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 .
Response to Arguments
Applicant’s arguments, see the section titled “II. Rejections Under 35 U.S.C. 101” starting on page 8 of the reply filed 08/26/2026, with respect to the rejection of claims 16, 22-29, and 31-33 under 35 U.S.C. 101 have been fully considered and are persuasive. In light of the amended claims, the rejection of claims 16, 22-29, and 31-33 under 35 U.S.C. 101 has been withdrawn.
Applicant's arguments, see the section titled “II. Rejections Under 35 U.S.C. 101” starting on page 8 of the reply filed 08/26/2026, with respect to the rejection of claims 17-19, 21, and 30 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Regarding claim 30, in the first paragraph beginning on page 10 of the reply filed 08/26/2026, Applicant argues that “claim 30 does not merely recite an abstract step, and is not insignificant extra-solution activity”; however, the Examiner disagrees. In the following paragraph, Applicant goes on to argue that “claim 30 does not recite a mental process” as “Obliteration, as claimed, is a destructive physical operation performed in information.”
Firstly, Examiner opines that “obliteration” as claimed may be broader than intended by Applicant, and does not require a destructive physical operation. For example, in paragraph [0067] of the instant specification, Applicant describes that “Obliterating such information may include any measure that is suitable to remove the sensitive information from the object-specific information or make it unrecognizable.” Whether or not the “obliteration” is a physical process, the Examiner further notes that 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, and further that the courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid or those performed on a computer (See MPEP 2106.14(a)(2)(III)). As the Examiner understands obliteration, such as removal or erasure of data, to be a well understood data manipulation technique that may be performed as a mental process, it is the Examiner’s opinion that the claim language comprises an abstract idea.
In the last paragraph beginning on page 10 of the reply filed 08/26/2026, Applicant further argues that “the obliteration limitation integrates the claim into a practical application under Step 2A, Prong Two”; however, the Examiner disagrees. As expressed above, it is the Examiner’s opinion that the “obliteration” is directed to an abstract idea without significantly more. Nothing in the “obliterating” limitation precludes the step from being performed in the mind and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In the first paragraph beginning on page 11 of the reply filed 08/26/2026, Applicant argues that “the rejection of claim 30 cannot be sustained at Step 2B”; the Examiner disagrees. It is the Examiner’s opinion that “obliteration” of information under its broadest reasonable interpretation does not amount to significantly more than the abstract idea.
Therefore, the rejection of claims 17-19, 21, and 30 under 35 U.S.C. 101 is maintained. See the rejection under 35 U.S.C. 101 below.
Applicant’s arguments, see the sections titled “III. Anticipation Rejection of Claim 16”, “IV. Anticipation Rejections of Claims 22-25, 28, 29, and 31-33”, and “VI. Obviousness Rejections of Claims 26 and 17” starting on page 11 of the reply filed 08/26/2026, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Claim 16 and corresponding dependent claims are now rejected under 35 U.S.C. 103 over at least Buerkle (US 2021/0107530 A1), in view of Hashimoto (US 2023/0097749 A1). See the rejections under 35 U.S.C. 103 below.
Applicant's arguments, see the sections titled “V. Anticipation Rejection of Claims 17-21 and 30” starting on page 14 of the reply filed 08/26/2026, have been fully considered but they are not persuasive.
Regarding claim 30, in the second paragraph beginning on page 14 of the reply filed 08/26/2026 Applicant argues that “Buerkle fails to disclose or suggest ‘wherein obtaining the object-specific information comprises obliterating a portion of the object-specific information based on the at least one monitoring criterion’”; however, the Examiner disagrees.
