DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office Action is in response to Amendments filed on 01/02/2026, wherein Claims 1, 2, 4, 7-9, 11, 14-16, and 18 have been amended. Claims 3, 10, and 17 were cancelled. Claims 1-2, 4-9, 11-16, and 18-20 are pending.
Response to Arguments
Regarding Examiner’s objections: Applicant's arguments, see Remarks (pp. 8-10), filed on 01/02/2026, with respect to the objections to the Claims have been fully considered. In view of the amendments to the Claims addressing the informalities raised in the previous office action, the objections to the Claims regarding the antecedent basis requirement, have been withdrawn.
However, a new objection has been issued (see below for details).
Regarding 35 USC 101 rejection: Applicant's arguments filed on 01/02/2026, with respect to 35 USC 101 rejection, have been fully considered and found persuasive. The 35 USC 101 rejection is withdrawn.
Regarding 35 USC 103 rejection: Applicant’s arguments filed 06/19/2025 with respect to claims 1-10, have been considered but are moot because of the new ground of rejection necessitated by the amendments.
Claim Objections
Regarding Claims 15-16 and 18-20:
Claim 15 is directed to non-statutory subject matter, more specifically, a computer program product comprising one or more computer-readable storage medium. The broadest reasonable interpretation of a claim drawn to a computer program product comprising one or more computer-readable storage medium covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent See MPEP 2111.01 and 2106.03. Similar language is recited in the dependent claims 16 and 18-20.
In order to overcome this objection and to avoid any potential issues under 35 U.S.C. 101, the following language is suggested for claim 15: “A non-transitory computer program product … , the non-transitory computer program product comprising: one or more non-transitory computer-readable storage medium and program instructions stored on at least one of the one or more non-transitory storage medium …”
For dependent claims 16 and 18-20, the following language is also suggested in order to avoid any potential issues under 35 U.S.C. 101: “The non-transitory computer program product …”.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5-8, 12-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over US20140358382A1 to Kou et al. (hereinafter Kou) in view of US20160298306 to de Kontz et al. (hereinafter de Kontz) and in further view of CN112012151A to Tan et al.(hereinafter Tan).
Regarding Claim 1: Kou discloses:
“A processor-implemented method for dust suppression” (para 0057 – “The mine management apparatus 10 is, for example, a computer. The processing device 12 is, for example, a CPU (Central Processing Unit)”; para 0058 – “The processing device 12 executes the operation control method according to the embodiment”; para 0039 – “a water truck 5 as a mining machine prevents the occurrence of dust (i.e. dust suppression, added by examiner) by watering the travel paths Rg and Rr, and the like.”), the method comprising:
“collecting, from a plurality of sensors, environmental data pertaining to one or more monitored segments comprising a route” (para 0046 – “The operation control system 1 includes a weather-observing device 3 installed as the travel path information collection device on the mine property, more specifically, in the vicinity of the travel path R”; para 0053 – “The weather-observing device 3 includes various measuring devices (i.e. sensors, added by examiner) for detecting the travel path's moisture information and information on the air temperature, humidity, and weather. Such measuring devices include, for example, a rain gauge, a thermometer, and a hygrometer. Such weather-observing devices 3 are installed in a plurality of places on the mine property”);
“based on the environmental data and historical data, identifying a moisture level and a dust level of the one or more monitored segments” (para 0018 – “an operation control method for a mining machine comprises: acquiring travel path information including at least information on moisture content of a travel path on which a mining machine operating at a mine runs, and position information being information on a position of the travel path corresponding to the travel path information”; para 0020 – “it is preferable that the travel path information includes information on a precipitation amount of the mine or information on a watering amount on the travel path (i.e. environmental and historical data, added by examiner))”); para 0004 – “in order to prevent the occurrence of such reduced visibility that hinders the travel of the vehicle, the reduced visibility being caused by dust rising in the it from the dry road surface”).
