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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 29, 2026 has been entered.
Terminal Disclaimer
The terminal disclaimer review decision of October 16, 2025 disapproved the terminal disclaimer because the power of attorney is missing (see the decision for further explanation).
Rule 132 Declaration
The examiner has reviewed the Rule 132 declaration. The examiner agrees with point (7). As to points (8)-(10), the concern is not that one of ordinary skill would not know what the word means, the concern is that these terms are not sufficiently precise for claim language (similar to how contract language needs to be more precise than two people talking). Taking the first example, “related” as used in item (c) of claim 1: it seems likely that most people would agree that an actual dimension of an object (such as height or width) qualifies as “related.” However, one could interpret this claim language to include dimensions of things other than the object at issue, such as determining the height of a car relative to the known height of a traffic sign. See, for example, US11378958B2, Fig. 5D. This could be a problem for this application because this application does not disclose technology for determining heights of objects based on the heights of other objects, and claiming technology that is not disclosed is grounds for a rejection under 112(a).
The examiner considered the statements regarding “based on,” but is unclear of their relevance because there is not a pending 112 rejection referring to “based on.”
Response to Arguments
Applicant’s arguments and amendments have persuasively overcome the claim objections, the specification objection, and some of the 112 rejections. The remaining issues are addressed below.
112
Applicant argues:
BUT the examiner did not provide any link between the prior art cited by the office and the 35 USC 112 rejections - especially how the prior art reflects the appropriate level.
Examiner responds:
The examiner does not understand what Applicant is arguing for. Does the Applicant feel that the level of skill is too high or too low? The examiner also does not understand what kind of a “link” is desired.
Applicant argues:
In addition - this vague statement is in contrary of other parts of the 35 USC 112 rejection that contradict the same prior art cited by the office.
Examiner responds:
The examiner does not know what is being referred to.
Applicant argues:
[dimension-indicative-properties] was properly defined in the specification - see paragraphs [0035]-[0040] that provide a general definition of the term and also explicit examples of the term in case of a vehicle and a pedestrian.
Examiner responds:
Specification [0035]-[0040] does not provide a binding definition. If Applicant would like to use the definition from Specification [0035]-[0040], they need to amend the claims accordingly.
Applicant provides a page of arguments providing examples of various terms that were rejected as subjective as well as alleged examples of where these terms were found to be definite.
The examiner’s concern with these terms is that they are not precise enough, and thus examples of their use does not address the problem. Additionally, the use of these terms elsewhere is not precedential.
103
Applicant argues:
Mell's disclosure of door size information is directed to predicting when a vehicle door will open, not to calculating actual dimensions of detected objects for the purpose of determining image dimensions for bounding shapes.
Examiner responds:
But Mell does actually calculate the dimensions – see, e.g., remarks, last line of p. 10 that “Mell uses door size information.”
Applicant argues:
Mell does not teach this calculation chain or its application to autonomous vehicle operation.
Examiner responds:
Nister was relied on for these limitations.
Applicant argues:
This passage [of Nister] merely identifies types of objects that may be present in an environment; it does not teach determining image dimensions based on both distance information from depth sensors and calculated actual dimensions.
Examiner responds:
Mell teaches use of the actual dimensions.
Applicant argues:
The Examiner's rationale that Mell's parameters would be used "for the purpose of refining vehicle dimensions" or "better predicting complexity associated with driving of the vehicle" does not address the claimed purpose of calculating actual dimensions to determine image dimensions for accurate tag generation.
Examiner responds:
Refining vehicle dimensions refers to the calculating actual dimensions to determine image dimensions. Additionally, KSR held that prior art references do not need to be combined for the same reasons as the invention.
Applicant argues:
Furthermore, neither Nister nor Mell teaches "generating tagged images that comprise the images and tags that are indicative of... the one or more images dimensions of the objects" where the image dimensions are determined based on the claimed calculation chain.
Examiner responds:
The combination of them does.
Applicant argues:
The Examiner has cited Nister Fig. 4E for showing "types of objects," but identifying object types is fundamentally different from generating tags indicative of calculated image dimensions derived from the specific process of steps (c)-(e).
Examiner responds:
Nister identifies objects, Mell provides the actual dimensions.
Specification
The abstract of the disclosure is objected to because it does not “enable the Office and the public generally to determine quickly from a cursory inspection the nature and gist of the technical disclosure.” 37 CFR 1.72(b). Specifically, the phrase “dimension-indicative-properties” requires consulting the specification.
