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 the Claims
Claims 1-19, as originally filed, are currently pending and have been considered below.
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.
Claim(s) 1-7, 10-16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Piron et al., International Publication No. WO 2009/153304, hereinafter, “Piron”, and further in view of Redden et al., U.S. Publication No. 2015/0015697, hereinafter, “Redden”.
As per claim 1, Piron discloses a method for treating a plant in a field using a farming machine on a second pass after a first pass through the field, the method comprising:
accessing, from an image sensor as the farming machine moves through the field on the second pass after the first pass through the field, an image of the field comprising one or more pixels representing the plant (Piron, page 3, lines 23-32, The weeding equipment may be arranged to travel along a line of crops several times during a critical weeding period … at the subsequent passage of the weeding equipment, given the difference in growth rates of the weeds and the crops, the difference between the corrected plant height and the expected crop height will be greater thus facilitating correct identification of that particular weed; Piron, page 5, lines 1-5, The experimental apparatus of Fig 1 comprises a video projector 11 arranged to illuminate a portion of a band of soil 12 which contains both crops and weeds and a camera 13 arranged to capture an image, in this case a top-down image approximately 200 mm by 250 mm. The video projector 11 and camera 13 are mounted on a carriage 10 adapted to move along the band of soil 12; Piron, page 4, lines 3-7, The stereoscopic data of plants growing on soil may be acquired in the form of an image, for example using a camera as a sensor. In this case, the captured plant data points (ie data points representing a position at which the presence of a plant has been detected) and the captured soil data points (ie data points representing a position at which the presence of soil has been detected) may be represented by pixels of the image);
applying a feature identification model to the image, the feature identification model: determining, based on the one or more pixels representing the plant, a second feature of the plant on the second pass (Piron, page 3, lines 23-32, The weeding equipment may be arranged to travel along a line of crops several times during a critical weeding period … at the subsequent passage of the weeding equipment, given the difference in growth rates of the weeds and the crops, the difference between the corrected plant height and the expected crop height will be greater thus facilitating correct identification of that particular weed).
Piron does not explicitly disclose the following limitations as further recited however Redden discloses
determining, using a location of the plant determined using the image, the plant was treated by the farming machine with a first treatment on the first pass (Redden, ¶0044, The system and/or method can additionally function to leverage historical data. Historical data can include historical weather data (e.g., for the geographic area of the plant field or the plant), historical treatment data, historical planting data, or any other suitable historical data. The treatments can be applied and recorded as population treatments (e.g., the plant field was indiscriminately blanketed with the treatment), area treatments (e.g., rows 1-8 had a first treatment while rows 9-16 had a second treatment), or individual treatments (e.g., individual plants within the same row or plant field can be treated with different treatments, wherein the individual treatments can be manually or automatically applied by the system); Redden, ¶0088, The plant data or plant index values that are determined from the data corresponding to the plant location are preferably recorded as the plant characteristics for the plant. For example, plant characteristics extracted from the area of an image determined to be associated with the identified plant (e.g., within the plant boundaries) are preferably stored in association with the plant. Individual plants are preferably identified by the plant location in successive data collection sessions);
determining, based on the first treatment of the plant corresponding to a first feature of the plant determined on the first pass, an expected feature of the plant (Redden, ¶0044, Treatments can include fertilization, necrosis inducement, fungicide application, pesticide application, fruit harvesting, drought, chemical application, water application, plant hormone application, salinity control, disease exposure, insect infestation exposure, distance from plant to plant and population, genetics, epigenetics, planting depth, planting depth control, a combination of the aforementioned treatments, or any other suitable plant treatment; Redden, ¶0129, The method can additionally include recording management data, which functions to enable correlation between a treatment received by the plant and the subsequent plant response … Because this method tracks plant growth on a plant-by-plant basis, the method can enable determination of how different plant phenotypes react to a given treatment);
determining, based on a difference between the expected feature of the plant and the second feature of the plant, an additional treatment for the plant on the second pass; and treating, using a treatment mechanism of the farming machine, the plant using the determined additional treatment (Redden, ¶0130, The method can additionally include treating the plants with the system. The plants are preferably treated before, during, or after plant parameter measurement in the same session, but can alternatively be treated in a separate session …. each plant is treated based on the measurements recorded in the same session, wherein the measurements are processed by the system in real-time to determine the individual plant treatment to obtain a predetermined goal. Predetermined goals can include a target parameter range (e.g., target size range), a target date at which the plant will be in a predetermined growth stage (e.g., a target ripening date … In a third variation of the method, each plant is treated based on the historic measurements for the given plant).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Redden with Piron because they are in the same field of endeavor. One skilled in the art would have been motivated to include the historical plant monitoring as taught by Redden in the system of Piron in order to track growth parameters of individual plants for management of the plants (Redden, ¶0130).
