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 .
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Status of the Claims
This Final Action is in response to Applicant’s amendment of 20 March 2026. Claims 1, 3-6, & 9-10 are pending and have been considered as follows. Claims 2 & 7-8 have been cancelled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/26/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Argument
Applicant's amendments and arguments with respect to the rejection of claim 8 under 35 USC 112(a) & 112(b) as set forth in the office action of 14 January 2026 have been considered and are persuasive. Therefore, the rejection of claim 8 under 35 USC 112(a) & 112(b) as set forth in the office action of 14 January 2026 has been withdrawn.
Applicant's amendments and arguments with respect to the rejection of claim 1-10 under 35 USC 112(b) as set forth in the office action of 14 January 2026 have been considered and are persuasive for the antecedent basis issues, however the remaining 112(b) rejections remain. Therefore, the rejection of claims 1-10 under 35 USC 112(b) as set forth in the office action of 14 January 2026 has been withdrawn for the antecedent basis issues however, please see the 112(b) rejections below for the remaining rejections and their justifications.
Applicant’s amendments and/or arguments with respect to the rejection of Claims 1-10 under 35 USC 101 as set forth in the office action of 14 January 2026 have been considered and are NOT persuasive. Specifically, Applicant argues:
Applicants respectfully submit that amended claim 1 is non-abstract for the reasons set below.
Applicants respectfully submit that amended claim 1 does not recite an abstract idea. Rather claim I addresses a technical challenge of how to enable the autonomous driving vehicle to precisely position based on a high-precision road map. It is clear that this is a particular problem arising in the realm of computer (When the codes run on a computer device, the computer device will carry out various steps of the method described above).
For the purpose of addressing the above problem, the claimed solution recites a method for generating a high-precision road map. Specifically, the method includes the steps of: collecting a plurality of road information to be processed; performing feature extraction on the plurality of the road information to be processed, and obtaining road feature information; and generating the high- precision road map according to the road feature information, vehicle body posture control information of a vehicle and mileage information. The collecting the plurality of the road information to be processed includes the steps of: in response to receiving a sensor control instruction, determining an information collection timestamp according to the sensor control instruction; and collecting the plurality of the road information to be processed according to the information collection timestamp. The generating the high-precision road map according to the road feature information, the vehicle body posture control information of the vehicle and the mileage information includes the steps of: determining predicted vehicle body posture control information of the vehicle according to the vehicle body posture control information and the mileage information; determining inertial navigation auxiliary information of the vehicle according to the predicted vehicle body posture control information; and generating the high- precision road map according to the road feature information and the inertial navigation auxiliary information.
It is clear that the claimed invention is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of the computer.
It is worth mentioning the caselaw "Enfish LLC Vs. Microsoft Corporation et al." (case no. 2015-1244), wherein the Federal Circuit Court (CAFC) observed that software can make non- abstract improvements to computer technology, just as hardware improvements can, and sometimes the improvements can be accomplished through either route. In other words, the court emphasized that such software resulting in the non-abstract improvements to the computer technology are not abstract in itself and therefore patent eligible under 35 U.S.C 101.
Applicants respectfully submit that since Applicants' claimed invention shows technical improvements pertaining to how to enable the autonomous driving vehicle to precisely position based on a high-precision road map, the claimed subject matter in claim I should hence be rendered as § 101 compliant. In view of the above, Applicants respectfully submit that claim 1 is non- abstract and therefore not related to judicial exception thereby satisfying requirements of 35 U.S.C 101.
Without prejudice to the above, Applicants further submit that even though claim 1 is considered to be an abstract idea (which Applicants respectfully do not agree with), Applicants' claim 1 recites one or more elements/features that, either alone or in combination, are sufficient to amount to significantly more than the judicial exception (i.e. abstract idea) for the reasons set below.
