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 .
Claim Objections
Claims 9 and 19 objected to because of the following informalities: each claim ends with a semicolon. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “computing devices” and neural network computing device” in claims 1-19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 1, 5-7, 10, 12, 15-17, and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bradlow et al (US2020/0124430A1).
Regarding claim 1, Bradlow teaches a system for estimating road roughness under real-world driving conditions through training and implementation of a convolutional neural network (CNN) (Paragraphs 0007-0008, 0087, 0130, 0154-0155), the system comprising: a. a plurality of computing devices (e.g., "handheld mobile device (smartphone, tablet) includes some or all of the components required to detect a surface type of a travel pathway" [Paragraphs 0045, 0056, 0091-0095]), wherein each computing device is mounted to a vehicle ("The handheld mobile device can be mounted and coupled to the PMV 300" in Paragraph 0095, as well as description of mounting means in Paragraph 0096 [which describes mounting of sensors, but given that paragraph 0095 recites that the sensors may be equipment of the handheld mobile device alternatively, it logically flows that the mounting may be applied to the smartphone as well]), wherein each computing device is capable of measuring, while the vehicle is driving on a road, a plurality of parameters comprising: i. global positioning system (GPS), ii. driving speed, iii. vertical acceleration, and iv. angular velocity of pitch motion (Paragraphs 0093-0095, 0100-0101, 0130); and b. a neural network computing device (either configuration of the computing device local to PMV, or remove server of cloud system [Paragraphs 0091, 0102, 0148-0149, 0161, 0179, etc.]) communicatively coupled to the plurality of computing devices, the neural network computing device comprising the CNN, the CNN comprising a plurality of convolutional layers (Paragraphs 0008, 0132, 0154-0155); wherein the CNN is capable of accepting the plurality of parameters from the plurality of computing devices as input and generating an international roughness index (IRI) value of the road as output based on the input (Paragraphs 0007-0008, 0087, 0130, 0154-0155 [additional description of the ML process is found at length throughout the specification]).
Regarding claim 5, Bradlow discloses the invention of claim 1 as discussed above, and Bradlow teaches measuring GPS is capable of GPS resolution enhancement by interpolation and grid snapping (no configuration of adequate structure to perform GPS resolution is recited).
Regarding claim 6, Bradlow discloses the invention of claim 1 as discussed above, and Bradlow teaches that the plurality of computing devices are capable of measuring vertical acceleration and angular velocity at 100 Hz (Paragraphs 0045, 0056, 0167).
Regarding claim 7, Bradlow discloses the invention of claim 1 as discussed above, and Bradlow teaches that each parameter of the plurality of parameters is capable of being converted into a fixed-size image array before being used as input to the CNN (Paragraphs 0048, 0108, 0134, 0154-0155).
Regarding claim 9, Bradlow discloses the invention of claim 1 as discussed above, and Bradlow teaches that the CNN is capable of being configured to be trained by a data set comprising previous data which is capable of being accepted from the plurality of computing devices comprising the plurality of parameters (Paragraphs 0154-0155, 0169, 0177).
Regarding claim 10, Bradlow teaches a method for estimating road roughness under real-world driving conditions through training and implementation of a convolutional neural network (CNN) (Paragraphs 0007-0008, 0087, 0130, 0154-0155), the method comprising: a. mounting a computing device (e.g., "handheld mobile device (smartphone, tablet) includes some or all of the components required to detect a surface type of a travel pathway" [Paragraphs 0045, 0056, 0091-0095]) to a vehicle ("The handheld mobile device can be mounted and coupled to the PMV 300" in Paragraph 0095, as well as description of mounting means in Paragraph 0096 [which describes mounting of sensors, but given that paragraph 0095 recites that the sensors may be equipment of the handheld mobile device alternatively, it logically flows that the mounting may be applied to the smartphone as well]); b. measuring, by the computing device, while the vehicle is driving on a road, a plurality of parameters comprising: i. global positioning system (GPS), ii. driving speed, iii. vertical acceleration, and iv. angular velocity of pitch motion (Paragraphs 0093-0095, 0100-0101, 0130, 0161); c. transmitting the plurality of parameters as input to a neural network computing device communicatively coupled to the computing device (either configuration of the computing device local to PMV, or remove server of cloud system [Paragraphs 0091, 0102, 0148-0149, 0161, 0179, etc.]) , the neural network computing device comprising a CNN comprising a plurality of convolutional layers (Paragraphs 0008, 0132, 0154-0155); d. processing, by the CNN, the input from the computing device to generate an IRI value of the road as output (Paragraphs 0007-0008, 0087, 0130, 0154-0155 [additional description of the ML process is found at length throughout the specification]).
