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 .
DETAILED ACTION
The instant application having Application No. 17623132 has a total of 23 claims pending in the application, of which claims 2, and 4-5 have been cancelled.
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, and 6-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a process type claim. Claim 8 is a machine type claim. Therefore, claims 1, 3, and 6-23 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“Generating context information by synthetically analyzing the sensor data” A driver mentally or with pencil and paper looks at data from the sensor data and forms an opinion.
“converting GPS coordinate information of the vehicle to an address of an administrative district to determine a location of the vehicle” The driver mentally or with pencil and paper notes their location and legal district by looking at the GPS information.
“determining a type of a road environment at the determined location of the vehicle” The driver mentally or with pencil and paper looks around and notes the current road environment.
“obtaining a weather condition based on the determined location and a time of acquiring the sensor data” The driver mentally or with pencil and paper looks around and notes the current weather.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“a vehicle” (mere instructions to apply the exception using a generic computer component);
“Training an AI (Artificial intelligence) network for autonomous driving using the hierarchically structured GT dataset”, “the AI network trained Using the hierarchically structured GT dataset achieves improved object recognition performance by enabling classification of training data according to the context information” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain nothing but generic training and generic artificial intelligence algorithm, with no additional limitations or details beyond that of an off the shelf AI algorithm.
“acquiring and storing vehicle data”, “acquiring and storing sensor data generated at a sensor installed in a vehicle”, “Storing the context information in a world information layer of a hierarchically structured GT dataset, wherein the world information layer is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data is stored, and an area in which an annotation data is stored” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A vehicle (mere instructions to apply the exception using a generic computer component)
“Training an AI (Artificial intelligence) network for autonomous driving using the hierarchically structured GT dataset”, “the AI network trained Using the hierarchically structured GT dataset achieves improved object recognition performance by enabling classification of training data according to the context information” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain nothing but generic training and generic artificial intelligence algorithm, with no additional limitations or details beyond that of an off the shelf AI algorithm.
“acquiring and storing vehicle data”, “acquiring and storing sensor data generated at a sensor installed in a vehicle”, “Storing the context information in a world information layer of a hierarchically structured GT dataset, wherein the world information layer is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data is stored, and an area in which an annotation data is stored” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” as well as “Storing and retrieving data from memory” are well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed acquiring and storing steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 3, 10-12, and 14, these claims contain additional mental steps similar to claim 1, and are rejected for similar reasons to claim 1.
As per claim 6 this claim contains additional generic hardware and mental steps similar to claim 1, and is rejected for similar reasons to claim 1.
As per claim 7 this claim contains additional extra-solutionary activity, mental steps, and generic machine learning to claim 1, and is rejected for similar reasons to claim 1.
As per claim 9 this claim contains additional generic AI and mental steps to claim 1, and is therefore rejected for similar reasons to claim 1.
As per claim 13, this claim contains additional extra-solutionary activity to claim 1, and is rejected for similar reasons to claim 1.
As per claim 15, this claim contains additional extra-solutionary activity and mental steps to claim 1, and is rejected for similar reasons to claim 1.
As per claim 8,
2A Prong 1:
“Generate context information by synthetically analyzing the sensor data” A driver mentally or with pencil and paper looks at data from the sensor data and forms an opinion.
“converting GPS coordinate information of the vehicle to an address of an administrative district to determine a location of the vehicle” The driver mentally or with pencil and paper notes their location and legal district by looking at the GPS information.
“determining a type of a road environment at the determined location of the vehicle” The driver mentally or with pencil and paper looks around and notes the current road environment.
“obtaining a weather condition based on the determined location and a time of acquiring the sensor data” The driver mentally or with pencil and paper looks around and notes the current weather.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“one or more processors” “a vehicle” (mere instructions to apply the exception using a generic computer component);
“Training an Artificial Intelligence (AI) network for autonomous driving using the hierarchically structured GT dataset”, “the AI network trained Using the hierarchically structured GT dataset achieves improved object recognition performance by enabling classification of training data according to the context information” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain nothing but generic training and generic artificial intelligence algorithm, with no additional limitations or details beyond that of an off the shelf AI algorithm.
