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 .
Status of Claims
This Office Action is in response to the Office Action Response dated December 22, 2025. Claims 1-12 and 15-16 and 18-20 are presently pending and are presented for examination.
Response to Arguments
Applicant’s amendments overcome the rejections under 35 USC 101. Applicant’s arguments with respect to claims 1 and 12 are moot in view of new grounds of rejection. Applicant’s arguments with respect to claim 16 is not persuasive, as indicated in the revised rejection addressing new claim features.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-8, 12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2023/0060112, to Marumo et al. (hereinafter Marumo), in view of U.S. Patent Publication No. 2017/0101103, to Foster et al. (hereinafter Foster), and in further view of U.S. Patent No. 6,330,505, to Schmitt et al. (hereinafter Schmitt).
As per claim 1, Marumo discloses an agricultural system (e.g. see Abstract and Fig. 1, wherein a vehicle system 1 is provided) comprising: a vehicle including one or more ground tractive elements (e.g. see Fig. 1, and para 0030, wherein the vehicle is provided including wheels 3LF, 3RF, 3LR and 3RR); a field sensor configured to capture data indicative of a moisture content within a field (e.g. see Fig. 1, and para 0040, wherein the vehicle includes a road surface detection sensor 33 configured for detecting moisture content of a road surface); and a computing system communicatively coupled to the field sensor (e.g. see Fig. 1, paras 0030-0031, wherein the vehicle includes a drive assist apparatus 50 and a vehicle control apparatus 41 (i.e. collectively a computing system)), the computing system including a processor and associated memory, the memory storing instructions that, when implemented by the processor, configure the computing system to: receive the data from the field sensor (e.g. see Fig. 1 and para 0040, wherein the drive assist apparatus 50 receives detected road conditions, including moisture content of a road surface, from the road surface detection sensor); identify one or more zones of the field having a moisture content that exceeds a defined moisture content (e.g. see Fig. 10 and para 0101, wherein an ice area 93 is identified, which at a minimum indicates a moisture content greater than zero); calculate a probability of the vehicle experiencing tractive element slippage while traversing through the one or more zones (e.g. see para 0088, wherein a probability of vehicle slippage is predicted);…
Marumo fails to specifically disclose an agricultural vehicle system determining a moisture content within a field. However, Foster teaches an agricultural vehicle system, which detects a soil moisture content (e.g. see Fig. 1, and para 0015, 0037 and 0044). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include detecting moisture content of a driving surface which is not a road, such as a field, for the purpose increasing slip monitoring to off-road systems to aid in the prevention of the vehicle becoming stuck.
Marumo fails to disclose all of the features of determine an ambient temperature; generate a first control command based at least in part on the probability of the vehicle experiencing tractive element slippage exceeding a defined probability value within the one or more zones and the ambient temperature exceeding a lower threshold temperature; and generate a second control command based at least in part on the probability of the vehicle experiencing tractive element slippage exceeding the defined probability value within the one or more zones and the ambient temperature being less than a lower threshold temperature. However, Schmitt teaches a method for controlling wheel performance to emphasize traction (e.g. see Abstract) including determining an ambient temperature using a measuring device 20 (e.g. see Fig. 1 and col. 3, lines 1-23). The method further includes a traction control system that modifies vehicle torque (i.e. at least a first and second control command) based upon, at a minimum, predefined slip threshold (i.e. probability of slippage) and outside air temperature (i.e. a plurality of different threshold temperature points used to determine the probability of slip) (e.g. see col. 3, line 62, to col. 4, lined 40). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include using outside temperature and probability of slip to determine vehicle torque control for the purpose of minimizing slippage and loss of vehicle control thereby improving safety to the driver and surrounding environment.
As per claim 2, Marumo, as modified by Foster and Schmitt, teaches the features of claim 1, and Marumo further discloses wherein the computing system is further configured to: receive a current condition of a power plant, wherein the probability of the vehicle experiencing tractive element slippage is based in part on the current condition of the power plant (e.g. see Fig. 2 and para 0041, wherein the drive assist apparatus receives data from a vehicle state sensor 35 including an engine speed).