For example, in the last paragraph beginning on page 14 of the reply filed 08/26/2026, Applicant further argues that Buerkle’s privacy-policy filtering is not obliteration based on the monitoring criterion, where “In Buerkle, the content withheld from the result is dictated by a privacy policy that exists independently of any particular sensor data request” and in the second paragraph beginning on page 15 of the reply filed 08/26/2026 that the cited validation of Buerkle is performed as part of validating results already received from the set of vehicles in contrast to validation as part of obtaining the object-specific information. Examiner is not persuaded that performing validation that the sensor data results comply with a privacy policy is necessarily precluded from being part of “obtaining the object-specific information” as argued by Applicant. For example, Examiner indicates that the claim language does not limit when the obliteration occurs in respect to the obtaining or providing steps, or by whom or what the obtaining and obliterating is performed. It is the Examiner’s opinion that validating the sensor data results comply with a privacy policy, and for example filtering information based on the privacy policy as disclosed, is part of “obtaining the object-specific information” as claimed in that the object-specific information obtained before validation may differ from the object-specific information obtained after validation, evidencing that the validation at least affects the “obtaining the object-specific information” step.
Therefore, the rejection of claims 17-21 and 30 under 35 U.S.C. 102(a)(1) and (a)(2) are maintained. See the rejections below.
Applicant's arguments, see the section titled “VII. New Claim 34” on page 16 of the reply filed 08/26/2026, have been fully considered but they are not persuasive.
As indicated by Applicant in the second paragraph beginning on page 16 of the reply filed 08/26/2026, new claim 34 represents subject matter of previously presented claim 20 rewritten in independent format. In the following paragraph, Applicant argues that “There is no comparison involving a reference software architecture” in Buerkle; however, the Examiner disagrees. The Examiner understands the data privacy and integrity check 435, which includes the data heuristics 445 generated from the testing for additional validation, as disclosed in paragraph [0038] to comprise at least a comparison to “at least one reference software architecture” under its broadest reasonable interpretation. See also paragraph [0036] where Buerkle discloses that the data heuristic 445 is generated in the pre-deployment testing, and paragraph [0039] where if anomalies are detected or the output results do not pass the data privacy and integrity check 435 using the data heuristics 445, then a warning may be generated and stored together with a recording of the input data and output results which may be used to analyze the input data and output results to determine errors such as a privacy violation.
Therefore, the previous rejection of claim 20 under 35 U.S.C. 102(a)(1) and (a)(2) is applied to new claim 34 which contains corresponding subject matter. See the rejection below.
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 17-19, 21, and 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Regarding independent claim 30, the claim falls under a statutory category as it recites a method including at least one step.
Step 2a) Prong One: Claim 30 recites a judicial exception. The claim recites:
“obtaining, using at least one sensor of the vehicle and a monitoring criterion, object-specific information on an object related to a third party;
providing the object-specific information to the third party, wherein obtaining the object-specific information comprises obliterating a portion of the object-specific information based on the at least one monitoring criterion.”
This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind.
Step 2a) Prong Two: Claim 30 does not integrate the abstract idea into a practical application. The claim recites an element of “obtaining”. The step is recited at a high level of generality (i.e. as a general means of obtaining information), and amounts to mere data gathering, which is a form of extra-solution activity. Other than reciting “using at least one sensor of the vehicle” nothing in the claim elements preclude the step from being performed in the mind. For example, but for the sensor language, the claim encompasses obtaining, providing, and obliterating, i.e. broadly manipulating and “outputting,” information, which is understood to be a simple judgment as the obtaining, providing, and obliterating are recited at a high level of generality. The Examiner notes that in paragraph [0067] of the instant specification, Applicant describes that “Obliterating such information may include any measure that is suitable to remove the sensitive information from the object-specific information or make it unrecognizable” where the Examiner opines that removing or altering data merely comprises data manipulation which may be performed in the abstract, such as by a mental process. The mere recitation of “using at least one sensor of the vehicle” does not take the claim limitations out of the mental process grouping. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2b: Claim 30 is ineligible, as the additional elements in the claim amounts to no more than insignificant extra-solution activity. The steps of obtaining information via sensors are not considered to be more than what is well-understood, routine, and conventional activity in the field. 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. Similarly, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 indicates that storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner. Therefore, the claimed element does not amount to significantly more than the abstract idea.