Kou does not explicitly disclose:
“based on the environmental data and the historical data, extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route; based on the historical data, the moisture levels and the dust levels for the monitored segments and the unmonitored segments, determining an amount and a type of dust suppressant to use in each of the monitored segments and the unmonitored segments; operating a dust suppression system to deploy the determined amount and the determined type of dust suppressant at each of the monitored segments and the unmonitored segments; and responsive to the deploying, modifying the machine learning model with effects of the deploying on the moisture levels and the dust levels”.
However, de Kontz discloses:
“based on the environmental data and the historical data, extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route and the dust level of one or more unmonitored segments comprising the route;” (para 0056 – “the disclosed systems and methods may analyze environmental and other data to develop a parametric dust model, which may be used to predict undue dust conditions. The disclosed systems and methods may also obtain real-time readings of dust conditions throughout a worksite and extrapolate the readings to determine the likely dust conditions at other areas in the worksite (i.e. unmonitored segments, added by examiner). Based on a comparison of the predicted and actual dust conditions throughout the worksite, the parametric dust model may be optimized and used to determine fluid delivery requirements and develop a fluid delivery plan to minimize undue dust conditions at the worksite. ”);
“based on the historical data, the moisture levels and the dust levels for the monitored segments and the unmonitored segments, determining an amount and a type of dust suppressant to use in each of the monitored segments and the unmonitored segments; operating a dust suppression system to deploy the determined amount and the determined type of dust suppressant at each of the monitored segments and the unmonitored segments” (para 0069 – “the fluid delivery plan identifies a plurality of geographic locations within a worksite and an amount of fluid to distribute per unit area to each geographic location.”; para 0077 – “a fluid delivery plan may be generated based on the first environmental data, second environmental data, and interpolated environmental conditions. The fluid delivery plan may identify an amount of fluid (e.g., water and/or other dust suppressant) to be distributed to each of the first, second, and third locations based on the first environmental data, second environmental data, and interpolated environmental conditions… The fluid delivery plan may also indicate a route for a fluid delivery machine to follow in order to deliver fluid to the first, second, and third locations.”; see also para 0081).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou, as taught by de Kontz, in order to obtain the real-time analytics of dust and moisture data and the ability to mitigate the levels of pollution quickly and efficiently.
Kou/de Kontz combination does not disclose “extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route” and “responsive to the deploying, modifying the machine learning model with effects of the deploying on the moisture levels and the dust levels”.
However, Tan discloses:
“extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route” (para 0007 – “By utilizing big data monitoring, machine learning, and brain-like intelligence, it achieves automatic sensing, identification, and adaptive spraying, thereby achieving efficient dust suppression”; para 0009 – “the system intelligently identifies dust generation conditions on mine roads through machine learning and brain-like intelligence technology. Furthermore, it adaptively determines the spraying pressure, time, and volume according to road conditions, ensuring simultaneous dust initiation and suppression, resulting in long-lasting and highly efficient dust suppression”);
“responsive to the deploying, modifying the machine learning model with effects of the deploying on the moisture levels and the dust levels” (para 0036 – “based on real-time monitoring data such as meteorological information of the open-pit mine area, location of the mine transportation road, road conditions, atmospheric humidity, temperature, wind force and vehicle operation in the vicinity of the road, and big data, machine learning and artificial intelligence technologies, the dust start conditions of the road are judged, and the invention adapts to the dynamic, random and uncertain complex environment, realizing the automated and intelligent control of the spray start and stop time, spray pressure, spray duration and spray volume of the transportation road”),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou/de Kontz combination, as taught by Tan, in order to obtain the real-time analytics of dust and moisture data and the ability to mitigate the levels of pollution quickly and efficiently.
Regarding Claim 5: Kou/de Kontz/Tan combination discloses the method of Claim 1.
