DETAILED ACTION
This action is filed in response to the application filed on 10/10/2023.
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 .
Information Disclosure Statement
Acknowledgement is made of Applicant’s Information Disclosure Statements (IDS) form PTO-1149 filed on 10/10/2023 and 5/29/2025. These IDS have been considered.
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 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. Claim 1, and similarly Claims 19-20 recite the following abstract concepts in BOLD of:
a method, comprising: using at least one computer hardware processor to perform: receiving magnetic field measurements of a vehicle, the magnetic field
measurements collected by a magnetic field sensor positioned proximate the vehicle; and
processing the magnetic field measurements using a trained ML model to detect, from the magnetic field measurements, a characteristic of the vehicle, the processing comprising:
generating magnetic field features from the magnetic field measurements; and
processing the magnetic field features using the trained ML model to obtain an output indicative of the characteristic of the vehicle.
Under Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category as Claim 1 discloses a method, Claim 19 discloses a system, and Claim 20 teaches a non-transitory computer readable storage medium.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
The step of using a trained machine learning model to detect vehicle characteristics can be interpreted as a mental process that can be performed in the human mind. Examiner notes the July 2024 Subject Matter Eligibility Guidance regarding Machine learning states that “the plain meaning of ‘detecting’ encompasses mental observations or evaluations e.g., a computer programmer’s mental identification of an anomaly in a data set” (e.g. see July 2024 Subject Matter Eligibility Examples pg. 6 paragraph 4). Examiner further notes the only detail provided regarding how the detection is made describes gathering data, which is extra-solution activity, and processing features. The limitation regarding processing features recites an additional abstract idea of performing mathematics while also providing mere instructions to implement an abstract idea on a generic computer. The July 2024 guidance states, “The limitations in (d) and (e) reciting ‘using the trained ANN’ provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it”… The trained ANN is used to generally apply the abstract idea without placing any limits on how the trained ANN functions,” (July 2024 Subject Matter Eligibility Examples pg. 8 paragraph 5).
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field.
The first limitations of Claim 1,19, and 20 disclosing at least one computer processor and a magnetic field sensor describe the elements composing the system and merely indicate a field of use as these elements impose no meaningful limitation of the claim. As recited in the MPEP 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Furthermore the limitations disclosing the use of a trained machine learning model also merely indicates a field of use or technological environment in which the judicial exception is performed, “Although the additional element ‘using a trained ANN’ limits the identified judicial exceptions ‘detecting one or more anomalies in a data set using the trained ANN’ and ‘analyzing the one or more detected anomalies using the trained ANN to generate anomaly data,’ this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h),” (e.g. see July 2024 Subject Matter Eligibility Examples pg. 9 paragraph 3).
Under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a processor and a magnetic field sensor are generic computer elements and not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Additionally, the steps of receiving magnetic field measurements and generating magnetic field features recite necessary data gathering and do not integrate the abstract ideas into a practical application. The limitation amounts to necessary data gathering and outputting. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Claims 2-18 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea:
Claims 2-5 further limit the data gathered and do not integrate the abstract ideas into a practical application. The limitation amounts to necessary data gathering and outputting. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Claims 6-8 and 14-18 further limit the type of data to be output and the way in which it is output which does not integrate the abstract idea into a practical application. As recited in MPEP section 2106.05(g), displaying analysis/results is considered extra solution activity. See MPEP 2106.05(g) “Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55”, see also MPEP 2106.05(h), As a whole the claim itself is analogous to the Electric Power Group decision in which it was determined that “ Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).”
Claims 9 and 10 further limit both the data gathered and the data output which does not integrate the abstract ideas into practical applications as both activities are insignificant extra solution activities. See MPEP 2106.05(g).
Claims 11-13 further limit the abstract idea of detecting characteristics by further defining the features of the machine learning model used without integrating the abstract idea into a practical application.