The dependent claims 17-19 and 21 do not add anything significantly more to the abstract idea, and merely recite additional abstract steps and insignificant extra-solution activity. Therefore, claims 17-19, 21, and 30 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 17-21, 30, and 34 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Buerkle (US 2021/0107530 A1).
Regarding claim 30, Buerkle discloses a method for monitoring an environment of a vehicle, the method comprising:
obtaining, using at least one sensor of the vehicle and a monitoring criterion, object-specific information on an object related to a third party (In paragraphs [0013-0014], Buerkle discloses that a vehicle 105 may be equipped with ADAS, and the ADAS system may include a camera, where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored; see also paragraph [0016] where Buerkle discloses that different types of vehicles may provide different interfaces to sensors, although the type of data (e.g. camera streams, lidar data, radar data) may be comparable or may be easily transformed into unified forms);
providing the object-specific information to the third party (In paragraphs [0013-0014], Buerkle discloses where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored),
wherein obtaining the object-specific information comprises obliterating a portion of the object-specific information based on the at least one monitoring criterion (In paragraph [0045], Buerkle discloses that the validation of the sensor data results may include verifying the sensor data results comply with a privacy policy, for example, a privacy policy may include not providing any images of people's faces in the results or may filter the amount of location data provided which may lead to tracking of a vehicle operator; see also paragraph [0044] where Buerkle discloses that the technique 500 includes an operation 512 to transmit a command to remove the application from the set of vehicles).
The Examiner notes that in paragraph [0067] of the instant specification, Applicant describes that “Obliterating such information may include any measure that is suitable to remove the sensitive information from the object-specific information or make it unrecognizable.” It is the Examiner’s opinion that “not providing any images of people's faces in the results or may filter the amount of location data provided which may lead to tracking of a vehicle operator” as disclosed by Buerkle in paragraph [0067] comprises an example of obliterating a portion of the information in that doing so removes sensitive information from the object-specific information under its broadest reasonable interpretation.
Regarding claim 17, Buerkle further discloses wherein obtaining the object-specific information comprises:
monitoring the environment of the vehicle for obtaining sensor data of the environment (In paragraphs [0013-0014], Buerkle discloses that a vehicle 105 may be equipped with ADAS, and the ADAS system may include a camera, where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored; see also paragraph [0016] where Buerkle discloses that different types of vehicles may provide different interfaces to sensors, although the type of data (e.g. camera streams, lidar data, radar data) may be comparable or may be easily transformed into unified forms);
and obtaining, based on the monitoring criterion, the object-specific information from the sensor data of the environment (In paragraphs [0013-0014], Buerkle discloses where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored).
Regarding claim 18, Buerkle further discloses wherein the obtained object-specific information comprises an object-specific portion of the sensor data of the environment (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole).
Regarding claim 19, Buerkle further discloses wherein the object-specific information comprises meta information on the object from the sensor data (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole).
Regarding claim 20, Buerkle further discloses wherein obtaining the object-specific information comprises obtaining the meta information from the sensor data using an aggregator and a privacy evaluator program for checking the aggregator by comparing the aggregator to at least one reference software architecture and/or based on training data used for training the aggregator (In paragraphs [0037-0039], Buerkle discloses that The AV sensor data is received by the input interface 415 and provided to the data collection application 420, the output results of the execution of the data collection application may be received at the output interface 430, which is then validated through the data privacy and integrity check 435, the data privacy and integrity check 435 includes the data heuristics 445 generated from the testing for additional validation).
Regarding claim 21, Buerkle further discloses wherein the obtained object-specific information comprises an object-specific portion of the sensor data of the environment (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole).