As per claim 5, Marumo, as modified by Foster and Schmitt, teaches the features of claim 2, and Foster further teaches wherein the computing system is further configured to: receive a current condition of a steering system, wherein the probability of the vehicle experiencing tractive element slippage is based in part on the current condition of the steering system (e.g. see para 0063, wherein the steering direction of the vehicle is determined so as to plan a route that would reduce slippage by avoiding slip regions). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include utilizing steering data of the vehicle so as to determine a likelihood of reducing slippage of the wheels by avoiding a slip region.
As per claim 6, Marumo, as modified by Foster and Schmitt, teaches the features of claim 1, and Foster further teaches wherein the first control command navigates the vehicle around the one or more zones, and wherein the second control command navigates the vehicle through the one or more zones (e.g. see para 0063, wherein the steering direction of the vehicle is determined so as to plan a route that would reduce slippage by avoiding slip regions). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include utilizing steering data of the vehicle so as to determine a likelihood of reducing slippage of the wheels by avoiding a slip region.
As per claim 7, Marumo, as modified by Foster and Schmitt, teaches the features of claim 6, and Foster further teaches wherein the computing system is configured to navigate the vehicle around the one or more zones through electronic control of at least one of a power plant, a transmission system, or a steering system of the vehicle (e.g. see para 0063, wherein the steering direction of the vehicle is determined so as to plan a route that would reduce slippage by avoiding slip regions). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include utilizing steering data of the vehicle so as to determine a likelihood of reducing slippage of the wheels by avoiding a slip region.
As per claim 8, Marumo, as modified by Foster and Schmitt, teaches the features of claim 6, and Foster further teaches further comprising: a display operably coupled with the computing system, the computing system configured to illustrate information related to the one or more zones (e.g. see para 0039, wherein a display 76 is provided for displaying a host vehicle in the field). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include providing a display of the vehicle in the field so that the driver can avoid slippage regions by following a slip region avoidance route.
As per claim 12, Marumo discloses a method for operating an agricultural system (e.g. see Abstract and Fig. 1, wherein a vehicle system 1 is provided), the method comprising: receiving data from a field sensor; identifying, with a computing system, one or more zones of a field having a moisture content that exceeds a defined moisture content based on data from the field sensor (e.g. see Fig. 1, and para 0040, wherein the vehicle includes a road surface detection sensor 33 configured for detecting moisture content of a road surface); and calculating, with the computing system, a probability of a vehicle experiencing tractive element slippage while traversing through the one or more zones(e.g. see para 0088, wherein a probability of vehicle slippage is predicted); generating, with the computing system, a control command based at least in part on the probability of the vehicle experiencing tractive element slippage within the one or more zones…(e.g. see para 0088, wherein a probability of slippage is determined); and electronically controlling at least one of a power plant, a transmission system, or a steering system of the vehicle to avoid the one or more zones…(e.g. see Fig. 10 and para 0108, wherein steering of the vehicle is controlled to avoid an ice area 93 (i.e. one or more zones)).
Marumo fails to specifically disclose an agricultural system determining a moisture content within a field. However, Foster teaches an agricultural vehicle system, which detects a soil moisture content (e.g. see Fig. 1, and para 0015, 0037 and 0044). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include detecting moisture content of a driving surface which is not a road, such as a field, for the purpose increasing slip monitoring to off-road systems to aid in the prevention of the vehicle becoming stuck.
Marumo fails to specifically disclose a control command based on an ambient temperature relative to a lower temperature threshold and controlling electronically controlling at least one of a power plant, a transmission system or a steering system based on the control command when the ambient temperature exceeds the lower temperature threshold. However, Schmitt teaches a method for controlling wheel performance to emphasize traction (e.g. see Abstract) including determining an ambient temperature using a measuring device 20 (e.g. see Fig. 1 and col. 3, lines 1-23). The method further includes a traction control system that modifies vehicle torque (i.e. at least a first and second control command) based upon, at a minimum, predefined slip threshold (i.e. probability of slippage) and outside air temperature (i.e. a plurality of different threshold temperature points used to determine the probability of slip) (e.g. see col. 3, line 62, to col. 4, lined 40). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include using outside temperature and probability of slip to determine vehicle torque control for the purpose of minimizing slippage and loss of vehicle control thereby improving safety to the driver and surrounding environment.
As per claim 15, Marumo, as modified by Foster and Schmitt, teaches the features of claim 13, and Marumo further discloses wherein the control command illustrates information related to the one or more zones on a display operably coupled with the computing system (e.g. see rejection of claim 8).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2023/0060112, to Marumo et al. (hereinafter Marumo), in further view of U.S. Patent Publication No. 2017/0101103, to Foster et al. (hereinafter Foster).