“acquire vehicle data”, “acquire sensor data generated at a sensor installed in a vehicle”, “Store the vehicle data and the sensor data and the context information in a hierarchically structured GT dataset, such that a world information layer in which the context information is stored is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data is stored, and an area in which an annotation data is stored” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A vehicle (mere instructions to apply the exception using a generic computer component)
“Training an AI (Artificial intelligence) network for autonomous driving using the hierarchically structured GT dataset”, “the AI network trained Using the hierarchically structured GT dataset achieves improved object recognition performance by enabling classification of training data according to the context information” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain nothing but generic training and generic artificial intelligence algorithm, with no additional limitations or details beyond that of an off the shelf AI algorithm.
“acquiring and storing vehicle data”, “acquiring and storing sensor data generated at a sensor installed in a vehicle”, “Storing the context information in a world information layer of a hierarchically structured GT dataset, wherein the world information layer is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data is stored, and an area in which an annotation data is stored” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” as well as “Storing and retrieving data from memory” are well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed acquiring and storing steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 16 this claim contains additional generic hardware and mental steps similar to claim 8, and is rejected for similar reasons to claim 8.
As per claim 17-19 and 23, these claims contain additional mental steps similar to claim 8, and are rejected for similar reasons to claim 8.
As per claim 20, this claim contains additional extra-solutionary activity to claim 8, and is rejected for similar reasons to claim 8.
As per claim 21 this claim contains additional extra-solutionary activity, mental steps, and generic machine learning to claim 8, and is rejected for similar reasons to claim 8.
As per claim 22 this claim contains additional generic AI and mental steps to claim 8, and is therefore rejected for similar reasons to claim 8.
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, 3, and 6-23 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.
As per claim 1, this claim calls for “Storing the context information in a world information layer of a hierarchically structured dataset, wherein the world information layer is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data I stored, and an area in which an annotation data is stored.” This limitation is not supported by the specification. While the specification clearly shows that data is arranged in a hierarchical manner (see instant specification Figure 2 and associated paragraphs) there is no discussion of the data actually being physically stored in this configuration. For example, paragraph 68 of the instant specification discloses that the data is stored in a storage unit 350. There is no designation of different storage units or things being stored at positions higher than where other data is stored. This causes the limitations to be new matter, and therefore rejected under U.S.C. 112(a).
As per claims 3, 6-7, and 9-15, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a) for new matter.
As per claim 8, this claim calls for “Store the vehicle data and the sensor data and the context information in a hierarchically a structured GT dataset such that a world information layer in which the context information is stored is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data I stored, and an area in which an annotation data is stored.” This limitation is not supported by the specification. While the specification clearly shows that data is arranged in a hierarchical manner (see instant specification Figure 2 and associated paragraphs) there is no discussion of the data actually being physically stored in this configuration. For example, paragraph 68 of the instant specification discloses that the data is stored in a storage unit 350. There is no designation of different storage units or things being stored at positions higher than where other data is stored. This causes the limitations to be new matter, and therefore rejected under U.S.C. 112(a).
As per claims 16-23, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a) for new matter.
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, and 6-23 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.
As per claim 1, this claim calls for “Storing the context information in a world information layer of a hierarchically structured dataset, wherein the world information layer is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data I stored, and an area in which an annotation data is stored.” This claim is confusing because the Examiner is unable to determine how data can be stored in “a hierarchically higher level” than other data. The specification at no time discloses multiple memories or the details of internal memory that would allow data to be stored at positions of “hierarchically higher level” than other data. This causes the claim to be confusing, and therefore rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claims 3, 6-7, and 9-15, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claim 8, this claim calls for “Store the vehicle data and the sensor data and the context information in a hierarchically a structured GT dataset such that a world information layer in which the context information is stored is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data I stored, and an area in which an annotation data is stored.” This claim is confusing because the Examiner is unable to determine how data can be stored in “a hierarchically higher level” than other data. The specification at no time discloses multiple memories or the details of internal memory that would allow data to be stored at positions of “hierarchically higher level” than other data. This causes the claim to be confusing, and therefore rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claims 16-23, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
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, 3, 6, 8, 10-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Luo et al (US 20190145765 A1) in view of Fields et al (US 9805601 B1) and Quinn (US 20200192872 A1).