Claim Rejections - 35 USC § 102
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 1-2, 4-5, 11, 14, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Burak Kolukisa, Veli Can Yildirim, Bahadir Elmas, Cem Ayyildiz, Vehbi Cagri Gungor, "Deep learning approaches for vehicle type classification with 3-D magnetic sensor," Computer Networks, Volume 217 (hereinafter “Kolukisa”).
Regarding Claim 1, Kolukisa teaches a method, comprising: using at least one computer hardware processor (e.g. see [pg. 2 section 2.2] “The developed mote is equipped with a CC1312 wireless MCU that has a 48-MHz Cortex-M4F microcontroller, a specific radio controller that is based on Cortex-M0, an ultralow-power 8-bit sensor controller IC, 80 kB of SRAM, UART, I2C, and SPI …The developed embedded software on the mote is used for calibration of the sensors, reading the sensor measurements in real-time, processing and storing the collected data temporarily, and finally forwarding the stored data to the gateway”) to perform:
receiving magnetic field measurements of a vehicle, the magnetic field
measurements collected by a magnetic field sensor (e.g. see [pg. 3 section 2.5] “The vector magnitude dependent measurement is used in the second method to take samples based on certain magnetic field changes (for example, every 10 microteslas). The main benefit of this method is the ability to capture all magnetic field changes, and it allows for processing larger sample amounts, which can obtain more accurate signatures.”) positioned proximate the vehicle (e.g. see [pg. 3 section 2.5] “As the vehicle passes over a 3-D magnetic sensor node, magnetic
sensor interrupt and record the vehicles’ measurements.”); and
processing the magnetic field measurements using a trained ML model to detect, from the magnetic field measurements, a characteristic of the vehicle (e.g. see [ pg. 3 section 3.3] “Therefore, in this study, vehicle signals are converted into 2-D images in order to perform vehicle classification by utilizing transfer learning models”) the processing comprising:
generating magnetic field features from the magnetic field measurements (pg. 3 Section 3.1] “As the vehicle passes over the 3-D magnetic sensor node, it records the signals of the X, Y, and Z axes. The signal lengths of the samples are equalized by padding them with zeros according to the maximum signal length. In total, there are 621 features available for each sample when considering the three axes. Also, the features in the dataset are scaled between 0 and 1 using MinMaxScaler”); and
processing the magnetic field features using the trained ML model to obtain an output indicative of the characteristic of the vehicle (e.g. see [pg. 6 section 5.1 “In this study, using a camera and a 3-D magnetic sensor node, data was collected on intermediate road traffic by taking 376 vehicle samples and identifying the types of vehicles. LSTM, GRU, SVM, and transfer learning algorithms are applied to the dataset for vehicle type classification”).
Regarding Claim 2, Kolukisa teaches the limitations of Claim 1. Kolukisa further discloses collecting the magnetic field measurements using the magnetic field sensor (e.g. see [pg. 2 section 2] “The proposed system consists of a 3-D magnetic sensor to measure the intensity of magnetic fields”).
Regarding Claim 4, Kolukisa teaches the limitations of Claim 1. Kolukisa further discloses wherein the magnetic field measurements comprise a respective time series of measurements (e.g. see [pg. 3 section 2.2.] “The developed embedded software on the mote is used for calibration of the sensors, reading the sensor measurements in real-time”) for each of multiple measurement axes (e.g. see [pg. 3 section 3.1] “As the vehicle passes over the 3-D magnetic sensor node, it records the signals of the X, Y, and Z axes”).
Regarding Claim 5, Kolukisa teaches the limitations of Claim 4. Kolukisa further discloses wherein the magnetic field measurements comprise a time series of x-axis measurements, a time series of y-axis measurements, and a time-series of z-axis measurements (e.g. see [pg. 3 section 2.2.] “The developed embedded software on the mote is used for calibration of the sensors, reading the sensor measurements in real-time,” and see [pg. 3 section 3.1] “As the vehicle passes over the 3-D magnetic sensor node, it records the signals of the X, Y, and Z axes”).