As per claim 16, Marumo discloses an agricultural system (e.g. see Abstract and Fig. 1, wherein a vehicle system 1 is provided) comprising: a field sensor configured to capture data indicative of a moisture content within a field (e.g. see Fig. 1, and para 0040, wherein the vehicle includes a road surface detection sensor 33 configured for detecting moisture content of a road surface); and a computing system communicatively coupled to the field sensor (e.g. see Fig. 1, paras 0030-0031, wherein the vehicle includes a drive assist apparatus 50 and a vehicle control apparatus 41 (i.e. collectively a computing system)), the computing system including a processor and associated memory, the memory storing instructions that, when implemented by the processor, configure the computing system to: receive data from the field sensor (e.g. see Fig. 1 and para 0040, wherein the drive assist apparatus 50 receives detected road conditions, including moisture content of a road surface, from the road surface detection sensor); identify one or more zones of the field having a moisture content that exceeds a defined moisture content (e.g. see Fig. 10 and para 0101, wherein an ice area 93 is identified, which at a minimum indicates a moisture content greater than zero); and calculate a probability of a vehicle experiencing tractive element slippage while traversing through the one or more zones (e.g. see para 0088, wherein a probability of vehicle slippage is predicted), wherein the probability of the vehicle experiencing tractive element slippage is based at least in part on a type of one or more ground contacting components operably coupled with the vehicle (e.g. see para 0088, wherein slippage is based upon a wear state of the tires (i.e. a worn or unworn type tire)); and generate a control command based at least in part on the probability of the vehicle experiencing tractive element slippage within the one or more zones; and a vehicle configured to navigate about the one or more zones based on the control command (e.g. see Fig. 10 and para 0108, wherein steering of the vehicle is controlled to avoid an ice area 93 (i.e. one or more zones)).
Marumo fails to specifically disclose an agricultural system determining a moisture content within a field. However, Foster teaches an agricultural vehicle system, which detects a soil moisture content (e.g. see Fig. 1, and para 0015, 0037 and 0044). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include detecting moisture content of a driving surface which is not a road, such as a field, for the purpose increasing slip monitoring to off-road systems to aid in the prevention of the vehicle becoming stuck.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2023/0060112, to Marumo et al. (hereinafter Marumo), in view of U.S. Patent Publication No. 2017/0101103, to Foster et al. (hereinafter Foster), in view of U.S. Patent No. 6,330,505, to Schmitt et al. (hereinafter Schmitt), and in further view of U.S. Patent Publication No. 2012/0095659, to Rodrigues et al. (hereinafter Rodrigues).
As per claim 3, Marumo, as modified by Foster and Schmitt, teaches the features of claim 2, but fails to teach wherein the computing system is further configured to: receive a current condition of a transmission system, wherein the probability of the vehicle experiencing tractive element slippage is based in part on the current condition of the power plant. However, Rodrigues teaches determining a probability of wheel slip based upon sensor data including a detected gear in which the transmission is operating (e.g. see para 0030). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include utilizing transmission data to further determine wheel rotation capabilities of the vehicle so as to determine a likelihood that a wheel may not maintain traction with a ground surface thereby leading to an increased awareness of slippage.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2023/0060112, to Marumo et al. (hereinafter Marumo), in view of U.S. Patent Publication No. 2017/0101103, to Foster et al. (hereinafter Foster), in view of U.S. Patent No. 6,330,505, to Schmitt et al. (hereinafter Schmitt), and in further view of U.S. Patent Publication No. 2024/0206363, to Kowalchuk et al. (hereinafter Kowalchuk).
As per claim 4, Marumo, as modified by Foster and Schmitt, teaches the features of claim 2, but fails to teach wherein the computing system is further configured to: receive a current condition of an application system, wherein the probability of the vehicle experiencing tractive element slippage is based in part on the current condition of the application system. However, Kowalchuk teaches an agriculture machine that takes into consideration ground engaging tools (i.e. application system) for analyzing potential likelihood of slip of the machines tires/tracks (e.g. see para 0055). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include utilizing application system data, which effects drag on a vehicle, to further determine an effect the drag has on potential slippage so as to maintain a safe travel direction.
