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
The information disclosure statements (IDS) were filed on 12/30/2024, 12/29/2025, 03/13/2026 and 08/10/2026 . The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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 limitations are: “data transmission unit configured to…” in claim 8, “server unit configured to…” in claims 8-10, “computer configured to…” in claim 14 and “sensor system configured to…” in claim 15.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they 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 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 limitations to avoid 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 limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10-11 and 14-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 14 recites the limitation "the computer unit" There is insufficient antecedent basis for this limitation in the claim. Examiner will construe this as “the computer” to ensure proper antecedent basis.
Regarding claim 10 the claims recites “…degree of diversity, in which rarer road conditions are sufficiently represented…” It is unclear from the context of the claim what “degree of diversity, in which rarer road conditions…” and “sufficiently represented” mean. One of ordinary skill in the art would ask “How much is sufficient?” “Is it half, more than 75% or more than 90%?” In addition “What if a vehicle is in a region where it snows and another is in a region where it barely snows?” “How can a rarity be determined if it depends on the region?”. Therefore one of ordinary skill in the art would not be able to apprise the scope of the claim for reasons regarding clarity and all limitations remain rejected. Examiner will construe these limitations based on the broadest reasonable interpretation (BRI) test.
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 and 12-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Saida et. al. (JP Pub. No. 2022089676-A) .
As per claim 1 Saida teaches “A method for training a machine learning system for monitoring the road condition, comprising:
providing or acquiring data by a sensor system of a vehicle, wherein the sensor system captures the surroundings of the vehicle as training input data;” (See page 2 paragraph 5-9 “The learning data set storage unit 22 stores a learning data set (teacher data) for machine learning in the trained model. Specifically, the learning data set is composed of a road surface image obtained by photographing the road surface in various road surface conditions and a road surface slip friction coefficient of the road surface actually measured at the same time as the photographing of the road surface image as one set…” Saida)
“providing or acquiring data which characterize a road condition of a road on which the vehicle is located by a reference sensor fitted in or on the vehicle as training target values; and
training the machine learning system;
wherein training data, which comprise training input data and training target values corresponding to the training input data, are provided, and the training data are used to adjust parameters of the machine learning system in such a manner that the machine learning system generates output data similar to the training target values when the training input data are input.” (See page 3 paragraphs 1-6, the slip resistance measuring device is the reference sensor and the slip resistance value into the road surface slip friction coefficient obtained is the training target value “On the other hand, as shown in FIG. 2, the road surface slip friction coefficient constituting the learning data set is calculated based on the road surface slip resistance value measured by the road surface slip resistance measuring device 13 mounted on the measuring vehicle 11. The road surface slip resistance measuring device 13 may be any device as long as it can continuously measure the road surface slip resistance value, and for example, a Real Time Traction Tool (Halliday Technologies) or the like can be used. In this case, the output data of the road surface slip resistance measuring device 13 is an index indicating the road surface slip resistance value called HFN (Halliday Friction Number), and this HFN is converted into the road surface friction friction coefficient by a predetermined conversion formula… The raw data acquisition unit 31 acquires raw data that is the basis of the learning data set. In the present embodiment, the raw data acquisition unit 31 acquires the road surface image taken by the in vehicle camera 12 and the road surface slip resistance value actually measured by the road surface slip resistance measuring device 13 at the same time as taking the road surface image as raw data… Further, the learning data set generation unit 32 converts the road surface slip resistance value acquired by the raw data acquisition unit 31 into the road surface slip friction coefficient by a predetermined conversion formula. Then, the learning data set generation unit 32 assigns a corresponding road surface slip friction coefficient to each of the cut out road surface images to form a set of learning data sets, which is stored in the learning data set storage unit 22.” See page 4 paragraphs 1-11 “The trained model generation unit 33 generates a trained model that outputs a road surface slip friction coefficient when a road surface image is input. In the present embodiment, the trained model generation unit 33 outputs a road surface image as input data by executing machine learning using a plurality of training data sets stored in the training data set storage unit 22. Generate a trained model that learns the correlation with the road surface slip friction coefficient as data… 1. 1. It is possible to generate a trained model that can estimate the road surface slip friction coefficient simply by inputting a road surface image…3. 3. It is possible to generate a highly accurate learning data set in which the road surface image and the slip friction coefficient are accurately matched, and it is possible to improve the estimation accuracy of the road surface slip friction coefficient. ” By utilizing both the slip resistance and images obtained at the same time, a trained model is developed to estimate the slip resistance from just an image, and therefore it serves the purpose of trying to get the output data to be as similar to the training values. It also serves the purpose of adjusting parameters by improving the estimation as seen in steps 1-4 and paragraphs 1-8 on page 4. See also page 6 paragraphs 1-10 and page 7 paragraphs 1-7 along with steps 1-9 shown in pages 6-7, the road condition is characterized by the obtained and developed data as well as improving the developed data “Further, the road surface slip friction coefficient estimation unit 52 may make it easier for the user to understand the estimation result by setting a predetermined threshold value in advance with respect to the estimated value of the road surface slip friction coefficient. For example, if the estimated value is equal to or more than the first threshold value, it is determined to be "very slippery", if it is less than the first threshold value and greater than or equal to the second threshold value, it is determined to be "slippery", and if it is less than the second threshold value, it is determined to be "difficult to slip". The determination result may be displayed as an estimation result… As a result, the road surface slip friction coefficient estimated with high accuracy is output in real time just by inputting the road surface image, so that the road surface condition can be grasped easily and inexpensively… 4. When applied to road surveillance cameras and road patrol vehicles owned by road managers, it is possible to objectively and efficiently grasp and manage real-time snow and ice conditions on the road surface…” See also all of pages 8-9. Saida)
