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
The action is in response to the Applicant’s communication filed on 10/16/2025.
Claims 1-12 are pending, where claims 1 and 10 are independent.
The specification objection has been withdrawn because the arguments and amendment overcome the specification objections.
The claim interpretation has been withdrawn because the arguments overcome the rejection.
The claim rejection under 112 set forth in the Non-Final Office Action is maintained because the arguments are not persuasive and amended claims.
Response to Arguments
Applicant's arguments filed on 10/16/2025 have been fully considered but they are not persuasive.
As to pages 10-12, applicant argues Allen does not teach or suggest "extra-atmospheric solar irradiance" as recited in claims 1 and 10.
Examiner respectfully disagrees because (Allen [0108-176] “irrigation system 200 comprises a monitoring and control server 201 connected to an irrigation actuator 202 and to a receiver 203 for wide-area meteorological prediction data - sensor network 204 comprising multiple sensors - to collect local-area sensor data - agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - control the sub-areas individually based on the calculated prediction specific to that sub-area - include protected cropping areas such as greenhouses, crops under nets, or other forms of protection - correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects how the conditions inside a greenhouse change for different weather outside - how the greenhouse warms up when there is sunshine outside - suggest some control measures at particular times of day - predicts that solar irradiation - numerical weather prediction model a gridded model output of parameters such as - solar radiation flux, - collected by sensors measuring the same variables, or easily comparable variables at the point of interest - determining the correlation between the historical wide-area meteorological prediction data and the historical local-area sensor data in order to create local area predictions - machine learning approach using neural networks - historical predicted wind speed from the meteorological prediction data - correlation between the historical meteorological prediction data and the historical sensor data - 10 parameters measured by the local-area sensors 205 including temperature, relative humidity, wind, rain, leaf wetness, solar irradiance, photosynthetic active radiation, frost detection, soil moisture and soil temperature” [abstract] see Fig. 1-11, monitoring and control server, predicts solar irradiance, meteorological prediction data including temperature, relative humidity, wind, rain, leaf wetness, solar irradiance; greenhouse, machine learning approach using neural networks, agricultural production area, multiple sub-areas, plurality of sensor located in each sub-area, processor determines correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects, greenhouse warms up at sunshine outside, predicts solar irradiation obviously provides extra-atmospheric solar irradiance calculating unit for calculating extra-atmospheric solar irradiance, where plurality of meteorological prediction data including solar irradiance as extra-atmospheric) teaches the limitations under 103 obviousness rejection. Therefore, Applicant’s arguments are not persuasive.
As to pages 8-9, applicant argues that “the term "quality" in the context of "quality of the crop" is broad but not indefinite”, as recited in the claim 7. In remarks page 9, applicant acknowledges the term “quality” is a broad term, as merely indefinite. In remarks page 8, applicant also acknowledges “Supreme Court has interpreted this to require that claims - about the scope of the invention with reasonable certainty".
Examiner respectfully disagrees because term "quality" has not been clearly or reasonable defined in the specification. It is a relative term and specification lacked standard for measuring the degrees intended. Thereby, it is rendering the scope of the claim unascertainable. See MPEP 2173.05b; “For example, in Ex parte Oetiker, 23 USPQ2d 1641 (Bd. Pat. App. & Inter. 1992), the phrases "relatively shallow," "of the order of," "the order of about 5mm," and "substantial portion" were held to be indefinite because the specification lacked some standard for measuring the degrees intended. The claim is not indefinite if the specification provides examples or teachings that can be used to measure a degree even without a precise numerical measurement”. Therefore, Applicant’s arguments are not persuasive.
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.
Claim 7 is rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 7 recites the limitation “quality of the corp”. The term “quality” renders the claim indefinite because the claim(s) include(s) elements not actually disclosed or clearly defined in the specification and it is a broad term, thereby rendering the scope of the claim(s) unascertainable. See MPEP § 2173.05.
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 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.
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.
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.
Claims 1-12 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Allen, et al. (USPGPub No. 20190254242 A1).
