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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 8, 2026 has been entered.
Response to Amendment
This office action is in response to the amendments filed September 8, 2026. Claims are 1, 4-6, 10, and 15-17 are amended. Claims 8 and 19 are cancelled. Claims 1-7, 9-18, and 20 are pending and addressed below.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been fully considered but are moot because the new ground of rejection does not rely on Somarowthu for any teaching or matter specifically challenged in the argument. Applicant’s arguments are only directed to the claim amendments, which add new limitations to the claims and are addressed below by the addition of US20220101554A1 (Fu).
Claim Objections
Claim 5 is objected to because of the following informalities:
Claim 5 was amended to remove “action signals are generated corresponding to”. However, claim 5’s amendment now reads “wherein the one or more exterior attribute sensors and the one or more interior attribute sensors are mounted on a first work machine, and the the operation is performed by a second work machine in the work area”, where an additional “the” appears to be in the claim.
Appropriate correction is required.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 1-3, 4, 8-13, 14-15, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US20230243761A1 (Somarowthu) in view of US20220101554A1 (Fu).
Regarding claims 1 and 10, Somarowthu discloses a computer-implemented method and a system comprising:
receiving one or more first input signals corresponding to a field of view comprising one or more plant parts in a work area yet to be traversed by a work machine, each of the one or more plant parts comprising an exterior visually obscuring an interior thereof, wherein the field of view is associated with one or more exterior attribute sensors associated with the work machine;
[0103] of Somarowthu, “Harvester 350 can include a plurality of field sensors configured to obtain indications of field topography in unharvested areas in the path of harvester 350 (Some examples of such sensors can include, but are not limited to, RADAR detection systems, ultrasonic sensors, cameras or other imaging sensors, etc.”, where the camera of harvester 350 receives a field of view of the path of harvester 350 (work area yet to be traversed) and one or more plant parts comprise an exterior visually obscuring an interior.
identifying an exterior attribute of at least one of the one or more plant parts based on the received one or more first input signals;
[0061] of Somarowthu, “In such embodiments, the controller 16 may identify the particular grain type or grain category presently processed by the combine harvester 10; e.g., based upon operator input received via operator interface 26, based on GPS data if correlated to grain type, and/or based on any type of automated grain identification technique, such as image processing of a live camera feed or surface response measurements of the harvested grain.
Identifying a type of the at least one of the one or more plant parts based on the identified exterior attribute and by reference to one or more first models in a first training stage to correlate exterior attributes with types of plant parts;
While Somarowthu does disclose identifying a type of the at least one of the one or more plant parts based on the identified exterior attribute ([0061], “In such embodiments, the controller 16 may identify the particular grain type or grain category presently processed by the combine harvester 10; e.g., based upon operator input received via operator interface 26, based on GPS data if correlated to grain type, and/or based on any type of automated grain identification technique, such as image processing of a live camera feed or surface response measurements of the harvested grain.”), Somarowthu does not explicitly disclose further identification by reference to one or more first models in a first training stage to correlate exterior attributes with types of plant parts.
However, Somarowthu does disclose the use of controller 17 that may identify the particular crop type or crop category through the use of “automated crop identification techniques”, such as image processing of a live camera feed (Somarowthu, [0077]). Combined with that the embodiment utilizing said automated crop identification techniques, the characteristic database 72 provided with the same embodiment contains datasets of characteristics, and the AI engine 218 (Somarowthu,[0075]) trained to perform classification (Somarowthu, [0115]), one of ordinary skill in the art would find it obvious that Somarowthu also identifies a type of the at least one of the one or more plant parts based on reference to one or more first models in a first training stage to correlate exterior attributes with types of plant parts as Somarowthu comprises a classification engine and a database of characteristics for identifying crops where exterior attribute sensors such as a camera ([0115]) are an input.
receiving one or more second input signals corresponding to penetrating radiation directed toward the at least one of the one or more plant parts, wherein the penetrating radiation is received by one or more interior attribute sensors associated with the work machine;
[0035] of Somarowthu, “The RF measurement systems can include any practical number of RF sensors (emitters, receivers, and other associated hardware), which collectively form an RF sensor subsystem. In certain embodiments, the RF sensor subsystem can include a single RF receiver and emitter pair, which cycles through multiple fixed frequencies during operation; or, instead, which modulates the emitted RF energy over a predetermined frequency range.”, where Fig. 9 of Somarowthu shows at least sensor 250-1 ([0093]).