Regarding claim 34, Buerkle discloses a method for monitoring an environment of a vehicle, the method comprising:
obtaining, using at least one sensor of the vehicle and a monitoring criterion, object-specific information on an object related to a third party (In paragraphs [0013-0014], Buerkle discloses that a vehicle 105 may be equipped with ADAS, and the ADAS system may include a camera, where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored; see also paragraph [0016] where Buerkle discloses that different types of vehicles may provide different interfaces to sensors, although the type of data (e.g. camera streams, lidar data, radar data) may be comparable or may be easily transformed into unified forms), by
monitoring the environment of the vehicle for obtaining sensor data of the environment (In paragraphs [0013-0014], Buerkle discloses that a vehicle 105 may be equipped with ADAS, and the ADAS system may include a camera, where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored; see also paragraph [0016] where Buerkle discloses that different types of vehicles may provide different interfaces to sensors, although the type of data (e.g. camera streams, lidar data, radar data) may be comparable or may be easily transformed into unified forms), and
obtaining, based on the monitoring criterion, the object-specific information from the sensor data of the environment (In paragraphs [0013-0014], Buerkle discloses where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored),
wherein the object-specific information comprises meta information on the object from the sensor data (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole);
providing the object-specific information to the third party (In paragraphs [0013-0014], Buerkle discloses where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored); and
wherein obtaining the object-specific information comprises obtaining the meta information from the sensor data using an aggregator and a privacy evaluator program for checking the aggregator by comparing the aggregator to at least one reference software architecture (In paragraphs [0037-0039], Buerkle discloses that The AV sensor data is received by the input interface 415 and provided to the data collection application 420, the output results of the execution of the data collection application may be received at the output interface 430, which is then validated through the data privacy and integrity check 435, the data privacy and integrity check 435 includes the data heuristics 445 generated from the testing for additional validation).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 16, 22-25, 28-29, and 31-33 are rejected under 35 U.S.C. 103 as being unpatentable over Buerkle (US 2021/0107530 A1) , in view of Hashimoto (US 2023/0097749 A1).
Regarding claim 16, Buerkle discloses a method for monitoring an environment of a vehicle, the method comprising:
obtaining, using at least one sensor of the vehicle and a monitoring criterion, object-specific information on an object related to a third party (In paragraphs [0013-0014], Buerkle discloses that a vehicle 105 may be equipped with ADAS, and the ADAS system may include a camera, where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored; see also paragraph [0016] where Buerkle discloses that different types of vehicles may provide different interfaces to sensors, although the type of data (e.g. camera streams, lidar data, radar data) may be comparable or may be easily transformed into unified forms), by
monitoring the environment of the vehicle for obtaining sensor data of the environment (In paragraphs [0013-0014], Buerkle discloses that a vehicle 105 may be equipped with ADAS, and the ADAS system may include a camera, where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored; see also paragraph [0016] where Buerkle discloses that different types of vehicles may provide different interfaces to sensors, although the type of data (e.g. camera streams, lidar data, radar data) may be comparable or may be easily transformed into unified forms),
and obtaining, based on the monitoring criterion, the object-specific information from the sensor data of the environment (In paragraphs [0013-0014], Buerkle discloses where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored),
wherein the object-specific information comprises meta information on the object from the sensor data (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole);
providing the object-specific information to the third party (In paragraphs [0013-0014], Buerkle discloses where the vehicle 105 may include a temporary application tasked with identifying roadways hazards, as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture roadway hazards, and this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, where a customer or third party client may provide a request for data including instructions for what should be detected or monitored); and
wherein obtaining the object-specific information comprises obtaining the meta information from the sensor data using an aggregator and a privacy evaluator program for checking the aggregator (In paragraphs [0037-0039], Buerkle discloses that The AV sensor data is received by the input interface 415 and provided to the data collection application 420, the output results of the execution of the data collection application may be received at the output interface 430, which is then validated through the data privacy and integrity check 435, the data privacy and integrity check 435 includes the data heuristics 445 generated from the testing for additional validation).
Buerkle does not explicitly disclose wherein obtaining the object-specific information comprises obtaining the meta information from the sensor data using an aggregator and a privacy evaluator program for checking the aggregator based on training data used for training the aggregator.