Applicants respectfully submit that the claimed solution indeed recites a series of limitations that when considered individually and as an ordered combination, provide an inventive concept sufficient to confer eligibility. As claimed in claim 1, the solution can collect a plurality of road information to be processed. Then the solution can perform feature extraction on the plurality of the road information to be processed and obtain road feature information. Finally, the solution can determine predicted vehicle body posture control information of the vehicle according to the vehicle body posture control information and the mileage information; determine inertial navigation auxiliary information of the vehicle according to the predicted vehicle body posture control information; and generate the high-precision road map according to the road feature information and the inertial navigation auxiliary information, thereby precisely positioning the autonomous driving vehicle based on the high-precision road map and improving the driver's experience of autonomous driving.
These are meaningful limitations that add more than generally linking the use of the abstract idea to the Internet, because they solve an Internet-centric problem with a claimed solution that is necessarily rooted in computer technology. These limitations, when taken as an ordered combination, provide unconventional steps that confine the abstract idea to a particular useful application. It is clear that the claimed solution purposefully arranges some particular steps in a particular order to achieve a technological solution to a technological problem specific to the computer.
It is worth mentioning case laws Alice Vs CLS Bank wherein the supreme court observed that if a claim contains one or more additional elements that show improvement in any technical field then such claim must be considered significantly more than the abstract idea itself thereby rendering the abstract idea patent eligible under 35 U.S.C 101. Since claim I provides an inventive solution possessing the technical advantages of enabling the autonomous driving vehicle to precisely position based on a high-precision road map, claim I shows improvement in the field of the computer and hence is patentable under 35 U.S.C 101.
in view of the above, claim 1 is patent eligible under 35 U.S.C 101 and should be allowable.
By virtue of their respective direct or indirect dependencies on independent claim 1, claims 3-6 and 9-10 should also be allowable.
In view of the above, Applicants respectfully request the 101 rejections be withdrawn
The Examiners Response:
Examiner has carefully considered Applicant’s amendments and arguments and respectfully disagrees. Regarding the claimed invention, the claims have a method that are used for the creation of a map, with the map being created from information collected/received from various sources. The claims do recite mental processes that can be performed in the human mind or with pen and paper as the current claims merely involve obtaining and either generating or determining additional data to create a map, the Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Furthermore, the “technical improvements pertaining to how to enable the autonomous driving vehicle to precisely position based on a high-precision road map “, is never explicitly mentioned in the currently amended claims and the claim itself does not reflect the improvement, “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107.” (MPEP 2106.05(a)). In addition, for the improvement to be indicative of integration into a practical application, it cannot be an improvement to an abstract idea, it must be "to the functioning of a computer, or to any other technology or technical field” (MPEP 2106.05(a)). Finally, the limitation of “generating the high-precision road map …,” is a well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere performance which in the instant application is creating a map is a well understood, routine, and conventional function. As such, even in combination, these additional elements, under broadest reasonable interpretation, do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea.
Applicant’s amendments and/or arguments with respect to the rejection of claims 1-10 under 35 USC 103 have been considered and are NOT persuasive. Specifically, Applicant argues:
Applicant respectfully disagrees that current claim 1 is anticipated by the reference documents, and submits that the current claim I is allowable. The reasons are as follows.
As shown in Li (US 20200109954 Al), Li discloses a method of map generation, which includes receiving data from a plurality of vehicles about environments within which the plurality of vehicles operate, and generating a three-dimensional map using the data from the plurality of vehicles. The data is collected by one or more sensors on-board the plurality of vehicles. According to the specification of Li, it discloses "the data herein can be collected by one or more sensors on- board the plurality of vehicles. The sensors may be removably attached to the vehicle or carried by the vehicle. In some cases, the sensors can be integrated or embedded into the vehicle. The one or more on-board sensors are capable of sensing the environment in proximity to or around the vehicle. In some embodiments, the one or more on-board sensors can be divided into external 14 sensors and internal sensors. The external sensors may be capable of collecting data from the environment around the vehicle while the internal sensors may be capable of sensing a condition of the vehicle, or sensing a characteristic of a component on-board the vehicle. In some cases, internal sensors may also be capable of collecting data regarding the vehicle itself'. It can be seen that Li only collects data through sensors. Li does not determine the information collection timestamp to unify the sampling frequency of multiple different sensors, and Li does not mention sensor control instructions, nor does Li disclose the steps of determining the timestamp based on the control instructions and guiding data collection.