As per claim 1, Luo discloses, “A ground truth dataset generation method comprising” (Pg.19, particularly paragraph 0200; EN: this denotes training with a labeled training data set (i.e. a ground truth set)).
“acquiring” (Pg.8, particularly paragraph 0082; EN: this denotes collecting data from sensors on the vehicle). “and storing vehicle data” (pg.8, particularly paragraph 0085; EN: this denotes storing the data).
“acquiring and storing sensor data generated at a sensor installed in a vehicle” (Pg.8, particularly paragraph 0082; EN: this denotes collecting data from sensors on the vehicle).
“generating context information” (Pg.4, particularly paragraph 0049-0050; EN: this denotes taking in map data about the locations the car is sensing). “by synthetically analyzing the sensor data, wherein the generating comprises” (pg.8, particularly paragraph 0079; EN: this denotes the computing system interacting with the sensors, thus synthetically analyzing the data).
“converting GPS coordinate information of the vehicle” (Pg.8, particularly paragraph 0082; EN: this denotes the use of GPS devices). “… to determine a location of the vehicle” (Pg.2, particularly paragraph 0025; EN: this denotes being aware of the vehicles geographical location).
“determining a type of a road environment of the determined location of the vehicle” (pg.3, particularly paragraph 0035; EN: this denotes looking at the roads around the environment).
“obtaining a weather condition…” (Pg.3, particularly paragraph 0035; EN: this denotes the system recognizing rain or snow).
“storing the context information…” (pg.8, particularly paragraph 0085; EN: this denotes storing the data). “… world information…” (pg.3, particularly paragraph 0035; EN: this denotes looking at the roads around the environment). “… vehicle data…” (Pg.8-9, particularly paragraph 0086; EN: this denotes vehicle data). “…sensor data…” (Pg.8, particularly paragraph 0082; EN: this denotes collecting data from sensors on the vehicle). “… annotation data…” (Pg.16, particularly paragraph 0164; EN: this denotes using sensor data as training data, along with other labeled training data).
“training an AI (artificial intelligence) network” (Pg.13, particularly paragraph 0128; EN: this denotes training a neural network with the training data). “for autonomous driving” (Pg.7, particularly paragraph 0077; EN: this denotes it being an autonomous driving vehicle). “… using the GT dataset” (Pg.16, particularly paragraph 0164; EN: this denotes using sensor data as training data, along with other labeled training data). “Wherein the AI network trained using the … GT dataset achieves improved object recognition performance by enabling classification of training data according to the context information” (Pg.5, particularly paragraph 0053; EN: this denotes using the neural network for object classification for the system).
However, Luo fails to explicitly disclose, “converting GPS coordinate information of the vehicle to an address of an administrative district”, “obtaining a weather condition based on the determined location and a time of acquiring the sensor data”, “Storing the context information in a world information layer of a hierarchically structured GT dataset, wherein the world information is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data is stored, and an area in which an annotation data is stored”, “… using the hierarchically structured GT dataset”
Fields discloses, “converting GPS coordinate information” (C35, particularly L1-17; EN: this denotes using GPS coordinates to determine location of the vehicle). “of the vehicle to an … of an administrative district” (C17, particularly L32-42; EN: this denotes looking at locations and their local laws such as speed limits).
“obtaining weather condition based on the determined location and a time of acquiring the sensor data” (C26, particularly L25-55; EN: this denotes querying the national weather service for current weather for their current situation).
While Luo and Fields fail to explicitly disclose, “Address” for contextual data of the vehicle, the Examiner is taking Official Notice that the use of an address to determine the laws of a location would be obvious to one of ordinary skill in the art, as the speed limit of a road will be associated with different addresses on the road in order to determine the speed limit at different portions of the road.
The Examiner made this Official Notice rejection under claim 5 in the previous office action mailed 4/23/26. Since Applicant has failed to argue or traverse this Official Notice, it is now considered Applicant Admitted Prior Art as per MPEP 2144.03(C).