However, Hashimoto teaches wherein obtaining the object-specific information comprises obtaining the meta information from the sensor data using an aggregator and a privacy evaluator program for checking the aggregator based on training data used for training the aggregator (In paragraphs [0020-0022], Hashimoto teaches that the ECU 4 includes a component to judge a data collection event 15 configured to determine a similarity between the invariant feature map from the feature extractor 2 and template data from a template storer 17, where the data collection event judge 15 includes a NN for similarity estimation 30, where the data collection event judge 15 is configured to determine whether the collected data contains private information, such as privacy information, or personal information (PI), and where the private information is filtered out using the NN for similarity estimation 30, or in some embodiments, NN 30 is trained so that NN 30 implicitly filters out private information without the data collection event judge 15 determining whether the collected data contains private information by rule-based algorithm).
Hashimoto is considered to be analogous to the claimed invention in that they both pertain to object recognition and sensor processing in a vehicle control setting including performing privacy evaluation based on training data for training an aggregator. It would be obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Hashimoto with the method as disclosed by Buerkle, where the Examiner understands that the use of training data, such as for a neural network or other machine-learning process, is well understood in the art and may be implemented without undue experimentation, and with a reasonable expectation of success and predictable results. Doing so may be advantageous in that the use of a trained neural network as taught by Hashimoto may increase the accuracy and contextual sensitivity of the detections and subsequent privacy evaluation, for example.
Regarding claim 22, Buerkle further discloses wherein the monitoring criterion is indicative of a predefined location where the object is located (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where, for example, the detection results 350 may be limited to the location and a still picture of the hazard), and wherein obtaining the object-specific information comprises:
determining whether the object-specific information is representative of the predefined location (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole);
and obtaining the object-specific information if the object-specific information is representative of the predefined location (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole).
Regarding claim 23, Buerkle further discloses wherein the monitoring criterion is indicative of one or more predefined scenarios for the environment (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data), and wherein obtaining the object-specific information comprises:
determining whether at least one of the predefined scenarios is present in the environment (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole); and
obtaining the object-specific information if at least one of the predefined scenarios is present (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole).
Regarding claim 24, Buerkle further discloses wherein the monitoring criterion is indicative of one or more predefined scenarios for the environment (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data), and wherein obtaining the object-specific information comprises:
determining whether at least one of the predefined scenarios is present in the environment (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole); and
obtaining the object-specific information if at least one of the predefined scenarios is present (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole).
Regarding claim 25, Buerkle further discloses wherein determining whether at least one of the predefined scenarios is present comprises determining whether the object-specific information is indicative of at least one of the predefined scenarios, and wherein obtaining the object-specific information based on the monitoring criterion comprises obtaining the object-specific information if the object-specific information is indicative of at least one of the predefined scenarios (In paragraphs [0030-0031], Buerkle discloses that the sensor data requests may include parameters, such as the location or times for collecting data, where these parameters may activate 330 the sensor data processing 325 which may receive sensor data from the sensors 320 and process the sensor data to extract the data requested, for example, if the requested task is the detection of pot holes, the complete video including sound with the tracked path of the vehicle is not needed, instead, the sensor data processing 325 may use all of this data provided from the sensors 320 to identify and locate a pothole, but the detection results 350 may be limited to the location and a still picture of the pothole).
Regarding claim 28, Buerkle further discloses wherein the environment comprises at least one infrastructure object, and wherein the object-specific information is indicative of the at least one infrastructure object (In paragraph [0002], Buerkle discloses that potential applications may include street maintenance, such as the detection of broken streetlamps and road damage; in paragraphs [0013-0014]; Buerkle discloses that as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture a pothole 120 or other roadway hazards, where this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, and where the request may include instructions for what should be detected or monitored, for example, a request may be for the detection of potholes in the roadway or detecting broken streetlights).