As shown in Mori (US 20210245777 Al), Mori discloses a map generation device, which includes a storage device that stores a program, and a hardware processor. The hardware processor executes the program stored in the storage device to acquire position information of a target outside a vehicle from an external sensor mounted on the vehicle, acquire a first movement amount of the vehicle based on the position information of the target, acquire a second movement amount of the vehicle based on information of the vehicle, and generate map information on a location, where the vehicle has traveled, based on the position information of the target, the first movement amount, and the second movement amount. According to the specification of Mori, it discloses "the first acquisitor 110 acquires position information of a target located outside the vehicle M from an external sensor such as the LIDAR 10 mounted on the vehicle M. For example, the first acquisitor 110 collects a data set (LiDAR data) input from the LIDAR 10 for each 1 scan and acquires point cloud data"; "the first acquisitor 110 compares a current value and a previous value (may be a value several times previously) of point cloud data acquired in time series, and derives and acquires a movement amount (first movement amount) of the vehicle M per cycle". It can be seen that Mori only collects data through sensors outside the vehicle. Mori does not determine the information acquisition timestamp to unify the sampling frequency of multiple different sensors. Moreover, Mori does not mention sensor control instructions, nor does Mori disclose the steps of determining the tirnestarnp based on the control instructions and guiding data collection.
As shown in He (US 20210323572 Al) discloses a systerm for registration of point clouds for autonomous driving vehicles (ADV). The system receives a number of point clouds and corresponding poses from ADVs equipped with LIDAR sensors capturing point clouds of a navigable area to be mapped, where the point clouds correspond to a first coordinate system. The system partitions the point clouds and the corresponding poses into one or more loop partitions based on navigable loop information captured by the point clouds. For each of the loop partitions, the system applies an optimization model to point clouds corresponding to the loop partition to register the point clouds. They system merges the one or more loop partitions together using a pose graph algorithm, where the merged partitions of point clouds are utilized to perceive a driving environment surrounding the ADV. According to the specification of He, in paragraphs [0 125]- [013 L], He disclose "synchronization may include a temporal synchronization using timestamps, e.g., matching RGB frames to the correct point clouds...the timestamp has a millisecond accuracy threshold...the camera unit can capture image frames and a time information provider can timestamp the image frame with a capture time. The time information provider may be shared between the LIDAR and the image capturing devices. in one embodiment, the time provider is a local real-time clock (RTC) provided by a CPU of the computing system. In another embodiment, the time provider is periodically synced to an external source such as an atomic clock from a time server over a network. In another embodiment, the time provider is synced to a GPS signal. Because point clouds and RGB are each associated with a timestamp, point clouds and RGB images received at different frequencies can be aligned...the image capturing device's RGB image capturing frequency may be adjusted to capture images at a similar frequency as the LIDAR sensor unit". It can be seen that He uses timestamps to receive point clouds and RGB images, but He does not determine the information acquisition timestamp in response to sensor control instructions. Furthermore, the timestamp of He is appended to the image frame by a time information provider, and He does not involve determining the information acquisition timestamp. In contrast, in the present application, to unify the sampling frequencies of multiple different sensors, the processing method for acquiring multiple road information to be processed includes determining the information acquisition timestamp upon receiving sensor control instruction, and collecting multiple road information to be processed based on the information acquisition timestamp. He does not mention sensor control instructions, nor does He disclose the steps of determining the timestarmp based on the control instructions and guiding data collection.
As shown in Su (CN 105674993 A), Su discloses a high-precision visual positioning map generation method. Su acquires binocular images through an image acquisition module, extracts and matches feature points from the binocular images, uses a binocular positioning method to locate the relative positions of visual feature points, obtains the spatial positional relationship of visual feature points relative to the camera, calculates the latitude and longitude coordinates of
visual feature points using high-precision GPS information, and analyzes visual feature points uploaded by different intelligent vehicles to finally obtain a reliable and stable visual feature point map. According to the specification of Su, it can be seen that Su does not determine the information acquisition timestamp to unify the sampling frequency of multiple different sensors. Moreover, Su does not mention sensor control instructions, nor does Su disclose the steps of determining the timestamp based on the control instructions and guiding data collection.
