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 arguments filed 03/10/2026 have been fully considered but they are not persuasive. On page 6 of the response Applicant requests for the Examiner to withdraw the 112(f) interpretation. The Examiner respectfully disagrees. The “image-capture devices” as laid out in the current specification and discussed by applicant could be several types of “image-capture devices” and could for example be eye balls. Applicant is not specific to say they are RGB camera for example. The interpretation is not a rejection. This is an interpretation of the claims giving how they are written and looking back to the specification. The 112(f) interpretation stands unless applicant amends the claims.
Further on page 7, applicant argues that Leger does not teach, "determine and dynamically select a spray mode from a predefined list of spray modes based on the determined health state of the crop plant, the growth state of the crop plant, and a type of chemical to be sprayed while the vehicle is in motion". The Examiner respectfully disagrees. In ¶[0105] “the treatment unit is in a moving vehicle” which clearly and pointed out teaches dynamically since the spray is being applied on a moving vehicle. Then “876 a prior projected path can be generated” which means there is planning according to that health of the crops ¶[0046] “crop health…crop status” would all be part of the projected path and what type of predetermined spray to spray as well as growth state of the plant. ¶[0079] “multiple fluid sources” would then be predefined list of spray modes based on all those factors. As for example a plant without any pest won’t be sprayed with the same amount an effected plant/crop for example. For these reasons the same rejection applies.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Claims 1 and 10 recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
· Claim 1; recites the limitation, “a plurality of image-capture devices configured to….”
· Claim 10; recites the limitation, “capturing, by a plurality of image-capture devices….”
Because this/these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1 and 10:
(i) “image-capture devices” (Fig. 1A, #118A-C. Paragraph [0030]- image-capture devices are described as may include suitable logic, circuitry, and/or interfaces that is configured to capture a plurality of field-of-views (FOVs) of a plurality of defined areas of the agricultural field 106 (of FIG. 1). In an implementation, the plurality of image-capture devices 118 are installed on the vehicle 104 (of FIG. lA) and may include a left-side camera (e.g., a RGB camera), a right-side camera and a central camera. Examples of each of the plurality of image-capture devices 118 may include but not limited to, a RGB camera, a high dynamic range (HDR) camera, and the like. Fig. 1A, illustrates the image-capture devices as black box. (Wherein the image-capture devices do have sufficient structure associated with it, RGB cameras.).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/22/2023 have been considered by the examiner.
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)(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 1-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Leger (US 2024/0130350).
As per claims 1 and 10, Leger teaches, a system, method mounted in a vehicle (Leger, fig.3A, 310 represents the vehicle and 311 represents the system), comprising: a plurality of image-capture devices configured to capture a sequence of colour images corresponding to a plurality of field-of-views (FOVs) of a plurality of defined areas of an agricultural field while the vehicle is in motion (Leger, ¶[0048] “ The navigation unit 230 can receive sensing signals from the sensors 232. In this example, the sensing signals can include images received from cameras or Lidar's.” therefore cameras are the sensors and image-capturing devices are being interpreted as cameras, ¶[0128] “For example, as a vehicle is moving the agricultural treatment system, the point of view of an obtained image may slightly change from one to another.” the point of view is changing and therefore plurality field of views are being taken and ¶ [0087] “In another example, in one mode of operation, in a first pass along a path along an agricultural environment, the agricultural treatment system obtains a first set of multiple images while the system moves along the path. For example, the agricultural treatment system uses onboard cameras and obtains multiple digital images of agricultural objects (e.g., plants, trees, crops, etc.). While obtaining the multiple images of the agricultural objects, the agricultural treatment system records positional and sensor information and associates this information for each of the obtained images.”); and one or more hardware processors (Leger, ¶ [0036] “Some embodiments are implemented by a computer system. A computer system may include a processor, a memory, and a non-transitory computer-readable medium.” Represents the processor) configured to: obtain the sequence of colour images corresponding to the plurality of FOVs from the plurality of image-capture devices (Leger, ¶[0046] “The communications module 226, as well as any telemetry modules on the computing unit, can be configured to receive and transmit data, including sensing signals, rendered images, indexed images, classifications of objects within images, data related to navigation and location, videos, agricultural data including crop yield estimation, crop health, cluster count, amount of pollination required, crop status, size, color, density, etc.,” Color information of the crops is being sent therefore color images are being sent and ¶[0103] “As the sensor scans the scene while a vehicle supporting the sensor is moving in a lateral direction, the sensor will capture one or more image frames in sequence from one to another illustrated in image frames 862, 