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 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 35 U.S.C. 112 (pre-AIA ), 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 2-3, 5-6 and 11 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation "the presence of harmful organism " in line 7. There is insufficient antecedent basis for this limitation in the claim. The same rejection applies to additional instances of the limitation.
Claim 3 recites the limitation "the presence of harmful organism " in line 7. There is insufficient antecedent basis for this limitation in the claim. The same rejection applies to additional instances of the limitation.
Claims 5-6 recite the limitation "the control data" in line 2 and 3, respectively. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation "the sub-field zone" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-6 and 8-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Peters (WO 2020201160).
Regarding claim 1, Peters discloses a method for modifying a treatment performance for treating an agricultural field by an agricultural machine, wherein the method comprises a modification function (Page 20, Machine learning unit, parametrization interface) and the agricultural machine comprises at least one treatment component (200), the method comprising:
- obtaining field data (Page 20, paragraphs 2-4, data is generated through sensors and images);
- determining a treatment performance by analyzing the field data (Page 21, paragraph 2, a machine learning analyzes whether weeds, insects and other unexpected items are in an area, relative to field data);
- providing a treatment performance modification via the modification function and a representation parameter (Page 21, Paragraph 3, treatment is adjusted based upon detected images and a signal is generated to make the treatment adjustment); and
- modifying the treatment performance with the treatment performance modification (Page 21, paragraphs 3-4, the updated treatment is applied).
Regarding claim 2, Peters discloses a method for modifying a treatment performance for treating an agricultural field by an agricultural machine, wherein the method comprises a modification function (Page 20, Machine learning unit, parametrization interface) and the agricultural machine comprises at least one treatment component (200), the method comprising:
- obtaining field data of the agricultural field in real-time while the agricultural machine moves through the agricultural field, wherein the field data at least comprises information about the presence of harmful organism on the field (Page 20, paragraphs 2-4, data is generated through sensors and images, which includes data on insects, pathogens, weeds);
- determining a treatment performance by analyzing the field data in real-time while the agricultural machine moves through the agricultural field (Page 21, paragraph 2, a machine learning analyzes whether weeds, insects and other unexpected items are in an area, relative to field data);
- providing a treatment performance modification via the modification function and a representation parameter in real-time while the agricultural machine moves through the agricultural field (Page 21, Paragraph 3, treatment is adjusted based upon detected images, and a signal is generated to make the treatment adjustment); and
- modifying the treatment performance with the treatment performance modification in real-time while the agricultural machine moves through the agricultural field (Page 21, paragraphs 3-4, the updated treatment is applied).
Regarding claim 3, Peters discloses a method for modifying a treatment performance for treating an agricultural field by an agricultural machine, wherein the method comprises a modification function (Page 20, Machine learning unit, parametrization interface) and the agricultural machine comprises at least one treatment component (200), the method comprising:
- obtaining field data of a sub-field zone (Page 11, paragraph 1 and Page 23, paragraph 1) of the agricultural field of the agricultural field in real-time while the agricultural machine moves through the agricultural field, wherein the field data at least comprises information about the presence of harmful organism on the field (Page 20, paragraphs 2-4, data is generated through sensors and images, which includes data on insects, pathogens, weeds);
- determining a treatment performance of the sub-field zone (Utilizing the data of the particular zone, as highlighted, the efforts of a particular zone are evaluated) by analyzing the field data in real-time while the agricultural machine moves through the agricultural field sub-zone) (Page 21, paragraph 2, a machine learning analyzes whether weeds, insects and other unexpected items are in an area, relative to field data);
- providing a treatment performance modification via the modification function and a representation parameter in real-time while the agricultural machine moves through the sub-field zone (Page 21, Paragraph 3, treatment is adjusted based upon detected images, and a signal is generated to make the treatment adjustment); and
- modifying the treatment performance for the sub-field zone with the treatment performance modification in real-time while the agricultural machine moves through the sub-field zone (Page 21, paragraphs 3-4, the updated treatment is applied).
