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 .
Response to Arguments
Applicant's arguments filed on 11/13/2025 with respect to the Double Patenting rejection have been fully considered but they are not persuasive.
Applicant argues on pg. 10 of Remarks filed on 11/13/2025, “The claims in this application contain elements not recited in the claims of U.S. Patent No. 11,287,411 and vice versa. For example, the claims above recite that training sensor data captured for a first plurality of plants includes sensor data captured for plants that are healthy and sensor data captured for plants that are unhealthy "as identified by the at least one human expert." This is not recited in the claims of U.S. Patent No. 11,287,411 - those claims do not recite that it is at least one human expert who determines which plants are healthy and which plants are unhealthy”.
Examiner disagrees. As mapped out on page 5 the Non-Final Office Action filed on 5/20/2025, US Pat. No. 11,287,411 clearly teaches a human expert identifying captured plant training sensor data as being either healthy plant or unhealthy plants (being “plants that are exhibiting the one or more particular problems”, as highlighted again below:
Instant Application Claims
US Pat. No. 11,287,411 Claims
Claim 21
…
wherein the training sensor data captured for the first plurality of plants includes sensor data captured for plants that are healthy and sensor data captured for plants that are unhealthy as identified by the at least one human expert.
Claim 1
…
wherein the first plurality of plants comprises plants that are healthy and plants that are exhibiting one or more particular problems, the human expert assessment based on visual inspection of the plants that are healthy and of the plants that are exhibiting the one or more particular problems, wherein the training sensor data is captured for each of the plants that are healthy and for each of the plants that are exhibiting the one or more particular problems; and
…
On page 11 of the Remarks filed on 11/13/2025, Applicant further argues “As another example, the claims of U.S. Patent No. 11,287,411 recite that supervised learning includes establishing statistical relationships between observed data that includes training sensor data with truth data that includes human expert assessment. The claims above do not include such recitations.”
Examiner finds this unpersuasive. Applicant appears to be referring to the additional limitation in US Pat. No. 11,287,411 of, “wherein the supervised learning comprises establishing statistical relationships between observed data that includes the training sensor data with truth data that includes the human expert assessment”. While it is true that this limitation is not present in the independent claims of the instant application, this further and strengthens Examiner’s basis for the double patent rejection insofar that the cited independent claims of the instant application are simply a broadened version of in US Pat. No. 11,287,411.
For the reasons above, the non-statutory double patenting rejection is maintained.
Applicant’s arguments filed on 11/13/2025, with respect to the prior art rejection have been fully considered and are persuasive. The 35 U.S.C. §103 rejection of claims 21-24, 28-34 and 38-40 has been withdrawn.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-24, 28-30, 31-34 and 38-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 5, 7 and 21 of U.S. Patent No. 11,287,411. Claims 21-24, 28-30, 31-34 and 38-40 of the instant application are broadened versions of the corresponding claims in the Patent and thus overlapping in scope and anticipated by the Patent. The non-statutory double patenting anticipatory analysis is demonstrated below between claims 21-24 and 28-30 of the instant application vis-à-vis claims 1, 3, 5, 7 and 21 of the Patent, wherein identical or equivalent claim language in claim limitations within the instant application and the Patent are highlighted in bold, whereas different or absent claim language between within the instant application and the Patent are italicized.
Instant Application Claims
US Pat. No. 11,287,411 Claims
Claim 21
A method comprising:
in a training phase:
receiving a human expert assessment of a state of each plant of a first plurality of plants based on visual inspection of the first plurality of plants by at least one human expert;
receiving training sensor data captured for each plant of the first plurality of plants; and
correlating the human expert assessment with the training sensor data using machine learning to generate a model; and
in an assessment phase:
receiving assessment sensor data captured for each plant of a second plurality of plants;
classifying a state of each plant of the second plurality of plants by applying the model to the assessment sensor data; and
transmitting information relating to the state of each plant of the second plurality of plants to at least one end-user device;
wherein the training sensor data captured for the first plurality of plants includes sensor data captured for plants that are healthy and sensor data captured for plants that are unhealthy as identified by the at least one human expert.
