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 . Claims 1-18 are pending and examined below. This action is in response to the claims filed 2/19/25.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claims discuss a device that falls under a machine in Step 1.
In Step 2A, Prong One, the device falls under an abstract idea as a mental process with nothing more than a generic computer.
Simply detecting position data, generating an obstacle candidate set and excluding candidate obstacles based on height can be performed within a human mind even with the use of a generic computer that does not recite any additional elements to integrate the judicial exception into a practical application in Step 2A, Prong Two. See MPEP § 2106.04(a)(2).
In Step 2B the claim does not recite additional claim elements that can amount to significantly more to overcome the Judicial Exception. Acquiring and detecting position data/data subsets is insignificant presolutionary activity as mere data gathering and computing a height of each candidate obstacle to determine whether it should be excluded from the set is the mental process.
Therefore, the claim is not eligible subject matter.
Including claim amendments to recite some form of physical control implementation such as that actually controlling the movement of the apparatus based on the determinations may overcome the rejection.
Dependent claims do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims are not patent eligible under the same rationale as provided for in the rejection of the independent claims.
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.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2020/0012869) in view of Van Der Merwe et al. (US 2024/0247945).
Regarding claims 1, 7, and 13, Li discloses an obstacle avoidance system including an obstacle detecting apparatus/method/non-transitory computer readable storage medium storing a program comprising: at least one memory that is configured to store instructions; and at least one processor that is configured to execute the instructions to (¶4-6):
acquire a position data set that includes a plurality of position data that indicates a three-dimensional position of a point of a target place (¶19-21 and Fig. 1 - element 101 discloses a three-dimensional point cloud corresponding to the recited a three-dimensional position of a point of a target place is established for the road based on the acquired image data corresponding to the recited acquire a position data set that includes a plurality of position data);
detect a first subset from the position data set, the first subset including a plurality of the position data that collectively represent a ground of the target place (¶33-39 and Fig. 1 – detecting ground information of the road corresponding to the recited detecting a first subset from the position data set including three dimensional point cloud data used to determine ground height and obstacle information corresponding to the recited a plurality of the position data that collectively represent a ground of the target place);
determine a vertical position of the ground based on vertical positions indicated by the plurality of position data in the first subset (¶33-39 and Fig. 1 – determining a ground position of the road based on the ground height; and performing traffic state detection for the ground position of the road to determine sag information on the road corresponding to the recited vertical positions indicated by the plurality of position data in the first subset);
detect one or more objects from the position data set to generate an obstacle candidate set (¶40-44 – detecting information for each obstacle corresponding to the recited generate an obstacle candidate set);
compute, for each object in the obstacle candidate set, a height of the object based on a vertical position of the object and the vertical position of the ground (¶40-44 and ¶58-60 – a prewarning grade for each obstacle is determined corresponding to the recited for each object in the obstacle candidate set based on information of the obstacle including a height based on the ground height corresponding to the recited height of the object based on a vertical position of the object and the vertical position of the ground); and
While Li does disclose assessing potential obstacles based on height pre-warning region relative to ground height as well as multiple tiers of determining the pass-ability of a region based on these pre-warning assessments such as very large and impassable obstacle (¶40-44 and ¶58-60), it does not explicitly disclose exclude an object whose height is larger than a height threshold from the obstacle candidate set.
However, Van Der Merwe discloses an autonomous navigation control system including exclude the object whose height is larger than a height threshold from the obstacle candidate set (¶121-122 – removing object data points above a preselected level from the ground level).
The combination of the obstacle avoidance system including height-based object filtering of Li with the filtering of object data points above a preselected level of Van Der Merwe fully discloses the elements as claimed.
It would have been obvious to one of ordinary skill in the art before the filing date to have combined the obstacle avoidance system including height-based object filtering of Li with the filtering of object data points above a pre-selected level of Van Der Merwe in order to more efficiently process the dataset (Van Der Merwe - ¶122).
Regarding claims 2, 8, and 14, Li further discloses wherein the determination of the vertical position of the ground includes (¶33-39 and Fig. 1 – determining a ground position of the road based on the ground height; and performing traffic state detection for the ground position of the road to determine sag information on the road corresponding to the recited vertical positions indicated by the plurality of position data in the first subset):
computing, as the vertical position of the ground, a statistical value of vertical positions indicated by the plurality of position data in the first subset (¶19-25 – ground information utilized to determine ground height corresponding to the recited vertical position of the ground is determined utilizing a scale normalized depth image corresponding to the recited statistical value of vertical positions indicated by the plurality of position data in the first subset).