864, and 866 where image frame 864 and 866 are frames captured by a sensor that captured image frame 862 subsequently” shows that the images are in sequence and as pointed out in ); determine a health state of a crop plant and a growth state of the crop plant from the obtained sequence of colour images (Leger, ¶[0046] “crop health, cluster count, amount of pollination required, crop status, size, color, density, etc., and processed either on a computer or computing device on-board the vehicle, such as one or more computing devices or components for the compute module 224” state of the crop would be the health and crop status growth state from the images which is the information); determine and dynamically select a spray mode from a predefined list of spray modes based on the determined health state of the crop plant, the growth state of the crop plant, and a type of chemical to be sprayed while the vehicle is in motion (Leger, ¶ [0105] “FIG. 9B. illustrates a diagram 803 to determine spray accuracy and spray health, spray health being whether external factors outside or correctly detecting target object and lining the treatment head onto the target object and tracking it as the target object moves away from the treatment unit, since the treatment unit is on a moving vehicle, a prior or predicted spray path 876 can be generated.” Spray health represents the type of chemical sprayed while vehicle is in motion and ¶ [005] “The agricultural treatment system may obtain imagery of emitted fluid projectiles at intended target locations. The system may identify positional parameters of a spraying head and/or motors used to maneuver the spraying head to emit the fluid projectile. The system may generate a calibration or lookup table based on a three-dimensional coordinate of the intended target location and of the positional parameters of the spraying head. The system may then use the lookup table to perform subsequent spray operations.” This represents determine and dynamically select a spray mode from a predefined list of spray modes based on the determined health state of the crop plant, the growth state of the crop plant, and a type of chemical and ¶[0079] “FIG. 7 is a block diagram illustrating an example configuration of the system with treatment unit 700 configured for various fluid source and spraying tip options as well as light source and laser emitting tip options. In one example, the agricultural treatment system has onboard circuitry, processors and sensors that allows the system to obtain imagery of agricultural objects and then identify a target object to be sprayed.” Identifies objects to be sprayed based on location which represents dynamically ); and automatically operate a different set of electronically controlled sprayer nozzles at different time instants to release the type of chemical from a predefined number of the set of electronically controlled sprayer nozzles (Leger, ¶[0059] “In one example, a chemical selection module, or chemical selection 280, of agricultural treatment system 200 agricultural treatment system 200 can be coupled to the compute module 224 and the treatment unit 270. The chemical selection module can be configured to receive instructions to send a chemical fluid or gas to the treatment unit 270 for treating a target plant or other object.” This represents automatically operate a different set of electronically controlled sprayer nozzles at different time instants to release the type of chemical from a predefined number of the set of electronically controlled sprayer nozzles as seen multiple in fig.6A and 6B and the chemical is changing and as they are set they are predefined), based on the selected spray mode while the vehicle is in motion (Leger, ¶[0079] “Over a period of time, the system may determine multiple poses of the vehicle and/or treatment unit and convert/translate these poses that the spraying head would need to be positioned into such that the spraying head would maintain an emit spray at the target object while the vehicle is moving.” This represents that the sprayer is being controlled while the vehicle is in motion).
As per claims 2 and 11, Leger teaches, the system and method according to claims 1 and 10, wherein the determination of the health state of the crop plant from the obtained sequence of colour images comprises determining whether the crop plant is a diseased crop plant or a non-diseased crop plant (Leger, ¶ [0046] “crop health” represents being able to tell a crop from being diseased or non-diseased plant as this is part of health of a crop).
As per claims 3 and 12, Leger teaches, the system and method according to claim 2, wherein the determination of the health state of the crop plant from the obtained sequence of colour images further comprises identifying a foliar discoloration in the crop plant (Leger, ¶[0046] and ¶[0097] “In one example, the color segmentation itself can be performed by a machine learning model configured to detect a specific type of color in each pixel ingested by an image sensor. In another example, the color segmentation can be manually predefined as pixels ranging between a specific range of a color format. For example, vegetation algorithm can be configured to analyze a given frame to partition any pixels having attributes of the color “green” form a Bayer filter. In another example, the algorithm can be configured to detect attributes of “green” under any color model where “green” is defined. For example, a numeric representation of RGB color being (r,g,b) where the value of g>0 in any digital number-bit per channel. The algorithm can itself be a machine learning algorithm to detect “green” or a different color that are of interest.” Between being able to tell the color and being able to tell the health and other factors would be taken into consideration that there is discoloration in the crop plant, as for example brown of plant as a health indicator).
