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 .
This communication is responsive to the Application No. 18/334,479 filled on 06/14/2023.
Claims 1-20 presented for examination.
Claim Objections
Claims 1, 8 and 15 are objected to because of the following informalities:
Regarding claim 1, the phrase “the processed vehicle-generated” should apparently be “the processed vehicle-generated data”. Claims 8 and 15 are also objected for the same reasons as discussed above. Appropriate correction is required.
On line 6 of claim 1, the acronym word, “GPS” should be presented for what it stands for (such as, Global Positioning System, Global Payment System, etc.). Claims 8 and 15 are also objected for the same reasons as discussed above. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 USC § 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 15-20 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter. The claim 15 recites “A computer program product, comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform…………”. Considering the open ended definition of the computer program product, it is computer software per se and is not a "process," a "machine," a "manufacture" or a "composition of matter," as defined in 35 USC § 101. In order to overcome 35 USC 101 rejection, the applicants are advised to amend the claim limitations as “A non-transitory computer readable storage medium having stored therein a computer program product comprising program instructions
Claims 16-20 are also rejected by the virtue of their dependency on claim 15.
Examiner's Note
Examiner has cited particular paragraphs/ columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching
all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to Applicants' definition which is not specifically set forth in the claims.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-5, 7-12 and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Leary et al. (US 2021/0080279 A1) (hereinafter Leary) in view of Brandon et al. (US 2024/0391420 A1) (hereinafter Brandon).
Claim 8. Leary teaches a system (See Fig. 1, “remote computing system 150”, and/or Para 3, Fig. 7, “computing system 700”) comprising:
a memory having computer readable instructions (See Para. [0095]-[0096], [0098]-[0099], discloses “the computing system includes storage device, stored therein software instructions and/or code , executed by processor”); and
one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations ((See Para. [0095]-[0096], [0098]-[0099], discloses “the computing system includes storage device, stored therein software instructions and/or code , executed by processor causing the system to perform function”) comprising:
receiving vehicle-generated data from a vehicle associated with a vehicle-generated data management system (See Para. [0026], [0030], “the remote computing system 150 receives data from autonomous vehicle 102/computing system 110 and analyze the data to train or evaluate machine learning algorithms for operating the autonomous vehicle 102”, and/or see Para. [0039], “remote computing system 150 receives sensor data (e.g., GPS data, image/video data, radar measurements, LIDAR measurements, inertial measurements, triangulation data, braking sensor data, ultrasonic sensor data, position sensor data, steering angle sensor data, steering wheel rotation sensor data, turn signal sensor data, parking sensor data, gear position sensor data, brake light sensor data, odometer data, speedometer data, accelerometer data, proximity sensor data, temperature measurements, light sensor data, etc.), provided by the autonomous vehicle 102”, and/or see Para. [0071], “the internal computing system 110 on the autonomous vehicle 102 can provide the sensor data 502 to the remote computing system 150”);
processing the vehicle-generated data to extract sensor data, GPS data, and time data (See Para. [0075], “the remote computing system 150 can filter out any portions of the sensor data 502”, where in para. [0068], discloses that the sensor for representing map data [constitutes GPS data], and AV state and context information”, and see Figs. 2A-4 discloses “AV arriving time”, which is time data, same as claimed. Furthermore, see Para. [0068], discloses “the sensor data 502 presents any of the information in the interfaces 200, 300 and/or 400 shown in FIGS. 2A-B, 3A-D, and 4, respectively”, and see Para. [0081], “the sensor data can include, for example and without limitation, GPS data, position measurements (e.g., x, y, z coordinates)”);
generating situational data using the sensor data, the GPS data and the time data (See Para. [0071], “the remote computing system process sensor data and supplements with other data before sending to the client device 170”. In other cases, the client device 170 can receive some or all of the sensor data 502 from the remote computing system 150 associated with the autonomous vehicle 102, and see Para. [0072], “the remote computing system 150 can process the sensor data 502 prior to providing it to the client device 170”, and see Para. [0076], “the sensor data 502 includes information about the environment around the autonomous vehicle”, hence the remote computing system generating situational data as claimed. Furthermore, see Para. [0081] ,discloses “sensor data can include “traffic data, weather data, image data depicting environment around the autonomous vehicle); and
transmitting the situational data and the processed vehicle-generated to a hybrid cloud system (See Para. 0071], “the internal computing system 110 on the autonomous vehicle 102 can provide the sensor data 502 to the remote computing system 150, which can then provide the sensor data 502 (e.g., as is, after being processed, and/or after being supplemented with other data) to the client device 170”).