Regarding claims 3, 9, and 15, Li further discloses wherein the determination of the vertical position of the ground includes (¶33-39 and Fig. 1 – determining a ground position of the road based on the ground height; and performing traffic state detection for the ground position of the road to determine sag information on the road corresponding to the recited vertical positions indicated by the plurality of position data in the first subset):
dividing the ground into two or more sections; and computing, for each section of the ground, the vertical position of the section based on vertical positions indicated by the plurality of position data that collectively represent the section (¶40-44 and ¶58-60 – image data is partitioned into MxN partitions corresponding to the recited dividing the ground into two or more sections to compute prewarning grade for each partition based on the obstacle information including a height based on the ground height corresponding to the recited height of the object based on a vertical position of the object and the vertical position of the ground for each partition corresponding to the recited computing vertical positions of each section representing the plurality of position data collectively representing that section).
Regarding claims 4, 10, and 16, Li further discloses wherein the division of the ground includes (¶40-44 and ¶58-60 – image data is partitioned into MxN partitions corresponding to the recited dividing the ground into two or more sections):
determining a lowest vertical position and a highest vertical position indicated by the plurality of the position data in the first subset (¶34-47 – determining a gradient for each partition relative to its adjacent partitions using a geometric invariable matrix corresponding to the recited lowest and highest vertical position indicated by the position data in the first subset);
dividing a range between the lowest vertical position and the highest vertical position by a predefined interval into two or more height ranges (¶40-¶47 – dividing the different partitions represented by the gradient values of all the partitions into pre-warning grade ranges based on obstacle size information including height corresponding to the recited dividing a range between lowest and highest vertical position by a proportional weighting to the pre-warning grade corresponding to the recited predefined interval into pre-warning grades 1, 2, or 3, corresponding to the recited two or more height ranges); and
dividing the first subset into two or more subsets that represent different sections of the ground from each other based on the two or more height ranges (¶40-44 and ¶58-60 – image data is partitioned into MxN partitions corresponding to the recited dividing the first subset into two or more subsets that represent different sections of the ground from each other utilizing the gradient value is calculated based on the weight of the partition and weights of adjacent partitions of the partition to assess the pre-warning grade of the partition corresponding to the recited based on the two or more height ranges).
Regarding claims 5, 11, and 17, Li further discloses wherein the predefined interval is proportional to the height threshold (¶44-47 - dividing the different partitions represented by the gradient values of all the partitions into pre-warning grade ranges based on obstacle size information including height based weight calculation where weight is positively proportional to the pre-warning grade corresponding to the recited predefined interval is proportional to the height threshold).
Regarding claims 6, 12, and 18, Li further discloses wherein the detection of the one or more objects includes (¶40-44 – detecting information for each obstacle corresponding to the recited detection of the one or more objects):
determining a partial area of the target place based on a region of the ground in top view (¶40-44 and ¶58-60 – image data is partitioned into MxN partitions corresponding to the recited determining a partial area of the target place based on a region of the ground in a top view); and
detecting the one or more objects from a set of a plurality of the position data that indicates a position included in the partial area (¶40-44 and ¶58-60 – each partition includes obstacle information corresponding to the recited detecting one or more objects from a set of the plurality of the position data indicating a position included in the partial area).
Additional References Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Luo et al. (US 2019/0147372) discloses an object detection system which determines static background objects such as a wall that are not of significance to navigation and separates them from general object detection and tracking (¶106).
Funaya (US 2020/0372670) discloses an object detection system including identifying obstacles that are considered outliers to be excluded based on detected vertical height relative to a ground plane (¶14-16).
Herman et al. (US 2020/0409382) discloses an intelligent cleaning robot including segmenting point cloud data into points below and above a threshold height (¶107).
Whitman et al. (US 2021/0333804) discloses a robotic navigation system which groups prospective obstacles and objects into height classifications relative to the floor (¶10 and ¶61).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Matthew J Reda whose telephone number is (408)918-7573. The examiner can normally be reached on Monday - Friday 7-4 ET.
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/MATTHEW J. REDA/Primary Examiner, Art Unit 3665
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