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 .
Claim Status
Claims 1-11 are pending for examination in the application filed 10/16/2024. Claims 1-10 are currently amended.
Priority
Acknowledgement is made of Applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been received for parent application JP2022-069071, filing date: 04/19/2022. Acknowledgement is additionally made of the present application as a national stage entry of PCT/JP2023/015602, international filing date: 04/19/2023.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/16/2024, 03/12/2026, and 04/23/2026 have been considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 7-9 and 11 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Farrand (US20220225584A1).
Regarding claim 1, Farrand teaches a forestry management system comprising ([0043] FIG. 2 is a schematic overview of a system for harvesting preparation, according to one or more embodiments):
at least one memory storing instructions, and at least one processor configured to execute the instructions, wherein the at least one processor is further configured to ([0080] The processing circuitry 210 may for example comprise one or more processors…the processor(s) may be configured to execute instructions (for example in the form of a computer program) stored in one or more memories 140):
acquire a low-altitude distance map generated by a distance measurement device that generates a distance map and a position of low-altitude observation equipment from the low-altitude observation equipment, the low-altitude observation equipment moving in a region of a forest near a ground surface where there are no branches and including the distance measurement device and a position measurement device that measures the position of the low-altitude observation equipment ([0129] The un-manned vehicle may be a drone that flies through the forest region under the canopy or may alternatively be a drone configured to move on the ground in the forest region. [0032] Suitably, the at least one sensor of the unmanned vehicle may comprise a stereoscopic camera, a time of flight sensor, an imaging sensor, a chemical sniffer, a LIDAR sensor or radar equipment. [0086] In one non-limiting example, one or more of the at least one sensor 120 is configured to gather terrain information. For example, the one or more of the at least one sensor 120 may be a Lidar sensor configured to gather information for generating a digital elevation model (DEM), a digital surface model (DSM), a digital terrain models (DTM), a triangular irregular networks (TIN) or any other suitable representation. [0141] the object identity can be a precise location where the object is located; this can be determined by the drone comprising equipment for detecting its own location accurately or by a remote control unit being able to determine accurately where the drone is and send this information to the drone. In some embodiments, the un-manned vehicle may comprise positioning equipment for determining a position using GPS, GLONASS, accelerometer(s), radio equipment for radio triangulation, a radio reference node, imaging device and access to image processing equipment for image matching, or any other system for global or other positioning in the real world, or a combination of any of these);
specify a position of a tree, based on a distance from the low-altitude observation equipment to the tree specified based on the low-altitude distance map, and on the position of the low-altitude observation equipment ([0092] In some embodiments, assigning an object ID to the object 110, using the processing circuitry 210, comprises analyzing the sensor information to determine a unique position associated with the object 110. [0032] Suitably, the at least one sensor of the unmanned vehicle may comprise a stereoscopic camera, a time of flight sensor, an imaging sensor, a chemical sniffer, a LIDAR sensor or radar equipment. [0086] In one non-limiting example, one or more of the at least one sensor 120 is configured to gather terrain information. For example, the one or more of the at least one sensor 120 may be a Lidar sensor configured to gather information for generating a digital elevation model (DEM), a digital surface model (DSM), a digital terrain models (DTM), a triangular irregular networks (TIN) or any other suitable representation. [0141] the object identity can be a precise location where the object is located; this can be determined by the drone comprising equipment for detecting its own location accurately or by a remote control unit being able to determine accurately where the drone is and send this information to the drone);
acquire identification information for identifying an individual of the tree based on the position of the tree ([0013] Alternatively, assigning an object identity to the object, using the processing circuitry, comprises analyzing the sensor information to determine a unique position associated with the object, generating a unique object identity, based on the determined unique position, and assigning the identity to the object. This allows for a very efficient association of the obtained sensor information with the specific location where the object is associated, so that harvesting is further improved); and
record low-altitude tree information, which is tree information of a portion of the individual lower than branches that is measured based on the low- altitude distance map, in a database in association with the identification information ([0014] Suitably, the method further comprises storing the information associated with the at least one object together with the identity of the object and the associated marker in a memory accessible to the un-manned vehicle. Thereby, digital markers relating to each object may be stored and can also form a point cloud or a map of the forest region comprising markers for each object and the information and possibly also the harvesting decisions associated with them. [0129] The un-manned vehicle may be a drone that flies through the forest region under the canopy or may alternatively be a drone configured to move on the ground in the forest region. [0032] Suitably, the at least one sensor of the unmanned vehicle may comprise a stereoscopic camera, a time of flight sensor, an imaging sensor, a chemical sniffer, a LIDAR sensor or radar equipment).