As per claim 2, Piron and Redden disclose the method of claim 1, wherein the first feature is a previous size of the plant, the second feature is a current size of the plant, and the expected feature is an expected size of the plant given the first treatment applied on the first pass (Redden, ¶0130, In a third variation of the method, each plant is treated based on the historic measurements for the given plant).
As per claim 3, Piron and Redden disclose the method of claim 1, wherein the first feature is a previous height of the plant, the second feature is a current height of the plant, and the expected feature is an expected height of the plant given the first treatment applied on the first pass (Piron, page 3, lines 17-32, Automated weeding equipment according to the invention may be arranged to work its way along a line or band of crops; it may be self propelling. It may operate on a step by step basis so that it remains stationary at a first position along a line a crops whilst detecting the position of weeds and dealing with them before move to a second position further along the line of crops and repeating this procedure ... The weeding equipment may be arranged to travel along a line of crops several times during a critical weeding period ... if the first time the weeding equipment passes a weed having a corrected plant height similar to the expected crop height it may not be identified as a weed. Nevertheless, at the subsequent passage of the weeding equipment, given the difference in growth rates of the weeds and the crops, the difference between the corrected plant height and the expected crop height will be greater thus facilitating correct identification).
As per claim 4, PIron and Redden disclose the method of claim 1, wherein the first feature is a previous physiological status of the plant, the second feature is a current physiological status of the plant, and the expected feature is an expected physiological status of the plant given the first treatment applied on the first pass (Redden, ¶0044, The system and/or method can additionally function to leverage historical data. Historical data can include historical weather data (e.g., for the geographic area of the plant field or the plant), historical treatment data, historical planting data, or any other suitable historical data. The treatments can be applied and recorded as population treatments (e.g., the plant field was indiscriminately blanketed with the treatment), area treatments (e.g., rows 1-8 had a first treatment while rows 9-16 had a second treatment), or individual treatments (e.g., individual plants within the same row or plant field can be treated with different treatments, wherein the individual treatments can be manually or automatically applied by the system); Redden, ¶0074, The data can include measurements, such as plant data and environmental data, extracted characteristics (e.g., derived data), such as plant characteristics and environment characteristics, treatment history, such as treatment history for an individual plant … compute the correlation between observed traits and management practices or environmental conditions, generate future treatments; Redden, ¶0096, the morphology measurement can be associated with the physiology measurement through the respective timestamps. For example, a first stereoview image taken at a first time can be associated with a set of near-field multispectral sensor images taken at a second time; Redden, ¶0113, Plant growth can be further determined from multiple measurements taken over a period of time, wherein a plant growth model can be determined for a plant feature, entire plant, plant population, or any other suitable set of plants based on the multiple measurements and associated with the respective measurement timestamp).
As per claim 5, Piron and Redden disclose the method of claim 1, wherein determining, using the location of the plant determined using the image, the plant was treated by the farming machine with the first treatment on the first pass further comprises: accessing a map of at least a portion of the field, the map generated on the first pass of the farming machine through the field and comprising the location of the plant and one or more previous features of the plant; and determining, based on the one or more previous features of the plant in the map, one or more differences between one or more current features of the plant and the one or more previous features of the plant (Redden, ¶0088, The plant data or plant index values that are determined from the data corresponding to the plant location are preferably recorded as the plant characteristics for the plant. For example, plant characteristics extracted from the area of an image determined to be associated with the identified plant (e.g., within the plant boundaries) are preferably stored in association with the plant. Individual plants are preferably identified by the plant location in successive data collection sessions ... the locations of each individual plant can be pre-mapped, wherein the plant characteristics extracted from each image is correlated to the pre-mapped plant based on the data location; Redden, ¶0096, the morphology measurement can be associated with the physiology measurement through the respective timestamps; Redden, ¶0044, The system and/or method can additionally function to leverage historical data. Historical data can include historical weather data (e.g., for the geographic area of the plant field or the plant), historical treatment data, historical planting data, or any other suitable historical data. The treatments can be applied and recorded).