Regarding claim 12, Bradlow discloses the invention of claim 10 as discussed above, and teaches multiple convolutional layers for deep learning, including the option of 7 layers (Paragraphs 0154-0155)
Regarding claim 15, Bradlow discloses the invention of claim 10 as discussed above, and Bradlow teaches measuring GPS is capable of GPS resolution enhancement by interpolation and grid snapping (no configuration of adequate structure to perform GPS resolution is recited).
Regarding claim 16, Bradlow discloses the invention of claim 10 as discussed above, and Bradlow teaches that the computing device is capable of measuring vertical acceleration and angular velocity at 100 Hz (Paragraphs 0045, 0056, 0167).
Regarding claim 17, Bradlow discloses the invention of claim 10 as discussed above, and Bradlow teaches that each parameter of the plurality of parameters is converted into a fixed-size image array before being used as input to the CNN (Paragraphs 0048, 0108, 0134, 0154-0155).
Regarding claim 19, Bradlow discloses the invention of claim 10 as discussed above, and Bradlow teaches that the CNN is trained by a data set comprising previous data from the plurality of computing devices comprising the plurality of parameters (Paragraphs 0154-0155, 0169, 0177).
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.
Claim(s) 2-3, 11, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bradlow et al (US2020/0124430A1) in view of Gao et al (US2020/0279157A1).
Regarding claim 2, Bradlow discloses the invention of claim 1 as discussed above, and teaches multiple convolutional layers for deep learning, which includes the option of 7 layers (Paragraphs 0154-0155) but does not teach a plurality of global average pooling layers.
Gao teaches a CNN application (Abstract [and throughout specification]) including a plurality of global average pooling layers in a multi-layer CNN in order to provide a much lower training cost with the same result (Paragraph 0091, 0162-0164).
Thus, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Bradlow, such that the CNN includes a plurality of global average pooling layers, as suggested and taught by Gao, in order to provide a much lower training cost with the same result.
Regarding claim 3, Bradlow discloses the invention of claim 1 as discussed above, but does not teach that each convolutional layer is zero-padded to maintain spatial features, batch normalized to accelerate training speed and prevent overfitting, and has leaky-relu activation for non-linear operations.
Gao teaches a CNN application with multiple convolutional layers (Abstract [and throughout specification]) wherein each convolutional layer is zero-padded to maintain spatial features, batch normalized to accelerate training speed and prevent overfitting, and has leaky-relu activation for non-linear operations, in order to achieve the apparent benefits above as well as greatly accelerating the convergence of stochastic gradient descent (Paragraphs 0129, 0162-0164, 0196, and 370).
Thus, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Bradlow, such each convolutional layer is zero-padded to maintain spatial features, batch normalized to accelerate training speed and prevent overfitting, and has leaky-relu activation for non-linear operations, as suggested and taught by Gao, in order to achieve the recited apparent benefits as well as greatly accelerating the convergence of stochastic gradient descent.
Regarding claim 11, Bradlow discloses the invention of claim 10 as discussed above, and teaches multiple convolutional layers for deep learning, including the option of 7 layers (Paragraphs 0154-0155), but does not teach a plurality of global average pooling layers.
Gao teaches a CNN application (Abstract [and throughout specification]) including a plurality of global average pooling layers in a multi-layer CNN in order to provide a much lower training cost with the same result (Paragraph 0091, 0162-0164).
Thus, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Bradlow, such that the CNN includes a plurality of global average pooling layers, as suggested and taught by Gao, in order to provide a much lower training cost with the same result.
Regarding claim 13, Bradlow discloses the invention of claim 10 as discussed above, but does not teach that each convolutional layer is zero-padded to maintain spatial features, batch normalized to accelerate training speed and prevent overfitting, and has leaky-relu activation for non-linear operations.