As per claim 2, Saida teaches “The method according to Claim 1, wherein the sensor system comprises a camera system of the vehicle so that the provided or acquired data are image data and the image data serve as training input data.” (See page 2 paragraph 5-9 “The learning data set storage unit 22 stores a learning data set (teacher data) for machine learning in the trained model. Specifically, the learning data set is composed of a road surface image obtained by photographing the road surface in various road surface conditions and a road surface slip friction coefficient of the road surface actually measured at the same time as the photographing of the road surface image as one set…” See also page 2 last paragraph and page 3 paragraphs 2-4 “The raw data acquisition unit 31 acquires raw data that is the basis of the learning data set. In the present embodiment, the raw data acquisition unit 31 acquires the road surface image taken by the in vehicle camera 12 and the road surface slip resistance value actually measured by the road surface slip resistance measuring device 13 at the same time as taking the road surface image as raw data.” Saida)
As per claim 12, Saida teaches “The method according to Claim 1, wherein the sensor data for the captured region of the road are divided into segments and the data provided or acquired by the reference sensor characterize the road condition for the segments.” (See page 3 paragraphs 4-8 “The learning data set generation unit 32 generates a learning data set from raw data. In the present embodiment, the learning data set generation unit 32 uses an image obtained by cutting out a region near the measurement point where the road surface slip friction coefficient is measured from the road surface image acquired by the raw data acquisition unit 31 as the road surface image… Since the angle of view of the vehicle-mounted camera 12 and the measurement position of the road surface slip resistance measuring device 13 are fixed, the region to be cut out as the road surface image can be set in advance as a fixed region… Further, the learning data set generation unit 32 converts the road surface slip resistance value acquired by the raw data acquisition unit 31 into the road surface slip friction coefficient by a predetermined conversion formula. Then, the learning data set generation unit 32 assigns a corresponding road surface slip friction coefficient to each of the cut out road surface images to form a set of learning data sets, which is stored in the learning data set storage unit 22.” See also page 4 paragraphs 4-10 “At this time, in the present embodiment, the learning data set generation unit 32 cuts out a region near the measurement point from the road surface image. As a result, the extra portion is removed from the road surface image, and only the minimum necessary portion is used for the learning data set, so that the estimation accuracy and the calculation speed of the road surface slip friction coefficient are improved.” Saida )
As per claim 13, Saida teaches “A method for monitoring the road condition utilizing a machine learning system , wherein the machine learning system is trained according to the method according to claim 1, data captured by the vehicle sensor system, which captures the surroundings of the vehicle, are provided to the trained machine learning system as input data, and the trained machine learning system generates output data which characterize the road condition from the input data.” (See page 4 paragraphs 1-12 and page 5 paragraph 5 “The trained model storage unit 42 stores a trained model for estimating the road surface slip friction coefficient. In the present embodiment, the trained model generated by the trained model generation device 1 and output to the road surface slip friction coefficient estimation device 10 is stored in the trained model storage unit 42. However, the present invention is not limited to this configuration, and is a learning data set composed of a road surface image obtained by photographing a road surface in various road surface conditions and a road surface slip friction coefficient of the road surface actually measured at the same time as taking the road surface image. Machine learning is performed using the above, and a trained model that outputs a road surface slip friction coefficient when a road surface image is input may be stored in the trained model storage unit 42.” See also page 6 paragraphs 1-10 and page 7 paragraphs 1-7 “Next, the road surface slip friction coefficient estimation unit 52 inputs the road surface image acquired in step S11 into the trained model, and estimates the output road surface slip friction coefficient as the road surface slip friction coefficient (step S12: road surface). Sliding friction coefficient estimation step). As a result, the road surface slip friction coefficient estimated with high accuracy is output in real time just by inputting the road surface image, so that the road surface condition can be grasped easily and inexpensively.” Saida)
As per claim 14, Saida teaches “A road condition monitoring system comprising an input unit for receiving the input data; a computer which is configured to carry out a method according to Claim 13; and an output interface coupled to the computer and which outputs the output data generated by the computer unit.” (See page 4 paragraphs 1-12 and page 5 paragraph 5, see also page 6 paragraphs 1-10 and page 7 paragraphs 1-7. See page 6 paragraph 1 “The road surface slip friction coefficient estimation unit 52 estimates the road surface slip friction coefficient of the road surface based on the road surface image. In the present embodiment, the road surface slip friction coefficient estimation unit 52 inputs the road surface image acquired by the road surface image acquisition unit 51 into the trained model stored in the trained model storage unit 42, and the trained model is used. The output road surface slip friction coefficient is estimated as the road surface slip friction coefficient of the road surface. Then, the estimated road surface slip friction coefficient is output to the display means 7 or the like and displayed.” Saida)
As per claim 15, Saida teaches “A vehicle comprising the sensor system, wherein the sensor system is configured to capture the surroundings of the vehicle and to provide the data from the sensor system to the input unit as the input data, and a road condition monitoring system according to Claim 14.” (See page 4 paragraphs 1-12 and page 5 paragraph 5 “The trained model storage unit 42 stores a trained model for estimating the road surface slip friction coefficient. In the present embodiment, the trained model generated by the trained model generation device 1 and output to the road surface slip friction coefficient estimation device 10 is stored in the trained model storage unit 42. However, the present invention is not limited to this configuration, and is a learning data set composed of a road surface image obtained by photographing a road surface in various road surface conditions and a road surface slip friction coefficient of the road surface actually measured at the same time as taking the road surface image. Machine learning is performed using the above, and a trained model that outputs a road surface slip friction coefficient when a road surface image is input may be stored in the trained model storage unit 42.” See also page 6 paragraphs 1-10 and page 7 paragraphs 1-7 “Next, the road surface slip friction coefficient estimation unit 52 inputs the road surface image acquired in step S11 into the trained model, and estimates the output road surface slip friction coefficient as the road surface slip friction coefficient (step S12: road surface). Sliding friction coefficient estimation step). As a result, the road surface slip friction coefficient estimated with high accuracy is output in real time just by inputting the road surface image, so that the road surface condition can be grasped easily and inexpensively.” Saida)
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.