Regarding claim 29, Buerkle further discloses wherein the monitoring criterion is indicative of at least one predefined infrastructure object (In paragraph [0002], Buerkle discloses that potential applications may include street maintenance, such as the detection of broken streetlamps and road damage; in paragraphs [0013-0014]; Buerkle discloses that as the vehicle 105 travels along the roadway 110, the field of view 115 of the camera may capture a pothole 120 or other roadway hazards, where this data captured by the camera of the vehicle 105 may then be reported by the application to a requesting party, for example, a municipality may have requested identifying roadway hazards, and where the request may include instructions for what should be detected or monitored, for example, a request may be for the detection of potholes in the roadway or detecting broken streetlights), and wherein obtaining the object-specific information comprises:
determining if the object-specific information is representative of the at least one predefined infrastructure object (In paragraph [0045], Buerkle discloses that the technique 500 may include further operations to validate that the sensor data results correspond to the request for sensor data, including validating that the results include the information requested, such as confirming the result data include the identification of potholes when the request was to identify potholes); and
obtaining the object-specific information if the object-specific information is representative of the at least one predefined infrastructure object (In paragraph [0045], Buerkle discloses that the technique 500 may include further operations to validate that the sensor data results correspond to the request for sensor data, including validating that the results include the information requested, such as confirming the result data include the identification of potholes when the request was to identify potholes).
Regarding claim 31, Buerkle further discloses a non-transitory computer readable medium having program code for performing at least one of the methods of claim 16 when the computer program is executed on a computer, a processor, or a programmable hardware component (In paragraphs [0046-0052], Buerkle discloses an example machine 600 upon which any one or more of the techniques (e.g., methodologies) discussed herein may be performed which includes a hardware processor 602 and a storage device (e.g., drive unit) 616, where storage device 616 may include a machine readable medium 622 on which is stored one or more sets of data structures or instructions 624 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein to be executed by the machine 600, where massed machine-readable media are not transitory propagating signals; see where Buerkle discloses the content of claim 16 above).
Regarding claim 32, Buerkle further discloses an apparatus (In paragraphs [0046-0052], Buerkle discloses an example machine 600 upon which any one or more of the techniques (e.g., methodologies) discussed herein may be performed) comprising:
one or more interfaces for communication (In paragraphs [0046-0052], Buerkle discloses an example machine 600 upon which any one or more of the techniques (e.g., methodologies) discussed herein may be performed which includes a user interface (UI) navigation device 614 and a network interface device 620 for example);
and a data processing circuit (In paragraphs [0046-0052], Buerkle discloses an example machine 600 upon which any one or more of the techniques (e.g., methodologies) discussed herein may be performed which includes a hardware processor 602) configured to:
control the one or more interfaces (In paragraphs [0046-0052], Buerkle discloses an example machine 600 upon which any one or more of the techniques (e.g., methodologies) discussed herein may be performed which includes a hardware processor 602, a user interface (UI) navigation device 614, and a network interface device 620 which may communicate with each other via an interlink (e.g., bus) 608); and
execute, using the one or more interfaces, the method of claim 16 (In paragraphs [0046-0052], Buerkle discloses an example machine 600 upon which any one or more of the techniques (e.g., methodologies) discussed herein may be performed which includes a hardware processor 602, a user interface (UI) navigation device 614, and a network interface device 620 which may communicate with each other via an interlink (e.g., bus) 608, where the instructions 624 may further be transmitted or received over a communications network 626 using a transmission medium via the network interface device 620 utilizing any one of a number of transfer protocols; see where Buerkle discloses the content of claim 16 above).
Regarding claim 33, Buerkle further discloses a vehicle comprising the apparatus of claim 32 (In paragraph [0029], Buerkle discloses that utilizing cloud based deployment 305, the deployed installation packages 310 may be wirelessly communicated, or OTA, to the vehicle, where the available processing systems 315 of the vehicle may execute the applications provided in the installation packages 310; see where Buerkle discloses the content of claim 32 above).
