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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/22/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached by the examiner.
Claim Objections
Claim 4 is objected to because of the following informalities: “if there are two or mores canes that are classified” should be corrected to “if there are two or more canes that are classified”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rosat (U.S. Patent Pub. No. 2022/0189329) in view of Koselka (U.S. Patent Pub. No. 2011/0137456).
Regarding Claim 1, Rosat teaches a method for using a computer or computers to generate cut-point data including information indicating a three-dimensional position of a point on a cane of a fruit tree where the cane is to be cut off, the method comprising (Fig. 6; ¶16 a series of images of the plant to be pruned are taken using the camera of the electronic device from a plurality of angles in step b. This step is followed by a step of generating a 3D digital model of the plant to be pruned according to the series of images of the plant. Reference features are extracted from the 3D digital model. These reference features represent a signature specific to the plant to be pruned:)
for each of two or more canes of the fruit tree, acquiring a measurement value(s) concerning one or more attributes, based on sensor data of the two or more canes acquired by a sensor or sensors (¶72 d. extracting “features”, referred to hereinafter as reference features, from the 3D digital model, for example the parts of the grapevine plant that correspond, in particular, to the bifurcations of the canes, and/or to the ends of the canes, etc; the broad term “measurement values” is being read as any feature point determined from the sensor data.)
based on the measurement value(s), determining the two or more canes each as a cane to be removed or a cane to be retained; and (¶73 e. transmitting these reference features to a neural network, previously trained with training images comprising only training features and with cutting instructions according to the configuration of the reference features in the image, so as to determine cutting instructions for the grapevine plant to be pruned)
generating the cut-point data for each cane determined as a cane to be removed; wherein (¶74 f. displaying the cutting instructions overlaid over the video image in real time, for example in the form of marks or points on the branches to be cut or explanatory text, images or video)
the determining the two or more canes each as a cane to be removed or a cane to be retained includes:
regarding each of the one or more attributes, based on the measurement value, classifying each of the two or more canes into one of a plurality of classes representing evaluation criteria for the attribute (¶77 The neural system 50 is designed to, first, classify the reference features extracted from the image captured using the camera, or from the 3D model generated on the basis of a plurality of images of a sequence, and then, second, deduce associated cutting instructions from this classification;)
regarding each of the one or more attributes, based on the measurement value, giving the two or more canes respectively different ranks; and
based on the classes and the ranks of the two or more canes, determining the two or more canes each as a cane to be removed or a cane to be retained (¶77 The neural system 50 is designed to, first, classify the reference features extracted from the image captured using the camera, or from the 3D model generated on the basis of a plurality of images of a sequence, and then, second, deduce associated cutting instructions from this classification. The cutting instructions are then displayed on the display area 16 of the glasses 12 in order to display these instructions in augmented reality overlaid over the image of the grapevine plant captured by the camera. The cutting instructions are, for example, in the form of marks, colors or points on the branches to be cut, in the form of images as illustrated in FIGS. 5a-5. Rosat does not explicitly disclose ranking canes, but instead classifies the canes, then cuts depending on how they are classified. The classification scheme is determined by one with ordinary skill in the art, and they could train the network to rank the classes that are determined.)
Rosat does not explicitly disclose for each of two or more canes of the fruit tree, acquiring a measurement value(s) concerning one or more attributes;
regarding each of the one or more attributes, based on the measurement value, giving the two or more canes respectively different ranks; and based on the classes and the ranks of the two or more canes, determining the two or more canes each as a cane to be removed or a cane to be retained.
Koselka is in the same field of art of image analysis. Further, Koselka teaches for each of two or more canes of the fruit tree, acquiring a measurement value(s) concerning one or more attributes (¶129 While it is examining the plant, it is looking for fruit and thick branches. This information is used to determine areas where both the scouting and task specific arms may be moved inside the canopy of the plant. The scouting arms may then moved into the canopy of the tree to map the fruit on the inside of the plant. For plant types with fruit exclusively on the outside of the plant such as a tomato plant, this step may not be performed. In addition, the scout may gather information such as the size or ripeness of each piece of fruit at 405.)
regarding each of the one or more attributes, based on the measurement value, giving the two or more canes respectively different ranks; and based on the classes and the ranks of the two or more canes, determining the two or more canes each as a cane to be removed or a cane to be retained (¶132 When it is time to operate in a field the worker implements the action plan to operate on the plants in the field. The operation implemented may involve picking, pruning, culling, thinning, spraying weeding or any other agricultural function… Once the actuator is looking approximately at the target location, the various camera(s) locates and operates on the item at 505, for example in one embodiment of the strategy pattern, the easiest (rank) piece of fruit to harvest. The actuator or arm is positioned to operate on the next intended item associated with the plant, then it moves to the next item location and the process continues until the entire plant is operated on.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Rosat by using a measurement and ranking the plants in the field that is taught by Koselka; thus, one of ordinary skilled in the art would be motivated to combine the references to pick fresh fruits and vegetables efficiently (Koselka ¶10).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 2, Rosat in view of Koselka discloses the method of claim 1, further comprising: inputting the generated cut-point data to a controller configured or programmed to control a three-dimensional position of a cutter that cuts a cane of the fruit tree (Koselka, ¶16 The end effector may be a mechanical hand that grabs and picks fruit, or may contain some mechanical cutting or thinning device, some type of spraying mechanism, or any other device or implement to perform an agricultural function or observation or measurement. The end effector may also contain a mechanism to cut or snip the fruit from the stem rather then just pulling it free.)