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 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Saida in view of Itoh et. al. (US Pub. No. 20170293812 A1) .
As per claim 3, Saida already teaches “The method according to Claim 1, wherein the reference sensor comprises a transmitting and receiving unit… receives and measures an intensity reflected by the road, and wherein the reference sensor is adapted to indicate probabilities for a presence…”, (See page 6 paragraphs 1-3 and paragraphs 6-10 along with page 7 paragraphs 1-7. “In the present embodiment, the output data of the trained model is the classification probability when the value of the road surface slip friction coefficient is classified into a plurality of classification classes divided in a predetermined numerical range. In this case, the road surface slip friction coefficient estimation unit 52 may calculate the estimated value of the road surface slip friction coefficient by using the output classification probability and the weighted average of the median road surface slip friction coefficient of each classification class.” It transmits and receives as seen on page 3 paragraphs 1-4 and page 5 paragraphs 1-6. Saida) however Saida does not teach “which emits electromagnetic radiation of at least one defined wavelength onto the road… and of various classes of road conditions on the basis of the measured intensity.”
Itoh teaches “which emits electromagnetic radiation of at least one defined wavelength onto the road and receives and measures an intensity reflected by the road, and wherein the reference sensor is adapted to indicate probabilities for a presence… and of various classes of road conditions on the basis of the measured intensity” (See paragraph 40 “[0040] Although a light source 3 that emits continuous-spectrum light, such as a halogen light or the like, may be used as the light source 3 for example, this is not restrictive. Examples of the light source 3 may include light emitting diode (LED) elements, semiconductor light-emitting elements such as semiconductor later or the like, electroluminescence (EL) elements such as organic EL and inorganic EL, and other solid-state light-emitting elements.” See also paragraphs 35-45, fig. 1 and fig. 4 (which shows the emitted, received and measured intensity reflected), the light source emits a wavelength onto a road to determine road conditions. “[0035]… The detection device 1 is a device that casts light upon objects, and detects states of the objects, as illustrated in FIG. 1. An example of an object in the present embodiment is the road surface of a road. The states of the object are a snow-accumulated state, a dry state, an under-water state, and a frozen state of the road surface of the road.” “[0038] Infrared light is used in the present embodiment for the light. S-polarized light is light of a first wavelength band and light of a second wavelength band. P-polarized light is also light of a first wavelength band and light of a second wavelength band. The light of the second wavelength band has a wavelength band that is different from the first wavelength band, and has a nature of being less readily absorbed by water as compared with the first wavelength band…” “[0039] The infrared light of the first wavelength band is light at an absorption peak regarding water, serving as an absorption wavelength of any one of near 740 nm, near 980 nm, near 1450 nm, and near 1940 nm…” See also paragraphs 53-67 for intensities. The probabilities for classes are shown in paragraphs 45-52 “[0049]… Whether or not snow is accumulated on the object can be determined based on whether or not the reflectance of the information P2 is above a threshold value. This threshold value has been calculated based on reflectance values obtained by casting light on the surfaces of asphalt, concrete, white lines, yellow lines, and accumulated snow, in a case where the object is a road surface. These reflectance values are approximately 0.11 for asphalt, approximately 0.42 for concrete, approximately 0.63 for white lines, approximately 0.52 for yellow lines, and approximately 0.9 for accumulated snow.” “[0050] Determination of a dry state of the object can be made by whether or not the information P1 and information P2 are in approximation…” See also paragraphs 60-67 “[0066] An arrangement may also be made where, in step S9, the magnitude of information P1 and information P2 is determined. If the information P1 is smaller than a value obtained by multiplying information P2 by 100%-20%, the control unit 7 may determine that water exists, or possibly exists, at the object, from the information P1 which is P-polarized light that has low reflectance at water and is also light of wavelength λ1 that is readily absorbed by water.” See also paragraphs 152 and 102-143. Itoh )
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Saida with the teachings of Itoh to emit electromagnetic radiation at defined wavelengths onto a road for classification of road conditions probabilities. The modification would have been motivated by the desire to have accurate detection of states along with suppressing manufacturing costs, therefore it is an improvement, as suggested by Itoh (See paragraphs 3-6 “[0004] According to the present disclosure, states of objects can be accurately detected.” See also paragraphs 68-77. “[0069] According to this configuration, the control unit 7 can determine the state of the object by combining light of wavelength λ1 that is the first wavelength band, light of wavelength λ2 that is the second wavelength band, S-polarized light, and P-polarized light. Thus, the detection device 1 can accurately detect the state of the object by using polarized light that is different from a case of detecting the state of an object using linearly polarized light.” “[0073] According to this configuration, light of desired wavelengths can be extracted from light emitted by the light source 3. Thus, there is no need to prepare a light source 3 that emits light of the desired wavelength, and increase in manufacturing costs can be suppressed.” Itoh)