Thus, Applicant respectfully submits that the reference documents fail to disclose the distinguishing technical features of the present application: the collecting the plurality of the road information to be processed comprises: in response to receiving a sensor control instruction, determining an information collection timestamp according to the sensor control instruction; and collecting the plurality of the road information to be processed according to the information collection timestamp.
The Examiner’s Response:
Examiner has carefully considered Applicant's arguments and respectfully disagrees. Examiner would like to point out that He does teach that the collection of road/environment information is done with a timestamp in mind, the collection timestamp is determined based the timestamp associated with the point cloud and based on that point cloud timestamp, additional road/environment information can be collected with that timestamp in mind for use in map generation. (see at least [¶0125] Synchronization submodule 1603 can synchronize the point clouds with the RGB images based a timestamp when these images are captured…..[¶0128] In another embodiment, synchronization may include a temporal synchronization using timestamps, e.g., matching RGB frames to the correct point clouds. For example, a timestamp provided by a time information provider can be associated to the image frame at a time when the LIDAR unit captures the image frame. In one embodiment, the timestamp has a millisecond accuracy threshold…..[¶0130] In this example, each point cloud can be associated with at least one RGB image by identifying the RGB image having a closest timestamp to a timestamp of the point cloud image. The RGB images with no associations would not be considered for integration/synchronization with the point clouds and can be discarded. In one embodiment, any RGB image can be associated with a point cloud so long as the RGB image timestamp is within a time threshold of the corresponding point cloud image….[¶0136] In one embodiment, synchronizing the RGB image with the point clouds to obtain RGB point clouds includes for each point cloud, determining a timestamp of the point cloud, locating a RGB image from the number of RGB images that has a timestamp within a temporal threshold of the timestamp of the point cloud, and associating the RGB image with the point cloud by associating one or more pixels with one or more points of the point cloud.) As such, "wherein the collecting the plurality of the road information to be processed comprises: in response to receiving a sensor control instruction, determining an information collection timestamp according to the sensor control instruction; and collecting the plurality of the road information to be processed according to the information collection timestamp", under broadest reasonable interpretation, can be taken as what is being disclosed in He.
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.
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, 3-6, & 9-10 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.
Regarding Claim 1, it is unclear what is being claimed by the limitation of “generating a high-precision road map according to the road feature information, vehicle body posture control information of a vehicle and mileage information”. The limitations are not clear as to how mileage would be used to assist in the generation of a high precision road map along with the vehicle posture information. The specification seems to merely repeat a lot of the claim language without going into further detail to explain the connection between mileage and posture information. It is unclear how the mileage information, road feature information, and posture information combined would lead to the creation of a high precision map as neither the claims or specification go into detailed information on the generation of the map. Examiner will interpret this limitation being met when mileage is used to assist in the generating of a road map.
Regarding Claim 1, it is unclear what is being claimed by the term of “inertial navigation auxiliary information”. From the presented arguments and cited paragraph ¶074, it seems the inertial navigation auxiliary information is the same as the predicted posture information, however, it is unclear as the specification cites that the inertial navigation auxiliary information is determined from the predicted posture information but does not go into detail what this would entail, see (¶074) “In this embodiment, determining predicted vehicle body posture control information of the vehicle according to the vehicle body posture control information and the mileage information; determining inertial navigation auxiliary information of the vehicle according to the predicted vehicle body posture control information; and it can generate the high-precision road map according to the road feature information and the inertial navigation auxiliary information.”. Examiner will interpret the inertial navigation auxiliary information being met with any predicted vehicle pose information that is obtained from a combination of mileage and previous vehicle pose information.