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7, 10, 12, 15-17 and 19-22 (all claims) are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 15 recite “(b) obtaining distance information, generated by a depth sensor selected of a LIDAR, a sonar and a radar, regarding distances between a vehicle and objects captured in the images,” but the claim does not specify how to determine if the objects whose depth is measured are the same as the objects in the camera images. Because of the wide variety of techniques that can be used to approach matching lidar, radar or sonar measurements to objects shown in camera images, this is unlimited functional claiming.
Dependent claims are likewise rejected.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7, 10, 12, 15-17 and 19-22 (all claims) 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.
Claims 1 and 15 recite “regarding,” but this is subjective because different people can have different opinions as to whether information “regards” a distance. MPEP 2173.05(b)(IV). One option to overcome this rejection is to recite an objective standard, such as “is.”
Claims 1 and 15 recite “dimension-indicative-properties,” but this is new terminology. MPEP 2173.05(a). One option to overcome this rejection is to recite a definition in the claim (as opposed to importing it from the specification).
Claims 1 and 15 recite “a dimension- indicative-properties to dimension mapping,” but this is new terminology. MPEP 2173.05(a).
Claims 1-3, 15-17, and 22 recite “related,” but this is subjective. MPEP 2173.05(b)(IV).
Claims 1 and 15 recites “the one or more actual dimensions,” but this lacks sufficient antecedent basis (note that this phrase had antecedent basis prior to the amendments). MPEP 2173.05(e).
Claim 1 twice recites “the vehicle,” but this lacks sufficient antecedent basis because claim 1 twice recites “a vehicle” (see (b) and (h)). MPEP 2173.05(e).
Claim 4 recites “the vehicle,” but the antecedent basis is unclear because the dimension-indicative-properties are of the “other vehicle,” rather than “the vehicle.” MPEP 2173.05(e).
Claim 7 recites “an age range of the pedestrian, wherein the age range is selected out of a child, an infant and an adult.” However, infants do not travel by foot (and the specification only makes reference to infants at [0040] and is silent regarding, for example, strollers).
Claim 19 recites “the one or more other machine learning processes,” but this lacks sufficient antecedent basis. MPEP 2173.05(e).
Claim 21 recites “indication,” but this is subjective. MPEP 2173.05(b)(IV).
Claim 21 recites “exact actual,” but it is unclear how this differs from simply “actual.” If “exact” is intended to require a level of precision, then “exact” is a term of degree without sufficient guidance. MPEP 2173.05(b)(I).
Claim 21 recites “a range of actual dimensions,” but the grammar is unclear. There can be a range of values around one actual dimension, or if there is more than one actual dimension (e.g., height and width), each dimension can have a range, but it is unclear how to interpret one range around multiple dimensions.
Dependent claims are likewise 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 therefor, subject to the conditions and requirements of this title.
Claims 1-7, 10, 12, 15-17 and 19-22 (all claims) are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Step 1: Claim 1 (and its dependents) recite a method, and processes are eligible subject matter.
Claim 15 (and its dependents) recite a non-transitory computer readable medium, and manufactures are eligible subject matter.
Step 2A, prong one: All of the elements of claims 1-7, 9-17 and 19-22 are a mental process because a person can look at objects, recognize them and image boxes around them. Further, the various models are also processes, see example 47, claim 2, element (d) (from the July 2024 AI subject matter eligibility examples). MPEP 2106.04(a)(2)(III)(C) explains that use of a generic computer or in a computer environment is still a mental process. In particular, this section begins by citing Gottschalk v. Benson, 409 US 63 (1972). “The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea.” In Benson the Supreme Court did not separately analyze the computer hardware at issue; the specifics of what hardware was claimed is only included in an appendix to the decision.
Because there are no additional elements, no further analysis is required for Step 2A, prong two or Step 2B.
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 1-7, 10, 12, 15-17 and 19-22 (all claims) are rejected under 35 U.S.C. 103 as being unpatentable over US20190243371A1 (“Nister”) in view of US20220024495A1 (“Mell”).