Kou further discloses:
“wherein the environmental data comprises trip dynamics including one or more vehicle interactions with a shoulder or adjacent terrain of the route” (para 0053 – “It is desirable that the installation place of the weather-observing device 3 be determined while associated with at least one of the design content of the travel path R at the mine (the characteristic of the travel path R such as a straight line, a curve, an upgrade, or a downgrade), and the terrain of the mine (the height of a place where there is the travel path R and a place susceptible to shade)”).
Regarding Claim 6: Kou/de Kontz/Tan combination discloses the method of Claim 1.
Kou further discloses:
“wherein the determining comprises adjusting a suppressant spray based on contours of the route” (para 0085 – “the vehicle control device 51 can compute and obtain a watering amount (i.e. a suppressant spray adjusted, added by examiner) using, as travel path information, information on the amount of the water W used by the water truck 5 to water the travel path R (the watering amount)… accordingly the watering amount per unit length for the travel path R can be obtained. If the width of the travel path R is constant (i.e. contours of the route, added by examiner), the watering flow rate by the pump 50P is divided by the travel speed of the water truck and the width of the travel path... data on the width of the travel path R may be selected to obtain the watering amount”).
Regarding Claim 7: Kou/de Kontz/Tan combination discloses the method of Claim 1.
Kou does not specifically disclose:
“wherein the amount and the type of dust suppressant are determined based on one or more environmental effects of one or more dust suppressant”.
However, de Kontz discloses:
“wherein the amount and the type of dust suppressant are determined based on one or more environmental effects of one or more dust suppressant” (para 0077 – “a fluid delivery plan may be generated based on the first environmental data, second environmental data, and interpolated environmental conditions. The fluid delivery plan may identify an amount of fluid (e.g., water and/or other dust suppressant) to be distributed to each of the first, second, and third locations based on the first environmental data, second environmental data, and interpolated environmental conditions… The fluid delivery plan may also indicate a route for a fluid delivery machine to follow in order to deliver fluid to the first, second, and third locations.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou/de Kontz/Tan combination, as taught by de Kontz, in order to obtain the real-time analytics of dust and moisture data and the ability to mitigate the levels of pollution quickly and efficiently.
Regarding Claim 8: Kou discloses:
“A computer system for dust suppression, the computer system comprising:
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer readable storage mediums for execution by at least one of the one or more processors via at least one of the one or more memories” (para 0057 – “The mine management apparatus 10 is, for example, a computer. The processing device 12 is, for example, a CPU (Central Processing Unit). The storage device 13 is, for example, RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, or a hard disk drive, or a combination thereof. The storage device 13 may be a server”; para 0058 – “the processing device 12 reads, from the storage device 13, a computer program that realizes the operation control method according to the embodiment and executes the computer program. The storage device 13 stores things such as the computer program that realizes the operation control method according to the embodiment, and a database including information ”)
“one or more sensors” (para 0053 – “The weather-observing device 3 includes various measuring devices (i.e. sensors, added by examiner) for detecting the travel path's moisture information and information on the air temperature, humidity, and weather. Such measuring devices include, for example, a rain gauge, a thermometer, and a hygrometer. Such weather-observing devices 3 are installed in a plurality of places on the mine property”), “wherein the computer system is capable of performing a method comprising:
“collecting, from a plurality of sensors, environmental data pertaining to one or more monitored segments comprising a route” (para 0046 – “The operation control system 1 includes a weather-observing device 3 installed as the travel path information collection device on the mine property, more specifically, in the vicinity of the travel path R”; para 0053 – “The weather-observing device 3 includes various measuring devices (i.e. sensors, added by examiner) for detecting the travel path's moisture information and information on the air temperature, humidity, and weather. Such measuring devices include, for example, a rain gauge, a thermometer, and a hygrometer. Such weather-observing devices 3 are installed in a plurality of places on the mine property”);
“based on the environmental data and historical data, identifying a moisture level and a dust level of the one or more monitored segments” (para 0018 – “an operation control method for a mining machine comprises: acquiring travel path information including at least information on moisture content of a travel path on which a mining machine operating at a mine runs, and position information being information on a position of the travel path corresponding to the travel path information”; para 0020 – “it is preferable that the travel path information includes information on a precipitation amount of the mine or information on a watering amount on the travel path (i.e. environmental and historical data, added by examiner))”); para 0004 – “in order to prevent the occurrence of such reduced visibility that hinders the travel of the vehicle, the reduced visibility being caused by dust rising in the it from the dry road surface”).