Regarding Claim 6, it is unclear what is being claimed by the term of “road pixel information”. The limitation that has the term road pixel information does not define it and neither does the specification. It is unclear if road pixel information is merely road images composed of pixels or if this term means something else and if this was the case, wouldn’t the already collected road images already be road pixel information. The specification merely repeats a lot of the claim language without going into further detail to explain the what the road pixel information is as it seems to imply it is a combination of image and point cloud data but does not go into further detail on the combination, see (¶086) “In this embodiment, if the current weather information is sunny weather information, then multiple road images to be processed in the collected multiple road information to be processed can be processed, and the road images to be processed can be processed; when the current weather information is not sunny weather information, and the current weather information is foggy weather information and rainy weather information, it is necessary to extract multiple point cloud data to be processed from the multiple road information to be processed, and then convert the multiple road images to be processed and the multiple point cloud data to be processed to obtain the corresponding multiple road pixel information, perform feature extraction on the multiple road pixel information, and obtain the road feature information, etc..”. Examiner will interpret the road pixel information being met when some form of road information is obtained from a combination of point cloud data and image data.
All dependent claims that depend on rejected claims are also 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, 3-6, & 9-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a method, and claim 8 is directed to a device. Therefore, claims 1 and 8 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. The other analogous claim 8 is rejected for the same reasons as the representative claim 1 as discussed here. Claim 1 recites:
A method for generating a high-precision road map, comprising:
collecting a plurality of road information to be processed;
performing feature extraction on the plurality of the road information to be processed, and obtaining road feature information;
and generating the high-precision road map according to the road feature information, vehicle body posture control information of a vehicle and mileage information;
wherein the collecting the plurality of the road information to be processed comprises: in response to receiving a sensor control instruction,
determining an information collection timestamp according to the sensor control instruction;
and collecting the plurality of the road information to be processed according to the information collection timestamp;
wherein the generating the high-precision road map according to the road feature information, the vehicle body posture control information of the vehicle and the mileage information comprises:
determining predicted vehicle body posture control information of the vehicle according to the vehicle body posture control information and the mileage information;
determining inertial navigation auxiliary information of the vehicle according to the predicted vehicle body posture control information;
and generating the high-precision road map according to the road feature information and the inertial navigation auxiliary information;
the inertial navigation auxiliary information is auxiliary information provided to an inertial navigation system.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “performing feature extraction …”, “generating …” and “determining …” all the various data in the context of this claim encompasses a person looking at data collected (received, detected, etc.) and forming a simple judgement (determination, analysis, comparison, etc.) either mentally or using a pen and paper. Accordingly, the claim recites at least one abstract idea. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for generating a high-precision road map, comprising:
collecting a plurality of road information to be processed;
performing feature extraction on the plurality of the road information to be processed, and obtaining road feature information;
and generating the high-precision road map according to the road feature information, vehicle body posture control information of a vehicle and mileage information;
wherein the collecting the plurality of the road information to be processed comprises: in response to receiving a sensor control instruction,
determining an information collection timestamp according to the sensor control instruction;
and collecting the plurality of the road information to be processed according to the information collection timestamp;
wherein the generating the high-precision road map according to the road feature information, the vehicle body posture control information of the vehicle and the mileage information comprises:
determining predicted vehicle body posture control information of the vehicle according to the vehicle body posture control information and the mileage information;
determining inertial navigation auxiliary information of the vehicle according to the predicted vehicle body posture control information;
and generating the high-precision road map according to the road feature information and the inertial navigation auxiliary information;
the inertial navigation auxiliary information is auxiliary information provided to an inertial navigation system.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations above, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. In particular, the collecting and obtaining steps from / using sensor system(s) are recited at a high level of generality (i.e. as a general means of receiving information for use in the performing and other steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Lastly, claims 9 and 10 further recite “A high-precision road map generation equipment, comprising: a memory, a processor and a high-precision road map generation program stored in the memory and executable on the processor, wherein the high-precision road map generation program is configured to implement the method” and “A non-transitory computer-readable storage medium, wherein a high-precision road map generation program is stored on the non-transitory computer-readable storage medium, and the method for generating the high-precision road map” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose map generation environment. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 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.”). The device(s) and processor(s) are recited at a high level of generality and merely automates the steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use 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 not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations discussed above are insignificant extra-solution activities.
The additional limitations of receiving information and values/features detecting/detectable are well-understood, routine and conventional activities because the background recites that the sensors are all conventional sensors, and the specification does not provide any indication that the processor is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. The limitation of “generating …,” is a well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere performance which in the instant application is creating a map is a well understood, routine, and conventional function. Hence, the claim is not patent eligible.