As to claims 1 and 10, Allen discloses An information processing device comprising: an extra-atmospheric solar irradiance calculating unit for calculating extra-atmospheric solar irradiance in each time span on each date at an installation location of a greenhouse in which a crop is cultivated (Allen [0108-176] “agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - control the sub-areas individually based on the calculated prediction specific to that sub-area - include protected cropping areas such as greenhouses, crops under nets, or other forms of protection - correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects how the conditions inside a greenhouse change for different weather outside - how the greenhouse warms up when there is sunshine outside - suggest some control measures at particular times of day - predicts that solar irradiation” [abstract] see Fig. 1-11, monitoring and control server, predicts solar irradiance, machine learning approach using neural networks, agricultural production area, multiple sub-areas, plurality of sensor located in each sub-area, processor determines correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects, conditions inside greenhouse change for different weather outside, greenhouse warms up at sunshine outside, suggest some control measures at particular times of day, predicts solar irradiation obviously provides calculating solar irradiance in each time span on each date at an installation location of a greenhouse in which a crop is cultivated);
a machine learning unit for performing machine learning of a greenhouse environment prediction model on the basis of actual result information relating to an environment inside the greenhouse using the extra-atmospheric solar irradiance and meteorological predicted information as inputs; a predicting unit for predicting the environment in the greenhouse using the greenhouse environment prediction model; (Allen [0108-176] “agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - control the sub-areas individually based on the calculated prediction specific to that sub-area - include protected cropping areas such as greenhouses, crops under nets, or other forms of protection - correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects how the conditions inside a greenhouse change for different weather outside - how the greenhouse warms up when there is sunshine outside - suggest some control measures at particular times of day - predicts that solar irradiation - create local area predictions - machine learning approach using neural networks - wind direction, wind speed, relative humidity, temperature, surface solar radiation, soil moisture, etc.” [abstract] see Fig. 1-11, monitoring and control server, predicts solar irradiance, machine learning approach using neural networks, agricultural production area, multiple sub-areas, plurality of sensor located in each sub-area, predicts solar irradiation, machine learning using neural networks obviously provides performing machine learning of a greenhouse environment prediction model on the basis of actual result information relating to an environment inside the greenhouse using the extra-atmospheric solar irradiance and meteorological predicted information as inputs; a predicting unit for predicting the environment in the greenhouse using the greenhouse environment prediction model) and
an output control unit for controlling the output of predicted information relating to the predicted environment inside the greenhouse (Allen [0108-176] “processor 302 store the calculated water supply relative to water demand or generate a user interface displaying the calculated water supply relative to water demand on data store 306 - send the determined values and/or user interface via communication port 308 to a webserver 320 - available to user 316 - agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - control the sub-areas individually based on the calculated prediction specific to that sub-area - include protected cropping areas such as greenhouses, crops under nets, or other forms of protection - correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects how the conditions inside a greenhouse change for different weather outside - how the greenhouse warms up when there is sunshine outside - suggest some control measures at particular times of day - predicts that solar irradiation - create local area predictions - machine learning approach using neural networks - wind direction, wind speed, relative humidity, temperature, surface solar radiation, soil moisture, etc.” [abstract] see Fig. 1-11, agricultural production area, multiple sub-areas, plurality of sensor located in each sub-area, predicts solar irradiation, machine learning using neural networks, processor, calculated water supply relative to water demand or generate a user interface displaying the calculated water supply relative to water demand, send the determined values and/or user interface via communication port to webserver, available to user obviously provides output control unit for controlling the output of predicted information relating to the predicted environment inside the greenhouse).
It would be therefore obvious to one having ordinary skill in the art at the time of the invention that predicts solar irradiation in agricultural production area includes multiple sub-areas, plurality of sensor located in each sub-area, conditions inside greenhouse change, suggest control measures at particular times of day are assumed as calculating extra-atmospheric solar irradiance in each time span on each date at an installation location of a greenhouse.
As to claims 2 and 11, Allen further discloses The information processing device according to claim 1, wherein the extra-atmospheric solar irradiance calculating unit calculates the extra-atmospheric solar irradiance on the basis of a latitude and a longitude obtained from an address of the installation location (Allen [0108-176] “processor 302 receives sensor data from sensor 204 via communications port 308 using a Wireless Sensor Network (WSN) - communications port 308 and user port 310 - network connection - IP sockets - agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - second input set actual sensor data at the location of prediction (i.e. local-area sensor data)” [abstract] see Fig. 1-11, agricultural production area, multiple sub-areas, plurality of sensor located in each sub-area such as regions, farms, paddocks, rows and even individual plants, network connection, WSN, communications port, user port, IP sockets obviously provides solar irradiance on the basis of a latitude and a longitude obtained from an address of the installation location).