As per claims 4 and 13, Leger teaches, the system and method according to claim 1, wherein the determination of the growth state of the crop plant from the obtained sequence of colour images comprises determining a canopy of growth of the crop plant (Leger, ¶[0036] “computer vision and automation to autonomously identify an agricultural object including any and all unique growth stages of agricultural objects identified, including crops or other plants or portions of a plant, characteristics and objects of a scene or geographic boundary, environment characteristics, or a combination thereof.” This represents determining a canopy of growth of the crop plant as it is measuring the growth of the plant).
As per claims 5 and 14, Leger teaches, the system and method according to claim 4, wherein the determination of the growth state of the crop plant from the obtained sequence of colour images comprises determining a crown of the crop plant (Leger, fig.4 showing measuring of the top or crown of the crop plant).
As per claims 6 and 15-16, Leger teaches, the system according to claim 1, wherein the one or more hardware processors are further configured to: distinguish crop plants from weeds based on the obtained sequence of colour images; and control the predefined number of the set of electronically controlled sprayer nozzles to concomitantly spray a first type of chemical exclusively over the weeds and a second type of chemical exclusively over the crop plants (Leger, ¶[0054] “In one example, an unwanted plant can be a weed that is undesirable for growing next or near a desirable plant such as a target crop or crop of interest. In one example, an unwanted plant can be a crop that is intentionally targeted for removal or blocking growth so that each crop growing on a specific plant or tree can be controlled and nutrients pulled from the plant can be distributed to the remaining crops in a controlled manner.” The weeds are targeted and this represents a second type of chemical exclusively over the crop plants, as the same chemical would not be sprayed on the plant versus weed to eliminate the weed ).
As per claims 7 and 17, Leger teaches, the system and method according to claim 1, wherein the predefined list of spray modes comprises a perceptive spot spray mode, a herbicide spray mode, a pesticide spray mode, a diseased-plant spray mode, a nutrient spray mode, a blanket spray mode, an all-green spray mode, a foliar discoloration spray mode, or a combinatorial spray mode in which any two or more spray modes are concomitantly selected (Leger, ¶[0066] “performed by treatment system 311 can be that of both the crop and the one or more different types of weeds” the treatment different for different types of weeds and ¶[0083] “Additionally, each modular sub systems of the treatment system including each modular spray subsystem, for example each modular spray subsystem or component treatment module including a structural mechanism, a computer unit, one or more sensors, one or more treatment units, and one or more illumination devices, can perform VSLAM and receive other non-visual based sensor readings, and continuously generate its own localized pose estimation, the pose being relative to specific objects detected by each of the component treatment modules” represents different types of spraying modes as seen in treatment system 412 in fig.4 and ¶[0062] “In this example, the treatment system 311 can perform a different variety of chemical treatments with varying treatment parameters, such as chemicals used, chemical composition, treatment frequency, and perform A/B type testing (A/B testing) on the agricultural scene by different zones of the same plant type, different chemical trials in the same or different zones or by different individual plant object for harvest, or a combination thereof.’).
As per claims 8 and 18, Leger teaches, the system and method according to claim 1, wherein the different set of electronically controlled sprayer nozzles are operated automatically to release two different types of chemicals concurrently when two different categories of crop plants are identified (Leger, ¶[0062] “In this example, the treatment system 311 can perform a different variety of chemical treatments with varying treatment parameters, such as chemicals used, chemical composition, treatment frequency, and perform A/B type testing (A/B testing) on the agricultural scene by different zones of the same plant type, different chemical trials in the same or different zones or by different individual plant object for harvest, or a combination thereof.” chemical composition represents having two different types of chemicals and a different variety of chemical treatments).
As per claims 9 and 19, Leger teaches, the system and method according to claim 1, wherein the different set of electronically controlled sprayer nozzles are operated to release two or more different types of chemicals concurrently or alternatively when two or more different categories of crop plants and different categories of weeds are identified (Leger, ¶[0062] “In this example, the treatment system 311 can perform a different variety of chemical treatments with varying treatment parameters, such as chemicals used, chemical composition, treatment frequency, and perform A/B type testing (A/B testing) on the agricultural scene by different zones of the same plant type, different chemical trials in the same or different zones or by different individual plant object for harvest, or a combination thereof.” This would represent two or more different types of chemicals as there is a chemical composition. And ¶[0066] “For example, weeds can grow in the same agricultural scene as that of a crop of interest such that the observation and treatment performed by treatment system 311 can be that of both the crop and the one or more different types of weeds, or just the weeds.“ represents different types of weeds).
Conclusion
THIS ACTION IS MADE FINAL. 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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/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/
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