Dependent claims 3-6 and 9-10 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 additional elements that do not integrate the judicial exception into a practical application. The dependent claims merely have additional steps such as “determining”, “collecting”, “obtaining”, “treating”, “adjusting”, “performing”,” extracting”, “converting” and “generating”. Therefore, dependent claims 3-6 and 9-10 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1, 3-6, & 9-10 are ineligible under 35 USC §101.
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, 3-6, & 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 20200109954 A1) in view of Mori (US 20210245777 A1) in view of He (US 20210323572 A1), in view of Su CN105674993A (English Translation).
Regarding Claim 1, Li teaches A method for generating a high-precision road map, comprising (see at least [¶0316-0320]):
collecting a plurality of road information to be processed (Collecting a plurality of road environment information to be processed. see at least [¶0322 & 0341]);
performing feature extraction on the plurality of the road information to be processed, and obtaining road feature information (Performing feature extraction on the plurality of road environment information that was collected by sensors, and obtaining road feature information by identifying objects. see at least [¶0334, 0341 & 0369]);
Li does not explicitly teach and generating a high-precision road map according to the road feature information, vehicle body posture control information of a vehicle and mileage information.
Shall be noted that Li teaches to improve mapping of a vehicle by using visual odometry to obtain better positioning of objects and the vehicle. (see at least [“[¶0416] In some cases, if the rotation matrix and translation vector of a camera obtained by the visual odometer using visual odometry are both correct, then feasible positioning and mapping can be obtained.”) For more clarification the examiner is using secondary reference of Mori.
Mori does teach and generating a high-precision road map according to the road feature information, vehicle body posture control information of a vehicle and mileage information (Generating a high precision road map based on collected road feature information, vehicle pose information and vehicle mileage information. see at least [¶036-039, 041, 057-059 & 065-066]);
Mori would be in a similar field as it also deals in the area of generating maps with vehicle information. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Li to use the technique of generating a high-precision road map according to the road feature information, vehicle body posture control information of a vehicle and mileage information as taught by Mori. Doing so would lead to improved generation of a map with higher accuracy (see at least [¶066]).
Li and Mori do not explicitly teach wherein the collecting the plurality of the road information to be processed comprises: in response to receiving a sensor control instruction, determining an information collection timestamp according to the sensor control instruction; and collecting the plurality of the road information to be processed according to the information collection timestamp.
However, He does teach wherein the collecting the plurality of the road information to be processed comprises: in response to receiving a sensor control instruction, determining an information collection timestamp according to the sensor control instruction (The collection of road/environment information is done with a timestamp in mind, the collection timestamp is determined based the timestamp associated with the point cloud and based on that point cloud timestamp, additional road/environment information can be collected with that timestamp in mind for use in map generation. see at least [¶070-071, 0124-0131 & 0135]);
and collecting the plurality of the road information to be processed according to the information collection timestamp (The collection of road/environment information is done with a timestamp in mind, the collection timestamp is determined based the timestamp associated with the point cloud and based on that point cloud timestamp, additional road/environment information can be collected with that timestamp in mind for use in map generation. see at least [¶070-071, 0124-0131 & 0135]).
He would be in a similar field as it also deals in the area of generating map for use by autonomous vehicles. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Li and Mori to use the technique of collecting the plurality of the road information to be processed comprises: in response to receiving a sensor control instruction, determining an information collection timestamp according to the sensor control instruction; and collecting the plurality of the road information to be processed according to the information collection timestamp as taught by He. Doing so would lead to improved road information collection for use in feature extraction (see at least [¶131]).
Li, Mori and He do not explicitly teach wherein the generating the high-precision road map according to the road feature information, the vehicle body posture control information of the vehicle and the mileage information comprises: determining predicted vehicle body posture control information of the vehicle according to the vehicle body posture control information and the mileage information; determining inertial navigation auxiliary information of the vehicle according to the predicted vehicle body posture control information; and generating the high-precision road map according to the road feature information and the inertial navigation auxiliary information; the inertial navigation auxiliary information is auxiliary information provided to an inertial navigation system.
