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 Arguments
Applicant’s remarks have been fully considered.
Applicant’s argument focuses on the alleged failure of Johnson to teach the new limitation of “the new machine settings information comprising a schedule of settings for one or more future operations of the one or more mobile machines.”
The Examiner respectfully disagrees.
First, to clarify the Examiner’s interpretation of the amended claim, the amended claim reads, in part:
“… using, by the computer system, the trained model to generate new machine settings information for a given task, the new machine settings information comprising a schedule of settings for one or more future operations of the one or more mobile machines.”
Since the “new machine settings information” are generated “for a given task,” the Examiner is interpreting this to mean that the “future operations” are part of performing “a given task.” For example, a task such as harvesting a field would include operations such driving, steering, harvesting of a crop, and unloading the crop from the harvester. The “schedule of settings” therefore represents the settings to be used during each of the different operations involved in performing the “given task.”
Examiner notes that this may not be entirely consistent with the specification. Specifically, paragraphs [0005]-[0006] and elsewhere recite the following:
“… some embodiments include a [method/system/technique/etc.] for using machine learning to generate, update, or enhance machine settings or generate schedules of settings for future operations.” Emphasis added.
No further description is provided regarding what it means to “generate schedules of settings for future operations” in the specification.
The “new machine settings information” of the claim appears analogous to “new machine settings information 108” of the specification. Nowhere in the specification is it described how “new machine settings information 108” may be “comprising a schedule of settings for one or more future operations of one or more mobile machines.”
“new machine settings information 108” and “generate a schedule of settings for future operations” never appear in the same paragraph.
Examiner will be evaluating the claim according to the interpretation provided in paragraph 8 above. If the Applicant disagrees with this interpretation, they are invited to explain their preferred interpretation and the support for that interpretation found in the specification.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 21 have been amended to read, in part:
“use the trained model to generate new machine settings information for a given task, the new machine settings information comprising a schedule of settings for one or more future operations of the one or more mobile machines.”
The specification recites:
“… some embodiments include a [method/system/technique/etc.] for using machine learning to generate, update, or enhance machine settings or generate schedules of settings for future operations.”
The specification does not describe how the “new machine settings information for a given task” may comprise a “schedule of settings for future operations.”
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 1-8, 10-15 and 17-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Johnson (US 20230242095 A1).
Regarding claim 1, Johnson teaches: A method, comprising:
receiving, by a computing system, initial machine settings information), the initial machine settings information comprising settings used by or to be used by one or more mobile machines performing one or more tasks, the mobile machine(s) comprising machines for farming, construction, forestry, or landscaping;
training, by the computing system, a deep learning model using the initial machine settings information; and (See Johnson Figs. 2-4. See [0106]-[0108] for obtaining measured values and performing machine learning. Recommended path and recommended settings are generated, then feedback is generated based on sensed values and actual settings, and used to perform additional machine learning, and generated new pathing and vehicle settings information. See feedback processing system 152, machine setting variation system 208, path and settings output 216, controllable subsystems 134. See [0071] for agricultural vehicles performing tasks such as planting, spraying or harvesting.)
using, by the computing system, the trained model to generate new machine settings information for a given task, the new machine settings information comprising a schedule of settings for one or more future operations of the one or more mobile machines. (See Johnson [0109]-[0114] for generation of recommended path and settings. See [0114] specifically for the example of reducing tire pressure while passing over a particular part of the field. Examiner considers this an example of scheduling different settings for different upcoming operations while performing a given task.)
Regarding claim 2, Johnson teaches: The method of claim 1, comprising using, by the computing system, the trained model to generate the new machine settings information for the given task and a given mobile machine. (See Johnson [0075] for vehicle index/data specific to the given vehicle. See Fig. 3 where vehicle characteristics 268 are used to determine vehicle force index, which is then used to determine vehicle settings)
Regarding claim 3, Johnson teaches: The method of claim 1, further comprising controlling a given mobile machine, by the computing system, to perform a task according to the new machine settings information. (See Johnson Fig. 4 for generation of recommended path and settings and control of vehicle based on recommended path and settings.)