1. A method for automatic tagging, the method comprises:
(a) obtaining images; (Nister, [0085] “(e.g., using the sensor data from the camera(s) of the vehicle 102),”)
(b) obtaining distance information, generated by a depth sensor selected of a LIDAR, a sonar and a radar, regarding distances between a vehicle and objects captured in the images; (Nister, [0085] For example, sensor data from the sensors of the vehicle 102 may be applied to one or more machine learning models in order to aid the vehicle 102 in determining the state of the objects 106 in the environment.” See, [0059] “the sensor data may be generated … RADAR … LIDAR”)
(c) identifying one or more dimension-indicative-properties related to the objects; (Nister, [0099] “bounding box”)
(e) determining one or more images dimensions related to the objects, based on the distance information and the one or more actual dimensions related to the objects; and (Nister, [0123] “ FIG. 4E illustrates examples where the objects 106 may be pedestrians, children, animals, motorcycles, bicyclists, vehicles, and/or other types of objects 106. … As such, the safety procedure determiner 134 may determine the appropriate safety procedure for each of the objects 106 perceived and/or determined to be in the environment 406.”)
(f) generating tagged images that comprise the images and tags that are indicative of at least one of (Nister, Fig. 4E. The identification of the various types of objects 106 teach the claimed tags.)
(i) locations of the objects within the images, and (Nister, Fig. 4E. Showing locations.)
(ii) the one or more images’ dimensions related of the objects; and (Nister, Fig. 4E. Showing types of objects.)
wherein at least steps (c), (e) and (f) are executed by using a machine learning process; (Nister, [0085] “For example, and without limitation, a convolutional neural network may be used for object detection and identification (e.g., using sensor data from camera(s) of the vehicle 102),”)
(g) training one or more other machine learning processes using at least some of the tagged images; (Nister, [0317] “The server(s) 104 may be used to train machine learning models (e.g., neural networks) based on training data.”)
(h) processing other images acquired by a vehicle during a driving session of the vehicle, the one or more other machine learning processes, the processing comprises detecting one or more sensed objects; and (Nister, [0317] “The training data may be generated by the vehicles, and/or may be generated in a simulation (e.g., using a game engine). In some examples, the training data is tagged”)
(i) autonomously driving the vehicle in response to the detecting of the one or more sensed objects. (Nister, [0317] “Once the machine learning models are trained, the machine learning models may be used by the vehicles (e.g., transmitted to the vehicles over the network(s) 1190, and/or the machine learning models may be used by the server(s) 104 to remotely monitor the vehicles.”)
Nister is not relied on for the below claim language.
However, Mell teaches (d) calculating the one or more actual dimensions related to the objects, based on the one or more dimension-indicative-properties related to the objects, using a dimension-indicative-properties to dimension mapping that maps dimension-indictive-properties to dimensions; (Mell, [0081] “The vehicle door predictor may refer… to a door size related to a certain model of a certain year.” Mell’s door size of a certain model teaches the claimed actual dimensions, Mell’s model and year teach the claimed dimension-indicative-properties and Mell’s door of a certain model teaches the claimed mapping. Claim interpretations based on specification [0036], e.g., “A dimension-indicative-property of the vehicle may be at least one out of a manufacturer of the vehicle, a model of the vehicle, a combination of a model and a year of manufacturing, or a type of a vehicle, a number of wheels of the vehicles.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Mell to the teachings of Nister such that Mell’s parameters are used by Nister for the purpose of refining vehicle dimensions (Mell, [0081]) or better predicting “complexity associated with driving of the vehicle” or vehicle behavior. Mell, [0057]. Further, Nister teaches using make and model of the vehicle as well as the size of the object. Nister, [0099]. Since Mell teaches the size of the objects, Nister would use that.
Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
2. The method according to claim 1, wherein one or more images’ dimensions related to the objects are indicative of one or more dimensions of bounding shapes that are indicative of dimensions of the objects. (Nister, [0099] “bounding box”)
3. The method according to claim 1, wherein one or more images’ dimensions related to the objects are indicative of one or more dimensions of objects within the one or more images. (Nister, [0099] “bounding box”)
4. The method according to claim 1, wherein one of the objects is another vehicle; (Nister, [0063] “(e.g., an object 106, such as a structure, entity, vehicle, etc.)”)
wherein dimension-indicative-properties of the vehicle comprises a manufacturer of the other vehicle and a model of the other vehicle. (Mell, [0057] “Various examples of a traffic parameter and the one or more contextual parameters may include the model and/or year and/or manufacturer of the vehicle that drives towards the first vehicle … .”)
5. The method according to claim 1, wherein one of the objects is another vehicle; (Nister, [0063] “(e.g., an object 106, such as a structure, entity, vehicle, etc.)”)
wherein dimension-indicative-properties of the other vehicle comprise a type of vehicle, wherein the type of the other vehicle is selected out of a truck, a van, a car, and a bus. (Nister, [0063] “In some examples, the object(s) 106 may include other vehicles (e.g., cars, trucks, motorcycles, busses, etc.)”)