However, Su does teach wherein the generating the high-precision road map according to the road feature information, the vehicle body posture control information of the vehicle and the mileage information comprises: determining predicted vehicle body posture control information of the vehicle according to the vehicle body posture control information and the mileage information (Determining a predicted vehicle posture according to previously collected vehicle posture information and mileage/odometer information. see at least [¶010-016, 044-047 & 097-0101]);
determining inertial navigation auxiliary information of the vehicle according to the predicted vehicle body posture control information (Determining auxiliary inertial navigation information of the vehicle from the predicted vehicle posture according to previously collected vehicle posture information and mileage/odometer information. see at least [¶010-016, 044-047 & 097-0101]);
and generating the high-precision road map according to the road feature information and the inertial navigation auxiliary information (Generating a high precision road map based on collected toad information and auxiliary inertial navigation information. see at least [¶010-016, 044-047 & 097-0101]).
the inertial navigation auxiliary information is auxiliary information provided to an inertial navigation system (Auxiliary inertial navigation information is additional information that can be provided to the inertial navigation system. see at least [¶044-047]).
Su would be in a similar field as it also deals in the area of generation of a high precision map. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Li, Mori and He to use the technique of generating the high-precision road map according to the road feature information, the vehicle body posture control information of the vehicle and the mileage information comprises: determining predicted vehicle body posture control information of the vehicle according to the vehicle body posture control information and the mileage information; determining inertial navigation auxiliary information of the vehicle according to the predicted vehicle body posture control information; and generating the high-precision road map according to the road feature information and the inertial navigation auxiliary information; the inertial navigation auxiliary information is auxiliary information provided to an inertial navigation system as taught by Su. Doing so would lead to generation of a high precision map for higher accuracy in vehicle positioning (see at least [¶018]).
Regarding Claim 3, Li, Mori, He and Su teach all of the limitations of claim 1 as shown above, furthermore, He teaches wherein the collecting the plurality of the road information to be processed according to the information collection timestamp comprises: collecting a plurality of initial road information according to the information collection timestamp (Collecting Road information according to the information collection timestamp. see at least [¶0125-0131]);
obtaining road sampling frequency information corresponding to each initial road information respectively (Obtaining a road sampling frequency information from the initial road information collected. see at least [¶0125-0131]);
determining whether the road sampling frequency information meets a preset frequency condition (Determining that the road sampling frequency meets a preset frequency. see at least [¶0125-0131]);
and in response to that the road sampling frequency information meets the preset frequency condition, treating the plurality of the initial road information as the plurality of the road information to be processed (If the road sampling frequency information meets the preset frequency, treating the set of initial road information as information that must be processed. see at least [¶0125-0131]).
He would be in a similar field as it also deals in the area of generating map for use by autonomous vehicles. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Li, Mori and Su to use the technique of collecting the plurality of the road information to be processed according to the information collection timestamp comprises: collecting a plurality of initial road information according to the information collection timestamp; obtaining road sampling frequency information corresponding to each initial road information respectively; determining whether the road sampling frequency information meets a preset frequency condition; and in response to that the road sampling frequency information meets the preset frequency condition, treating the plurality of the initial road information as the plurality of the road information to be processed as taught by He. Doing so would lead to improved road information collection for use in feature extraction (see at least [¶131]).
Regarding Claim 4, Li, Mori, He and Su teach all of the limitations of claim 3 as shown above, furthermore, He teaches wherein after the determining whether the road sampling frequency information meets the preset frequency condition, the method further comprises: in response to that the road sampling frequency information does not meet the preset frequency condition (Determining that the road sampling frequency meets a preset frequency or not. see at least [¶0125-0131]),
adjusting the road sampling frequency information according to a preset rule, and obtaining target sampling frequency information (Adjusting the road sampling frequency to a preset rule and obtaining the new target sampling frequency information. see at least [¶0125-0131]);
and collecting the plurality of the road information to be processed according to the target sampling frequency information (Collecting the set of road information that must be processed according to the new target sampling frequency information. see at least [¶0125-0131]).