Gao teaches a CNN application with multiple convolutional layers (Abstract [and throughout specification]) wherein each convolutional layer is zero-padded to maintain spatial features, batch normalized to accelerate training speed and prevent overfitting, and has leaky-relu activation for non-linear operations, in order to achieve the apparent benefits above as well as greatly accelerating the convergence of stochastic gradient descent (Paragraphs 0129, 0162-0164, 0196, and 370).
Thus, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Bradlow, such each convolutional layer is zero-padded to maintain spatial features, batch normalized to accelerate training speed and prevent overfitting, and has leaky-relu activation for non-linear operations, as suggested and taught by Gao, in order to achieve the recited apparent benefits as well as greatly accelerating the convergence of stochastic gradient descent.
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bradlow et al (US2020/0124430A1) in view of Gutierrez et al (US2016/0173667A1).
Regarding claim 4, Bradlow discloses the invention of claim 1 as discussed above, and Bradlow teaches that the plurality of computing devices comprise a plurality of portable computing devices, a plurality of vehicle-embedded computing devices, or a combination thereof, wherein each computing device of the plurality of computing devices is mounted to the vehicle by a mounting device (as previously discussed and cited), but does not explicitly teach that the mounting device is selected from a group comprising a vent clip, a vent magnet, a section clip, a suction magnet, and a CDP clip.
Gutierrez teaches a mounting device for a mobile device selected from a group comprising a vent clip, a vent magnet, a section clip, a suction magnet, and a CDP clip (Paragraphs 0042-0043), in order to achieve the predictable of easily mounting the mobile device securely.
Thus, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Bradlow, such that the mounting device is selected from a group comprising a vent clip, a vent magnet, a section clip, a suction magnet, and a CDP clip, as suggested and taught by Gutierrez, in order to achieve the predictable of easily mounting the mobile device securely.
Regarding claim 14, Bradlow discloses the invention of claim 10 as discussed above, and Bradlow teaches that the plurality of computing devices comprise a plurality of portable computing devices, a plurality of vehicle-embedded computing devices, or a combination thereof, wherein each computing device of the plurality of computing devices is mounted to the vehicle by a mounting device (as previously discussed and cited), but does not explicitly teach that the mounting device is selected from a group comprising a vent clip, a vent magnet, a section clip, a suction magnet, and a CDP clip.
Gutierrez teaches a mounting device for a mobile device selected from a group comprising a vent clip, a vent magnet, a section clip, a suction magnet, and a CDP clip (Paragraphs 0042-0043), in order to achieve the predictable of easily mounting the mobile device securely.
Thus, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Bradlow, such that the mounting device is selected from a group comprising a vent clip, a vent magnet, a section clip, a suction magnet, and a CDP clip, as suggested and taught by Gutierrez, in order to achieve the predictable of easily mounting the mobile device securely.
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bradlow et al (US2020/0124430A1) in view of Guo et al (US2020/0082264A1).
Regarding claim 8, Bradlow discloses the invention of claim 1 as discussed above, but does not teach that a batch size of the CNN is 64 and a learning rate of the CNN is 0.0001.
Guo teaches a CNN application (Abstract [and throughout specification]), wherein a batch size of the CNN is 64 and a learning rate of the CNN is 0.0001, in order to achieve a high accuracy level (Paragraphs 0154, 0249).
Thus, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Bradlow, such that a batch size of the CNN is 64 and a learning rate of the CNN is 0.0001, as suggested and taught by Guo, in order to achieve a high accuracy level.
Regarding claim 18, Bradlow discloses the invention of claim 10 as discussed above, but does not teach that a batch size of the CNN is 64 and a learning rate of the CNN is 0.0001.
Guo teaches a CNN application (Abstract [and throughout specification]), wherein a batch size of the CNN is 64 and a learning rate of the CNN is 0.0001, in order to achieve a high accuracy level (Paragraphs 0154, 0249).
Thus, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Bradlow, such that a batch size of the CNN is 64 and a learning rate of the CNN is 0.0001, as suggested and taught by Guo, in order to achieve a high accuracy level.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN R STECKBAUER whose telephone number is (571)270-0433. The examiner can normally be reached Monday - Thursday 9:30-7:30 PST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Logan Kraft can be reached at 571-270-5065. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KEVIN R STECKBAUER/Primary Examiner, Art Unit 3747