determining at least one interior plant part attribute with respect to at least one of the one or more plant parts, based on at least the received one or more second input signals and by reference to one or more second models trained in a second training stage to correlate penetrating radiation characteristics with interior plant part attributes for respective types of plant parts, wherein the interior plant part attributes correspond to one or more of identified interior silhouettes, layers, boundaries, voids, shapes, and/or sizes of visually obscured interior elements wherein a selected one of the one or more second models corresponds to the identified type of plant part; and
Somarowthu discloses determining at least one interior plant part attribute with respect to at least one of the one or more plant parts, based on at least the one or more second input signals ([0093] of Somarowthu, “By way of example, THz-base sensor 250-1 can detect objects or constituents or contaminants or other characteristics or harvest-related parameters of the plants that have yet to be engaged by cotton harvester 230.” See also Fig. 3 of Somarowthu for other interior plant part attributes.) and by reference to one or more second models trained in a second training stage to correlate penetrating radiation characteristics with interior plant part attributes for respective types of plant parts, wherein a selected one of the one or more second models corresponds to the identified type of plant part (See Fig. 13 of Somarowthu, where the flowchart described refers to AI engine 218 that is trained to perform classification between sensor signal inputs and characteristics (Somarowthu, [0115]) within the flowchart of the process of Fig. 7), where the interior plant part attributes correspond to visually obscured interior elements ([0078], “Next, at block 306, controller 16 determines one or more harvest-related parameters relative to the current harvest operation of the combine harvester 10. In various embodiments, such parameters will include foreign object/material detection, crop characteristics, contaminant (e.g., pesticide, fungus, GMO, soil/ash) presence and/or level.”, where block 306 pertains to penetrating radiation characteristics with interior plant part attributes in Fig. 7).
automatically controlling performance of an operation in the work area, wherein treatment is selectively provided to plant parts in the work area based on at least the determined at least one interior plant part attribute.
While Somarowthu discloses automatically controlling performance of an operation in the work area (see Fig. 3 of Somarowthu, where step 110 automatically controls performance of an operation in the work area), Somarowthu does not disclose selectively providing treatment to plant parts in the work area based on at least the determined at least one interior plant part attribute.
From a similar field of endeavor, Fu discloses a farming machine that comprises a treatment system (Fu, [0084], treatment mechanism 120) for selectively providing treatment to plant parts in the work area (see at least Fu, Fig. 1A, where a treatment area exists within a geographic area comprising one or more plants 102) based on at least the determined plant characteristic (see at least Fu, [0083-0084], where detection mechanism 110 identifies a plant for treatment and treatment mechanism 120 applies treatments to an identified plant 102).
One of ordinary skill in the art would find it obvious, prior to the applicant’s effective filing date, to combine the treatment system of Fu to the system of Somarowthu as Fu would provide a direct improvement to the system of Somarowthu by allowing for treating the detected problems of the plant part’s interior to keep harvested crops clean to improve the yield quality.
Regarding claims 2 and 11, with all of the limitations of claims 1 and 10, the method and system further comprise:
wherein at least one of the exterior attribute sensors and at least one of the one or more interior attribute sensors are the same device.
See Fig. 7 of Somarowthu. The figure identifies the process taken by controller 16 ([0076]), where sensing is completed by RF sensors (interior attribute sensors). The process ends with step 310 identifying more to harvest, where it checks for more crops to harvest (exterior attribute).
Regarding claims 3 and 14, with all of the limitations of claim 1 and 10, the method and system further comprise:
wherein training the one or more first models and the one or more second models comprises optimizing one or more variable governing parameters during training to obtain an accurate input-output mapping for a training dataset; and
While Somarowthu does not explicitly disclose training the one or more first models and the one or more second models comprises optimizing one or more variable governing parameters during training to obtain an accurate input-output mapping for a training dataset, one of ordinary skill in the art would find it obvious that Somarowthu performs the training in such a way as the AI engine 218 of Somarowthu is a trained classifier, where the models described may be a deep neural network or machine learning system that requires optimizing parameters with respect to a training dataset.
wherein for a current set of one or more first input signals and a corresponding set of one or more second input signals, the at least one interior plant part attribute is determined with respect to the at least one of the one or more plant parts by reference to the selected one of the one or more first trained models, wherein the selected one of the one or more first models is retrieved to identify the type of plant part, and by reference to the selected one of the one or more second models, wherein the selected one of the one or more second models is retrieved based on the identified type of plant part to determine the at least one interior plant part attribute.
In light of the rationale of claim 1 regarding the one or more first models trained in a first training stage and the process of Figs. 7 and 13 of Somarowthu, one of ordinary skill in the art would find it obvious that the identified plant part type, found by one or more first models, is combined with one or more second models to determine the at least one interior plant part attribute.
Regarding claims 4 and 15, with all of the limitations of claims 1 and 10, the method and system further comprise:
comprising automatically controlling the performance of the operation based on a determined aggregate of interior plant part attributes with respect to a defined period of time and/or with respect to a defined distance traversed by the work machine and/or with respect to the work area being traversed.