Kou does not specifically disclose:
“based on the environmental data and the historical data, extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route; based on the historical data, the moisture levels and the dust levels for the monitored segments and the unmonitored segments, determining an amount and a type of dust suppressant to use in each of the monitored segments and the unmonitored segments; operating a dust suppression system to deploy the determined amount and the determined type of dust suppressant at each of the monitored segments and the unmonitored segments; and responsive to the deploying, modifying the machine learning model with effects of the deploying on the moisture levels and the dust levels”.
However, de Kontz discloses:
“based on the environmental data and the historical data, extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route and the dust level of one or more unmonitored segments comprising the route;” (para 0056 – “the disclosed systems and methods may analyze environmental and other data to develop a parametric dust model, which may be used to predict undue dust conditions. The disclosed systems and methods may also obtain real-time readings of dust conditions throughout a worksite and extrapolate the readings to determine the likely dust conditions at other areas in the worksite (i.e. unmonitored segments, added by examiner). Based on a comparison of the predicted and actual dust conditions throughout the worksite, the parametric dust model may be optimized and used to determine fluid delivery requirements and develop a fluid delivery plan to minimize undue dust conditions at the worksite. ”);
“based on the historical data, the moisture levels and the dust levels for the monitored segments and the unmonitored segments, determining an amount and a type of dust suppressant to use in each of the monitored segments and the unmonitored segments; operating a dust suppression system to deploy the determined amount and the determined type of dust suppressant at each of the monitored segments and the unmonitored segments” (para 0069 – “the fluid delivery plan identifies a plurality of geographic locations within a worksite and an amount of fluid to distribute per unit area to each geographic location.”; para 0077 – “a fluid delivery plan may be generated based on the first environmental data, second environmental data, and interpolated environmental conditions. The fluid delivery plan may identify an amount of fluid (e.g., water and/or other dust suppressant) to be distributed to each of the first, second, and third locations based on the first environmental data, second environmental data, and interpolated environmental conditions… The fluid delivery plan may also indicate a route for a fluid delivery machine to follow in order to deliver fluid to the first, second, and third locations.”; see also para 0081).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou, as taught by de Kontz, in order to obtain the real-time analytics of dust and moisture data and the ability to mitigate the levels of pollution quickly and efficiently.
Kou/de Kontz combination does not specifically disclose “extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route” and “responsive to the deploying, modifying the machine learning model with effects of the deploying on the moisture levels and the dust levels”.
However, Tan discloses:
“extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route” (para 0007 – “By utilizing big data monitoring, machine learning, and brain-like intelligence, it achieves automatic sensing, identification, and adaptive spraying, thereby achieving efficient dust suppression”; para 0009 – “the system intelligently identifies dust generation conditions on mine roads through machine learning and brain-like intelligence technology. Furthermore, it adaptively determines the spraying pressure, time, and volume according to road conditions, ensuring simultaneous dust initiation and suppression, resulting in long-lasting and highly efficient dust suppression”);
“responsive to the deploying, modifying the machine learning model with effects of the deploying on the moisture levels and the dust levels” (para 0036 – “based on real-time monitoring data such as meteorological information of the open-pit mine area, location of the mine transportation road, road conditions, atmospheric humidity, temperature, wind force and vehicle operation in the vicinity of the road, and big data, machine learning and artificial intelligence technologies, the dust start conditions of the road are judged, and the invention adapts to the dynamic, random and uncertain complex environment, realizing the automated and intelligent control of the spray start and stop time, spray pressure, spray duration and spray volume of the transportation road”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou/de Kontz combination, as taught by Tan, in order to obtain the real-time analytics of dust and moisture data and the ability to mitigate the levels of pollution quickly and efficiently.