The reasons for combining Rosat and Koselka are similar to that stated in the rejection of claim 1. In addition, this same reasoning is pertinent and applicable to the rejections of claims 13-20 below.
Regarding Claim 12, Rosat in view of Koselka discloses the method of claim 1, further comprising: grouping a plurality of canes of the fruit tree into a plurality of groups based on sensor data of the plurality of canes; wherein the acquisition of the measurement value(s), the determination as to a cane to be removed or a cane to be retained, and the generation of the cut-point data are performed for two or more canes having been grouped into a same group among the plurality of groups (Rosat, ¶17 the reference features correspond in particular to the bifurcations of the branches of the plant to be pruned, and preferably to the ends of the branches; ¶20 the cutting instructions comprise cut points or marks or coloring of branches that are not to be retained overlaid over the branches of the real plant image and/or explanatory videos/images.)
Regarding Claim 13, Rosat in view of Koselka discloses the method of claim 1, further comprising: acquiring information on a number of buds to be retained on the cane to be retained; and based on the number of buds to be retained, generating the cut-point data for each cane determined as a cane to be retained (Koselka, ¶21 Using oranges as an example, an agricultural robot configured for picking, i.e., a picking robot, is provided a map comprising the number and approximate locations of oranges in each specific region of a tree. The map may originate from a scout robot, or other source whether robotic or not or any other system capable of producing a map such as a computer system that is configured to generate a map from photographs.)
Regarding Claim 14, Rosat in view of Koselka discloses the method of claim 13, wherein the generating the cut-point data for each cane having been determined as a cane to be retained includes: generating the cut-point data so that each cane having been determined as a cane to be retained includes one or more buds after being cut (This is simply a design choice one with ordinary skill in the art could make to ensure proper growth; Koselka, ¶89 A robot picker may efficiently pick fruit of a given size or ripeness to maximize crop value. The process of picking the fruit may be aided by multi-spectral sensing devices. This may involve multiple harvests and readjusting the map in the database to update the status of remaining fruit.)
Regarding Claim 15, Rosat in view of Koselka discloses the method of claim 1, wherein the one or more attributes include at least one of a color of the cane, a direction in which the cane extends, a thickness of the cane, a height of a base of the cane, a size of buds on the cane, a direction in which buds on the cane are facing, a length of the cane, or a length between nodes of the cane (Koselka, ¶20 Embodiments of the invention pre-map the individual fruit/vegetable size, color, and/or locations on the plant and preplan a picking sequence. Similarly, a pruning, culling, thinning, or spraying sequence or any other function may be preplanned.)
Regarding Claim 16, Rosat in view of Koselka discloses the method of claim 1, wherein the determining the two or more canes each as a cane to be removed or a cane to be retained includes: among the two or more canes, determining that any cane other than the cane(s) determined as a cane(s) to be retained is a cane to be removed (This is simply a design choice one with ordinary skill in the art could make to ensure proper growth of the fruit; Rosat, Figs 5a-c; Koselka, ¶89 A robot picker may efficiently pick fruit of a given size or ripeness to maximize crop value. The process of picking the fruit may be aided by multi-spectral sensing devices. This may involve multiple harvests and readjusting the map in the database to update the status of remaining fruit.)
Regarding claim 17, claim 17 has been analyzed with regard to claim X and is rejected for the same reasons of obviousness as used above as well as in accordance with Koselka further teaching on: A system for generating cut-point data including information indicating a three-dimensional position of a point on a cane of a fruit tree where the cane is to be cut off, the system comprising: a sensor or sensors to acquire sensor data of two or more canes of the fruit tree; and a data processor (Koselka; Fig. 10; ¶117 Processor system 1006 communicates with tractor 1002 via an electronic tether 1007… Robotic arms 1004 may be mounted on trailer 1003 and configured to harvest, prune, scout, measure or perform any other agricultural task desired.)
Regarding Claim 18, Rosat in view of Koselka discloses the system of claim 17, further comprising: a cutter to cut a cane of the fruit tree and a controller configured or programmed to control a three-dimensional position of the cutter; wherein the data processor is configured or programmed to input the generated cut-point data to the controller; and the controller is configured or programmed to control the three-dimensional position of the cutter based on the cut-point data (Koselka, ¶16 The end effector may be a mechanical hand that grabs and picks fruit, or may contain some mechanical cutting or thinning device, some type of spraying mechanism, or any other device or implement to perform an agricultural function or observation or measurement. The end effector may also contain a mechanism to cut or snip the fruit from the stem rather then just pulling it free; ¶131 he plan may include the order of fruit to pick with each arm and the approximate arm motions to reach each piece. Once the plan is complete, the scout transmits it to the appropriate worker at 503 (or to a server). Alternatively, the scout robot may merely transmit the map to a worker robot or server where the action plan is calculated and coordination between a plurality of worker robots is performed. Use of the system without a centralized server comprises a peer-to-peer architecture. The peer-to-peer architecture may be used in order to balance processing loads of the various robots depending on their current work load in order to most efficiently utilize their associated computing elements.)