Regarding 11, Kolukisa teaches the limitations of Claim 1. Kolukisa further discloses wherein the magnetic field measurements comprise a respective time series of measurements for each of multiple measurement axes (e.g. see [pg. 3 section 2.2.] “The developed embedded software on the mote is used for calibration of the sensors, reading the sensor measurements in real-time,” and see [pg. 3 section 3.1] “As the vehicle passes over the 3-D magnetic sensor node, it records the signals of the X, Y, and Z axes”), and
wherein generating magnetic field features from the magnetic field measurements comprises: resizing each of the time series of measurements (e.g. see [pg. 4 section 3.3] “Therefore, in this study, vehicle signals are converted into 2-D images in order to perform vehicle classification by utilizing transfer learning models. Firstly, the lengths of samples are scaled to the maximum signal length of 207”), and normalizing each of the time series of measurements (e.g. see [pg. 5 section 4.2] “parameters of the models have been regularized using batch normalization and dropout,” and [Figure 4]).
Regarding Claim 14, Kolukisa teaches the limitations of Claim 1. Kolukisa further discloses recording the magnetic field measurements using the magnetic field sensor; and transmitting the magnetic field measurements via at least one communication network to a computing device comprising the at least one computer hardware processor (e.g. see [pg. 3 section 2.2] “The built-in software on the mote is used to calibrate the sensor, get measurements from the sensor in real time, process and temporarily store the gathered data, and finally transmit the stored data to the gateway”).
Regarding Claim 19, Kolukisa teaches a system comprising: A system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor- executable instructions that, when executed by the at least one computer hardware processor cause the at least one computer hardware processor (e.g. see [pg. 2 section 2.2] “The developed mote is equipped with a CC1312 wireless MCU that has a 48-MHz Cortex-M4F microcontroller, a specific radio controller that is based on Cortex-M0, an ultralow-power 8-bit sensor controller IC, 80 kB of SRAM, UART, I2C, and SPI …The developed embedded software on the mote is used for calibration of the sensors, reading the sensor measurements in real-time, processing and storing the collected data temporarily, and finally forwarding the stored data to the gateway”) to perform:
receiving magnetic field measurements of a vehicle, the magnetic field
measurements collected by a magnetic field sensor (e.g. see [pg. 3 section 2.5] “The vector magnitude dependent measurement is used in the second method to take samples based on certain magnetic field changes (for example, every 10 microteslas). The main benefit of this method is the ability to capture all magnetic field changes, and it allows for processing larger sample amounts, which can obtain more accurate signatures.”) positioned proximate the vehicle (e.g. see [pg. 3 section 2.5] “As the vehicle passes over a 3-D magnetic sensor node, magnetic
sensor interrupt and record the vehicles’ measurements.”); and
processing the magnetic field measurements using a trained ML model to detect, from the magnetic field measurements, a characteristic of the vehicle (e.g. see [ pg. 3 section 3.3] “Therefore, in this study, vehicle signals are converted into 2-D images in order to perform vehicle classification by utilizing transfer learning models”) the processing comprising:
generating magnetic field features from the magnetic field measurements (pg. 3 Section 3.1] “As the vehicle passes over the 3-D magnetic sensor node, it records the signals of the X, Y, and Z axes. The signal lengths of the samples are equalized by padding them with zeros according to the maximum signal length. In total, there are 621 features available for each sample when considering the three axes. Also, the features in the dataset are scaled between 0 and 1 using MinMaxScaler”); and
processing the magnetic field features using the trained ML model to obtain an output indicative of the characteristic of the vehicle (e.g. see [pg. 6 section 5.1 “In this study, using a camera and a 3-D magnetic sensor node, data was collected on intermediate road traffic by taking 376 vehicle samples and identifying the types of vehicles. LSTM, GRU, SVM, and transfer learning algorithms are applied to the dataset for vehicle type classification”).