Regarding Claim 12: Kou/de Kontz/Tan combination discloses the method of Claim 8.
Kou further discloses:
“wherein the environmental data comprises trip dynamics including one or more vehicle interactions with a shoulder or adjacent terrain of the route” (para 0053 – “It is desirable that the installation place of the weather-observing device 3 be determined while associated with at least one of the design content of the travel path R at the mine (the characteristic of the travel path R such as a straight line, a curve, an upgrade, or a downgrade), and the terrain of the mine (the height of a place where there is the travel path R and a place susceptible to shade)”).
Regarding Claim 13: Kou/de Kontz/Tan combination discloses the method of Claim 8.
Kou further discloses:
“wherein the determining comprises adjusting a suppressant spray based on contours of the route” (para 0085 – “the vehicle control device 51 can compute and obtain a watering amount (i.e. a suppressant spray adjusted, added by examiner) using, as travel path information, information on the amount of the water W used by the water truck 5 to water the travel path R (the watering amount)… accordingly the watering amount per unit length for the travel path R can be obtained. If the width of the travel path R is constant (i.e. contours of the route, added by examiner), the watering flow rate by the pump 50P is divided by the travel speed of the water truck and the width of the travel path... data on the width of the travel path R may be selected to obtain the watering amount”).
Regarding Claim 14: Kou/de Kontz/Tan combination discloses the method of Claim 8.
Kou does not specifically disclose:
“wherein the amount and the type of dust suppressant are determined based on one or more environmental effects of one or more dust suppressant”.
However, de Kontz discloses:
“wherein the amount and the type of dust suppressant are determined based on one or more environmental effects of one or more dust suppressant” (para 0077 – “a fluid delivery plan may be generated based on the first environmental data, second environmental data, and interpolated environmental conditions. The fluid delivery plan may identify an amount of fluid (e.g., water and/or other dust suppressant) to be distributed to each of the first, second, and third locations based on the first environmental data, second environmental data, and interpolated environmental conditions… The fluid delivery plan may also indicate a route for a fluid delivery machine to follow in order to deliver fluid to the first, second, and third locations.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou/de Kontz/Tan, as taught by de Kontz, in order to obtain the real-time analytics of dust and moisture data and the ability to mitigate the levels of pollution quickly and efficiently.
Regarding Claim 15: Kou discloses:
“A computer program product for dust suppression, the computer program product comprising: one or more computer-readable storage medium and program instructions stored on at least one of the one or more storage medium, the program instructions executable by a processor to cause the processor to perform a method comprising” (para 0058 – “The processing device 12 executes the operation control method according to the embodiment… the processing device 12 reads, from the storage device 13, a computer program that realizes the operation control method according to the embodiment and executes the computer program. The storage device 13 stores things such as the computer program that realizes the operation control method according to the embodiment, and a database including information … necessary for the computer program that realizes the operation control method according to the embodiment.”):
“collecting, from a plurality of sensors, environmental data pertaining to one or more monitored segments comprising a route” (para 0046 – “The operation control system 1 includes a weather-observing device 3 installed as the travel path information collection device on the mine property, more specifically, in the vicinity of the travel path R”; para 0053 – “The weather-observing device 3 includes various measuring devices (i.e. sensors, added by examiner) for detecting the travel path's moisture information and information on the air temperature, humidity, and weather. Such measuring devices include, for example, a rain gauge, a thermometer, and a hygrometer. Such weather-observing devices 3 are installed in a plurality of places on the mine property”);
“based on the environmental data and historical data, identifying a moisture level and a dust level of the one or more monitored segments” (para 0018 – “an operation control method for a mining machine comprises: acquiring travel path information including at least information on moisture content of a travel path on which a mining machine operating at a mine runs, and position information being information on a position of the travel path corresponding to the travel path information”; para 0020 – “it is preferable that the travel path information includes information on a precipitation amount of the mine or information on a watering amount on the travel path (i.e. environmental and historical data, added by examiner))”); para 0004 – “in order to prevent the occurrence of such reduced visibility that hinders the travel of the vehicle, the reduced visibility being caused by dust rising in the it from the dry road surface”);
Kou does not specifically disclose:
“based on the environmental data and the historical data, extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route; based on the historical data, the moisture levels and the dust levels for the monitored segments and the unmonitored segments, determining an amount and a type of dust suppressant to use in each of the monitored segments and the unmonitored segments; operating a dust suppression system to deploy the determined amount and the determined type of dust suppressant at each of the monitored segments and the unmonitored segments; and responsive to the deploying, modifying the machine learning model with effects of the deploying on the moisture levels and the dust levels”.