Regarding Claim 19, Rosat in view of Koselka discloses an agricultural machine comprising the system of claim 18 (Koselka, Fig. 10.)
Regarding Claim 20, Rosat in view of Koselka discloses the agricultural machine of claim 19, further comprising: an arm supporting the cutter, a support supporting the arm, and a driver to move the support; wherein the controller is configured or programmed to control the three-dimensional position of the cutter by controlling an operation of the arm (Koselka, Fig. 10, 1004; ¶114 although the processor system 1006 may control the speed of tractor 1002 and therefore trailer to allow robotic arms 1004 to adequately perform assigned tasks according to an action plan in the shortest time possible. Robotic arms 1004 may be mounted on trailer 1003 and configured to harvest, prune, scout, measure or perform any other agricultural task desired.)
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Rosat (U.S. Patent Pub. No. 2022/0189329) in view of Koselka (U.S. Patent Pub. No. 2011/0137456) in view of Sibley (U.S. Patent Pub. No. 2021/0185942).
Regarding Claim 3, Rosat in view of Koselka teaches the method of claim 1.
Rosat in view of Koselka does not explicitly disclose wherein the giving the two or more canes respectively different ranks includes: regarding each of the one or more attributes, if the two or more canes are classified into respectively different classes, giving the two or more canes respectively different ranks based on the classes found in the classification.
Sibley is in the same field of art of image analysis. Further, Sibley teaches wherein the giving the two or more canes respectively different ranks includes: regarding each of the one or more attributes, if the two or more canes are classified into respectively different classes, giving the two or more canes respectively different ranks based on the classes found in the classification (Sibley, ¶82 Limb data 264 may include status data 264a that may identify or classify a limb (e.g., a branch, a shoot, etc.) as being in a particular state at one point in time, which may be determined (at another point in time) to be in another state when the limb develops and grows. For example, limb data 264 can include data specifying a limb as being in a “non-supportive” state (i.e., the limb size and structure may be identified as being less likely to support growth of one or more apples to harvest). In this state, an agricultural treatment delivery system may be configured to apply a treatment, such as a growth hormone, to promote growth of the limb into, for example, a “supportive” state to facilitate growth of apples. Physical data 264b may describe any attribute or characteristic of an agricultural object identified as a limb. For example, physical data 264b may include a shape, size, color, orientation, anomaly, or the like, including image data, or any characteristic that may be associated with a limb as an agricultural object.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Rosat in view of Koselka by ranking in different classes that is taught by Sibley; thus, one of ordinary skilled in the art would be motivated to combine the references to reduce costs of produced resources (Sibley ¶3).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 4, Rosat in view of Koselka in view of Sibley discloses the method of claim 1, wherein the giving the two or more canes respectively different ranks includes: if there are two or mores canes that are classified into a same class among the plurality of classes regarding each of the one or more attributes, making a relative evaluation of the canes; and based on the classes classified in the classification and results of the relative evaluation, giving the two or more canes respectively different ranks (Sibley, ¶82 Limb data 264 may include status data 264a that may identify or classify a limb (e.g., a branch, a shoot, etc.) as being in a particular state at one point in time, which may be determined (at another point in time) to be in another state when the limb develops and grows. For example, limb data 264 can include data specifying a limb as being in a “non-supportive” state (i.e., the limb size and structure may be identified as being less likely to support growth of one or more apples to harvest). In this state, an agricultural treatment delivery system may be configured to apply a treatment, such as a growth hormone, to promote growth of the limb into, for example, a “supportive” state to facilitate growth of apples. Physical data 264b may describe any attribute or characteristic of an agricultural object identified as a limb. For example, physical data 264b may include a shape, size, color, orientation, anomaly, or the like, including image data, or any characteristic that may be associated with a limb as an agricultural object.)
The reasons for combining Rosat, Koselka, and Sibley are similar to that stated in the rejection of claim 1.
Allowable Subject Matter
Claims 5-11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 5, no prior art teaches wherein the determining the two or more canes each as a cane to be removed or a cane to be retained includes: regarding each of the one or more attributes, assigning one of a plurality of scores corresponding to the plurality of classes to each of the two or more canes; for each of the two or more canes, regarding each of the one or more attributes, calculating a factor score by multiplying the score with a coefficient that is in accordance with the result of the relative evaluation; and based on the factor score regarding each of the one or more attributes, determining the two or more canes each as a cane to be removed or a cane to be retained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUSTIN BILODEAU whose telephone number is (571)272-1032. The examiner can normally be reached 9am-5pm.
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/DUSTIN BILODEAU/Examiner, Art Unit 2664
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