Regarding Claim 20, Kolukisa teaches at least one non-transitory computer-readable storage medium storing processor- executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor (e.g. see [pg. 2 section 2.2] “The developed mote is equipped with a CC1312 wireless MCU that has a 48-MHz Cortex-M4F microcontroller, a specific radio controller that is based on Cortex-M0, an ultralow-power 8-bit sensor controller IC, 80 kB of SRAM, UART, I2C, and SPI …The developed embedded software on the mote is used for calibration of the sensors, reading the sensor measurements in real-time, processing and storing the collected data temporarily, and finally forwarding the stored data to the gateway”) to perform:
receiving magnetic field measurements of a vehicle, the magnetic field
measurements collected by a magnetic field sensor (e.g. see [pg. 3 section 2.5] “The vector magnitude dependent measurement is used in the second method to take samples based on certain magnetic field changes (for example, every 10 microteslas). The main benefit of this method is the ability to capture all magnetic field changes, and it allows for processing larger sample amounts, which can obtain more accurate signatures.”) positioned proximate the vehicle (e.g. see [pg. 3 section 2.5] “As the vehicle passes over a 3-D magnetic sensor node, magnetic
sensor interrupt and record the vehicles’ measurements.”); and
processing the magnetic field measurements using a trained ML model to detect, from the magnetic field measurements, a characteristic of the vehicle (e.g. see [ pg. 3 section 3.3] “Therefore, in this study, vehicle signals are converted into 2-D images in order to perform vehicle classification by utilizing transfer learning models”) the processing comprising:
generating magnetic field features from the magnetic field measurements (pg. 3 Section 3.1] “As the vehicle passes over the 3-D magnetic sensor node, it records the signals of the X, Y, and Z axes. The signal lengths of the samples are equalized by padding them with zeros according to the maximum signal length. In total, there are 621 features available for each sample when considering the three axes. Also, the features in the dataset are scaled between 0 and 1 using MinMaxScaler”); and
processing the magnetic field features using the trained ML model to obtain an output indicative of the characteristic of the vehicle (e.g. see [pg. 6 section 5.1 “In this study, using a camera and a 3-D magnetic sensor node, data was collected on intermediate road traffic by taking 376 vehicle samples and identifying the types of vehicles. LSTM, GRU, SVM, and transfer learning algorithms are applied to the dataset for vehicle type classification”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Burak Kolukisa, Veli Can Yildirim, Bahadir Elmas, Cem Ayyildiz, Vehbi Cagri Gungor, "Deep learning approaches for vehicle type classification with 3-D magnetic sensor," Computer Networks, Volume 217 (hereinafter “Kolukisa”), in view of Chen (US20180292471 A1).
Regarding Claim 3, Kolukisa teaches the limitations of Claim 2. Kolukisa does not explicitly disclose wherein the magnetic field sensor is a magnetometer part of a smartphone positioned proximate the vehicle. In the same field of endeavor, Chen teaches disclose wherein the magnetic field sensor is a magnetometer part of a smartphone positioned proximate the vehicle (e.g. see [0030] “In the case that the magnetic features and the acceleration features are associated with a vehicle, the classification output by the classifier may be for a vehicle type, thereby inferring a mode of transportation utilized by a user of a mobile device having a magnetometer and accelerometer that captured the magnetometer and accelerometer data,”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the magnetic senor of Kolukisa with the magnetometer and smartphone embodiment of Chen for the purpose of classifying vehicles with the advantage of low cost and easily accessible sensing techniques.
Regarding Claim 15, Kolukisa teaches the limitations of Claim 1. Kolukisa further discloses generating an electronic vehicle condition report (e.g. see [pg. 2 section 2] “proposed system consists of a 3-D magnetic sensor to measure the intensity of magnetic fields, a mote responsible for reading the sensor outputs, a gateway responsible for transmitting the data provided by the sensor mote to the data center, and a web server responsible for analyzing and displaying the data that has been collected”).
