., CNNs) or equivalent machine learning models/ algorithms. For example, neural networks have shown unprecedented ability to recognize objects”)
With respect to claims 4 and 17, as best understood in view of the 112(b) rejection above, it is important to note that the limitation “configuring the generic obstacle detection model to predict” introduces limitations that entirely consist of intended use limitations. With respect to system claims, a claim containing a “recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus” if the prior art apparatus teaches all the structural limitations of the claim. See Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987); MPEP 2114, section II. With respect to method claims, in Federal Circuit case, Teva Pharms. USA, Inc. v. Sandoz Inc. (In re Copaxone Consol. Cases), 906 F.3d 1013, the court stated that “Claim language without any bearing on the claimed methods should be deemed non-limiting when it does not result in "a manipulative difference in the steps of the claim. See Teva Pharms. USA, Inc. v. Sandoz Inc. (In re Copaxone Consol. Cases), 906 F.3d 1013, 1023, 2018 U.S. App. LEXIS 28751, *20.
Guo in view of Fowe disclose
generating the generic obstacle detection model from the drivable area model includes configuring the generic obstacle detection model to predict, from the road image:
a drivable area category corresponding to a drivable area in the road image;
(Guo, i.e., 630, Fig. 6, excluding subareas on road surface 640, 650 and corresponding description)
a background category3 corresponding to one or more background areas in the road image; and
(Guo, 610, 620, FIG. 5 and corresponding description)
a generic obstacle category indicating an existence of one or more objects on a road surface in the road image, wherein the drivable area category, the background category and the generic obstacle category are mutually exclusive categories
(Guo, 650, Fig. 6 and corresponding description)
With respect to claims 5 and 19, Guo in view of Fowe disclose generating the generic obstacle detection model from the drivable area model includes training the drivable area model using machine learning via a corpus of training images
(Fowe, ¶ 35; 61-63; 76, 97 i.e., Fig. 1 training database 125, unknown object platform 113 and corresponding description)
(Guo, i.e., ¶¶ 32 “network architecture 300 may determine the obstacles parameters 295 more accurately by combining reconstruction, adversarial, and segmentation losses of RGB data to train the neural network model 320. Once the neural network model 320 reaches a steady-state, the obstacle parameters 295 may be estimated. In one approach, the network architecture 300 may also perform more independent obstacle detection . . .obstacle location, map data . . . In this way, the network architecture 300 improves detecting unknown objects or obstacles in unmapped areas”; 44 detection system 170 may determine the obstacle parameters 295 more accurately by combining reconstruction, adversarial, and segmentation losses of image data to train the neural network model 320”; 35 neural network model 320 may use a skip 330 connection to provide a cross-layer connection by feeding a previous layer's information to concatenate with another layer”)
Allowable Subject Matter
Claims 6-7 and 20 may include allowable subject matter if the 35 USC § 101 rejection above is complied with . The following is an examiner’s statement of reasons for allowance. The prior art does not teach, disclose or suggest the limitation of:
the corpus of training images includes a plurality of synthetic training images; and
the method further comprises creating the plurality of synthetic training images, including:
obtaining a first set of images, each of the first set of images including a respective unoccluded road surface; and
placing, in each image of the first set of images, one or more respective obstacle images onto the respective unoccluded road surface of the image to create the plurality of synthetic training images
as recited in claim 6 and in combination with all other limitations recited in claims 1 and 5; and similarly recited in claim 20 and in combination with all other limitations recited in claims 18 and 19. Failure to include intervening claim limitations negates any indication of potentially allowable subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH J MALKOWSKI whose telephone number is (313)446-4854. The examiner can normally be reached 8:00 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faris Almatrahi can be reached at 313-446-4821. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KENNETH J MALKOWSKI/Primary Examiner, Art Unit 3667
1 “generic object or obstacle broadly refers to an object on a road surface”, Spec ¶ 6.
2 The specification broadly indicates that “one or more background areas 814 include remaining areas in the first road image 810 that are not part of the drivable area 812”. Parent Spec. publication number 20240338951 is used for reference here and throughout (hereinafter “spec.”) ¶ 103.
3 The specification broadly indicates that “one or more background areas 814 include remaining areas in the first road image 810 that are not part of the drivable area 812”. Spec ¶ 103.
Prosecution Insights