Quinn discloses, “storing the context information in a world information layer” (Pg.7, particularly paragraph 0073; EN: this denotes the storing of environmental information (i.e. world information)). “of a hierarchically structured GT dataset” (Figure 5 and associated paragraphs, particularly pg.6, paragraph 0056-0057; EN: this denotes the hierarchical setup of the data). “wherein the world information layer is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data is stored, and an area in which an annotation data is stored” (Fig.8 and associated paragraphs; Pg.7, particularly paragraph 0066; EN: this denotes the system allowing data to be stored and modified as needed by the system).
“… using the hierarchically structured GT dataset” (Pg.11, particularly paragraph 0106; EN: this denotes using the data for machine learning).
Luo and Fields are analogous art because both involve autonomous vehicles.
Before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Fields in order to consider weather and laws based on location when controlling a vehicle.
The motivation for doing so would be to “ determine the occurrence of an anomalous condition based upon the received sensor data. Such anomalous conditions may include accidents, weather conditions, traffic conditions, construction or other roadway condition and/or high risk conditions” or in the case of Luo, allow the use of weather and local law data such as speed limits to determine information for an autonomous vehicle as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Fields in order to consider weather, location, and date when controlling a vehicle.
Luo and Quinn are analogous art because both involve autonomous vehicles.
Before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Quinn in order to allow data to be stored hierarchically.
The motivation for doing so would be to “more efficiently store data received from one or more devices, such as autonomous or semi-autonomous vehicles” (Quinn, Pg.6, particularly paragraph 0056) or in the case of Luo, allow the system to store the various data in a hierarchy that makes the system more easily to access and run.
Therefore before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Quinn in order to allow data to be stored hierarchically.
As per claim 3, Luo discloses, “wherein the context information is information that is referred to when the context at the time when the data is acquired is reconstituted” (Pg.4, particularly paragraph 0049-0050; EN: this denotes taking in map data about the locations the car is sensing. It is “reconstituted” whenever the map data is used in relation to the sensors).
As per claims 6 and 16, Luo discloses, “wherein the vehicle data comprises information regarding the vehicle and information regarding sensors mounted in the vehicle” (Pg.8, particularly paragraph 0082; EN: this denotes collecting data from sensors on the vehicle).
“wherein the storing the sensor data comprises synchronizing” (Pg.4, particularly paragraph 0049-0050; EN: this denotes taking in map data about the locations the car is sensing). “the sensor data sensed by the sensors through at least one of interpolation, up-sampling, and down-sampling and storing the sensor data” (pg.8, particularly paragraph 0085; EN: this denotes storing the data. Data that will be used together, such as the map data for a location is “synchronized” as it allow the data to be used with that location).
As per claim 8, Luo discloses, “A ground truth (GT) dataset generation system comprising” (Pg.19, particularly paragraph 0200; EN: this denotes training with a labeled training data set (i.e. a ground truth set)).
“one or more processors configured to” (Pg.18, particularly paragraph 0185; EN: this denotes the hardware of the system).
“acquire vehicle data” (Pg.8, particularly paragraph 0082; EN: this denotes collecting data from sensors on the vehicle). “and acquire sensor data generated at a sensor installed in a vehicle” (Pg.8, particularly paragraph 0082; EN: this denotes collecting data from sensors on the vehicle).
“generate context information” (Pg.4, particularly paragraph 0049-0050; EN: this denotes taking in map data about the locations the car is sensing). “by synthetically analyzing the sensor data, wherein the generating comprises” (pg.8, particularly paragraph 0079; EN: this denotes the computing system interacting with the sensors, thus synthetically analyzing the data).
“converting GPS coordinate information of the vehicle” (Pg.8, particularly paragraph 0082; EN: this denotes the use of GPS devices). “… to determine a location of the vehicle” (Pg.2, particularly paragraph 0025; EN: this denotes being aware of the vehicles geographical location).
“determining a type of a road environment of the determined location of the vehicle” (pg.3, particularly paragraph 0035; EN: this denotes looking at the roads around the environment).