Claims 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over Buerkle (US 2021/0107530 A1) and Hashimoto (US 2023/0097749 A1), in view of Chen (US 10,953,877 B2).
Regarding claim 26, the combination of Buerkle and Hashimoto does not explicitly disclose wherein the method further comprises:
determining information on false positive detections of at least one of the scenarios from the object-specific information; and
determining a malfunction based at least in part on the information on false positive detections of at least one of the scenarios.
However, Chen teaches wherein the method further comprises:
determining information on false positive detections of at least one of the scenarios from the object-specific information (Starting with column 6 lines 56-67, Chen teaches a method for road condition prediction; in column 10 lines 22-60, Chen teaches that to determine whether there is a common problematic location, the number of vehicles that report the same or similar problematic location (i.e., the location with abnormal road condition) may be counted, and if the number of the vehicles reporting the same or similar problematic location is lower than the reporting threshold, for example only one, it is likely that the noises in the sensor data have caused a false positive); and
determining a malfunction based at least in part on the information on false positive detections of at least one of the scenarios (Starting with column 6 lines 56-67, Chen teaches a method for road condition prediction; in column 10 lines 22-60, Chen teaches that to determine whether there is a common problematic location, the number of vehicles that report the same or similar problematic location (i.e., the location with abnormal road condition) may be counted, and if the number of the vehicles reporting the same or similar problematic location is lower than the reporting threshold, for example only one, it is likely that the noises in the sensor data have caused a false positive).
Chen is considered to be analogous to the claimed invention in that they both pertain to determining false positives of road condition or infrastructure detection. It would be obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Chen with the method as disclosed by the combination of Buerkle and Hashimoto where the sensor data received from multiple source vehicles can be analyzed and cross-checked “to reduce the impact of the noises” in the data as suggested by Chen in column 10 lines 31-43, advantageously increasing the accuracy of the determined information, for example.
Regarding claim 27, the combination of Buerkle and Hashimoto does not explicitly disclose wherein the method further comprises:
determining information on a frequency of detections of at least one of the scenarios from the object-specific information; and
determining a malfunction based at least in part on the frequency of detections of at least one of the scenarios.
However, Chen teaches wherein the method further comprises:
determining information on a frequency of detections of at least one of the scenarios from the object-specific information (Starting with column 6 lines 56-67, Chen teaches a method for road condition prediction; in column 10 lines 22-60, Chen teaches that to determine whether there is a common problematic location, the number of vehicles that report the same or similar problematic location (i.e., the location with abnormal road condition) may be counted, and if the number of the vehicles reporting the same or similar problematic location is lower than the reporting threshold, for example only one, it is likely that the noises in the sensor data have caused a false positive); and
determining a malfunction based at least in part on the frequency of detections of at least one of the scenarios (Starting with column 6 lines 56-67, Chen teaches a method for road condition prediction; in column 10 lines 22-60, Chen teaches that to determine whether there is a common problematic location, the number of vehicles that report the same or similar problematic location (i.e., the location with abnormal road condition) may be counted, and if the number of the vehicles reporting the same or similar problematic location is lower than the reporting threshold, for example only one, it is likely that the noises in the sensor data have caused a false positive).
Chen is considered to be analogous to the claimed invention in that they both pertain to determining false determinations based on frequency of detections of road condition or infrastructure detection. It would be obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Chen with the method as disclosed by the combination of Buerkle and Hashimoto where the sensor data received from multiple source vehicles can be analyzed and cross-checked “to reduce the impact of the noises” in the data as suggested by Chen in column 10 lines 31-43, advantageously increasing the accuracy of the determined information, for example.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Torre (US 2022/0179090 A1) teaches systems and methods for detecting and addressing a potential danger, including where in various embodiments, the data deletion component redacts image data of the scanned individuals by blacking out faces or redacting facial features of the scanned individuals.
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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/HARRISON HEFLIN/ Examiner, Art Unit 3665
/HUNTER B LONSBERRY/ Supervisory Patent Examiner, Art Unit 3665
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