As per claim 6, Piron and Redden disclose the method of claim 1, further comprising: storing a map of the plant comprising one or more of the location of the plant, the expected feature of the plant, and the second feature of the plant (Redden, ¶0088, The plant data or plant index values that are determined from the data corresponding to the plant location are preferably recorded as the plant characteristics for the plant. For example, plant characteristics extracted from the area of an image determined to be associated with the identified plant (e.g., within the plant boundaries) are preferably stored in association with the plant. Individual plants are preferably identified by the plant location in successive data collection sessions ... the locations of each individual plant can be pre-mapped, wherein the plant characteristics extracted from each image is correlated to the pre-mapped plant based on the data location; Redden, ¶0096, the morphology measurement can be associated with the physiology measurement through the respective timestamps; Redden, ¶0044, The system and/or method can additionally function to leverage historical data. Historical data can include historical weather data (e.g., for the geographic area of the plant field or the plant), historical treatment data, historical planting data, or any other suitable historical data. The treatments can be applied and recorded).
As per claim 7, Piron and Redden disclose the method of claim 1, wherein the location of the plant is in a substrate of the field between a first crop row and a second crop row, and the determined additional treatment for the plant is based on its determined location between the first crop row and the second crop row (Piron, page 1, lines 2-5, automatically guiding the mechanical or chemical destruction of weeds between rows of crops; knowledge of the position of the rows where crops should be growing and the assumption that plants growing outside such positions are weeds may be used).
As per claim 10, PIron discloses a farming machine configured to treat a plant in a field on a second pass after a first pass, the farming machine comprising:
an image sensor configured to capture images of the field as the farming machine travels through the field (Piron, a camera 13 arranged to capture an image, in this case a top-down image approximately 200 mm by 250 mm. The video projector 11 and camera 13 are mounted on a carriage 10 adapted to move along the band of soil 1);
a treatment mechanism configured to treat plants in the field (Piron, page 3, lines 17-32, Automated weeding equipment according to the invention may be arranged to work its way along a line or band of crops; it may be self propelling. It may operate on a step by step basis so that it remains stationary at a first position along a line a crops whilst detecting the position of weeds and dealing with them before move to a second position further along the line of crops and repeating this procedure);
one or more processors; and a non-transitory computer readable storage medium comprising computer program instructions that, when executed by the one or more processors, cause the farming machine to: access, from the image sensor as the farming machine moves through the field on the second pass after the first pass through the field, an image of the field comprising one or more pixels representing the plant (Piron, page 3, lines 23-32, The weeding equipment may be arranged to travel along a line of crops several times during a critical weeding period … at the subsequent passage of the weeding equipment, given the difference in growth rates of the weeds and the crops, the difference between the corrected plant height and the expected crop height will be greater thus facilitating correct identification of that particular weed; Piron, page 5, lines 1-5, The experimental apparatus of Fig 1 comprises a video projector 11 arranged to illuminate a portion of a band of soil 12 which contains both crops and weeds and a camera 13 arranged to capture an image, in this case a top-down image approximately 200 mm by 250 mm. The video projector 11 and camera 13 are mounted on a carriage 10 adapted to move along the band of soil 12; Piron, page 4, lines 3-7, The stereoscopic data of plants growing on soil may be acquired in the form of an image, for example using a camera as a sensor. In this case, the captured plant data points (ie data points representing a position at which the presence of a plant has been detected) and the captured soil data points (ie data points representing a position at which the presence of soil has been detected) may be represented by pixels of the image);
apply a feature identification model to the image, the feature identification model: determining, based on the one or more pixels representing the plant, a second feature of the plant on the second pass (Piron, page 3, lines 23-32, The weeding equipment may be arranged to travel along a line of crops several times during a critical weeding period … at the subsequent passage of the weeding equipment, given the difference in growth rates of the weeds and the crops, the difference between the corrected plant height and the expected crop height will be greater thus facilitating correct identification of that particular weed).