Somarowthu discloses automatically controlling the performance of the operation (Fig. 7, step 308) based on a determined aggregate of interior plant part attributes ([0062], “controller 16 determines multiple unknown parameters describing the currently processed grain harvested … such parameters will include grain mass and the fraction of harvested grain composed of a particular constituent type or types”) with respect to the work are being traversed (Fig. 7, Termination process 312 in response to having no more field to harvest).
Regarding claims 9 and 20, with all of the limitations of claim 1 and 10, the method and system further comprise:
wherein the penetrating radiation comprises non-ionizing radiation generated from at least one emitter associated with the work machine.
[0028] of Somarowthu, “Terahertz radiation is subject to significant laboratory research and shows promise for agricultural applications. It lies between microwave and infrared (IR) on the electromagnetic spectrum and provides the advantage of at least partial penetration into objects, but is not considered ionizing radiation, like X-rays.”, where the RF sensors contain emitters and receivers ([0055] of Somarowthu).
Regarding claim 12, with all of the limitations of claim 10, the system further comprises:
wherein the one or more interior attribute sensors comprise at least one paired emitter and detector mounted on at least one side of the work machine and configured for reflection and backscatter measurements with respect to the emitted penetrating radiation.
Somarowthu discloses interior attribute sensors with a paired emitter and detector (see Fig. 6 of Somarowthu) mounted on at least one side of the work machine and configured for reflection ([0071]) and backscatter ([0071]) measurements with respect to the emitted penetrating radiation.
Regarding claim 13, with all of the limitations of claim 10, the system further comprises:
wherein the one or more interior attribute sensors comprise at least one paired emitter and detector arranged such that plant parts pass between the respective emitter and detector pairing for attenuation measurements with respect to the emitted penetrating radiation.
See Fig. 6 of Somarowthu, which discloses a detection area 214 in between emitters and detectors. The sensor may only detection attenuation of Terahertz after the plant part pass through the area ([0071]).
Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US20230243761A1 (Somarowthu) in view of US20150348419A1 (Matthews).
Regarding claims 5 and 16, with all of the limitations of claim 1 and 10, the method and system further comprise:
wherein the one or more exterior attribute sensors and the one or more interior attribute sensors are mounted on a first work machine, and
See Fig. 9 of Somarowthu, where sensors 250-1, 250-2, and 250-3 are all Terahertz-based sensors that can act as exterior attribute sensors (see rationale of claim 2) and interior attribute sensors.
the the operation is performed by a second work machine in the work area.
While Somarowthu does not disclose a second work machine in the work area to perform the operation by a first work machine, Matthews discloses a system of agricultural machines communicably coupled to provide information to a central node or master robot (see Fig. 1B of Matthews).
One of ordinary skill in the art would find it obvious to try a plurality of slave harvesters with a master harvester providing the sensed data to distribute the total amount of work needed to minimize the time needed.
Claims 6-7 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over US20230243761A1 (Somarowthu) in view of US20150348419A1 (Matthews) and in further view of US20210022283A1 (Vandike).
Regarding claims 6 and 17, with all of the limitations of claim 5 and 16, the method and system further comprise:
wherein action signals corresponding to the operation are generated by the first work machine and are utilized for mapping the determined at least one interior plant part attribute to a location of the respective plant part in a selectively retrievable data structure, for each of the one or more plant parts having an interior plant part attribute determined therefor.
While Somarowthu in view of Matthews does not disclose mapping the determined at least one interior plant part attribute to a location of the respective plant part in a selectively retrievable data structure, for each of the one or more plant parts having an interior plant part attribute determined therefor, Vandike discloses the use of a prior information map stored in data store 202 of the harvester ([0039] of Vandike) that contains contextual information such as a vegetation map from a prior operation in the field comprising information of the crop type ([0038] of Vandike).
One of ordinary skill in the art would find it obvious, prior to the applicant’s effective filing date, to combine the system of Vandike to Somarowthu in view of Matthews as the mapping functionality with the internal plant part attribute sensing system of Somarowthu in view of Matthews would allow for modelling the determined interior plant part attribute to historical data ([0042] of Vandike) and other sensed information for future crop harvests.
Regarding claim 7 and 18, with all of the limitations of claims 6 and 17, the method and system further comprise:
wherein the operation by the second work machine is controlled based on the mapped attributes in the selectively retrievable data structure.
In light of the rationale of claims 5 and 6, one of ordinary skill in the art would find it obvious to utilize the mapped information of the first work machine to operate the second work machine as the first work machine transmits (see the rationale of claim 5) the mapping in the selectively retrievable data structure (see the rationale of claim 6) to a second work machine to perform the harvesting.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAEWOOK JUNG whose telephone number is (571)272-5470. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached on (571) 270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.J./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656
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