Regarding claim 7, Farrand teaches the system of claim 1. Farrand further teaches wherein the database stores the identification information for identifying the individual of the tree, a position of the individual, and the low-altitude tree information of the individual in association with each other ([0092] In some embodiments, assigning an object ID to the object 110, using the processing circuitry 210, comprises analyzing the sensor information to determine a unique position associated with the object 110. [0032] Suitably, the at least one sensor of the unmanned vehicle may comprise a stereoscopic camera, a time of flight sensor, an imaging sensor, a chemical sniffer, a LIDAR sensor or radar equipment. [0086] In one non-limiting example, one or more of the at least one sensor 120 is configured to gather terrain information. For example, the one or more of the at least one sensor 120 may be a Lidar sensor configured to gather information for generating a digital elevation model (DEM), a digital surface model (DSM), a digital terrain models (DTM), a triangular irregular networks (TIN) or any other suitable representation. [0141] Or alternatively the object identity can be a precise location where the object is located; this can be determined by the drone comprising equipment for detecting its own location accurately or by a remote control unit being able to determine accurately where the drone is and send this information to the drone. [0014] Suitably, the method further comprises storing the information associated with the at least one object together with the identity of the object and the associated marker in a memory accessible to the un-manned vehicle. Thereby, digital markers relating to each object may be stored and can also form a point cloud or a map of the forest region comprising markers for each object and the information and possibly also the harvesting decisions associated with them),
and the at least one processor is further configured to: provide new identification information to the individual in a case in which identification information corresponding to the specified position is not recorded in the database ([0091] According to a non-limiting example, wherein the object 110 is a tree, the image pattern determined from the analysis of the obtained sensor information may be a bark print, indicative of visible features in the bark of the tree. This is illustrated in FIGS. 6a to 6c, wherein the bark of the object (tree) 110 comprises a number of features 601 recognizable from the 2D or 3D image data, or 3D point cloud data, gathered in relation to the object 110 by at least one sensor 120 of the un-manned vehicle 100. By analyzing the sensor information, in this case the 2D or 3D image data, or 3D point cloud data, comprising the features 601, the processing circuitry 210 is configured to, and method step 420 comprises, determining a pattern 602, as exemplified in FIG. 6b where the detected features 601 of the bark are connected to generate the pattern 602. The pattern 602, shown on without the object 110 in FIG. 6c, is unique to the object 110, and therefore suitable to be used as its unique object ID. [0105] To associate a marker can be to store a digital marker connected to the property or to the object identity in a memory, and/or to apply a physical marker to the object itself).
Regarding claim 8, Farrand teaches the system of claim 1. Farrand further teaches wherein the low-altitude observation equipment moves through the forest along a traveling route along which a forestry machine that fells the tree travels, and the at least one processor is further configured to: record low-altitude tree information of a tree standing in a vicinity of the traveling route in the database ([0015] Preferably, the method also comprises that a harvester recognizes the marker, using at least one sensor of the harvester and that the harvester also obtains the harvesting decision for the object associated with the recognized marker and performs a harvesting action based on the harvesting decision related to the object. Thereby, harvesting can be made more efficient by using the preparations of the un-manned vehicle. [0035] Suitably, the un-manned vehicle operates in the forest region first and the harvester follows later and uses the markers associated with the objects when harvesting. [0105] To associate a marker can be to store a digital marker connected to the property or to the object identity in a memory, and/or to apply a physical marker to the object itself. [0022] The object may be a tree to be cut, and making a harvesting decision based on the obtained sensor information may comprise: [0024] calculating, by the processing circuitry, an optimal manner of cutting the tree trunk in at least one identified place in order to maximize the possible yield of high-quality wood for the tree).