Piron does not explicitly disclose the following limitations as further recited however Redden discloses
determine, using a location of the plant determined using the image, the plant was treated by the farming machine with a first treatment on the first pass (Redden, ¶0044, The system and/or method can additionally function to leverage historical data. Historical data can include historical weather data (e.g., for the geographic area of the plant field or the plant), historical treatment data, historical planting data, or any other suitable historical data. The treatments can be applied and recorded as population treatments (e.g., the plant field was indiscriminately blanketed with the treatment), area treatments (e.g., rows 1-8 had a first treatment while rows 9-16 had a second treatment), or individual treatments (e.g., individual plants within the same row or plant field can be treated with different treatments, wherein the individual treatments can be manually or automatically applied by the system); Redden, ¶0088, The plant data or plant index values that are determined from the data corresponding to the plant location are preferably recorded as the plant characteristics for the plant. For example, plant characteristics extracted from the area of an image determined to be associated with the identified plant (e.g., within the plant boundaries) are preferably stored in association with the plant. Individual plants are preferably identified by the plant location in successive data collection sessions);
determining, based on the first treatment of the plant corresponding to a first feature of the plant determined on the first pass, an expected feature of the plant (Redden, ¶0044, Treatments can include fertilization, necrosis inducement, fungicide application, pesticide application, fruit harvesting, drought, chemical application, water application, plant hormone application, salinity control, disease exposure, insect infestation exposure, distance from plant to plant and population, genetics, epigenetics, planting depth, planting depth control, a combination of the aforementioned treatments, or any other suitable plant treatment; Redden, ¶0129, The method can additionally include recording management data, which functions to enable correlation between a treatment received by the plant and the subsequent plant response … Because this method tracks plant growth on a plant-by-plant basis, the method can enable determination of how different plant phenotypes react to a given treatment);
determine, based on a difference between the expected feature of the plant and the second feature of the plant, an additional treatment for the plant on the second pass; and treat, using the treatment mechanism of the farming machine, the plant using the determined additional treatment (Redden, ¶0130, The method can additionally include treating the plants with the system. The plants are preferably treated before, during, or after plant parameter measurement in the same session, but can alternatively be treated in a separate session …. each plant is treated based on the measurements recorded in the same session, wherein the measurements are processed by the system in real-time to determine the individual plant treatment to obtain a predetermined goal. Predetermined goals can include a target parameter range (e.g., target size range), a target date at which the plant will be in a predetermined growth stage (e.g., a target ripening date … In a third variation of the method, each plant is treated based on the historic measurements for the given plant).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Redden with Piron because they are in the same field of endeavor. One skilled in the art would have been motivated to include the historical plant monitoring as taught by Redden in the system of Piron in order to track growth parameters of individual plants for management of the plants (Redden, ¶0130).
As per claim 11, Piron and Redden disclose the farming machine of claim 10, wherein the first feature is a previous size of the plant, the second feature is a current size of the plant, and the expected feature is an expected size of the plant given the first treatment applied on the first pass (Redden, ¶0130, In a third variation of the method, each plant is treated based on the historic measurements for the given plant).
As per claim 12, Piron and Redden disclose the farming machine of claim 10, wherein the first feature is a previous height of the plant, the second feature is a current height of the plant, and the expected feature is an expected height of the plant given the first treatment applied on the first pass (Piron, page 3, lines 17-32, Automated weeding equipment according to the invention may be arranged to work its way along a line or band of crops; it may be self propelling. It may operate on a step by step basis so that it remains stationary at a first position along a line a crops whilst detecting the position of weeds and dealing with them before move to a second position further along the line of crops and repeating this procedure ... The weeding equipment may be arranged to travel along a line of crops several times during a critical weeding period ... if the first time the weeding equipment passes a weed having a corrected plant height similar to the expected crop height it may not be identified as a weed. Nevertheless, at the subsequent passage of the weeding equipment, given the difference in growth rates of the weeds and the crops, the difference between the corrected plant height and the expected crop height will be greater thus facilitating correct identification).