As per claim 4, Saida in view of Itoh already teaches “The method according to Claim 3, wherein the reference sensor outputs probabilities for the presence of the various road condition classes comprising:”, however Itoh teaches "dry", "wet", "snow", "ice" and "unknown/error" (See paragraphs 35-45, fig. 1 and fig. 4 (which shows the emitted, received and measured intensity reflected), the light source emits a wavelength onto a road to determine road conditions. “[0035]… The detection device 1 is a device that casts light upon objects, and detects states of the objects, as illustrated in FIG. 1. An example of an object in the present embodiment is the road surface of a road. The states of the object are a snow-accumulated state, a dry state, an under-water state, and a frozen state of the road surface of the road.” See also paragraphs 25-29. “[0025]… Particularly, water has a nature where the longer the wavelength of light is at the absorption peak of light, the more readily light is absorbed. Accordingly, in principle, if light containing wavelengths near the absorption peak is cast on a road, the presence or absence of water or ice can be detected by the amount of reflected light thereof.” See paragraphs 50, which show that error is also measured “[0050] Determination of a dry state of the object can be made by whether or not the information P1 and information P2 are in approximation. The reason is that in a case where generally uniform random polarized light is cast upon an object in a dry state, both light of wavelength λ1 and light of wavelength λ2 are absorbed at the object by about the same amount. Accordingly, the quantity of light of the wavelength λ1 and the quantity of light of the wavelength λ2 are generally the same in the scattered light at the object. Note that the expression in the present embodiment that the information P1 and information P2 are in approximation means that the error of the information P1 and information P2 is within ±20%. This error range is preferable in a case where the object is a road surface, for example, since the information P1 and information P2 will differ depending on the road material.” See also paragraphs 63-64. Itoh )
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Saida in view of Petrov et. al. (Petrov, A. V., et al. "A pyrometric sensor for the thermal mapping of highways." Journal of Optical Technology 69.5 (2002): 354.) .
As per claim 5, Saida already teaches “The method according to claim 1 wherein the reference sensor comprises”, however Saida does not teach “a pyrometer which measures the temperature of the road.”
Petrov teaches “a pyrometer which measures the temperature of the road.” (See page 1 abstract along with columns 1-2 and page 4 column 1 last paragraph and column 2 paragraphs 1-5. See also fig. 1 (it shows a pyrometer attached to the roof of a car). The abstract mentions “A pyrometric sensor has been created for operation under conditions of intense air flows in the temperature range 20... 40°C. It is intended for the thermal mapping of highways and has been tested as part of a measurement-and-information system based on the KP-514MP automotive laboratory. The sensor is compact, has low power requirements, and is suitable for the automatic control systems of technological processes that use noncontact temperature monitoring.” Petrov)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Saida with the teachings of Petrov to utilize a pyrometer as a reference sensor to measure the temperature of the road. The modification would have been motivated by the desire to have a compact sensor (space efficient) and lower power requirements while also operating in a wide temperature range, therefore it is an improvement, as suggested by Petrov (See page 1 abstract along with columns 1-2 and page 4 column 1 last paragraph and column 2 paragraphs 1-5. See also fig. 1 (it shows a pyrometer attached to the roof of a car). The abstract mentions “A pyrometric sensor has been created for operation under conditions of intense air flows in the temperature range 20... 40°C. It is intended for the thermal mapping of highways and has been tested as part of a measurement-and-information system based on the KP-514MP automotive laboratory. The sensor is compact, has low power requirements, and is suitable for the automatic control systems of technological processes that use noncontact temperature monitoring.” Page 4 column 1 last paragraph mentions “The design allows the DP-1 sensor a pyrometric sensor to operate in a wide temperature range Fig. 2.” Petrov)
In addition, the motivation of the modification is also obvious since there is some teaching, suggestion or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention, see MPEP § 2143(g), because it is knowledge generally available to one of ordinary skill in the art that a pyrometer is used for measuring temperature.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Saida in view of Itoh and further in view of Riehm et. al. (Riehm, Mats, et al. "Ice formation detection on road surfaces using infrared thermometry." Cold Regions Science and Technology 83 (2012): 71-76.) .
As per claim 6, Saida in view of Itoh already teaches “The method according to Claim 3 wherein the reference sensor utilizes the following wavelengths: 1550 nm and 980 nm in order to detect a presence of water by a comparison of the reflected intensities,” (See paragraphs 39 “[0039] The infrared light of the first wavelength band is light at an absorption peak regarding water, serving as an absorption wavelength of any one of near 740 nm, near 980 nm, near 1450 nm, and near 1940 nm. The infrared light of the second wavelength band is light of which the wavelength is shorter than the first wavelength band. For example, in a case where the infrared light of the first wavelength band is light near 980 nm, the infrared light of the second wavelength band may be light near 800 nm. In a case where the infrared light of the first wavelength band is light near 1940 nm, the infrared light of the second wavelength band may be light near 1550 nm.” See also paragraphs 25 “[0025] Water has an absorption spectrum where light is absorbed at a particular wavelength region. Accordingly, the Present Inventors have taken note of the absorption spectrum of water as a way to detect the state of the road surface. In the relationship between wavelengths of light and absorption coefficient of water, water has peaks of light absorption (absorption peaks) near 740 nm, 980 nm, 1450 nm, and 1940 nm, in the near-infrared region of light (said to be 700 nm to 2500 nm), for example. Particularly, water has a nature where the longer the wavelength of light is at the absorption peak of light, the more readily light is absorbed. Accordingly, in principle, if light containing wavelengths near the absorption peak is cast on a road, the presence or absence of water or ice can be detected by the amount of reflected light thereof.” ), however Saida in view of Itoh does not teach “and a wavelength in the range of 2 to 10 µm in order to measure a temperature of the road.”