However, de Kontz discloses:
“based on the environmental data and the historical data, extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route and the dust level of one or more unmonitored segments comprising the route;” (para 0056 – “the disclosed systems and methods may analyze environmental and other data to develop a parametric dust model, which may be used to predict undue dust conditions. The disclosed systems and methods may also obtain real-time readings of dust conditions throughout a worksite and extrapolate the readings to determine the likely dust conditions at other areas in the worksite (i.e. unmonitored segments, added by examiner). Based on a comparison of the predicted and actual dust conditions throughout the worksite, the parametric dust model may be optimized and used to determine fluid delivery requirements and develop a fluid delivery plan to minimize undue dust conditions at the worksite. ”);
“based on the historical data, the moisture levels and the dust levels for the monitored segments and the unmonitored segments, determining an amount and a type of dust suppressant to use in each of the monitored segments and the unmonitored segments; operating a dust suppression system to deploy the determined amount and the determined type of dust suppressant at each of the monitored segments and the unmonitored segments” (para 0069 – “the fluid delivery plan identifies a plurality of geographic locations within a worksite and an amount of fluid to distribute per unit area to each geographic location.”; para 0077 – “a fluid delivery plan may be generated based on the first environmental data, second environmental data, and interpolated environmental conditions. The fluid delivery plan may identify an amount of fluid (e.g., water and/or other dust suppressant) to be distributed to each of the first, second, and third locations based on the first environmental data, second environmental data, and interpolated environmental conditions… The fluid delivery plan may also indicate a route for a fluid delivery machine to follow in order to deliver fluid to the first, second, and third locations.”; see also para 0081).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou, as taught by de Kontz, in order to obtain the real-time analytics of dust and moisture data and the ability to mitigate the levels of pollution quickly and efficiently.
Kou/de Kontz combination does not specifically disclose “extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route” and “responsive to the deploying, modifying the machine learning model with effects of the deploying on the moisture levels and the dust levels”.
However, Tan discloses:
“extrapolating, by a machine learning model, the moisture level and the dust level of one or more unmonitored segments comprising the route” (para 0007 – “By utilizing big data monitoring, machine learning, and brain-like intelligence, it achieves automatic sensing, identification, and adaptive spraying, thereby achieving efficient dust suppression”; para 0009 – “the system intelligently identifies dust generation conditions on mine roads through machine learning and brain-like intelligence technology. Furthermore, it adaptively determines the spraying pressure, time, and volume according to road conditions, ensuring simultaneous dust initiation and suppression, resulting in long-lasting and highly efficient dust suppression”);
“responsive to the deploying, modifying the machine learning model with effects of the deploying on the moisture levels and the dust levels” (para 0036 – “based on real-time monitoring data such as meteorological information of the open-pit mine area, location of the mine transportation road, road conditions, atmospheric humidity, temperature, wind force and vehicle operation in the vicinity of the road, and big data, machine learning and artificial intelligence technologies, the dust start conditions of the road are judged, and the invention adapts to the dynamic, random and uncertain complex environment, realizing the automated and intelligent control of the spray start and stop time, spray pressure, spray duration and spray volume of the transportation road”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou/de Kontz/Tan combination, as taught by Tan, in order to obtain the real-time analytics of dust and moisture data and the ability to mitigate the levels of pollution quickly and efficiently.