As per claim 13, Piron and Redden disclose the farming machine of claim 10, wherein the first feature is a previous physiological status of the plant, the second feature is a current physiological status of the plant, and the expected feature is an expected physiological status of the plant given the first treatment applied on the first pass (Redden, ¶0044, The system and/or method can additionally function to leverage historical data. Historical data can include historical weather data (e.g., for the geographic area of the plant field or the plant), historical treatment data, historical planting data, or any other suitable historical data. The treatments can be applied and recorded as population treatments (e.g., the plant field was indiscriminately blanketed with the treatment), area treatments (e.g., rows 1-8 had a first treatment while rows 9-16 had a second treatment), or individual treatments (e.g., individual plants within the same row or plant field can be treated with different treatments, wherein the individual treatments can be manually or automatically applied by the system); Redden, ¶0074, The data can include measurements, such as plant data and environmental data, extracted characteristics (e.g., derived data), such as plant characteristics and environment characteristics, treatment history, such as treatment history for an individual plant … compute the correlation between observed traits and management practices or environmental conditions, generate future treatments; Redden, ¶0096, the morphology measurement can be associated with the physiology measurement through the respective timestamps. For example, a first stereoview image taken at a first time can be associated with a set of near-field multispectral sensor images taken at a second time; Redden, ¶0113, Plant growth can be further determined from multiple measurements taken over a period of time, wherein a plant growth model can be determined for a plant feature, entire plant, plant population, or any other suitable set of plants based on the multiple measurements and associated with the respective measurement timestamp).
As per claim 14, Piron and Redden disclose the farming machine of claim 10, wherein determining, using the location of the plant determined using the image, the plant was treated by the farming machine with the first treatment on the first pass further causes the one or more processors to: access a map of at least a portion of the field, the map generated on the first pass of the farming machine through the field and comprising the location of the plant and one or more previous features of the plant; and determine, based on the one or more previous features of the plant in the map, one or more differences between one or more current features of the plant and the one or more previous features of the plant (Redden, ¶0088, The plant data or plant index values that are determined from the data corresponding to the plant location are preferably recorded as the plant characteristics for the plant. For example, plant characteristics extracted from the area of an image determined to be associated with the identified plant (e.g., within the plant boundaries) are preferably stored in association with the plant. Individual plants are preferably identified by the plant location in successive data collection sessions ... the locations of each individual plant can be pre-mapped, wherein the plant characteristics extracted from each image is correlated to the pre-mapped plant based on the data location; Redden, ¶0096, the morphology measurement can be associated with the physiology measurement through the respective timestamps; Redden, ¶0044, The system and/or method can additionally function to leverage historical data. Historical data can include historical weather data (e.g., for the geographic area of the plant field or the plant), historical treatment data, historical planting data, or any other suitable historical data. The treatments can be applied and recorded).
As per claim 15, Piron and Redden disclose the farming machine of claim 10, wherein executing the computer program instructions causes the one or more processors to: Store a map of the plant comprising one or more of the location of the plant, the expected feature of the plant, and the second feature of the plant (Redden, ¶0088, The plant data or plant index values that are determined from the data corresponding to the plant location are preferably recorded as the plant characteristics for the plant. For example, plant characteristics extracted from the area of an image determined to be associated with the identified plant (e.g., within the plant boundaries) are preferably stored in association with the plant. Individual plants are preferably identified by the plant location in successive data collection sessions ... the locations of each individual plant can be pre-mapped, wherein the plant characteristics extracted from each image is correlated to the pre-mapped plant based on the data location; Redden, ¶0096, the morphology measurement can be associated with the physiology measurement through the respective timestamps; Redden, ¶0044, The system and/or method can additionally function to leverage historical data. Historical data can include historical weather data (e.g., for the geographic area of the plant field or the plant), historical treatment data, historical planting data, or any other suitable historical data. The treatments can be applied and recorded).