Riehm teaches “and a wavelength in the range of 2 to 10 µm in order to measure a temperature of the road.” (See page 3 section 2.2 Infrared Thermometry paragraphs 1- 4 “Infrared radiation is the part of the electromagnetic wavelength spectrum that goes from visible light to microwaves, which means wave lengths between approximately 0.7 and 1000 μm. Infrared radiation is often called heat radiation due to thermal radiation being emitted largely within the infrared wavelength band….” Column 2 mentions “An infrared thermometer measures the outgoing infrared radiation, which is a direct effect of the temperature and the emissivity of the surface according to the Stefan–Boltzmann law. However, infrared thermometers do not measure over all the wavelengths for which the Stefan–Boltzmann law is defined. Typically, an infrared thermometer measures the heat radiation in a specified wavelength band between 7 and 14 μm and the temperature of the object can then be calculated by the thermometer based on prior calibration…” Riehm)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Saida with the teachings of Itoh and Riehm for the reference sensor to utilize a wavelength in the range of 2 to 10 µm in order to measure a temperature of the road. The motivation of the modification is obvious since there is some teaching, suggestion or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention, see MPEP § 2143(g), because it is knowledge generally available to one of ordinary skill in the art that a wavelength in the range of 2 to 10 µm is commonly used to measure temperature in the infrared region as suggested by Riehm “(See page 3 section 2.2 Infrared Thermometry paragraphs 1- 4 “Infrared radiation is the part of the electromagnetic wavelength spectrum that goes from visible light to microwaves, which means wave lengths between approximately 0.7 and 1000 μm. Infrared radiation is often called heat radiation due to thermal radiation being emitted largely within the infrared wavelength band….” Column 2 mentions “An infrared thermometer measures the outgoing infrared radiation, which is a direct effect of the temperature and the emissivity of the surface according to the Stefan–Boltzmann law. However, infrared thermometers do not measure over all the wavelengths for which the Stefan–Boltzmann law is defined. Typically, an infrared thermometer measures the heat radiation in a specified wavelength band between 7 and 14 μm and the temperature of the object can then be calculated by the thermometer based on prior calibration…There are a number of sources of error that arise when infrared thermometers are used to measure road surface temperature. One is atmospheric conditions such as absorption and emission of energy from gas molecules and particles. In clean air the absorption of energy is mainly due to water vapour and carbon dioxide. In some sections of the wavelength spectrum known as atmospheric windows, which fall between 3–4, 4.5–5 and 7–14 μm, the absorption of water vapour is small. The atmospheric window between 7 and 14 μm is suitable for measuring objects around room temperature due to the fact that most heat radiation from objects around room temperature is within this range.” See also page 4 section 2.3 Climate chamber experiment paragraph 2 “an Optris infrared (IR) thermometer that absorbs the radiation between wavelengths of 8 to 14 um was used (see Fig. 4 for a diagram of the chamber setup…)” See also page 5 conclusion. Riehm). There is reasonable expectation of success because most heat radiation from objects are within the wavelength range and it can be used to detect the freezing of water on an asphalt surface.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Saida in view of Cheng et. al. (Cheng, Lushan, Xu Zhang, and Jie Shen. "Road surface condition classification using deep learning." Journal of Visual Communication and Image Representation 64 (2019): 102638. (Year: 2019)) .
As per claim 7, Saida already teaches “The method according to claim 1, wherein the machine learning system”, however Saida does not teach “is a neural network.”