Regarding claim 4, Johnson teaches: The method as set forth in claim 1, further comprising:
receiving, by the computing system, initial mobile machine information; and
further training, by the computing system, the deep learning model according to the initial mobile machine information. (See Johnson [0106]-[0108] for feedback processing system 152 which performs machine learning to improve accuracy of system. See vehicle index generator/estimator 170 as part of the feedback system. See [0084] where vehicle index generator/estimator 170 uses information including vehicle characteristics 158 and load varying characteristics 160.)
Regarding claim 5, Johnson teaches: The method of claim 4, wherein the initial mobile machine information comprises one or more of machine model information, machine type information, machine size information, machine shape information, machine ground footprint information, machine turn radius information, and energy usage information. (See Johnson [0083] where vehicle characteristics may include physical dimensions, weight, make and model, among other things. )
Regarding claim 6, Johnson teaches: The method as set forth in claim 1, further comprising:
receiving, by the computing system, initial field information; and
further training, by the computing system, the deep learning model according to the initial field information. (See Johnson Fig. 2 for soil measure detection system 148, incorporating feedback system 152, terrain identifier 192, soil type identifier 194. See Johnson [0106]-[0108] for feedback processing system 152 which performs machine learning to improve accuracy of system.)
Regarding claim 7, Johnson teaches: The method of claim 6, wherein the initial field information comprises one or more of field size information, field shape information, field elevation information, field topology information, soil type information, soil condition information, crop type information, crop lodging information, soil compaction information, weed density information, and weed location information. (See Johnson [0083] for maps 162 including terrain type, soil type, soil measure, soil damage (compaction), moisture, yield, or other characteristics.)
Regarding claim 8, Johnson teaches: The method as set forth in claim 1, further comprising:
receiving, by the computing system, machine performance results associated with the initial machine settings; and
further training, by the computing system, the deep learning model according to the machine performance results. (See Johnson [0106]-[0108] for feedback processing system 152 which performs machine learning to improve accuracy of system. See comparison of measured values to soil damage estimates to improve accuracy of predictions.)
Regarding claim 10, Johnson teaches: The method as set forth in claim 1, wherein the trained model is configured to generate the new machine settings information to minimize operation time of the mobile machine when performing a given field operation. (See Johnson [0091] for optimization of operation speed.)
Regarding claim 11, Johnson teaches: The method as set forth in claim 1, wherein the trained model is configured to generate the new machine settings information to minimize soil compaction caused by the mobile machine when performing a given field operation. (See Johnson [0106]-[0108] for feedback processing system 152 which performs machine learning to improve accuracy of system. See comparison of measured values to soil damage estimates to improve accuracy of predictions. See [0076] for soil damage metric indicative of compaction of soil, [0003] for avoiding undesired levels of compaction, [0088] for threshold level of damage. See [0091] for optimization of soil damage score.)
Regarding claim 12, Johnson teaches: The method as set forth in claim 1, further comprising:
receiving, by the computing system, secondary information; and
further training, by the computing system, the deep learning model according to the secondary information. (See Johnson [0086] for soil moisture identifier 196 using weather data. See [0106]-[0108] for feedback processing system 152 which performs machine learning to improve accuracy of system.)
Regarding claim 13, Johnson teaches: The method of claim 12, wherein the secondary information comprises weather data, ambient condition data, time of year data, geographic region data, or any combination thereof. (See Johnson [0086] for weather data, precipitation, sun, temperature, wind. See [0119] for historical weather data.)
Regarding claim 14, Johnson teaches: The method as set forth in claim 13, the secondary information comprising ambient temperature, ambient precipitation and/or ambient humidity. (See Johnson [0086] for weather data, precipitation, sun, temperature, wind.)