The cited prior art, Leary discloses that the remote computing system [i.e., system as claimed] received sensor data, process sensor data and supplement the sensor data with other data and sends the processed data to client device. Leary further discloses in Para. [0016], “that the remote computing system 150 [i.e., system as claimed] can communicate to computing device 170 over hybrid cloud network”, and in Para. [0033], discloses “the computing device 170 can include a server. Therefore, the remote computing system [i.e., claimed “system”] transmit processed sensor data including situational data to a server. But, he doesn’t explicitly spell out, where the client device/server is a hybrid cloud system.
Brandon discloses in para. [0060]-[0061] that “data center 650 can be a private cloud, public cloud, hybrid cloud or multi-cloud” and in Para. [0049], disclose “The data center 650 can send and receive various signals to and from the AV 602 and the client computing device 670. These signals can include sensor data captured by the sensor systems 604-608”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to have modified the teaching of Leary with a hybrid cloud deployed as a data center as taught by Brandon in order to store a large volume of different types of sensor data and to provide respective services.
Claim 9. The teaching of Leary as modified by the teaching of Brandon teaches the system of claim 8, wherein the operations further comprise:
intercepting sensor data from a sensor of the vehicle (See Leary, Para. [0075]-[0076], “the remote computing system 150 receives sensor data from computing system 110 and can filter out any portions of the sensor data 502”, which constitutes intercepting sensor data); and
generating the vehicle-generated data by associating the sensor data with a current time and a current GPS location of the vehicle (See Leary, Para. [0071]-[0072], “the remote computing system process sensor data and supplements with other data before sending to the client device 170”, and see Para. [0081] ,discloses “sensor data can include “traffic data, weather data, image data depicting environment around the autonomous vehicle).
Claim 10. The teaching of Leary as modified by the teaching of Brandon teaches the system of claim 8, wherein the operations further comprise:
transmitting a request for the vehicle-generated data from the vehicle (See Leary, Para. [0029]-[0030], “the remote computing system 150 [i.e., claimed “system”] can be configured to send signals to computing system 110 for sensor data”. Additionally, see Brandon, Para. [0042], “the central computer requests updated vehicle data from the vehicle, receives updated vehicle data from the vehicle, and transmits the updated vehicle data to the fleet vehicle access application.”); and
receiving the vehicle-generated data from the vehicle in response to the request (See Brandon, Para. [0042], “the central computer requests updated vehicle data from the vehicle, receives updated vehicle data from the vehicle, and transmits the updated vehicle data to the fleet vehicle access application”), and/or see Leary, Para. [0071], “he internal computing system 110 on the autonomous vehicle 102 can provide the sensor data 502 to the remote computing system 150”).
Claim 11. The teaching of Leary as modified by the teaching of Brandon teaches the system of claim 10, wherein the operations further comprise:
receiving a notification that the vehicle is in an identified geographic area (See Leary, Para. [0076], “the internal computing system 110 can send the sensor data 502 to the remote computing system 150, where the sensor data 502 includes information about the environment around the autonomous vehicle”, same as claimed); and
transmitting the request for the vehicle-generated data of the vehicle in response to receiving the notification See Leary, Para. [0029]-[0030], “the remote computing system 150 [i.e., claimed “system”] can be configured to send signals to computing system 110 for sensor data”. Additionally, see Brandon, Para. [0042], “the central computer requests updated vehicle data from the vehicle, receives updated vehicle data from the vehicle, and transmits the updated vehicle data to the fleet vehicle access application”).