As per claim 16, Piron and Redden disclose the farming machine of claim 10, wherein the location of the plant is in a substrate of the field between a first crop row and a second crop row, and the determined additional treatment for the plant is based on its determined location between the first crop row and the second crop row (Piron, page 1, lines 2-5, automatically guiding the mechanical or chemical destruction of weeds between rows of crops; knowledge of the position of the rows where crops should be growing and the assumption that plants growing outside such positions are weeds may be used).
As per claim 19, Piron discloses a non-transitory computer readable storage medium comprising computer program instructions for treating a plant in a field using a farming machine on a second pass after a first pass through the field, the computer program instructions, when executed by one or more processors, causing the one or more processors to:
access, from an image sensor as the farming machine moves through the field on the second pass after the first pass through the field, an image of the field comprising one or more pixels representing the plant (Piron, page 3, lines 23-32, The weeding equipment may be arranged to travel along a line of crops several times during a critical weeding period … at the subsequent passage of the weeding equipment, given the difference in growth rates of the weeds and the crops, the difference between the corrected plant height and the expected crop height will be greater thus facilitating correct identification of that particular weed; Piron, page 5, lines 1-5, The experimental apparatus of Fig 1 comprises a video projector 11 arranged to illuminate a portion of a band of soil 12 which contains both crops and weeds and a camera 13 arranged to capture an image, in this case a top-down image approximately 200 mm by 250 mm. The video projector 11 and camera 13 are mounted on a carriage 10 adapted to move along the band of soil 12; Piron, page 4, lines 3-7, The stereoscopic data of plants growing on soil may be acquired in the form of an image, for example using a camera as a sensor. In this case, the captured plant data points (ie data points representing a position at which the presence of a plant has been detected) and the captured soil data points (ie data points representing a position at which the presence of soil has been detected) may be represented by pixels of the image);
apply a feature identification model to the image, the feature identification model: determining, based on the one or more pixels representing the plant, a second feature of the plant on the second pass (Piron, page 3, lines 23-32, The weeding equipment may be arranged to travel along a line of crops several times during a critical weeding period … at the subsequent passage of the weeding equipment, given the difference in growth rates of the weeds and the crops, the difference between the corrected plant height and the expected crop height will be greater thus facilitating correct identification of that particular weed).
Piron does not explicitly disclose the following limitations as further recited however Redden discloses
determine, using a location of the plant determined using the image, the plant was treated by the farming machine with a first treatment on the first pass (Redden, ¶0044, The system and/or method can additionally function to leverage historical data. Historical data can include historical weather data (e.g., for the geographic area of the plant field or the plant), historical treatment data, historical planting data, or any other suitable historical data. The treatments can be applied and recorded as population treatments (e.g., the plant field was indiscriminately blanketed with the treatment), area treatments (e.g., rows 1-8 had a first treatment while rows 9-16 had a second treatment), or individual treatments (e.g., individual plants within the same row or plant field can be treated with different treatments, wherein the individual treatments can be manually or automatically applied by the system); Redden, ¶0088, The plant data or plant index values that are determined from the data corresponding to the plant location are preferably recorded as the plant characteristics for the plant. For example, plant characteristics extracted from the area of an image determined to be associated with the identified plant (e.g., within the plant boundaries) are preferably stored in association with the plant. Individual plants are preferably identified by the plant location in successive data collection sessions);
determining, based on the first treatment of the plant corresponding to a first feature of the plant determined on the first pass, an expected feature of the plant (Redden, ¶0044, Treatments can include fertilization, necrosis inducement, fungicide application, pesticide application, fruit harvesting, drought, chemical application, water application, plant hormone application, salinity control, disease exposure, insect infestation exposure, distance from plant to plant and population, genetics, epigenetics, planting depth, planting depth control, a combination of the aforementioned treatments, or any other suitable plant treatment; Redden, ¶0129, The method can additionally include recording management data, which functions to enable correlation between a treatment received by the plant and the subsequent plant response … Because this method tracks plant growth on a plant-by-plant basis, the method can enable determination of how different plant phenotypes react to a given treatment);
determine, based on a difference between the expected feature of the plant and the second feature of the plant, an additional treatment for the plant on the second pass; and treat, using a treatment mechanism of the farming machine, the plant using the determined additional treatment (Redden, ¶0130, The method can additionally include treating the plants with the system. The plants are preferably treated before, during, or after plant parameter measurement in the same session, but can alternatively be treated in a separate session …. each plant is treated based on the measurements recorded in the same session, wherein the measurements are processed by the system in real-time to determine the individual plant treatment to obtain a predetermined goal. Predetermined goals can include a target parameter range (e.g., target size range), a target date at which the plant will be in a predetermined growth stage (e.g., a target ripening date … In a third variation of the method, each plant is treated based on the historic measurements for the given plant).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Redden with Piron because they are in the same field of endeavor. One skilled in the art would have been motivated to include the historical plant monitoring as taught by Redden in the system of Piron in order to track growth parameters of individual plants for management of the plants (Redden, ¶0130).
