DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-16 are pending.
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 Claim(s) 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 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more Claim(s) particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 5 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 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 pre-AIA the applicant regards as the invention.
Claim 5 recites limitation “The method of claim 1, … the one or more attributes”. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1, 6, 11 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Picon Ruiz et al (US2022/0327815).
Regarding claim 1, Picon Ruiz teaches a method for identification of weeds in images, the method comprising:
receiving one or more images of an area of interest (AOI) being captured by one or more mobile devices (104) associated with one or more registered users (106), and a corresponding location of the AOI;
(Picon Ruiz, Figs. 1-2, s1100, “An image recording device 90 (e.g., a digital camera capable of recording high resolution pictures with a resolution in the range of 1024 up-to 10000 px) takes an image of section 1 and provides the image as a test input image 91 to the computer system 100”, [0043], AOI: “identify the species and the location of plants which grow between crop plants in a section 1 of an agricultural field (freeland or greenhouse)”, [0043]; Fig. 1, the image recording device 90 may be a smartphone ([0107]); the user of the smartphone may be a registered user so that he/she can access the crop/weed identification system in computer system 100)
identifying one or more weeds in the received one or more images, and training a computing unit (102) with the identified one or more weeds;
(Picon Ruiz, identifying weeds via DTCNN segmentation (identifying one or more weeds/species in images) and training the neural network (computing unit) with annotated training images including identified weed; training with datasets of manually/automatically annotated images identifying weeds, “the dual task CNN modules are jointly trained based on an image training data set which includes a combination of two training data subsets with one subset including manually annotated training images and the other subset including automatically annotated training images”, [0020]; “Before applying DTCNN 120 to a test input, the network gets trained with images of a training dataset whereby the intermediate module 121 and the segmentation module 122 are trained together”, [0049]; “An original image 41 showing a real-world situation in an agricultural field with crop plants of a crop species and weed plants of one or more weed species amongst the crop plants is provided to a human user for manual annotation”, [0068]; obviously, those images with crop and weed annotations are the training dataset for training the DTCNN)
extracting one or more details pertaining to the identified one or more weeds based on the determined location of the AOI and the associated weeds, and
(Picon Ruiz, Fig. 2, “s1700, generating a mask for each weed species class as segmentation output’, identifying/segmenting weeds in different locations/tiles of the image; “the output is the information about which plant species are present on a particular image showing, for example, crop and weed plants”, [0012]), “s1800, combining the generated masks into a final image”, “A final post-process interprets and combines those masks to reconstruct all tiles into the final segmented image”, [0047])
transmitting a first set of data packets to the one or more first mobile devices (104).
(Picon Ruiz, Fig. 1, the image recording device 90 may be a smartphone associated with a user; it may transmit the captured image 91 to a server with a request for crop/weed identification because device 90 and computer system 100 may have a client-server relationship to each other, [0125]; obviously, the final segmented image processed in the computer system server 100 may be transmitted back to user’s smartphone in response to the request from the smartphone; “an image reconstruction module 140 is used to reconstruct the processed tiles at the end into a full-blown segmented image 92 which is output to the user (e.g. a farmer)”, [0044])
Regarding claim 6, Picon Ruiz teaches its/their respective base claim(s).
Picon Ruiz further teaches the method of claim 1, further comprising enabling the one or more registered users (106) to access and select, using the one or more first mobile devices (104), at least one of the images for identification of the one or more weeds from the selected images and corresponding one or more details.
(Picon Ruiz, “An original image 41 showing a real-world situation in an agricultural field with crop plants of a crop species and weed plants of one or more weed species amongst the crop plants is provided to a human user for manual annotation”, [0068])
Regarding claim 11, Picon Ruiz teaches a system (100) for identifying weeds in images, the system (100) comprising:
one or more first mobile devices (104) associated with one or more registered users (106);
a computing unit (102) in communication with the one or more first mobile devices (104), the computing unit (102) comprising one or more processors (302) coupled with a memory (304),
(Picon Ruiz, Fig. 1, the image recording device 90 may be a smartphone associated with a user (farmer); it may transmit the captured image 91 to a server with a request for crop/weed identification because device 90 and computer system 100 may have a client-server relationship to each other, [0125]; Fig. 5, computer device 900, processor 902, memory 904, [0108])
wherein the computing unit (102) is configured to receive one or more images and location of an area of interest (AOI) from one or more devices (104); and
(Picon Ruiz, see comments on claim 1; Fig. 1, the image recording device 90 may be a smartphone such as a “Samsung A8 mobile phone”, [0072]; it may be a client to the computer system 100 (may be a server), [0125]; the image captured by the image recording device 90 and sent to server computer system 100 may be embedded with location information (geotagging), “GPS”, [0118])
identify one or more weeds in the received one or more images, and correspondingly train for upcoming weed identification,
wherein the computing unit (102) extracts one or more details pertaining to the identified one or more weeds based on the determined location of the AOI and the associated weeds, and correspondingly transmits a first set of data packets to the one or more first mobile devices (104).
(Picon Ruiz, see comments on claim 1)
Regarding claim 16, Picon Ruiz teaches its/their respective base claim(s).
Picon Ruiz further teaches the system (100) of claim 11, wherein the computing unit (102) is in communication with one or more second mobile devices (108) associated with the one or more registered sellers (110).
(Picon Ruiz, see comments on claim 1; the user of the smartphone 982 of Fig. 5 may be a farmer, “an image reconstruction module 140 is used to reconstruct the processed tiles at the end into a full-blown segmented image 92 which is output to the user (e.g. a farmer)”, [0044]; a farmer can be a seller)
Allowable Subject Matter
Claim(s) 2-4, 7-10 and 12-15 is/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 Claim(s).
The following is a statement of reasons for the indication of allowable subject matter:
Claim(s) 2-4, 7-10 and 12-15 recite(s) the limitation(s) to which no explicit teachings are found in the prior art cited in this
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on (571)272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 10/2/2025