Cheng teaches “is a neural network.” (See page 1 abstract and pages 1-2 section Introduction “Traditional image recognition technology currently cannot achieve the fast real-time high-accuracy performance necessary for road recognition in intelligent driving. Deep learning models have been recently emerging as promising tools to achieve this performance. The recognition performance of such models can be boosted using appropriate selection of the activation functions. This paper proposes a deep learning approach for the classification of road surface conditions, and constructs a new activation function based on the rectified linear unit Rectified Linear Units (ReLu) activation function. The experimental results show a classification accuracy of 94.89% on the road state database. Experiments on public data sets demonstrate that the proposed convolutional neural network model with the improved activation function has better generalization and excellent classification performance.” See also page 7 section Conclusion “Deep convolutional neural networks have powerful recognition and learning abilities. These networks produce excellent performance in solving image classification and recognition problems. In this paper, a deep learning method, combined with an improved activation function and a GPU with high computing power, is used to classify five common types of road surface conditions. The results show that the deep model equipped with a Gai-ReLU acti vation function has a high classification accuracy of 94.89%. More over, the deep learning model with this activation function has good generalization and applicability to real-world applications.” Cheng)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Saida with the teachings of Cheng to utilize a neural network as the machine learning system. The modification would have been motivated by the desire to have better generalization, higher classification and recognition performance, better learning and higher classification accuracy, therefore it is an improvement, as suggested by Cheng (See page 1 abstract and pages 1-2 section Introduction “Traditional image recognition technology currently cannot achieve the fast real-time high-accuracy performance necessary for road recognition in intelligent driving. Deep learning models have been recently emerging as promising tools to achieve this performance. The recognition performance of such models can be boosted using appropriate selection of the activation functions. This paper proposes a deep learning approach for the classification of road surface conditions, and constructs a new activation function based on the rectified linear unit Rectified Linear Units (ReLu) activation function. The experimental results show a classification accuracy of 94.89% on the road state database. Experiments on public data sets demonstrate that the proposed convolutional neural network model with the improved activation function has better generalization and excellent classification performance.” See also page 7 section Conclusion “Deep convolutional neural networks have powerful recognition and learning abilities. These networks produce excellent performance in solving image classification and recognition problems. In this paper, a deep learning method, combined with an improved activation function and a GPU with high computing power, is used to classify five common types of road surface conditions. The results show that the deep model equipped with a Gai-ReLU acti vation function has a high classification accuracy of 94.89%. More over, the deep learning model with this activation function has good generalization and applicability to real-world applications.” Cheng)
As per claim 16, Saida in view of Cheng already teaches “The method according to Claim 7, wherein the neural network comprises a convolutional neural network.” (See page 1 abstract and pages 1-2 section Introduction “Traditional image recognition technology currently cannot achieve the fast real-time high-accuracy performance necessary for road recognition in intelligent driving. Deep learning models have been recently emerging as promising tools to achieve this performance. The recognition performance of such models can be boosted using appropriate selection of the activation functions. This paper proposes a deep learning approach for the classification of road surface conditions, and constructs a new activation function based on the rectified linear unit Rectified Linear Units (ReLu) activation function. The experimental results show a classification accuracy of 94.89% on the road state database. Experiments on public data sets demonstrate that the proposed convolutional neural network model with the improved activation function has better generalization and excellent classification performance.” See also page 7 section Conclusion “Deep convolutional neural networks have powerful recognition and learning abilities. These networks produce excellent performance in solving image classification and recognition problems. In this paper, a deep learning method, combined with an improved activation function and a GPU with high computing power, is used to classify five common types of road surface conditions. The results show that the deep model equipped with a Gai-ReLU acti vation function has a high classification accuracy of 94.89%. More over, the deep learning model with this activation function has good generalization and applicability to real-world applications.” It is motivated by the same reasons presented in claim 7. Cheng)
Claims 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Saida in view of Yamashina et. al. (US Pub. No. 2021/0334440 A1) .
As per claim 8, Saida teaches “The method according to Claim 1 ,wherein the vehicle comprises a data transmission unit, and the data transmission unit is configured to transfer training data…” (See page 2 last paragraph, the camera captures training data that is transferred, see also page 3 paragraphs 2-4 “Next, the arithmetic processing means 3 is composed of a CPU (Central Processing Unit) or the like, and is generated by executing the trained model generation program 1a installed in the program storage unit 21 as shown in FIG. It functions as a data acquisition unit 31, a training data set generation unit 32, and a trained model generation unit 33. Hereinafter, each component will be described in more detail. The raw data acquisition unit 31 acquires raw data that is the basis of the learning data set. In the present embodiment, the raw data acquisition unit 31 acquires the road surface image taken by the in vehicle camera 12 and the road surface slip resistance value actually measured by the road surface slip resistance measuring device 13 at the same time as taking the road surface image as raw data.” See page 5 paragraph 6 “…Further, the road surface slip friction coefficient estimation program 10a and the trained model may be packaged and made available for download as an application for smartphones and tablets. Further, the road surface slip friction coefficient estimation program 10a and the trained model may be stored in an external server or the like and used by a cloud computing method or an ASP (Application Service Provider) method.” See also page 8 paragraphs 1-3 “On the other hand, road surface images reflecting objects such as other vehicles and wipers were not excluded, and about 600,000 learning data sets were generated. Furthermore, in order to increase the amount of training data set (Data augmentation), the road surface image is subjected to various processing (left-right inversion, 32 pixels moved to the right and left from the original image, increased brightness) for training. We added it as data and prepared about 3.3 million learning data sets… Next, the training data set prepared as described above was input to the image classification model for machine learning to generate a trained model. In the first embodiment, as the image classification model, MobileNet V2, which is capable of high-speed processing even with a device having a low arithmetic processing capacity such as a smartphone, is used… Next, apart from the above-mentioned training data set, 3200 daytime and nighttime road surface images cut out from the video of the in-vehicle camera 12 are prepared, and the road surface slip is input to the trained model generated as described above. ” In addition, the trained model already contains training data, therefore by transferring the model, the training data is also transferred. Saida), however Saida does not completely teach “…transfer training data to a server unit”