Regarding claim 9, Farrand teaches the system of claim 1. Farrand further teaches wherein the at least one processor is further configured to: generate a felling plan indicating an individual to be felled by a forestry machine that fells the tree, based on information recorded in the database ([0022] The object may be a tree to be cut, and making a harvesting decision based on the obtained sensor information may comprise: [0024] calculating, by the processing circuitry, an optimal manner of cutting the tree trunk in at least one identified place in order to maximize the possible yield of high-quality wood for the tree);
and transmit an instruction signal that instructs the forestry machine to perform felling according to the generated felling plan ([0015] Preferably, the method also comprises that a harvester recognizes the marker, using at least one sensor of the harvester and that the harvester also obtains the harvesting decision for the object associated with the recognized marker and performs a harvesting action based on the harvesting decision related to the object. Thereby, harvesting can be made more efficient by using the preparations of the un-manned vehicle).
Regarding claim 11, Farrand teaches a forestry management method comprising ([Abstract] The present invention relates to a method for preparing for harvesting of forest using an un-manned vehicle (100) configured to move under the canopy in a forest region):
a step of acquiring a low-altitude distance map generated by a distance measurement device that generates a distance map and a position of low-altitude observation equipment from the low- altitude observation equipment, the low-altitude observation equipment moving in a region of a forest near a ground surface where there are no branches and including the distance measurement device and a position measurement device that measures the position of the low-altitude observation equipment ([0129] The un-manned vehicle may be a drone that flies through the forest region under the canopy or may alternatively be a drone configured to move on the ground in the forest region. [0032] Suitably, the at least one sensor of the unmanned vehicle may comprise a stereoscopic camera, a time of flight sensor, an imaging sensor, a chemical sniffer, a LIDAR sensor or radar equipment. [0086] In one non-limiting example, one or more of the at least one sensor 120 is configured to gather terrain information. For example, the one or more of the at least one sensor 120 may be a Lidar sensor configured to gather information for generating a digital elevation model (DEM), a digital surface model (DSM), a digital terrain models (DTM), a triangular irregular networks (TIN) or any other suitable representation. [0141] Or alternatively the object identity can be a precise location where the object is located; this can be determined by the drone comprising equipment for detecting its own location accurately or by a remote control unit being able to determine accurately where the drone is and send this information to the drone. In some embodiments, the un-manned vehicle may comprise positioning equipment for determining a position using GPS, GLONASS, accelerometer(s), radio equipment for radio triangulation, a radio reference node, imaging device and access to image processing equipment for image matching, or any other system for global or other positioning in the real world, or a combination of any of these);
a step of specifying a position of a tree, based on a distance from the low-altitude observation equipment to the tree specified based on the low-altitude distance map, and on the position of the low-altitude observation equipment ([0092] In some embodiments, assigning an object ID to the object 110, using the processing circuitry 210, comprises analyzing the sensor information to determine a unique position associated with the object 110. [0032] Suitably, the at least one sensor of the unmanned vehicle may comprise a stereoscopic camera, a time of flight sensor, an imaging sensor, a chemical sniffer, a LIDAR sensor or radar equipment. [0086] In one non-limiting example, one or more of the at least one sensor 120 is configured to gather terrain information. For example, the one or more of the at least one sensor 120 may be a Lidar sensor configured to gather information for generating a digital elevation model (DEM), a digital surface model (DSM), a digital terrain models (DTM), a triangular irregular networks (TIN) or any other suitable representation. [0141] Or alternatively the object identity can be a precise location where the object is located; this can be determined by the drone comprising equipment for detecting its own location accurately or by a remote control unit being able to determine accurately where the drone is and send this information to the drone);
a step of acquiring identification information for identifying an individual of the tree based on the position of the tree ([0013] Alternatively, assigning an object identity to the object, using the processing circuitry, comprises analyzing the sensor information to determine a unique position associated with the object, generating a unique object identity, based on the determined unique position, and assigning the identity to the object. This allows for a very efficient association of the obtained sensor information with the specific location where the object is associated, so that harvesting is further improved); and
a step of recording low-altitude tree information, which is tree information of a portion of the individual lower than branches that is measured based on the low-altitude distance map, in a database in association with the identification information ([0014] Suitably, the method further comprises storing the information associated with the at least one object together with the identity of the object and the associated marker in a memory accessible to the un-manned vehicle. Thereby, digital markers relating to each object may be stored and can also form a point cloud or a map of the forest region comprising markers for each object and the information and possibly also the harvesting decisions associated with them. [0129] The un-manned vehicle may be a drone that flies through the forest region under the canopy or may alternatively be a drone configured to move on the ground in the forest region. [0032] Suitably, the at least one sensor of the unmanned vehicle may comprise a stereoscopic camera, a time of flight sensor, an imaging sensor, a chemical sniffer, a LIDAR sensor or radar equipment).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Farrand in view of Jupp (US20040130702A1).