Claim(s) 8, 9, 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Piron et al., International Publication No. WO 2009/153304, hereinafter, “Piron”, in view of Redden et al., U.S. Publication No. 2015/0015697, hereinafter, “Redden” as applied to claims 1 and 10 above, and further in view of Chowdhary et al., U.S. Publication No. 2021/0158041, hereinafter, “Chowdhary”.
As per claim 8. Piron and Redden disclose the method of claim 1, wherein applying the feature identification model to the image comprises: determining, based on the one or more pixels representing the plant, a distance between each pixel and the image sensor (Piron, page 1, lines 9-11, use of stereoscopic images to assess the distance between a plant and a camera has been used with the aim of differentiating between crops and weeds and thus allowing the position of weeds to be determined; Piron, page 4, lines 3-7, The stereoscopic data of plants growing on soil may be acquired in the form of an image, for example using a camera as a sensor. In this case, the captured plant data points (ie data points representing a position at which the presence of a plant has been detected) and the captured soil data points (ie data points representing a position at which the presence of soil has been detected) may be represented by pixels of the image; Piron, page 8, lines 1-3, The raw data acquired by the stereoscopic device was not plant height but the distance of the plants relative to the measurement device);
determining, based on the one or more pixels representing the plant, a classification for each pixel (Piron, page 8, lines 10-32, The crop/weed discrimination process is illustrated Figure 4. The camera 13 was used to acquire a stereoscopic image 41 of the plants growing on the soil. First we segmented the stereoscopic image 41 in to ground pixels 42 in the images and plant pixels 43 using only the multispectral data ... For the classification between crops and weeds (by means of a quadratic discriminant analysis), we used two parameters. The first parameter is for each plant pixel the distance between the plant pixel and the reconstructed soil underneath (corrected plant height). The second is the expected crop height).
Piron and Redden do not explicitly disclose the following limitation as further recited however Chowdhary discloses
generating, using the determined distances and classifications for each pixel, a point cloud representing the plant (Chowdhary, ¶0089, reference will now be made to Data Acquisition according to an embodiment. In this embodiment, video data, encoder readings (for instantaneous robot velocity estimation) and LIDAR point cloud data (for lateral distance estimation) are acquired at 90 fps, 5 fps and 20 fps respectively; Chowdhary, ¶0124, As described herein are algorithms for estimation of crop stem width on small mobile robots. Stem width is an important phenotype needed by breeders and plant-biologists to measure plant growth ... Using the extracted foreground, one algorithmic approach described herein uses estimates of robot velocity from wheel encoders and structure from motion to estimate depth, while another approach described herein employs use of the LIDAR 2-D point cloud to estimate the depth. These algorithms have been validated against available hand-measurements on biomass sorghum (Sorghum bicolor) in real experimental fields. Experiments indicate that both methods are also applicable to other crops with cylindrical stems without significant modifications. As described herein, the width estimation match on sorghum is 92.5% (using vision) and 98.2% (using vision and LIDAR).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Chowdhary with Piron and Redden because they are in the same field of endeavor. One skilled in the art would have been motivated to substitute the point cloud as taught by Chowdhary for the stereoscopic image as taught by Piron and Redden as an alternate means to determine distances and dimensions (Chowdhary, ¶0124).