He would be in a similar field as it also deals in the area of generating map for use by autonomous vehicles. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Li, Mori and Su to use the technique of after the determining whether the road sampling frequency information meets the preset frequency condition, the method further comprises: in response to that the road sampling frequency information does not meet the preset frequency condition, adjusting the road sampling frequency information according to a preset rule, and obtaining target sampling frequency information; and collecting the plurality of the road information to be processed according to the target sampling frequency information as taught by He. Doing so would lead to improved road information collection for use in feature extraction (see at least [¶131]).
Regarding Claim 5, Li, Mori, He and Su teach all of the limitations of claim 1 as shown above, furthermore, Li teaches wherein the road information to be processed comprises road images to be processed and to-be-processed point cloud data (Road information collected from sensors can include road images and point cloud data. see at least [¶0369-0370 & 0429-0430]);
the performing feature extraction on the plurality of the road information to be processed, and obtaining the road feature information comprises (Performing feature/object extraction on the plurality of road information collected to obtain the road feature/object information can include. see at least [¶0369-0370 & 0429-0430]):
determining whether current weather information meets a preset weather condition (Determining if the current weather conditions meet a preset weather condition. see at least [¶0427-0430]);
in response to that the current weather information meets the preset weather condition, extracting a plurality of the road images to be processed from the plurality of the road information to be processed (In response to the current weather conditions meeting a preset weather condition (such as foggy conditions), extracting a set of road images that must be processed from the plurality of road information that is collected. see at least [¶0427-0430]);
converting the plurality of the road images to be processed respectively, and obtaining a plurality of road point cloud data (Converting the set of road images and obtaining a plurality of point cloud data from lidar sensors. see at least [¶0427-0430]);
and performing feature extraction on the plurality of the road point cloud data and the plurality of the to-be-processed point cloud data, and obtaining the road feature information (Performing feature/object extraction on the set of point cloud data to obtain road feature/object information. see at least [¶0427-0430]).
Regarding Claim 6, Li, Mori, He and Su teach all of the limitations of claim 5 as shown above, furthermore, Li teaches wherein after the determining whether the current weather information meets the preset weather condition, the method further comprises: in response to that the current weather information does not meet the preset weather condition, extracting the plurality of the to-be-processed point cloud data from the plurality of the road information to be processed (In response to the current weather conditions not meeting a preset weather condition (such as foggy conditions), extracting a set of point cloud data from lidar sensors that must be processed from the plurality of road information that is collected (since image data may not be fully reliable). see at least [¶0427-0430]);
converting the plurality of the road images to be processed and the plurality of the to-be-processed point cloud data respectively, to generate a plurality of road pixel information (Converting the set of road images and a plurality of point cloud data from lidar sensors by fusing them to obtain combined road information. see at least [¶0427-0430]);
and performing feature extraction on the plurality of the road pixel information, to generate the road feature information (Performing feature/object extraction on the set of road fused information to obtain road feature/object information. see at least [¶0427-0430]).
Regarding Claim 9, Li, Mori, He and Su teach all of the limitations of claim 1 as shown above, furthermore, Li teaches A high-precision road map generation equipment, comprising: a memory, a processor and a high-precision road map generation program stored in the memory and executable on the processor, wherein the high-precision road map generation program is configured to implement the method for generating the high-precision road map according to claim 1 (A map generation equipment can have a memory, processor and a program that can implement the method for map generation. see at least [¶0316, 0340, 0354-0355 & 0470-0472]).
Regarding Claim 10, Li, Mori, He and Su teach all of the limitations of claim 1 as shown above, furthermore, Li teaches A non-transitory computer-readable storage medium, wherein a high-precision road map generation program is stored on the non-transitory computer-readable storage medium, and the method for generating the high-precision road map according to claim 1 is implemented when the high-precision road map generation program is executed by a processor (A non-transitory medium can store the map generation program and the method can be executed by a processor. see at least [¶0316, 0340, 0354-0355 & 0470-0472]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/MOISES GASCA ALVA/Examiner, Art Unit 3667
/FARIS S ALMATRAHI/Supervisory Patent Examiner, Art Unit 3667
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