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 .
DETAILED ACTION
Claims 1 – 20 are pending in this application. Claims 1, 11 and 16 are independent.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. § 112 (b):
(B) CONCLUSION – The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of pre-AIA 35 U.S.C. 112, second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 – 7, 12 – 13 and 17 – 18 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 2 – 3, 7, 12 and 17, the claim language recites: "…frequency data…". Although recited in the said claim(s), Inventor(s) (or (pre-AlA) Applicant(s)) fails to explicitly define and describe, within the written description of the specification as originally filed, the claimed subject matter other than merely reciting it within Applicant’s disclosure. In other words, it is unclear what the claimed frequency data actually is or what type of data it represents when, for example, the first hyperspectral image is converted or when a band-pass filter is applied to it. Inventor(s) (or (pre-AlA) Applicant(s)) is therefore required to provide sufficient explanation regarding the claimed "… frequency data …" with respect to the said claims.
Appropriate action is required.
Regarding claims 4 – 7, 13 and 18, the claim language recites: "…spectral band factors…". Although recited in the said claim(s), Inventor(s) (or (pre-AlA) Applicant(s)) fails to explicitly define and describe, within the written description of the specification as originally filed, the claimed subject matter other than merely reciting it within Applicant’s disclosure. In other words, it is unclear what the claimed spectral band factors actually is or what type of data they represent when added to a three-dimensional tensor. Inventor(s) (or (pre-AlA) Applicant(s)) is therefore required to provide sufficient explanation regarding the claimed "…spectral band factors…" with respect to the said claims.
Appropriate action 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 of this title, 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.
Claim(s) 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bongartz, Timo (US-20190259108-A1, hereinafter simply referred to as Timo) in view of Coen, Lior (US-20220307971-A1, hereinafter simply referred to as Lior).
Regarding independent claim(s) 1, 11 and 16, Timo teaches:
A system for variably predicting vase life of cut flowers (e.g., A product (cut flowers) of Timo) (See at least Timo, ¶ [0501]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…"), the system comprising: a photographing device (e.g., e.g. cameras, including thermal and hyperspectral cameras of Timo) configured to photograph the cut flowers and provide a thermal image and a hyperspectral image of the cut flowers (e.g., A product (cut flowers) of Timo) (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…"); and a prediction device configured to discriminate a cut flower variety on the basis of the thermal image and the hyperspectral image (See at least Timo, ¶ [0661, 1242]; FIGS. 11, 15; "…sensors are able to detect flowers and/or buds of plants…identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…").
Timo teaches the subject matter of the claimed inventive concept as expressed in the rejections above.
But, Timo does not expressly disclose the concept of setting weights of lifespan impact factors affecting the vase life according to the cut flower variety, and setting up an artificial intelligence model optimized for the cut flower variety, so as to predict a disease and the vase life of the cut flowers.
Nevertheless, Lior teaches the concept of setting weights (e.g., weights pre-trained on a classification task of Lior) of lifespan impact factors affecting the vase life according to the cut flower variety (e.g., images of various plants or plant parts may be preprocessed and registered as on the training phase of Lior) (See at least Tior, ¶ [0256]; FIGS. 1 – 4, 7; "…The middle panel of FIG. 7 shows the ROI of the pictures taken by each of the imaging sensors. The images of the ROI were analyzed by making use of a deep neural network (ResNet50), with weights pre-trained on a classification task from the ImageNet database…"), and setting up an artificial intelligence model optimized for the cut flower variety, so as to predict a disease and the vase life of the cut flowers (See at least Tior, ¶ [0128, 0138, 0156]; FIGS. 1 – 4, 7; "…features and annotations may then be used for training an artificial intelligence engine, such as a neural network, a deep neural network…", "…the disclosure provides a system for automatic determination of phenotypes of plants, such as but not limited to: yield components, yield prediction, properties of the plants, biotic stress, a-biotic stress, harvest time and any combination thereof…", "…a thermal camera for measuring the temperature of plant parts may provide indication due to the presence of diseases…").
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use and apply the known technique of setting weights of lifespan impact factors affecting the vase life according to the cut flower variety, and setting up an artificial intelligence model optimized for the cut flower variety, so as to predict a disease and the vase life of the cut flowers as disclosed in the device of Lior to modify and improve the known and similar device of Timo for the desirable and advantageous purpose of providing for unified data enabling extracting the features in a highly accurate, reproducible manner, as discussed in Lior (See ¶ [0035]); thereby, achieving the predictable result of improving the overall efficiency and speed of the system with a reasonable expectation of success while enabling others skilled in the art to best utilize the invention along with various implementations and modifications as are suited to the particular use contemplated.