6. The method according to claim 1, wherein one of the objects is another two-wheeled vehicle; (Nister, Fig. 4E. Showing a bicycle and a motorcycle.)
wherein dimension-indicative-properties of the other two-wheeled vehicle comprise a type of the other vehicle, wherein the type of the other vehicle is selected out of a bicycle, a motorcycle and a scooter. (Nister, Fig. 4E. Showing a bicycle and a motorcycle.)
7. The method according to claim 1, wherein one of the object is a pedestrian; (Nister, Fig. 4E. Showing pedestrians.)
wherein dimension-indicative-properties of the pedestrian comprise an age range of the pedestrian, wherein the age range is selected out of a child, an infant and an adult. (Nister, [0123] “ FIG. 4E illustrates examples where the objects 106 may be pedestrians, children, animals, motorcycles, bicyclists, vehicles, and/or other types of objects 106.”)
8. (Canceled)
9. (Canceled)
10. The method according to claim 1, wherein the at least some of the tagged images is a first part of the tagged images and (This language does not limit the claim, there is nothing to map.)
the method further comprises testing one or more other machine learning processes with at least a second part of the tagged images. (Nister, [0317] “The training data may be generated by the vehicles.” Training discloses the claimed testing, note that [0317] discusses using after training. See also “remotely monitor.”)
11. (Canceled)
12. The method according to claim 1, comprising performing an autonomous advance driver assistance system (ADAS) operation using the one or more other machine learning processes;
wherein the ADAS operation is executed without human driver intervention and is selected out of braking (Nister, claim 3, “braking”) and changing lane (Nister, [0070] “The behavior planner may determine the feasibility of basic behaviors of the vehicle 102, such as staying in the lane or changing lanes left or right”)
13. (Cancelled)
14. (Cancelled)
Claims 15-17 are rejected as per claims 1-3. Nister’s memory 1204 teaches the claimed “non-transitory computer readable medium.”
18. (Cancelled)
19. (New) The method according to claim 1, further comprising exporting parameters of the one or more other machine learning processes to the vehicle. (Nister, [0317] “Once the machine learning models are trained, the machine learning models may be used by the vehicles (e.g., transmitted to the vehicles over the network(s) 1190, and/or the machine learning models may be used by the server(s) 104 to remotely monitor the vehicles.”)
20. (Previously Presented) The method according to claim 1, wherein at least one of the objects is a vehicle or a pedestrian. (Nister, [0123] “With respect to FIG. 4E, FIG. 4E illustrates examples where the objects 106 may be pedestrians, children, animals, motorcycles, bicyclists, vehicles, and/or other types of objects 106.”)
21. (Previously Presented) The method according to claim 1, wherein for each object, each dimension-indicative property provides
(i) an indication of an exact actual dimension of the object or (Mell, [0081] “The vehicle door predictor may refer… to a door size related to a certain model of a certain year.”)
(ii) an indication about a range of actual dimensions of the object. (Mell, [0082] “To provide a vehicle specific (or model and/or year specific) vehicle door estimate” Mell’s disclosure of not necessarily using the specific year teaches the claimed range (i.e., each model year is a point in the claimed range))
Interpretation of Claim 22
The mathematical relationship described in this claim is a property of the photo having been taken, it is not something that needs to be calculated. See, for example, specification, [0045] admitting that this is known, and demonstrating that this relationship is true because of the nature of taking photos of objects. Thus, claim 22 is understood as limiting which dimensions are at issue to those dimensions where this relationship holds (e.g., height and width are in, but weight is not).
22. (Previously Presented) The method according to claim 1, wherein an image dimension related to an object equals an actual dimension related to the object multiplied by a ratio between a focal length for a camera that acquired the images and the distance between the camera and the object. (Nister, [0099] “With respect to the objects 106 in the environment, the size of the objects 106 may be determined using the sensors and sensor data therefrom (e.g., from the sensor manager 108), and/or one or more machine learning models (e.g., convolutional neural networks).” Nister’s “size” teaches height and width (e.g., [0099]’s rectangle), each of which meet the claim.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US11734473B2 – Claim 1, “determining, based at least in part on the vehicle data and the ground truth data, an error;”
US20240233387A1 – Fig. 4
US11222219B2 – Claim 22 “determining at least one of a vehicle make or a vehicle model corresponding to the at least one target vehicle.”
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID ORANGE whose telephone number is (571)270-1799. The examiner can normally be reached Mon-Fri, 9-5.
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/DAVID ORANGE/Primary Examiner, Art Unit 2663
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