As per claim 9, Piron, Redden and Chowdhary disclose the method of claim 8, wherein determining the second feature of the plant comprises analyzing distance information and classification information of the point cloud (Chowdhary, ¶0124, As described herein are algorithms for estimation of crop stem width on small mobile robots. Stem width is an important phenotype needed by breeders and plant-biologists to measure plant growth ... Using the extracted foreground, one algorithmic approach described herein uses estimates of robot velocity from wheel encoders and structure from motion to estimate depth, while another approach described herein employs use of the LIDAR 2-D point cloud to estimate the depth. These algorithms have been validated against available hand-measurements on biomass sorghum (Sorghum bicolor) in real experimental fields. Experiments indicate that both methods are also applicable to other crops with cylindrical stems without significant modifications. As described herein, the width estimation match on sorghum is 92.5% (using vision) and 98.2% (using vision and LIDAR).
Regarding claim(s) 17 and 18:
A corresponding reasoning as given earlier (see rejection of claim(s) 8 and 9) applies, mutatis mutandis, to the subject-matter of claim(s) 17 and 18, and therefore is/are also considered rejected under the grounds given in the rejection of claim(s) 8 and 9.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1 and 10 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 9, 10,13 and 16 of U.S. Patent No. 11,367,207. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1 and 10 of the current application are encompassed by the scope of claims 1, 9, 10,13 and 16 of U.S. Patent No. 11,367,207 in that claims 1, 9, 10,13 and 16 of U.S. Patent No. 11,367,207 contain similar limitations as claims 1 and 10 of the current application and are therefore not patentably distinct from claims 1, 9, 10,13 and 16 of U.S. Patent No. 11,367,207.
Claims 1 and 10 of the current application recite similar limitations as claims 1, 9, 10 ,13 and 16 of U.S. Patent No. 11,367,207 as follows:
Current Application # 18/949,732
U. S. Patent No. 11,367,207
1. A method for treating a plant in a field using a farming machine on a second pass after a first pass through the field, the method comprising: accessing, from an image sensor as the farming machine moves through the field on the second pass after the first pass through the field, an image of the field comprising one or more pixels representing the plant; determining, using a location of the plant determined using the image, the plant was treated by the farming machine with a first treatment on the first pass; applying a feature identification model to the image, the feature identification model: determining, based on the one or more pixels representing the plant, a second feature of the plant on the second pass; and determining, based on the first treatment of the plant corresponding to a first feature of the plant determined on the first pass, an expected feature of the plant; determining, based on a difference between the expected feature of the plant and the second feature of the plant, an additional treatment for the plant on the second pass; and treating, using a treatment mechanism of the farming machine, the plant using the determined additional treatment.
1. A method for treating a plant in a field by a farming machine that moves through the field, the farming machine including a plurality of treatment mechanisms, the method comprising: accessing a single image of the field from an image sensor as the farming machine moves through the field, the single image comprising one or more pixels representing a plurality of objects including the plant; applying a depth identification module to the image, the depth identification module to determine, for each pixel in the single image, a representative depth for each pixel, the representative depth quantifying distance between the sensor and an object represented by the pixel, and the depth identification module including a plurality of layers in a convolutional neural network configured to identify representative depths defining distances between objects and sensors in single images; classifying, based on the determined representative depth for each pixel, one or more pixels in the single image as the plant; and actuating a treatment mechanism of the plurality of treatment mechanisms to treat the plant as the farming machine moves past the plant in the field.
Claim 10 of the current application corresponds to claim 13 of U.S. Patent No. 11,367,207.
The table above shows that, although the corresponding claims are not identical, claims 1 and 10 of the current invention are not patentably distinct from claims 1, 9, 10 ,13 and 16 of U.S. Patent No. 11,367,207.
Conclusion
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/TRACY MANGIALASCHI/Examiner, Art Unit 2668
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