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2023/0060112, to Marumo et al. (hereinafter Marumo), in view of U.S. Patent Publication No. 2017/0101103, to Foster et al. (hereinafter Foster), in view of U.S. Patent No. 6,330,505, to Schmitt et al. (hereinafter Schmitt), and in further view of Chinese Patent Application No. CN115372282, to Univ Shenzhen (hereinafter Shenzhen). For the purpose of examination a machine translation of CN115372282 is utilized and attached herewith.
As per claim 9, Marumo, as modified by Foster and Schmitt, teaches the features of claim 1, but fails to teach wherein the field sensor is configured as a hyperspectral sensor. However, Shenzhen teaches using a deep neural network regression model to analyze reflectance spectrum in hyperspectral image data for the purpose of determining a moisture content within a field (e.g. see p. 8, line 1-19). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include using reflective spectrum model analysis to determine a moisture content within a field for the purpose of reducing cost associate with determining a field state by using image data as oppose to direct soil sampling.
As per claim 10, Marumo, as modified by Foster, Schmitt and Shenzhen, teaches the features of claim 9, and Shenzhen further teaches wherein the data collected from the hyperspectral sensor is associated with a reflectivity value of a soil within the field (e.g. see p. 8, line 1-19, wherein Shenzhen teaches using a deep neural network regression model to analyze reflectance spectrum in hyperspectral image data for the purpose of determining a moisture content within a field). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include using reflective spectrum model analysis to determine a moisture content within a field for the purpose of reducing cost associate with determining a field state by using image data as oppose to direct soil sampling.
As per claim 11, Marumo, as modified by Foster, Schmitt and Shenzhen, teaches the features of claim 10, and Shenzhen further teaches wherein the computing system is configured to identify one or more zones of the field having a moisture content that exceeds the defined moisture content by inputting the reflectivity values in a machine-learned model (e.g. see p. 8, line 1-19, wherein Shenzhen teaches using a deep neural network regression model to analyze reflectance spectrum in hyperspectral image data for the purpose of determining a moisture content within a field). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include using reflective spectrum model analysis to determine a moisture content within a field for the purpose of reducing cost associate with determining a field state by using image data as oppose to direct soil sampling.
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2023/0060112, to Marumo et al. (hereinafter Marumo), in view of U.S. Patent Publication No. 2017/0101103, to Foster et al. (hereinafter Foster), and in further view of Chinese Patent Application No. CN115372282, to Univ Shenzhen (hereinafter Shenzhen). For the purpose of examination a machine translation of CN115372282 is utilized and attached herewith.
As per claim 18, Marumo, as modified by Foster, teaches the features of claim 16, but fails to teach wherein the field sensor is configured as a hyperspectral sensor. However, Shenzhen teaches using a deep neural network regression model to analyze reflectance spectrum in hyperspectral image data for the purpose of determining a moisture content within a field (e.g. see p. 8, line 1-19). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include using reflective spectrum model analysis to determine a moisture content within a field for the purpose of reducing cost associate with determining a field state by using image data as oppose to direct soil sampling.
As per claim 19, Marumo, as modified by Foster and Shenzhen, teaches the features of claim 18, and Shenzhen further teaches wherein the data collected from the hyperspectral sensor is associated with a reflectivity value of a soil within the field (e.g. see p. 8, line 1-19, wherein Shenzhen teaches using a deep neural network regression model to analyze reflectance spectrum in hyperspectral image data for the purpose of determining a moisture content within a field). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include using reflective spectrum model analysis to determine a moisture content within a field for the purpose of reducing cost associate with determining a field state by using image data as oppose to direct soil sampling.
As per claim 20, Marumo, as modified by Foster and Shenzhen, teaches the features of claim 19, and Shenzhen further teaches wherein the computing system is configured to identify one or more zones of the field having a moisture content that exceeds the defined moisture content by inputting the reflectivity values in a machine-learned model (e.g. see p. 8, line 1-19, wherein Shenzhen teaches using a deep neural network regression model to analyze reflectance spectrum in hyperspectral image data for the purpose of determining a moisture content within a field). It would have been obvious to a person of ordinary skill in the art at the time of Applicants’ invention to modify the vehicle system of Marumo to include using reflective spectrum model analysis to determine a moisture content within a field for the purpose of reducing cost associate with determining a field state by using image data as oppose to direct soil sampling.
Conclusion
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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/JAMES M MCPHERSON/Primary Examiner, Art Unit 3663B