Regarding claim 4, Peters discloses the method according to claim 1, further comprising generating control data based on the modified treatment performance (Page 22, paragraph 1, validation data of the modified treatment is generated, to control future application/treatment).
Regarding claim 5, Peters discloses the method according to claim 1, wherein the control data relates to a specific on/off operation of at least one treatment of the agricultural machines (Page 22, paragraph 1, the validation data relates to the effectiveness of a particular component being on or off in a particular location of the agricultural machine).
Regarding claim 6, Peters discloses the method according to claim 1, further comprising controlling the agricultural machines and/or at least one treatment component based on the control data (Page 22, paragraph 1, The validation data is provided to the field manager system/machine learning unit, and the parametrization is adjusted based on the validation data).
Regarding claim 8, Peters discloses the method according to claim 1, wherein the determined treatment performance is based on at least one threshold level (Page 21, paragraph 2, biomass weed percentage is used to determine whether an area is properly treated).
Regarding claim 9, Peters discloses the method according to claim 1, wherein a form of the representation parameter is inputted via an HMI device (Page 18, paragraph 4)
Regarding claim 10, Peters discloses the method according to claim 9, wherein the treatment performance is outputted via the HMI device before the form of a representation parameter is inputted via the HMI device (Page 21, paragraph 1, Page 22, paragraph 1).
Regarding claim 11, Peters discloses the method according to claim 1, wherein the method further comprises calculating a difference between the planned treatment performance for the sub- field zone and the determined treatment performance for the sub-field zone (Page 21, paragraph 2, and Page 11, paragraph 1 and Page 23, paragraph 1), and based on this calculated difference or depending on whether this calculated difference exceeds a predetermined treatment performance difference threshold value, generating an adjustment signal to the HMI device (Page 21, paragraph 2), and wherein modifying the treatment performance comprises:
modifying the treatment performance for the sub-field zone with the treatment performance modification in real-time while the agricultural machine moves through the sub-field zone based on the adjustment signal (Page 21, paragraph 2).
Regarding claim 12, Peters discloses the method according to claim 1, wherein obtaining field data and modifying the treatment performance is conducted in real-time (Page 2, paragraph 4).
Regarding claim 13, Peters discloses a non-transitory computer readable medium having instructions encoded thereon that, when executed by a computing device, cause the computing device to perform the method of claim 1 (Page 18, paragraph 4 and claim 1).
Regarding claim 14, Peters discloses a control system for an agricultural machine, which, when receiving control data, causes the agricultural machine to perform the method according to claim 1 (Page 4, paragraph 3, page 18, paragraph 4 and claim 1).
Regarding claim 15, Peters discloses an agricultural machine comprising the control system according to claim 14 (Page 4, paragraph 3, page 18, paragraph 4 and claim 1).
Claim Rejections - 35 USC § 103
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.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Peters in view of Wintemute (US 2018/0255696).
Regarding claim 7, Peters discloses the method according to claim 1, but fails to disclose the method including obtaining identity data connected with a farmer of the agricultural field from a database; and storing or updating the identity data of the database with the treatment performance modification.
Wintemute discloses an agricultural data tracking method that includes obtaining identity data connected with a farmer of the agricultural field from a database and storing or updating the identity data of the database with use data (Paragraphs 48-50 and 51, use data is tracked in a database using a Farmer ID, which includes specific user identity data).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Peters with the disclosures of Wintemute, providing the method including obtaining identity data connected with a farmer of the agricultural field from a database; and storing or updating the identity data of the database (Wintemute, Paragraphs 48-50 and 51, use data is tracked in a database using a Farmer ID, which includes specific user identity data) with the treatment performance modification (Peters, Page 22, paragraph 1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER R. DANDRIDGE whose telephone number is (571)270-1505. The examiner can normally be reached M-T 9am-7pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arthur O. Hall can be reached at (571)270-1814. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHRISTOPHER R. DANDRIDGE
Primary Examiner
Art Unit 3752
/CHRISTOPHER R DANDRIDGE/Primary Examiner, Art Unit 3752