Regarding claim 2, Farrand teaches the system of claim 1. Farrand does not explicitly teach wherein the at least one processor is further configured to: calculate an altitude of a ground surface on which the tree stands, which is the low-altitude tree information, based on the low-altitude distance map and on a three-dimensional position of the low-altitude observation equipment in a global coordinate system.
Jupp, in the same field of endeavor of forestry management, teaches wherein the at least one processor is further configured to: calculate an altitude of a ground surface on which the tree stands, which is the low-altitude tree information, based on the low-altitude distance map and on a three-dimensional position of the low-altitude observation equipment in a global coordinate system ([0011] This application relates to a ground based forest survey system and method variously utilising multi-angle sounding, controlled, variable beam width and shape and recording the return waveform with calibration to provide apparent reflectance as a function of range for each choice of angle and beam size and shape. [0391] The terrain surface is measured by estimating the "envelope" under the last significant returns, eliminating anomalous values and then interpolating the data to a DTM. [0788] Canopy and ground elevations are estimated by identifying, respectively, the first and last return above a threshold. A plot of ground and canopy elevation against time allows the operator to verify that the recorded data reflect a visual assessment of the terrain and vegetation. [0934] GPS and sensor attitude data are used to geolocate and orient earn shot).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the system of Farrand with the teachings of Jupp to calculate an altitude of a ground surface on which the tree stands based on a low altitude distance map and the position of the observation equipment "to verify that the recorded data reflect a visual assessment of the terrain and vegetation" [0788].
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Farrand in view of Tham (US20200066034A1).
Regarding claim 3, Farrand teaches the system of claim 1. Farrand does not explicitly teach wherein the at least one processor is further configured to: record high-altitude tree information, which is tree information of the individual measured from a portion of high-altitude captured data captured from a sky of the forest at which the individual is shown, in the database in association with the identification information of the individual.
Tham, in the same field of endeavor of forestry management, teaches wherein the at least one processor is further configured to: record high-altitude tree information, which is tree information of the individual measured from a portion of high-altitude captured data captured from a sky of the forest at which the individual is shown, in the database in association with the identification information of the individual
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([0122] The 3D model, possibly along with the parameters and any measurements made, may be saved for later retrieval. The storing may be done locally or externally such as in a server or in a cloud server. [0108] Also as the image stream has been received, possibly in conjunction with determine the point cloud, trees are identified in the image stream and possibly associated with a location and/or various tree attributes. The trees are identified by splitting the point cloud into a 2D grid from the normal direction of the ground plane, filter out all points that are within a threshold distance to the plane, finding all clusters with many remaining points (i.e. points that are outside a threshold distance to the plane) and assume these are trees).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the system of Farrand with the teachings of Tham to record high-altitude tree information "To enable for a height to be calculated correctly, and for matching an upper portion of a tree (such as the top of the tree or where the tree ends, the highest visible point of the tree or simply a point in the crown area of the tree to give a few examples) to a lower (detected) portion" [0152].
Regarding claim 4, Farrand teaches the system of claim 1. Farrand does not explicitly teach wherein the at least one processor is further configured to: calculate a height of the individual based on an altitude of a treetop of the individual measured from a portion of a high-altitude distance map measured from an altitude higher than a canopy of the forest at which the individual is shown and on an altitude of a ground surface on which the individual stands, wherein the recording unit records the height of the individual in the database in association with the identification information of the individual.