Regarding Claim 19: Kou/de Kontz/Tan combination discloses the method of Claim 15.
Kou further discloses:
“wherein the environmental data comprises trip dynamics including one or more vehicle interactions with a shoulder or adjacent terrain of the route” (para 0053 – “It is desirable that the installation place of the weather-observing device 3 be determined while associated with at least one of the design content of the travel path R at the mine (the characteristic of the travel path R such as a straight line, a curve, an upgrade, or a downgrade), and the terrain of the mine (the height of a place where there is the travel path R and a place susceptible to shade)”).
Regarding Claim 20: Kou/de Kontz/Tan combination discloses the method of Claim 15.
Kou further discloses:
“wherein the determining comprises adjusting a suppressant spray based on contours of the route” (para 0085 – “the vehicle control device 51 can compute and obtain a watering amount (i.e. a suppressant spray adjusted, added by examiner) using, as travel path information, information on the amount of the water W used by the water truck 5 to water the travel path R (the watering amount)… accordingly the watering amount per unit length for the travel path R can be obtained. If the width of the travel path R is constant (i.e. contours of the route, added by examiner), the watering flow rate by the pump 50P is divided by the travel speed of the water truck and the width of the travel path... data on the width of the travel path R may be selected to obtain the watering amount”).
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kou in view of de Kontz in further view of Tan and in further view of NPL to Niraj et al., Managing mine dust pollution in near real time leveraging IoT and Analytics, Analytics India Magazine, June 30, 2018, pp. 1-9 (hereinafter Niraj).
Regarding Claim 2: Kou/de Kontz/Tan combination discloses the method of Claim 1.
Kou does not specifically disclose:
“further comprising: based on the moisture levels, the dust levels, the environmental data, and the historical data, modelling a three-dimensional visualization of the dust levels and the moisture levels along the route”.
However, Naraj discloses:
“further comprising: based on the moisture levels, the dust levels, the environmental data, and the historical data, modelling a three-dimensional visualization of the dust levels and the moisture levels along the route” (see Figures 1-5 presenting the three-dimensional visualization of the dust levels and moisture levels along the road (route), Figure 1 – Dust Level at Haul road; Figure 2 – Moisture level at haul road; Figure 3 – Dust and moisture level at haul road, Figure 4 – The delta moisture required at the location of haul road; Figure 5 – Real time dust conditioning dashboard architecture (historical data)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou/de Kontz/Tan combination, as taught by Naraj, in order to obtain the real-time analytics of dust and moisture data and the ability to mitigate the levels of pollution quickly and efficiently.
Regarding Claim 9: Kou/de Kontz/Tan combination discloses the method of Claim 8.
Kou does not specifically disclose:
“further comprising: based on the moisture levels, the dust levels, the environmental data, and the historical data, modelling a three-dimensional visualization of the dust levels and the moisture levels along the route”.
However, Naraj discloses:
“further comprising: based on the moisture levels, the dust levels, the environmental data, and the historical data, modelling a three-dimensional visualization of the dust levels and the moisture levels along the route” (see Figures 1-5 presenting the three-dimensional visualization of the dust levels and moisture levels along the road (route), Figure 1 – Dust Level at Haul road; Figure 2 – Moisture level at haul road; Figure 3 – Dust and moisture level at haul road, Figure 4 – The delta moisture required at the location of haul road; Figure 5 – Real time dust conditioning dashboard architecture (historical data)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou/de Kontz/Tan combination, as taught by Naraj, in order to obtain the real-time analytics of dust and moisture data and the ability to mitigate the levels of pollution quickly and efficiently.