Regarding claim 15, Johnson teaches: The method as set forth in claim 1, wherein the initial machine settings information is recorded by the one or more mobile machines while operating in one or more fields. (See Johnson [0106]-[0108] for feedback processing system 152 which performs receives sensor and setting information and uses it to perform machine learning to improve the accuracy of the estimation systems)
Regarding claim 17, Johnson teaches: The method as set forth in claim 1, wherein the initial settings and the new settings comprise implement positions or implement heights. (See Johnson [0097] for frame configuration system 228 which can be controlled to reconfigure the frame of the machine to change the weight distribution.
Regarding claim 18, Johnson teaches: The method as set forth in claim 1, wherein the initial settings and the new settings comprise one or more of implement or actuator operation speeds or rates or one or more of dispensing rates, evacuation rates, flow rates, spray rates, or seeding rates. (See Johnson [0071] and [0083] for variation of vehicle weight based on seed rate, harvest rate at a particular speed. See [0115] for controlling weight of harvester by unloading while only partially full)
Regarding claim 19, Johnson teaches: The method as set forth in claim 1, wherein the initial settings and the new settings comprise one or more of mobile machine default ground speeds, mobile machine maximum ground speeds, or mobile machine minimum ground speeds. (See Johnson [0083] for relationship between ground speed and weight change. See [0091] for optimization based on operation speed, which will inherently involve vehicle speed.)
Regarding claim 20, Johnson teaches: The method as set forth in claim 2, wherein the initial settings and the new settings comprise one or more of default hydraulic pressures, maximum hydraulic pressures, or minimum hydraulic pressures, or one or more of default operating temperatures or pressures, maximum operating temperatures or pressures, or minimum operating temperatures or pressures. (See Johnson [0022], [0110] for control of tire inflation pressure. See [0097] for operation of hydraulic or pneumatic systems.)
Regarding claim 21, the claim is directed to a system for performing the method of claim 1 and is rejected under the same rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 20230242095 A1) in view of Dix et al. (US 20170357262 A1).
Regarding claim 9, Johnson teaches: The method as set forth in claim 1,
Johnson does not explicitly teach: wherein the trained model is configured to generate the new machine settings information to minimize fuel consumption of the mobile machine when performing a given field operation.
Johnson [0091] teaches optimization criteria such as the time to complete an operation or the damage done to the soil during the operation or other criteria.
Dix teaches a method of pathfinding for an off-road agricultural vehicle (See Dix Fig. 1 and [0014]) based on a cost function which may include the time to complete the path, fuel consumed, or wear on the vehicle. (See Dix [0033] and [0043]).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application, to modify the optimization of Johnson to include fuel consumption, as taught in Dix, in order to improve the efficiency of the operation of the vehicle.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 20230242095 A1) in view of Birnie (US 20090118904 A1).
Regarding claim 16, Johnson teaches: The method as set forth in claim 1,
Johnson does not explicitly teach: wherein the initial machine settings information is predetermined machine settings information derived from designed machine settings to be used by the one or more mobile machines.
Examiner notes that the specification of the present application does not clearly state how a “designed” setting is distinct from any other generated setting. However, based on paragraphs [0003] and [0018], Examiner is assuming that “designed” settings are ones which have been generated manually rather than recorded during operation or generated by a model.
Birnie teaches a method of path determination for an agricultural vehicle (See Birnie [0009]) which begins by accessing a series of planned paths (See Birnie Fig. 3 and [0060]) which may include previously recorded paths, manually estimated paths, or generated paths (See Birnie [0060]).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the application, to modify the route and settings generation system of Johnson to incorporate the manually estimated paths of Birnie in order to allow for manual input or modification of the control algorithm. It would have further been obvious, as Johnson is directed to generating both path and vehicle settings, to include manually designed control settings with the manually designed paths.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB KENT BESTEMAN-STREET whose telephone number is (571)272-2501. The examiner can normally be reached M-TH 8:00-5:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan can be reached on 571-270-7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format.
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/JACOB KENT BESTEMAN-STREET/
Examiner, Art Unit 3661
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