While Kolukisa teaches analyzing the magnetic field data and displaying that analysis, Kolukisa does not explicitly disclose the vehicle condition report including the characteristic of the vehicle. In the same field of endeavor, Chen teaches generating an electronic vehicle condition report including the characteristic of the vehicle (e.g. see [0030] “i e magnetic features and the acceleration features can then be input to a classifier, which may be configured to evaluate the magnetic features and the acceleration features and output a classification that corresponds to the magnetic features and the acceleration features. In the case that the magnetic features and the acceleration features are associated with a vehicle, the classification output by the classifier may be for a vehicle type”).
It would have been obvious to one of ordinary skill in the art to combine the data output of Kolukisa with the vehicle classification report of Chen for the purpose of classifying vehicles with the advantage of transmitting the determined information to a user.
Regarding Claim 16, Kolukisa and Chen teach the limitations of Claim 15. Kolukisa further discloses transmitting the electronic vehicle condition report via at least one communication network to a remote device of an inspector of the vehicle (e.g. see [Figure 1] and [pg. 2 section 2] “proposed system consists of a 3-D magnetic sensor to measure the intensity of magnetic fields, a mote responsible for reading the sensor outputs, a gateway responsible for transmitting the data provided by the sensor mote to the data center, and a web server responsible for analyzing and displaying the data that has been collected”).
Furthermore, Chen also teaches transmitting the electronic vehicle condition report via at least one communication network to a remote device of an inspector of the vehicle (e.g. see [0046] “in one example, a mechanical device classification may be returned to the mobile device 302 where the mechanical device classification may be utilized by applications hosted on the mobile device 302. In another example, a mechanical device classification may be utilized by services and applications hosted on the server 310, or other servers included in a computing service environment”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the data output of Kolukisa with the remote device communication of Chen for the purpose of classifying a vehicle with the advantage of quickly disseminating the classification results.
Regarding Claim 17, Kolukisa and Chen teach the limitations of Claim 15. Kolukisa does not explicitly disclose transmitting the electronic vehicle condition report, via at least one communication network, to one or more reviewers. In the same field of endeavor, Chen teaches transmitting the electronic vehicle condition report, via at least one communication network, to one or more reviewers (e.g. see [0043-0044] “After identifying a classification for a mechanical device, the classifier module 210 may be configured to output the classification for use by applications 214. For example, applications 214 that utilize a vehicle classification output by the classifier module 210 may include personal healthcare applications, life coaching applications, or recommender systems. [0044] The various processes and/or other functionality contained within the mobile device 202 may be executed on one or more processors 216 that are in communication with one or more memory devices 218. Application components may be may be rendered on a mobile device display. The mobile device display may be a touchscreen that displays an interactive graphical user interface. The mobile device 202 may Input/Output (I/O) device communication to enable communication between hardware devices and I/O components,” Examiner notes the prior art teaches displaying the classification result on the display of a mobile device, it is inherent that this display is being shown to the user of the phone, i.e. the reviewer.).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the data output of Kolukisa with the reviewer display of Chen for the purpose of classifying a vehicle with the advantage of quickly disseminating the classification results to interested parties.
Claims 6-7 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Burak Kolukisa, Veli Can Yildirim, Bahadir Elmas, Cem Ayyildiz, Vehbi Cagri Gungor, "Deep learning approaches for vehicle type classification with 3-D magnetic sensor," Computer Networks, Volume 217 (hereinafter “Kolukisa”) in view of Yang (WO2020140049A1).
Regarding Claim 6, Kolukisa teaches the limitations of Claim 1. Kolukisa does not explicitly disclose wherein the characteristic of the vehicle is a model of the vehicle. In the same field of endeavor Yang teaches wherein the characteristic of the vehicle is a model of the vehicle (e.g. see [pg. 27 lines 12-19] “In examples where the machine learning model(s) 104 is trained to predict the bounding shapes, the ground truth encoding 110 may further include encoding the locations of the bounding shapes generated by an object detector (e.g., the object detector 214) as ground truth data. In some embodiments, a class of object or obstacle may also be encoded as ground truth and associated with each bounding shape. For example, where an object is a vehicle, a classification of vehicle, vehicle type, vehicle color, vehicle make, vehicle model, vehicle year, and/or other information may be associated with the bounding shape corresponding to the vehicle”).