Tham, in the same field of endeavor of forestry management, teaches wherein the at least one processor is further configured to: calculate a height of the individual based on an altitude of a treetop of the individual measured from a portion of a high-altitude distance map measured from an altitude higher than a canopy of the forest at which the individual is shown and on an altitude of a ground surface on which the individual stands, wherein the recording unit records the height of the individual in the database in association with the identification information of the individual ([0086] A base plane and an up direction in the point cloud are determined 540. In one embodiment this is done by filtering points and fitting a base plane to the data filtered points. [0087] A height map is generated 550, in one embodiment by dividing the point cloud into 2D cells along a plane, and finding a median distance from each point cloud point in the corresponding cell to the plane. When detecting trees, points that are near the ground—as specified by the plane and height map—will be filtered out. [0152] To facilitate the extrapolation, and for enabling capture of a highest top, the extrapolation may be supplemented by further video recording 1122, possibly in combination with further sensor readings, this time aimed at the top of the trees, or at least their crowns. To enable for a height to be calculated correctly, and for matching an upper portion of a tree (such as the top of the tree or where the tree ends, the highest visible point of the tree or simply a point in the crown area of the tree to give a few examples) to a lower (detected) portion. [0112] The UE may also be configured to calculate an absolute scale 592. In one embodiment this is done by determining an average distance between the camera and the ground plane. The measured height and the determined average distanced thereby giving the scale factor as they represent the same height. [0183] The sets are found to be matching by comparing characteristics of the objects. The characteristics may be the size of the object(s), the individual position(s), the actual position of an object, tree species (kind of trees), branch structure, shape, profile, vertical bole transaction, barch texture or pattern, tree height and/or other characteristics such as discussed above).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the system of Farrand with the teachings of Tham to calculate the height based on a high-altitude distance map and the altitude of the ground surface "To enable for a height to be calculated correctly, and for matching an upper portion of a tree (such as the top of the tree or where the tree ends, the highest visible point of the tree or simply a point in the crown area of the tree to give a few examples) to a lower (detected) portion" [0152].
Regarding claim 5, Farrand and Tham teach the system of claim 4. Farrand does not explicitly teach wherein the at least one processor is further configured to: specify an altitude of a treetop of which a plane position is closest to a plane position of the individual related to the identification information, among a plurality of treetops specified from a high-altitude distance map measured from a sky of the forest, as the altitude of the treetop of the individual.
Tham, in the same field of endeavor of forestry management, teaches wherein the at least one processor is further configured to: specify an altitude of a treetop of which a plane position is closest to a plane position of the individual related to the identification information, among a plurality of treetops specified from a high-altitude distance map measured from a sky of the forest, as the altitude of the treetop of the individual ([0087] A height map is generated 550, in one embodiment by dividing the point cloud into 2D cells along a plane, and finding a median distance from each point cloud point in the corresponding cell to the plane. [0181] As a first set S1 has been found, a second set S2 is found 2020 in the second area A2. [0182] The second set S2 is found by finding a set of objects that correspond to the first set S1, whereby a matching of the two sets and therefore also the two areas is achieved. In one embodiment, the first set S1 and the second set S2 are found as a set of objects that exist in both areas. [0183] The sets are found to be matching by comparing characteristics of the objects. The characteristics may be the size of the object(s), the individual position(s), the actual position of an object, tree species (kind of trees), branch structure, shape, profile, vertical bole transaction, barch texture or pattern, tree height and/or other characteristics such as discussed above).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the system of Farrand with the teachings of Tham to specify an altitude of a treetop of which a plane position is closest to a plane position of the individual related to the identification information, among a plurality of treetops specified from a high-altitude distance map measured from a sky of the forest, as the altitude of the treetop of the individual so that "a matching of the two sets and therefore also the two areas is achieved. In one embodiment, the first set S1 and the second set S2 are found as a set of objects that exist in both areas" [0182] and "generation of a base plane and a height map may be optional and are not essential, but do provide a clear benefit in that it is easier to find the clusters representing trees" [0094].
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Farrand in view of Hosomi (US20220225583A1).
Regarding claim 6, Farrand teaches the system of claim 1. Farrand does not explicitly teach wherein the low-altitude observation equipment includes an illuminance sensor, and the at least one processor is further configured to acquire illuminance data measured by the illuminance sensor, and record a value related to the illuminance data in the database in association with the identification information of the tree.