“obtaining a weather condition…” (Pg.3, particularly paragraph 0035; EN: this denotes the system recognizing rain or snow).
“store the vehicle data and the sensor data and the context information” (pg.8, particularly paragraph 0085; EN: this denotes storing the data). “… world information…” (pg.3, particularly paragraph 0035; EN: this denotes looking at the roads around the environment). “… vehicle data…” (Pg.8-9, particularly paragraph 0086; EN: this denotes vehicle data). “…sensor data…” (Pg.8, particularly paragraph 0082; EN: this denotes collecting data from sensors on the vehicle). “… annotation data…” (Pg.16, particularly paragraph 0164; EN: this denotes using sensor data as training data, along with other labeled training data).
“train an artificial intelligence (AI) network” (Pg.13, particularly paragraph 0128; EN: this denotes training a neural network with the training data). “for autonomous driving” (Pg.7, particularly paragraph 0077; EN: this denotes it being an autonomous driving vehicle). “… using the… GT dataset” (Pg.16, particularly paragraph 0164; EN: this denotes using sensor data as training data, along with other labeled training data). “Wherein the AI network trained using the … GT dataset achieves improved object recognition performance by enabling classification of training data according to the context information” (Pg.5, particularly paragraph 0053; EN: this denotes using the neural network for object classification for the system).
However, Luo fails to explicitly disclose, “converting GPS coordinate information of the vehicle to an address of an administrative district”, “obtaining a weather condition based on the determined location and a time of acquiring the sensor data”, “Store the vehicle data and the sensor data and the context information in a hierarchically structured GT dataset such that a world information layer in which the context information is stored is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data is stored, and an area in which an annotation data is stored”, “… using the hierarchically structured GT dataset”
Fields discloses, “converting GPS coordinate information” (C35, particularly L1-17; EN: this denotes using GPS coordinates to determine location of the vehicle). “of the vehicle to an … of an administrative district” (C17, particularly L32-42; EN: this denotes looking at locations and their local laws such as speed limits).
“obtaining weather condition based on the determined location and a time of acquiring the sensor data” (C26, particularly L25-55; EN: this denotes querying the national weather service for current weather for their current situation).
While Luo and Fields fail to explicitly disclose, “Address” for contextual data of the vehicle, the Examiner is taking Official Notice that the use of an address to determine the laws of a location would be obvious to one of ordinary skill in the art, as the speed limit of a road will be associated with different addresses on the road in order to determine the speed limit at different portions of the road.
The Examiner made this Official Notice rejection under claim 5 in the previous office action mailed 4/23/26. Since Applicant has failed to argue or traverse this Official Notice, it is now considered Applicant Admitted Prior Art as per MPEP 2144.03(C).
Quinn discloses, “store the vehicle data” (Pg.6, particularly paragraph 0058; EN: this denotes storing data related to the vehicle in the hierarchy). “and the sensor data” (Pg.7, particularly paragraph 0069; EN: this denotes storing sensor data in the hierarchy). “and the context information” (Pg.7, particularly paragraph 0073; EN: this denotes the storing of environmental information (i.e. world information)). “in a hierarchically structured GT dataset” (Figure 5 and associated paragraphs, particularly pg.6, paragraph 0056-0057; EN: this denotes the hierarchical setup of the data). “such that a world information layer in which the context information is stored is positioned at a hierarchically higher level than an area in which the vehicle data is stored, an area in which the sensor data is stored, and an area in which an annotation data is stored” (Fig.8 and associated paragraphs; Pg.7, particularly paragraph 0066; EN: this denotes the system allowing data to be stored and modified as needed by the system).
“… using the hierarchically structured GT dataset” (Pg.11, particularly paragraph 0106; EN: this denotes using the data for machine learning).
Luo and Fields are analogous art because both involve autonomous vehicles.
Before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Fields in order to consider weather and laws based on location when controlling a vehicle.
The motivation for doing so would be to “ determine the occurrence of an anomalous condition based upon the received sensor data. Such anomalous conditions may include accidents, weather conditions, traffic conditions, construction or other roadway condition and/or high risk conditions” or in the case of Luo, allow the use of weather and local law data such as speed limits to determine information for an autonomous vehicle as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Fields in order to consider weather, location, and date when controlling a vehicle.