It would have been obvious top one of ordinary skill in the art before the effective filling date to combine the vehicle characteristics of Kolukisa with those determined in Yang for the purpose of classifying vehicles with the advantage of a more in depth classification result.
Regarding Claim 7, Kolukisa teaches the limitations of Claim 1. Kolukisa does not explicitly disclose wherein the characteristic of the vehicle is the trim type of the vehicle. In the same field of endeavor, Yang teaches wherein the characteristic of the vehicle is the trim type of the vehicle (e.g. see [pg. 27 lines 12-19] “In examples where the machine learning model(s) 104 is trained to predict the bounding shapes, the ground truth encoding 110 may further include encoding the locations of the bounding shapes generated by an object detector (e.g., the object detector 214) as ground truth data. In some embodiments, a class of object or obstacle may also be encoded as ground truth and associated with each bounding shape. For example, where an object is a vehicle, a classification of vehicle, vehicle type, vehicle color, vehicle make, vehicle model, vehicle year, and/or other information may be associated with the bounding shape corresponding to the vehicle”).
It would have been obvious top one of ordinary skill in the art before the effective filling date to combine the vehicle characteristics of Kolukisa with those determined in Yang for the purpose of classifying vehicles with the advantage of a more in depth classification result.
Regarding Claim 9, Kolukisa teaches the limitations of Claim 1. Kolukisa does not explicitly disclose wherein the vehicle is an electric vehicle. In the same field of endeavor, Yang teaches wherein the vehicle is an electric vehicle (e.g. see [pg. 51 lines 8-10] “The vehicle 1400 may include components such as a chassis, a vehicle body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of a vehicle. The vehicle 1400 may include a propulsion system 1450, such as an internal combustion engine, hybrid electric power plant, an all-electric engine, and/or another propulsion system type.”).
It would have been obvious top one of ordinary skill in the art before the effective filling date to combine the vehicle of Kolukisa with the expanded vehicle types of Yang for the purpose of classifying vehicles with the advantage of identifying a wider variety of vehicles.
Regarding Claim 10, Kolukisa teaches the limitations of Claim 1. Kolukisa does not explicitly disclose wherein the vehicle is a hybrid vehicle. In the same field of endeavor, Yang teaches wherein the vehicle is a hybrid vehicle (e.g. see [pg. 51 lines 8-10] “The vehicle 1400 may include components such as a chassis, a vehicle body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of a vehicle. The vehicle 1400 may include a propulsion system 1450, such as an internal combustion engine, hybrid electric power plant, an all-electric engine, and/or another propulsion system type.”).
It would have been obvious top one of ordinary skill in the art before the effective filling date to combine the vehicle of Kolukisa with the expanded vehicle types of Yang for the purpose of classifying vehicles with the advantage of identifying a wider variety of vehicles.
Allowable Subject Matter
Examiner notes there are no prior art rejections for claims 8, 12-13, and 18. The following is a statement of reasons for indication of allowable subject matter:
Regarding Claim 8, none of the prior art discloses or renders obvious a method as claimed wherein “the characteristic of the vehicle is a battery type in the vehicle.”
Regarding Claim 12, none of the prior art discloses or renders obvious a method as claimed wherein “the trained ML model comprises a 1-dimensional (1D) convolutional neural network (CNN) trained to detect, from magnetic field measurements of a vehicle, the characteristic of the vehicle.”
Claim 13 would be allowable based on its dependence on claim 12.
Regarding Claim 18, none of the prior art discloses or renders obvious a method as claimed wherein “upon review and approval of the electronic vehicle condition report, initiating an online vehicle auction to auction the vehicle.”
Conclusion
Examiner notes while Claims 8, 12-13, and 18 have no prior art rejections, Examiner is unable to determine the allowability of any claims until the 35 U.S.C. 101 Rejections are addressed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NYLA GAVIA whose telephone number is (703)756-1592. The examiner can normally be reached M-F 8:30-5:30pm.
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857