Claim 12. The teaching of Leary as modified by the teaching of Brandon teaches the system of claim 8, wherein the operations further comprise:
receiving a request for data associated with an identified location from a user device (See Leary, [0003], [0034], discloses “the rideshare service 158 can receive requests from passenger ridesharing application 172, such as user requests to be picked up or dropped off”);
retrieving a set of processed vehicle-generated data from the hybrid cloud system (See Brandon, Para. [0061], “the client computing device 670 [i.e., the system as claimed] receives from the data center 650 [i.e., hybrid cloud] can send and receive various signals to the client computing device 670. These signals can include sensor data captured by the sensor systems 604-608, roadside assistance requests”);
in response to the request, generating new situational data using the set of processed vehicle-generated data (See Leary, Para. [0029], “the remote computing system 150 can be configured to send and receive signals to and from the autonomous vehicle 102. The signals can include, for example and without limitation, data reported for training and evaluating services such as machine learning services, data for requesting assistance from remote computing system 150 or a human operator, software service updates, rideshare pickup and drop off instructions, etc.”); and
transmitting the new situational data to the user device (See Leary, Para. [0071], “the client device 170 can receive some or all of the sensor data 502 from the remote computing system 150 associated with the autonomous vehicle 102. For example, the internal computing system 110 on the autonomous vehicle 102 can provide the sensor data 502 to the remote computing system 150, which can then provide the sensor data 502 (e.g., as is, after being processed, and/or after being supplemented with other data) to the client device 170. In yet other cases, the client device 170 can receive a portion of the sensor data 502 from the internal computing system 110 on the autonomous vehicle 102 and another portion of the sensor data 502 from the remote computing system 150”. Additionally, see Brandon, Para. [0012], “systems and methods are provided for receiving a vehicle data request from an operator mobile device application, identifying a vehicle in close proximity to the operator mobile device, obtaining most recent vehicle data, and transmitting the requested vehicle data to the operator mobile device”).
Claims 1-5 and 7 are method claims corresponding to the system claims 8-12 and 14 and having substantially the same technical features as claims 8-12 and 14, differing only in the category of invention. Therefore, the claims 1-5 and 7 are rejected for the same rationales set forth as above for claims 8-12 and 14.
Claims 15-19 are computer program product claims corresponding to the system claims 8-12 and having substantially the same technical features as claims 8-12, differing only in the category of invention. Therefore, the claims 15-19 are rejected for the same rationales set forth as above for claims 8-12.
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Leary et al. (US 2021/0080279 A1) (hereinafter Leary) in view of Brandon et al. (US 2024/0391420 A1) (hereinafter Brandon) and further in view of Brahma et al. (US 2021/0383209 A1) (hereinafter Brahma).
Claim 13. The teaching of Leary as modified by the teaching of Brandon teaches the system of claim 8, wherein the operations to process the vehicle-generated data further comprises:
analyzing content of the vehicle-generated data (See Leary, Para. [0030], discloses “The remote computing system 150 can include an analysis service 152 configured to receive data from autonomous vehicle 102 and analyze the data to train or evaluate machine learning algorithms for operating the autonomous vehicle 102. The analysis service 152 can also perform analysis pertaining to data associated with one or more errors or constraints reported by autonomous vehicle 102”, and/or see Para. [0072], “the remote computing system 150 can process the sensor data 502 prior to providing it to the client device 170”);
extracting the sensor data, the GPS data, and the time data from the vehicle-generated data (See Leary, Para. [0075], “(See Para. [0075], “the remote computing system 150 can filter out any portions of the sensor data 502”, where in para. [0068], discloses that the sensor data for representing map data [constitutes GPS data], and AV state and context information”, and see Figs. 2A-4 discloses “AV arriving time”, which is time data, same as claimed. Furthermore, see Leary Para. [0068], discloses “the sensor data 502 presents any of the information in the interfaces 200, 300 and/or 400 shown in FIGS. 2A-B, 3A-D, and 4, respectively”, and in Para. [0081], discloses “the sensor data can include, for example and without limitation, GPS data, position measurements (e.g., x, y, z coordinates)”, same as claimed); and
Nevertheless, the teaching of Leary as modified by Brandon fails to teach, associating one or more category tags with the vehicle-generated data.