Luo and Quinn are analogous art because both involve autonomous vehicles.
Before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Quinn in order to allow data to be stored hierarchically.
The motivation for doing so would be to “more efficiently store data received from one or more devices, such as autonomous or semi-autonomous vehicles” (Quinn, Pg.6, particularly paragraph 0056) or in the case of Luo, allow the system to store the various data in a hierarchy that makes the system more easily to access and run.
Therefore before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Quinn in order to allow data to be stored hierarchically.
As per claims 10 and 17, Quinn discloses, “Wherein the hierarchically structured GT dataset further comprises a dataset header positioned at a top layer of the hierarchically structured GT dataset above the world information layer, the dataset header including at least one of license information, production institute information, and distribution related information of the GT dataset” (pg.4, particularly paragraph 0045; EN: this denotes having permissions requirements for access (i.e. distribution related information) at the root layer, the highest layer of the hierarchy).
As per claims 11 and 18, Luo discloses, “The vehicle information layer including at least one of: a model of the vehicle; coordinates of a reference point of the vehicle; sensor information including a type of a sensor mounted in the vehicle, a mounting position of the sensor, a mounting environment of the sensor, and characteristics of the sensor including at least one of a resolution, a view angle, a frequency, a channel number, and an operating frequency; and intrinsic and extrinsic parameter information of the sensor for calibration” (pg.2, particularly paragraph 0027; EN: this denotes being aware of the types of sensors in the system).
Quinn discloses, “Wherein the hierarchically structured GT dataset further comprises a vehicle information layer” (Pg.6, particularly paragraph 0058; EN: this denotes vehicle data in the hierarchy). “positioned below the world information layer” (Fig.8 and associated paragraphs; Pg.7, particularly paragraph 0066; EN: this denotes the system allowing data to be stored and modified as needed by the system).
As per claims 12 and 19, Luo discloses, “Wherein the weather condition comprises at least one of: a time of day condition including dawn, daytime, or nighttime; and a weather type condition including sunny, cloudy, foggy, rainy or snowy” (Pg.3, particularly paragraph 0035; EN: this denotes snow covered roads and rainy days).
As per claims 13 and 20, Luo discloses, “Wherein the sensor data … includes at least one of: posture information of the vehicle at a time of the data acquisition; accelerator or deceleration information of the vehicle at the time of the data acquisition; steering information of the vehicle at the time of the data acquisition; and a sensor data file list acquired at the time of the data acquisition for linking variation and acquired data” (Pg.9, particularly paragraph 0087; EN: this denotes the system being aware of the position of the vehicle (i.e. posture information of the vehicle)).
Quinn discloses, “Wherein the sensor data stored in the hierarchically structured GT dataset…” (Pg.7, particularly paragraph 0069; EN: this denotes storing sensor data in the hierarchy).
As per claim 15, Luo discloses, “further comprising combining the synchronized sensor data from a plurality of sensors on a same coordinates system to generate description information” (Pg.4, particularly paragraph 0047; EN: this denotes the sensors working in x, y, z coordinate systems).
Quinn discloses, “storing the description information in the hierarchically structured GT dataset” (Pg.7, particularly paragraph 0069; EN: this denotes storing sensor data in the hierarchy).
Claim Rejections - 35 USC § 103
Claims 7, 14, 21, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Luo et al (US 20190145765 A1) in view of Fields et al (US 9805601 B1) and Quinn (US 20200192872 A1) and further in view of Feng et al (“Deep Active Learning for Efficient Training of a LiDar 3D object Detector”).
As per claims 7 and 21, Luo discloses, “further comprising acquiring and storing GT information regarding the sensor data” (Pg.19, particularly paragraph 0200; EN: this denotes training with a labeled training data set (i.e. a ground truth set).
However, Luo fails to explicitly disclose, “acquiring and storing an annotation comprises acquiring the GT information which is manually correctable after generating by using an AI network which receive sensor data and infers the GT information.”