Yamashina teaches “…transfer training data to a server unit” (See paragraphs 39-46. “[0042] FIG. 2 is a block diagram schematically illustrating a configuration of a prediction system 1 (1B) including a model optimization device 100 (100B) according to the embodiment. As illustrated in FIG. 2, the prediction system 1 (1B) includes one or more sensors 300 provided in a facility such as a plant, a transmission device 500 configured to acquire measured values from the one or more sensors 300 and transmit the measured values to a server device 400 (400B) via a network NW, and the server device 400 (400B) configured to communicate with the transmission device 500 via the network NW. The server device 400 (400B) includes the model optimization device 100 (100B) for optimizing the prediction model, and makes a prediction based on a stored prediction model.” Yamshina)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Saida with the teachings of Yamashina to transmit training data obtained from the vehicle’s sensors to a server. The modification would have been motivated by the desire to optimize training, optimize predictions, improve prediction accuracy, enable remote learning, improve robustness and account for component degradation due to weather conditions (vehicles are in constantly affected by changing weather conditions) therefore it is an improvement, as suggested by Yamashina (See paragraphs 5, 6, “[0005] In prediction models using machine learning in the related art, robustness may not be ensured due to the unbalance of training data or evaluation data. For example, in a case where a prediction model is applied to a plant having varying characteristics depending on the outdoor temperature, if the prediction model is taught only with training data acquired in summer, prediction accuracy in winter may decrease. For example, component degradation of the plant may change performance, which may decrease prediction accuracy. In addition, due to individual differences in components and differences in fuel properties, operating conditions may deviate from the operating conditions at the time of learning, and the prediction accuracy may decrease.” “[0011] According to the present disclosure, it is possible to provide a model optimization device or the like capable of improving the robustness of a prediction model.” See paragraphs 38-47 “0044]… Furthermore, the prediction system 1 can be configured to make a prediction at a local location and perform processing necessary for optimization at a remote location. For example, such a configuration can be realized by disposing the prediction device 200 having a prediction model stored thereon at a local location, disposing the model optimization device 100 at a remote location, and communicatively connecting the two.” “[0039] As illustrated in FIG. 1, the prediction system 1 (1A) includes one or more sensors 300 provided in a facility such as a plant, a prediction device 200 configured to acquire measured values from the one or more sensors 300 and predict target variables in a case where the measured values are used as explanatory variables, and a server device 400 (400A) configured to communicate with the prediction device 200 via a network NW. The prediction device 200 includes the model optimization device 100 (100A) for optimizing the prediction model, and makes a prediction based on a stored prediction model.” See paragraphs 67-69, 79, 94-106 and 115-121. Yamashina)
As per claim 9, Saida in view of Yamashina already teaches “The method according to Claim 8, wherein the server unit is configured to update a training data set comprising a quantity of training data, wherein a size of the training data set is kept constant…” (See also page 8 paragraphs 1-3 “On the other hand, road surface images reflecting objects such as other vehicles and wipers were not excluded, and about 600,000 learning data sets were generated. Furthermore, in order to increase the amount of training data set (Data augmentation), the road surface image is subjected to various processing (left-right inversion, 32 pixels moved to the right and left from the original image, increased brightness) for training. We added it as data and prepared about 3.3 million learning data sets… Next, the training data set prepared as described above was input to the image classification model for machine learning to generate a trained model. In the first embodiment, as the image classification model, MobileNet V2, which is capable of high-speed processing even with a device having a low arithmetic processing capacity such as a smartphone, is used… Next, apart from the above-mentioned training data set, 3200 daytime and nighttime road surface images cut out from the video of the in-vehicle camera 12 are prepared, and the road surface slip is input to the trained model generated as described above. Saida ) and both Saida and Yamashina already teaches “and wherein based on one or more quality criteria, a relevance of the training data in terms of the road condition determination is increased when the training data set is updated.” (See page 3 paragraphs 4-8 “Further, in the present embodiment, the learning data set generation unit 32 excludes the road surface slip friction coefficient measured when the traveling speed of the measuring vehicle 11 is equal to or less than a predetermined value from the learning data set. This is because when the traveling speed is equal to or less than a predetermined value, the reliability of the value measured by the road surface slip resistance measuring device 13 is lowered” “… the learning data set generation unit 32 calculates the time when the road surface slip resistance measuring device 13 passes through the region captured by the road surface image based on the traveling speed of the measuring vehicle 11, and the time is the same. A training data set is generated by matching the road surface slip friction coefficient in the road surface image with the road surface image. As a result, the deviation between the photographing area and the measurement point is corrected, and the road surface slip friction coefficient corresponding to the road surface image is accurately matched.” “Further, in the present embodiment, when the road surface slip resistance measuring device 13 whose reliability of the measured value is lowered when the measuring vehicle 11 makes a right turn or a left turn is used, the learning data set generation unit 32 uses the steering angle of the measuring vehicle 11. The road surface slip friction coefficient measured when is greater than or equal to a predetermined value is excluded from the training data set. These processes exclude unreliable road slip friction coefficients from the training dataset” See also page 4 paragraphs 1-7. See also page 8 paragraphs 1-6 along with page 9 paragraphs 1-5 which also show utilizing quality measures to update training. Saida)