Regarding dependent claim(s) 2, 12 and 17, Timo modified by Lior above teaches:
wherein the photographing device comprises: a thermal imaging camera for generating the thermal image (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Tior, ¶ [0016, 0128, 0138, 0156]; FIGS. 1 – 4, 7); and a hyperspectral camera for generating the hyperspectral image comprising RGB information comprising red, green, and blue, infrared information, and ultraviolet information, and the prediction device obtains a first hyperspectral image for a first variety sensed by the hyperspectral camera among a plurality of plant varieties (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Tior, ¶ [0016, 0128, 0138, 0156]; FIGS. 1 – 4, 7), converts the first hyperspectral image into frequency data in frequency bands, applies a first band-pass filter corresponding to the first variety to the frequency data (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Tior, ¶ [0016, 0128, 0138, 0156]; FIGS. 1 – 4, 7), and performs post-processing on feature values representing features for each band in the first hyperspectral image (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Tior, ¶ [0016, 0128, 0138, 0156]; FIGS. 1 – 4, 7).
Regarding dependent claim 3, Timo modified by Lior above teaches:
wherein the prediction device generates a first feature map by inputting the frequency data to a first artificial intelligence model comprised in a first learning model for processing the thermal image (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), and outputs a probability value for a temperature of the cut flowers by inputting the first feature map into a second artificial intelligence model comprising a plurality of layers (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7).
Regarding dependent claims 4, 13 and 18, Timo modified by Lior above teaches:
wherein the prediction device adds spectral band factors reflected with the features of the spectral bands to a three-dimensional tensor comprising two-dimensional coordinates and signal intensity, so as to generate a four-dimensional tensor comprising the two-dimensional coordinates (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), the signal intensity, and the spectral band factors, and performs preprocessing on the hyperspectral image on the basis of the four-dimensional tensor (See at least Timo, ¶ [0661, 1242]; FIGS. 11, 15; "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7).
Regarding dependent claim 5, Timo modified by Lior above teaches:
wherein the prediction device learns the four-dimensional tensor generated for the thermal image by using a network function (See at least Timo, ¶ [0661, 1242]; FIGS. 11, 15; "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7).
Regarding dependent claim 6, Timo modified by Lior above teaches:
wherein the prediction device learns the three-dimensional tensor generated for the thermal image by using a first network (See at least Timo, ¶ [0661, 1242]; FIGS. 11, 15; "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), and relearns the four-dimensional tensor, in which weights corresponding to the spectral band factors are given to the three-dimensional tensor, by using the first network (See at least Timo, ¶ [0661, 1242]; FIGS. 11, 15; "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7).
Regarding dependent claim 7, Timo modified by Lior above teaches:
wherein the prediction device performs first learning of the three-dimensional tensor generated for the thermal image by using the first network (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), performs second learning of a one-dimensional tensor comprising the spectral band factors by using the first network (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), generates a latent vector (or z) by converting respective results of the first and second learning into a latent space, and restores the thermal image and an image for the frequency data by decoding the latent vector (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7).
Regarding dependent claims 8, 14 and 19, Timo modified by Lior above teaches:
wherein the photographing device first generates the hyperspectral image and then generates the thermal image (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), and the prediction device generates a bounding box for a specific spot of the cut flowers comprised in the hyperspectral image and the thermal image (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), extracts features of the cut flowers within the bounding box (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), determines an abnormal state of the cut flowers on the basis of the cut flower variety and the features of the cut flowers (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), and outputs a lifespan prediction value for the vase life of the cut flowers on the basis of the abnormal state of the cut flowers and the lifespan impact factors (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7).
Regarding dependent claims 9, 15 and 20, Timo modified by Lior above teaches:
wherein the prediction device obtains training images of the cut flowers (See at least Timo, ¶ [0501, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), designates cut flower quality factors for quality factors to the cut flowers (See at least Timo, ¶ [0661, 1242]; FIGS. 11, 15; "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), learns the training images in order to detect objects for the cut flower variety and each of the quality factors of the cut flowers (See at least Timo, ¶ [0501, 0661, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), extracts object detection item scores to indicate items of detecting the objects as scores on the basis of the results of the learning (See at least Timo, ¶ [0501, 0661, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), and creates a cut flower lifespan prediction model for predicting the vase life of the cut flowers on the basis of the object detection item scores (See at least Timo, ¶ [0501, 0661, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7).
Regarding dependent claim 10, Timo modified by Lior above teaches:
wherein the prediction device obtains new images of the cut flowers (See at least Timo, ¶ [0501, 0661, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), performs preprocessing on data of the new images, detects the cut flower quality factors of the cut flowers on the basis of the preprocessed data (See at least Timo, ¶ [0501, 0661, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), switches the lifespan prediction model with another on the basis of the cut flower quality factors (See at least Timo, ¶ [0501, 0661, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7), and predicts the vase life of the cut flowers by using the lifespan prediction model (See at least Timo, ¶ [0501, 0661, 1242]; FIGS. 11, 15; "…analysis can be performed using methods connected to artificial intelligence (AI), such as deep learning to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation. The analysis may also include the plant state…", "…sensors are able to detect flowers and/or buds of plants… identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device…", "…the controlled agricultural system may also comprise sensors to detect the growth state of the plants, e.g. cameras, including thermal and hyperspectral cameras…" Also, see at least Timo, ¶ [0146, 0398, 0413, 0561, 0764] & at least Tior, ¶ [0016, 0128, 0138, 0155, 0156]; FIGS. 1 – 4, 7).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure: See the Notice of References Cited (PTO–892)
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/IDOWU O OSIFADE/Primary Examiner, Art Unit 2675