Feng discloses, “acquiring and storing an annotation comprises acquiring the GT information which is manually correctable after generating by using an AI network which receive sensor data and infers the GT information” (Pg.667, particularly the Introduction section; EN: this denotes active learning, where a machine learning algorithm designates images to be used for classification and requests manual labeling from humans as needed).
Luo and Feng are analogous art because both involve autonomous vehicles.
Before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Feng in order to use manual labeling for images identified by machine learning for machine learning.
The motivation for doing so would be to “evluat[] the informativeness of unlabeled data, select the most informative samples to be labeled by human annotators, and updates the training set with the newly-labeled data” (Feng, Pg.667, Introduction section, C1 last paragraph, C2 first paragraph) or in the case of Luo, allow the system to manually label images for the system to train on as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Feng in order to use manual labeling for images identified by machine learning for machine learning.
As per claims 14 and 23, Luo discloses, “Wherein the GT information comprises at least one of: a class ID of an object; two-dimensional (2D) or three-dimensional (3D) bounding box coordinates of the object; segmentation information of the object; and attribute information of the object including at least one of visibility information and characteristic information of the object” (Pg.3, particularly paragraph 0031; EN: this denotes classifying objects, with the class of that object being the class ID; Pg.10, particularly paragraph 0100; EN: this denotes using segmentation to detect the objects).
Claim Rejections - 35 USC § 103
Claims 9 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Luo et al (US 20190145765 A1) in view of Fields et al (US 9805601 B1), Quinn (US 20200192872 A1) and Feng et al (“Deep Active Learning for Efficient Training of a LiDar 3D object Detector”) and further in view of Breed et al (US 6405132 B1).
As per claims 9 and 22, Luo fails to disclose, “Wherein the AI network is trained using a superset training dataset that includes a superset class encompassing a GT class, such that the AI network identifies objects corresponding to the GT class from input sensor data and extracts the GT information therefrom.”
Breed discloses, “Wherein the AI network is trained using a superset training dataset that includes a superset class encompassing a GT class, such that the AI network identifies objects corresponding to the GT class from input sensor data and extracts the GT information therefrom” (C76, particularly L40-63; EN: this denotes general classes (i.e. superset class) and subclass (I.e. GT class) for neural networks. When combined with the Luo reference, this denotes allowing classes and subclasses to be used together for object recognition).
Luo and Breed are analogous art because both involve autonomous vehicles.
Before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Breed in order to use subclasses when classifying and training neural networks.
The motivation for doing so would be because “The human mind operates with modular neural networks where the object to be identified is first determined to belong to a general class and then to a subclass etc. Object recognition neural networks can frequently make use of this principle with a significant simplification resulting” (Breed, C76, L49-63) or in the case of Luo, allow the system to have subclasses relating to classes in order to further identify objects as needed by the system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of autonomous vehicles to combine the work of Luo and Breed in order to use subclasses when classifying and training neural networks.
Response to Arguments
In pg.10, the Applicant argues in regards to the rejection under U.S.C. 101 of the independent claims,
The present invention solves this technical problem by generating a hierarchically structured GT dataset (e.g., comprising a dataset header, a world information layer, a vehicle information layer, a data layer, and an annotation layer) in which context information (generated by the specific synthetic analysis of sensor data) is stored in the world information layer positioned at a hierarchically higher level than the other data layers.
This specific hierarchical data structure enables training data to be classified and analyzed according to acquisition context, directly resulting in improved object recognition performance of the trained AI network for autonomous driving. (see paragraphs [0065] and [0072] of Specification).
IN response, the Examiner maintains the rejection as shown above. The Applicant merely claims that data is stored in a way that relates to one another. However, merely describing data as stored is insignificant extra-solutionary activity, and not enough to cause the claim to be significantly more than the abstract idea. Manipulating the data that goes into or comes out of a generic machine learning algorithm does not disclose improving the machine learning algorithm, it discloses improving the abstract idea that the generic machine learning algorithm has applied to it. Therefore the rejection is maintained as shown above.
Applicant's remaining arguments with respect to claims 1, 3, and 6-23 have been considered but are either repetitions of the above argument or are moot in view of the new ground(s) of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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