(See paragraphs 106 and 94- 109 “[0094] Hereinafter, optimization based on performance evaluation of the prediction model by the model optimization device 100 according to some embodiments will be described. This performance evaluation is performed when considering the effectiveness of relearning using MR data, the need for updating the prediction model or updating the weighting, and the like, and when applying the update.” “[0106] FIG. 12 is a schematic diagram illustrating a specific example of evaluation results using the model optimization device 100 according to the embodiment. In FIG. 12, seven examples (Case 1 to Case 7) are shown as examples of evaluation results for the third evaluation score, the fourth evaluation score, the fifth evaluation score, and the sixth evaluation score, and the determination results of the performance evaluation are indicated by “GOOD” for a good result and “POOR” for a poor result.” See also fig. 12. “[0101] For example, the optimization unit 153 may perform optimization by causing the prediction model to learn so that the performance of the prediction model is increased in accordance with the evaluation index. It can be said that, if the evaluation index is J, the smaller the evaluation index, the higher the performance of the prediction model. On the other hand, if the evaluation index is the reciprocal of J, the smaller the evaluation index, the lower the performance of the prediction model. In other words, the relationship between the performance of the prediction model and the evaluation index depends on the definition. Thus, as long as the learning is performed such that the performance of the prediction model is increased, a configuration in which the evaluation index is increased may be used, or a configuration in which the evaluation index is decreased may be used.” See also paragraphs 117-121. It is also motivated by the same reasons presented in claim 8. Yamashina)
As per claim 10, Saida in view of Yamashina already teaches “The method according to Claim 9, wherein the server unit is configured to attain a balance of the training data set during the updating, so that the training data set has a degree of diversity, in which rarer road conditions are sufficiently represented by the training data.” (“ [0005] In prediction models using machine learning in the related art, robustness may not be ensured due to the unbalance of training data or evaluation data. For example, in a case where a prediction model is applied to a plant having varying characteristics depending on the outdoor temperature, if the prediction model is taught only with training data acquired in summer, prediction accuracy in winter may decrease. For example, component degradation of the plant may change performance, which may decrease prediction accuracy. In addition, due to individual differences in components and differences in fuel properties, operating conditions may deviate from the operating conditions at the time of learning, and the prediction accuracy may decrease.” “[0006] In this way, in a case where robustness cannot be ensured, the prediction model may be refrained from being applied to an actual machine, and the prediction model may be used with doubts about prediction results. For this reason, a technique has also been proposed in which new data is generated using MR to complement the unbalance of the training data and the evaluation data, and the MT is executed using the data. For example, in image recognition, a method of generating MR data obtained by rotating training data and executing MT has been proposed. However, such newly generated MR data may also include data that cannot actually occur. In addition, MT is used exclusively for system evaluation (for example, performance evaluation of a prediction model)…” See also paragraph 143 “[0143] According to the above configuration, the balance between the first evaluation score and the second evaluation score in the evaluation index used in the optimization can be adjusted by weighting. In this way, it is possible to prevent overlearning due to learning based on MR data for expanding training data.” See also paragraph 115-121 “[0121] The optimization unit 153 determines the necessity of updating the prediction model and updating the weighting based on the various evaluation scores (step S22). The optimization unit 153 determines whether updating the prediction model or the weighting is necessary as a result of the necessity determination (step S23). When the update is determined to be unnecessary (step S23; No), the process ends. On the other hand, if the update is determined to be necessary (step S23; Yes), the optimization unit 153 updates the prediction model or updates the weighting assigned by the assigning unit 155 (step S24).” See also paragraphs 94-99, It is motivated by the same reasons presented in claim 8. Yamashina)
As per claim 11, Saida in view of Yamashina already teaches “The method according to Claim 9, wherein the relevance of the training data is evaluated in such a manner that a degree of relevance is attributed to the training data which are orthogonal to other training data already contained in the training data set.” (Examiner interprets “orthogonal” as “different”. See paragraphs 5-6. See paragraphs 94-109 “[0108] First, in Case 1, all of the evaluation scores are good. In this case, because performance is good regardless of the presence or absence of relearning based on the MR data, it may be determined that updating the prediction model is unnecessary. In Case 2, because all performance is poor, it is thought that actual data completely different from the learned data or the MR data is input. In this case, updating the prediction model may be determined to be necessary, or may be considered an outlier and determined to be unnecessary. In Case 3, only the third evaluation score is good, and the other evaluation scores are poor. In this case, it can be seen that relearning using the MR data was effective. It may also be considered to reflect the results in weight updates.” See also paragraph 143 “[0143] According to the above configuration, the balance between the first evaluation score and the second evaluation score in the evaluation index used in the optimization can be adjusted by weighting. In this way, it is possible to prevent overlearning due to learning based on MR data for expanding training data.” See also paragraph 115-121 “[0121] The optimization unit 153 determines the necessity of updating the prediction model and updating the weighting based on the various evaluation scores (step S22). The optimization unit 153 determines whether updating the prediction model or the weighting is necessary as a result of the necessity determination (step S23). When the update is determined to be unnecessary (step S23; No), the process ends. On the other hand, if the update is determined to be necessary (step S23; Yes), the optimization unit 153 updates the prediction model or updates the weighting assigned by the assigning unit 155 (step S24).” See also paragraphs 94-99, It is motivated by the same reasons presented in claim 8. See also paragraphs 69-82. The changes in speed of temporal change, slope change are different and processed independently “0070] Hereinafter, specific examples of the MR data generated by the generating unit 152 will be described. The generating unit 152 may generate the MR data by adding at least one of offset processing, slope change processing of the temporal change, time axis inversion processing, time constant change processing of the temporal change, filtering processing, noise addition processing, and transformation processing using generative adversarial networks (GAN) to the waveform indicating the temporal change of the training data.” Paragraphs 71-81 also present independent variables. It is motivated by the same reasons presented in claim 8. Yamashina)
Conclusion
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/DYLAN JOHN MENDEZ MUNIZ/Examiner, Art Unit 2675
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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