However, Brahma et al. (US 2021/0383209 A1) teaches, associating one or more category tags with the vehicle-generated data (See Para. [0097], “the augmentation module 805 may augment data, such as sensor data, candidate data, training data, etc., to include additional tags, labels, and/or annotations”, and/or see Para. [0009], [0102]-[0103], “The data analysis module 2010 may determine whether the candidate data can be associated with one or more categories of a set of categories for data, and a category may indicate that the data includes information about different environments where a vehicle may be located. The data analysis module 2010 may associate data (e.g., candidate data, training data, sensor data, etc.) with a category (or multiple categories) by associating the data with a tag (e.g., a label, an annotation, a name, etc.) for that category (or for multiple categories). For example, the data analysis module 2010 may update the data to include the tag. In another example, the data analysis module 2010 may generate metadata indicating an associating between the data and the tag (e.g., may update a table, a list, etc.). This may be referred to as tagging the data.”, and see Para. [0100], “the candidate data should be associated with a tag that indicates that the candidate data depicts a daytime environment.”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to have modified the teaching of Leary in view of the teaching of Brandon with a category by associating data with tag as taught by Brahma in order manage more efficiently the sensor data that is received from various vehicles.
Claim 14. The teaching of Leary as modified by the teaching of Brandon teaches the system of claim 8, wherein the operations further comprise:
receiving additional vehicle-generated data from a different vehicle (See Leary, Para. [0076], discloses “sensors on other vehicles in the fleet, and the internal computing system 110 can send the sensor data 502 to the remote computing system 150);
processing the additional vehicle-generated data (See Para. [0076], “The remote computing system 150 can receive such sensor data 502 and supplement it with other information about the environment obtained from one or more other sources, such as sensors on other autonomous vehicles in the fleet, the Internet, government sources, news sources, a third-party map/navigation application service, a geographic information system, a data repository associated with the remote computing system 150, etc.”);
generating additional situational data using the additional vehicle-generated data; and generating a set of situational data comprising the situational data and the additional situational data based on an identified GPS location or an identified time (See Leary, Para. [0071], “the remote computing system process sensor data and supplements with other data before sending to the client device 170”, and see Para. [0081] ,discloses “sensor data can include “traffic data, weather data, image data depicting environment around the autonomous vehicle [i.e., GPS location data], see Leary Para. [0039], “some or all of the information presented in the interface 200 can be based on sensor data (e.g., GPS data”, and Para. [0041], discloses “the information section 212 can display a notification 214 indicating an estimated time of arrival of the autonomous vehicle 102”); and
transmitting the set of situational data to the hybrid cloud system (see Brandon, Para. [0060]-[0061], discloses “data center 650 can be a private cloud, public cloud, hybrid cloud or multi-cloud” and in Para. [0049], discloses “The data center 650 can send and receive various signals to and from the AV 602 and the client computing device 670. These signals can include sensor data captured by the sensor systems 604-608”).
Claim 6 is method claim corresponding to the system claim 13 and having substantially the same technical features as claim 13, differing only in the category of invention. Therefore, the claim 6 is rejected for the same rationales set forth as above for claim 13.
Claim 20 is computer program product claim corresponding to the system claim 13 and having substantially the same technical features as claim 13, differing only in the category of invention. Therefore, the claim 20 is rejected for the same rationales set forth as above for claim 13.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to B M M HANNAN whose telephone number is (571)270-0237. The examiner can normally be reached MONDAY-FRIDAY at 8:30AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Mott can be reached at 5712705376. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B M M HANNAN/Primary Examiner, Art Unit 3657
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