Claim 1
A method for assessing a state of plants in a crop, the
method comprising:
in a training phase:
receiving a human expert assessment of a state of each plant of a first plurality of plants, wherein the human expert assessment is based on visual inspection of each plant of the first plurality of plants by a human expert;
receiving training sensor data captured for each plant of the first plurality of plants, the training sensor data related to at least one plant-related parameter, the training sensor data comprising sensor data from a device other than an illumination-based sensor; and
correlating the human expert assessment with the training sensor data using supervised learning to generate a set of trained data and a data-derived model based on the set of trained data; and
in an assessment phase:
receiving crop assessment sensor data captured for each plant of a second plurality of plants in the crop, the crop assessment sensor data related to at least one plant-related parameter, at least part of the crop assessment sensor data comprising sensor data from at least one device other than an illumination-based sensor;
classifying a state of each plant of the second plurality of plants by applying the data-derived model to the crop assessment sensor data; and
transmitting information relating to the state of each plant of the second plurality of plants to at least one end-user device;
wherein the first plurality of plants comprises plants that are healthy and plants that are exhibiting one or more particular problems, the human expert assessment based on visual inspection of the plants that are healthy and of the plants that are exhibiting the one or more particular problems, wherein the training sensor data is captured for each of the plants that are healthy and for each of the plants that are exhibiting the one or more particular problems; and
wherein the supervised learning comprises establishing statistical relationships between observed data that includes the training sensor data with truth data that includes the human expert assessment.
Claim 22
The method of Claim 21, wherein the human expert assessment comprises, for each plant of the first plurality of plants, at least one of:
an indication that the plant appears healthy; or
a ranking of a level, from among a plurality of levels, that the plant is suffering from each of multiple problems.
Claim 21
The method of claim 1, wherein the human expert assessment comprises, for each plant of the first plurality of plants,
a human-assessed ranking of a level, from among a plurality of levels, that the plant is suffering from each of multiple problems.
Claim 23
The method of Claim 22, wherein the indication or ranking is obtained using a software program or application executed by at least one mobile electronic device used by the at least one human expert.
Claim 1
…
wherein the first plurality of plants comprises plants that are healthy and plants that are exhibiting one or more particular problems, the human expert assessment based on visual inspection of the plants that are healthy and of the plants that are exhibiting the one or more particular problems; …
Claim 3
The method of claim 1, wherein, in the training phase, the human expert assessment and the training sensor data are captured and transmitted by a hand-held sensory device operated by the human expert.
Claim 24
The method of Claim 23, wherein the software program or application is configured to receive a custom assessment associated with positive or negative plant attributes selected by the at least one human expert.
Claim 1
…
receiving a human expert assessment of a state of each plant of a first plurality of plants, wherein the human expert assessment is based on visual inspection of each plant of the first plurality of plants by a human expert;…
wherein the first plurality of plants comprises plants that are healthy and plants that are exhibiting one or more particular problems, the human expert assessment based on visual inspection of the plants that are healthy and of the plants that are exhibiting the one or more particular problems;…
Claim 28
The method of Claim 21, further comprising:
receiving an additional human expert assessment of a state of each plant of a third plurality of plants and additional training sensor data captured for each plant of the third plurality of plants; and
correlating the additional human expert assessment with the additional training sensor data using machine learning to update or enhance the model.
Claim 7
The method of claim 1, wherein:
the first plurality of plants comprises first, second, and third groups of plants; and
receiving the human expert assessment of the state of each plant of the first plurality of plants comprises:
receiving an assessment by a first human expert of a state of each plant of the first group of plants;
receiving an assessment by a second human expert of: a state of each plant of the second group of plants; and
receiving an assessment by a third human expert of a
state of each plant of the third group of plants.
Claim 29
The method of Claim 21, wherein the sensor data captured for the plants of the first plurality of plants that are unhealthy comprises sensor data captured for plants that are identified by the at least one human expert as suffering from a particular pest, disease, or condition.
Claim 1
…
wherein the first plurality of plants comprises plants that are healthy and plants that are exhibiting one or more particular problems, the human expert assessment based on visual inspection of the plants that are healthy and of the plants that are exhibiting the one or more particular problems, wherein the training sensor data is captured for each of the plants that are healthy and for each of the plants that are exhibiting the one or more particular problems;…
Claim 30
The method of Claim 21, wherein the assessment sensor data is received from at least one mobile sensory platform each configured to place one or more sensors on or proximate to individual plants of the second plurality of plants.
Claim 5
The method of claim 1, wherein, in the assessment
phase, at least some of the crop assessment sensor data is captured and transmitted by a mobile sensory platform comprising at least one non-imaging sensor configured to be positioned proximate to each plant of the second plurality of plants during capture of the crop assessment sensor data for that plant.
Instant claims 31-34 and 38-40 are substantially similar in scope and spirit as instant claims 21-24 and 28-30. Therefore, the double patenting rejection with claims 1, 3, 5, 7 and 21 of U.S. Patent No. 11,287,411 is applied accordingly.
Allowable Subject Matter
Claims 25-27 and 35-37 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.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN CHEN whose telephone number is (571)272-4143. The examiner can normally be reached M-F 10-7.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALAN CHEN/Primary Examiner, Art Unit 2125