As to claim 3, Allen further discloses The information processing device according to claim 1, wherein the information input into the greenhouse environment prediction model includes information relating to the extra-atmospheric solar irradiance at a prediction target time (Allen [0108-176] “agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - control the sub-areas individually based on the calculated prediction specific to that sub-area - include protected cropping areas such as greenhouses, crops under nets, or other forms of protection - correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects how the conditions inside a greenhouse change for different weather outside - how the greenhouse warms up when there is sunshine outside - suggest some control measures at particular times of day - predicts that solar irradiation” [abstract] see Fig. 1-11, agricultural production area, multiple sub-areas, plurality of sensor located in each sub-area, processor determines correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects, conditions inside greenhouse change for different weather outside, greenhouse warms up at sunshine outside, suggest some control measures at particular times of day, predicts solar irradiation obviously provides solar irradiance at a prediction target time).
As to claim 4, Allen further discloses The information processing device according to claim 1, wherein the predicted information includes information relating to at least one of temperature, humidity, solar irradiance, carbon dioxide concentration, soil temperature, and soil moisture content inside the greenhouse (Allen [0108-176] “agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - control the sub-areas individually based on the calculated prediction specific to that sub-area - include protected cropping areas such as greenhouses, crops under nets, or other forms of protection - correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects how the conditions inside a greenhouse change for different weather outside - how the greenhouse warms up when there is sunshine outside - suggest some control measures at particular times of day - predicts that solar irradiation - create local area predictions - machine learning approach using neural networks - wind direction, wind speed, relative humidity, temperature, surface solar radiation, soil moisture, etc.”” [abstract] see Fig. 1-11, agricultural production area, multiple sub-areas, plurality of sensor located in each sub-area, processor determines correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects, conditions inside greenhouse change for different weather outside, greenhouse warms up at sunshine outside, suggest some control measures at particular times of day, predicts solar irradiation, humidity, temperature obviously provides information relating to at least one of temperature, humidity, solar irradiance, carbon dioxide concentration, soil temperature, and soil moisture content inside the greenhouse).
As to claim 5, Allen further discloses The information processing device according to claim 1, wherein the predicted information includes information relating to a pest outbreak (Allen [0108-176] “agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - control the sub-areas individually based on the calculated prediction specific to that sub-area - include protected cropping areas such as greenhouses, crops under nets, or other forms of protection - correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects how the conditions inside a greenhouse change for different weather outside - how the greenhouse warms up when there is sunshine outside - suggest some control measures at particular times of day - predicts that solar irradiation - major concern disease and pest outbreaks as well as managing shoot growth and root growth” [abstract] see Fig. 1-11).
As to claim 6, Allen further discloses The information processing device according to claim 1, wherein the predicted information includes information relating to a weed outbreak (Allen [0108-176] “agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - control the sub-areas individually based on the calculated prediction specific to that sub-area - include protected cropping areas such as greenhouses, crops under nets, or other forms of protection - correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects how the conditions inside a greenhouse change for different weather outside - how the greenhouse warms up when there is sunshine outside - suggest some control measures at particular times of day - predicts that solar irradiation - major concern disease and pest outbreaks as well as managing shoot growth and root growth - one day at 15 C with more than six hours of leaf wetness and black spot a risk - coddling moth outbreak detected - automatically suggest appropriate product as a control of the agricultural production area - reduces the risk to the farmer of losing profit” [abstract] see Fig. 1-11, pest outbreaks, suggest some control measures, managing shoot growth and root growth, coddling moth outbreak detected, automatically suggest appropriate product as a control of the agricultural production area, reduces the risk to the farmer of losing profit obviously provides information relating to a weed outbreak as one of the unwanted element).
As to claim 7, Allen further discloses The information processing device according to claim 1, wherein the predicted information includes information relating to at least one of yield of the crop and quality of the crop (Allen [0108-176] “agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - control the sub-areas individually based on the calculated prediction specific to that sub-area - include protected cropping areas such as greenhouses, crops under nets, or other forms of protection - correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects how the conditions inside a greenhouse change for different weather outside - how the greenhouse warms up when there is sunshine outside - suggest some control measures at particular times of day - predicts that solar irradiation - major concern disease and pest outbreaks as well as managing shoot growth and root growth - one day at 15 C with more than six hours of leaf wetness and black spot a risk - coddling moth outbreak detected - automatically suggest appropriate product as a control of the agricultural production area - reduces the risk to the farmer of losing profit” [abstract] see Fig. 1-11, suggest some control measures - managing shoot growth and root growth - coddling moth outbreak detected - automatically suggest appropriate product as a control of the agricultural production area - reduces the risk to the farmer of losing profit obviously provides information relating to at least one of yield of the crop and quality of the crop).