Regarding Claim 16: Kou/de Kontz/Tan combination discloses the method of Claim 15.
Kou does not specifically disclose:
“further comprising: based on the moisture levels, the dust levels, the environmental data, and the historical data, modelling a three-dimensional visualization of the dust levels and the moisture levels along the route”.
However, Naraj discloses:
“further comprising: based on the moisture levels, the dust levels, the environmental data, and the historical data, modelling a three-dimensional visualization of the dust levels and the moisture levels along the route” (see Figures 1-5 presenting the three-dimensional visualization of the dust levels and moisture levels along the road (route), Figure 1 – Dust Level at Haul road; Figure 2 – Moisture level at haul road; Figure 3 – Dust and moisture level at haul road, Figure 4 – The delta moisture required at the location of haul road; Figure 5 – Real time dust conditioning dashboard architecture (historical data)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou/de Kontz/Tan combination, as taught by Naraj, in order to obtain the real-time analytics of dust and moisture data and the ability to mitigate the levels of pollution quickly and efficiently.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kou in view of de Kontz in further view of Tan and in further view of NPL to Susanto et al., A Kriging Method for Mapping Underground Mine Air Pollution, Adv. Sci. Lett, 2017, Vol. 23, No. 3, 2329-2332 (hereinafter Susanto).
Regarding Claim 4: Kou/de Kontz/Tan combination discloses the method of Claim 1.
Kou does not specifically disclose:
“wherein the extrapolating is performed by the machine learning model utilizing a Kriging method”.
However, Susanto discloses:
“wherein the extrapolating is performed by a machine learning model utilizing a Kriging method” (page 2332 – “The universal kriging added into the spatial coordinate was used for estimating global trend (interpreted as extrapolating, added by examiner)… The mapping of the CO and DPM pollutants with universal kriging indicated a differential spatial distribution at the track haulage area at the DOZ underground mining”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou/de Kontz/Tan combination, as taught by Susanto, in order to quickly, accurately and efficiently extrapolate existing environmental data without additional sensors.
Regarding Claim 11: Kou/de Kontz/Tan combination discloses the method of Claim 8.
Kou does not specifically disclose:
“wherein the extrapolating is performed by the machine learning model utilizing a Kriging method”.
However, Susanto discloses:
“wherein the extrapolating is performed by the machine learning model utilizing a Kriging method” (page 2332 – “The universal kriging added into the spatial coordinate was used for estimating global trend (interpreted as extrapolating, added by examiner)… The mapping of the CO and DPM pollutants with universal kriging indicated a differential spatial distribution at the track haulage area at the DOZ underground mining”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou/de Kontz/Tan combination, as taught by Susanto, in order to quickly, accurately and efficiently extrapolate existing environmental data without additional sensors.
Regarding Claim 18: Kou/de Kontz/Tan combination discloses the method of Claim 15.
Kou does not specifically disclose:
“wherein the extrapolating is performed by the machine learning model utilizing a Kriging method”.
However, Susanto discloses:
“wherein the extrapolating is performed by the machine learning model utilizing a Kriging method” (page 2332 – “The universal kriging added into the spatial coordinate was used for estimating global trend (interpreted as extrapolating, added by examiner)… The mapping of the CO and DPM pollutants with universal kriging indicated a differential spatial distribution at the track haulage area at the DOZ underground mining”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Kou/de Kontz/Tan combination, as taught by Susanto, in order to quickly, accurately and efficiently extrapolate existing environmental data without additional sensors.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20100301266 to Marsden et al. (hereinafter Marsden) discloses Coal Topper Dust Control Formulation, System and Method.
US20110266360A1 to Gudat et al. (hereinafter Gudat) discloses methods and systems for executing fluid delivery mission.
US20190294136 to Iacobone et al. (hereinafter Iacobone) discloses systems and methods for providing monitoring and response measures in connection with remote sites.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LYUDMILA ZAYKOVA-FELDMAN/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857
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