Hosomi, in the same field of endeavor of tree management, teaches wherein the low-altitude observation equipment includes an illuminance sensor, and the at least one processor is further configured to acquire illuminance data measured by the illuminance sensor, and record a value related to the illuminance data in the database in association with the identification information of the tree ([0106] Further, regarding environment state information such as a temperature, a humidity, and an illuminance that can be detected by detectors such as a temperature sensor, a humidity sensor, and an illuminance sensor; the control unit 10 may control these sensors through the external interface 16 to acquire the training environment state information. [0108] The training data storage unit 131, which is a predetermined area of the auxiliary storage unit 13, stores the training environment state information, the training work history information, and the training yield information acquired through the training environment state information acquisition unit 21, the training work history information acquisition unit 22, and the training yield acquisition unit 23. [0109] Corresponding to each case, the training environment state information, the training work history information, and the training yield information are associated with each other by information that identifies each stem or the group of stems).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the system of Farrand with the teachings of Hosomi to record illuminance data with identification information of the tree "to generate a learning model configured to determine and output a work including the shape change work for the fruit vegetable plant or the fruit tree with respect to inputs of environment state information and preplanned cultivation evaluation index information of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree" [0024].
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Farrand in view of Trundle (US10755443B1).
Regarding claim 10, Farrand teaches the system of claim 1. Farrand further teaches wherein the at least one processor is further configured to: determine whether the tree shown in low- altitude captured data captured by the low-altitude observation equipment is a fallen tree or a living tree based on an angle of the tree with respect to horizontal ([0129] The un-manned vehicle may be a drone that flies through the forest region under the canopy or may alternatively be a drone configured to move on the ground in the forest region. [0029] Suitably, the marker may be a physical marker and the un-manned vehicle may be configured to associate the marker with the object by attaching the physical marker to the object or a location in the immediate vicinity of the object, or placing the physical marker on the object or a location in the immediate vicinity of the object. Thereby, the un-manned vehicle may place markers in the forest regions so that the harvester or an operator using the harvester can identify the markers and harvest objects based on them…The wireless radio messages may encode information such as: “I am here”, “Do not go here”, “I am a log”, “I am a fallen log”, I am a bog”, “Go here”, “Cut me”, “Do not cut me” and others. [0017] Suitably, each of the at least one object is a tree or a part of a tree, a boundary of an area, an area un-fitted for a harvester to travel across, an existing path in the forest region, ancient remnants or monuments, a fallen tree with environmental heritage value, or another biological object of protection. The harvesting decision may be a decision to harvest the object, how to harvest the object, to not harvest the object or to avoid a location where the object is situated or a specified area within which the object is situated. [0090] The at least one property of the object may be an object type such as a tree, species of a tree, a swamp, a river, a downed log; or it could alternatively be a color, a surface roughness co-efficient, a size, a volume, a shape or a pattern of the object, or alternatively it could be a geographical location or an interaction with other object such as positioning with respect to terrain that could be upright, horizontal or at a particular angle to any of these directions).
Farrand does not explicitly teach acquire the identification information of the tree in a case in which the tree is determined to be the living tree.
Trundle, in the same field of endeavor of tree management, teaches acquire the identification information of the tree in a case in which the tree is determined to be the living tree ([col. 2 ln. 48-64] In the example shown in FIG. 1, the monitoring server 30 collects and analyzes images of outdoor areas of the property 10 to identify the vegetation areas 80A, 80B, and 80C with living vegetation such as lawns, flowers, and trees. The monitoring server 30 may analyze colors of the outdoor images, change of the colors, a shape of an object in the outdoor areas, or movement of the object to identify the vegetation areas from the images of the outdoor areas. For example, the monitoring server 30 may have a database that includes a reference range of colors, and/or shapes of general vegetation at a particular period of time (e.g., season) and at a geographic location where the property 10 is located. In this case, the monitoring server 30 may compare the outdoor images of the property 10 with the reference range of colors and shapes of vegetation in the database to find matching areas from the outdoor images of the property 10 to identify vegetation areas).
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Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the system of Farrand with the teachings of Trundle to acquire identification information of a tree when it is determined to be a living tree to "control how much areas including outdoor living vegetation are watered based on the color of those areas in images or videos captured through an outdoor camera. For example, the monitoring system may provide more water to areas that are known to include vegetation and that are yellow in images, and provide less water to areas that are known to include vegetation and that are brown in images" [col. 1 ln. 66 - col. 2 ln. 6].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Flood (US20190102623A1) teaches forestry image analysis for tree inventory.
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/JACQUELINE R ZAK/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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