As to claim 8, Allen further discloses The information processing device according to claim 1, wherein the predicted information includes information relating to a date and time at which irrigation of the crop is required, or an amount of required irrigation (Allen [0108-176] “agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - control the sub-areas individually based on the calculated prediction specific to that sub-area - include protected cropping areas such as greenhouses, crops under nets, or other forms of protection - correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects how the conditions inside a greenhouse change for different weather outside - how the greenhouse warms up when there is sunshine outside - suggest some control measures at particular times of day - predicts that solar irradiation” [abstract] see Fig. 1-11, agricultural production area, multiple sub-areas, plurality of sensor located in each sub-area, processor determines correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects, conditions inside greenhouse change for different weather outside, greenhouse warms up at sunshine outside, suggest some control measures at particular times of day obviously provides information relating to a date and time at which irrigation of the crop is required, or an amount of required irrigation).
As to claim 9, Allen further discloses An information processing system comprising the information processing device according to claim 1 and at least one measurement and control device in communication with the information processing device, the at least one measurement and control device configured to obtain the actual result information relating to the environment inside the greenhouse (Allen [0108-176] “processor 302 receives sensor data from sensor 204 via communications port 308 using a Wireless Sensor Network (WSN) - communications port 308 and user port 310 - network connection - IP sockets - agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - second input set actual sensor data at the location of prediction (i.e. local-area sensor data)” [abstract] see Fig. 1-11, agricultural production area, multiple sub-areas, plurality of sensor located in each sub-area, network connection, WSN, communications port, user port, IP sockets obviously provides measurement and control device in communication with the information processing device, the at least one measurement and control device configured to obtain the actual result information relating to the environment inside the greenhouse).
As to claim 12, Allen further discloses. An information processing system comprising the information processing device according to claim 10 and at least one measurement and control device in communication with the information processing device, the at least one measurement and control device configured to obtain the actual result information relating to the environment in the open field (Allen [0108-176] “agricultural production area 206 comprises multiple sub-areas, such as regions, farms, paddocks, rows and even individual plants - at least one sensor located in each sub-area and processor 302 determines the correlation for each sub-area based on the sensor data from that sub-area - control the sub-areas individually based on the calculated prediction specific to that sub-area - include protected cropping areas such as greenhouses, crops under nets, or other forms of protection - correlation between the wide-area meteorological prediction data and historical local-area sensor data reflects how the conditions inside a greenhouse change for different weather outside - how the greenhouse warms up when there is sunshine outside - suggest some control measures at particular times of day - predicts that solar irradiation” [abstract] see Fig. 1-11, agricultural production area, multiple sub-areas, plurality of sensor located in each sub-area, greenhouses or other forms, suggest some control measures at particular times of day, predicts solar irradiation obviously provides measurement and control device configured to obtain the actual result information relating to the environment in the open field).
Citation of Pertinent Prior Art
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2141.02 VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, i.e., as a whole and 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record:
a. Satoshi, et al. WO 2018047726 A1 discloses a mechanism to improve the accuracy in the determination of occurrence of a plant pest includes machine learning unit for performing on the basis of plant pest occurrence information and crop cultivation information, determination model using the cultivation information.
b. Allen, et al. USPGPub No. 2020/0390044 A1 discloses an irrigation system for an agricultural production area to receive wide-area meteorological prediction data and sensors deployed within the agricultural production area collect local-area sensor data.
c. Thomas, et al. USPGPub No. 2021/0080615 A1 discloses a DLI method of greenhouse, polytunnel or other controlled environment at the specified geographic location for calculations a prediction based on the proximity of the weather stations to provide sufficient monthly Daily Light Integral (DLI) for a given crop with known DLI requirements an amount of supplemental electric lighting specified.
d. Itzhaky, et al. USPGPub No. 2017/0161560 A1 discloses a method for predicting harvest yield receiving monitoring data analyzing and extracting plurality of features and generating harvest yield prediction based on extracted features and prediction model.
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 extension fee 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 Md Azad whose telephone @(571)272-0553 or email: md.azad@uspto.gov. The examiner can normally be reached on Mon-Thu 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on (571)272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Md Azad/
Primary Examiner, Art Unit 2119