DETAILED ACTION
Remarks
This final office action is in response to the amendments filled on 02/02/2026. Claims 1, 2, 5, 6, 8, 9, 13, 15, 16 and 20 are amended. Claims 21-23 are newly added. Claims 17-19 are canceled. Claims 1-16 and 20-23 are pending and examined below.
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 Objections
Claim(s) 3 and 14 is/are objected to because of the following informalities:
Claim 3, line 1, “comprising;” should be “comprising: ”. Claim 14 is rejected likewise.
Appropriate correction is required.
Specification
Two sets of specification were submitted on 02/02/2026. It is not clear which one should be included.
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(s) 1-16 and 20-23 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.
Regarding claim 1 (and similarly claim 13 and 20), which recites “the robot utilizes computer vision in order to estimate the height, growth and mass of plants” line 4 and “the robot uses computer vision to estimate the height, growth and mass of plants” line 9 have same subject matter. It is not clear whether the computer vision is estimating height, growth and mass of the same plant or different plant or different time period or something else.
Dependent claim(s) 1-12 and 14-19 is/are also rejected because they do not resolve their parent deficiencies.
Regarding claim 9, which recites “the artificial intelligence energy optimization software” is unclear and lacks antecedent basis. Artificial intelligence optimization software is mentioned previously. It is not clear whether the artificial intelligence energy optimization software is referring the artificial intelligence optimization software or not.
Regarding claim 21, which recites “the method of claim 1” is not clear since claim 1 is a system. It Is not clear which method is referred by claim 21.
Regarding claim 23, which recites “the solar grid and the solar battery” is not clear since claim 20 does not disclose the solar grid and the solar battery. Claim 23 is dependent on claim 20. It Is not clear which the solar grid and the solar battery is referred by claim 23.
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) 1-3, 7, 10-14 and 20-22 is/are rejected 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over US 2023/0143014 (“Nguyen”).
Regarding claim 1 (and similarly claim 13 and 20), as best understood in view of indefiniteness rejection explained above, Nguyen discloses a system to guide robotic automation of vertical farming, comprising (see at least fig 9A, where a vertical farming arrangement is shown. see also [0016], where “the entire process may be executed autonomously via the control unit AI and the robotic units.”):
wherein the artificial intelligence optimization software is coupled to a robot (see at least [0012], where “The control system may incorporate artificial intelligence (AI) algorithms to optimize and control the environmental parameters.”; see also [0061], where “The AI system may also instruct another system or a robotic system to move or transplant the plants”);
wherein the robot utilizes computer vision in order to estimate the height, growth and mass of multiple plants in a vertical farm at multiple time periods (see at least fig 8, [0087], [0084], [0049] and [0039]);
wherein the robot has a robotic arm that (see at least [0076], where robotic arm is moving, harvesting and packaging plants.);
wherein once a seed grows past a seedling into a plant, the robot moves the plant to a hydroponics greenhouse (see at least fig 1, block 110 and [0053]);
wherein in the hydroponics greenhouse the robot uses computer vision to estimate the height, growth and mass of multiple plants at multiple time periods (see at least fig 8, [0087], [0084], [0049] and [0039]);
wherein the artificial intelligence optimization software provides guidance and feedback on when and where the robot should make changes to plants in the hydroponic greenhouse (see at least [0061], where AI software is providing guidance to the robot. See also fig 7, [0036] and [0083]), wherein such changes include cutting vines near the plants or changing the location of the plants (see at least [0047-48], [0051] and [0088]);
wherein the artificial intelligence optimization software is configured to:
(i) apply computer vision techniques to image data to estimate at least one of plant size, plant mass, growth, or predicted yield (see at least [0066] and [0083]);
(ii) determine, based on the estimated plant size, plant mass, growth, or predicted yield, a transfer timing for each plant (see at least fig 8); and
(iii) determining an optimal time to harvest each plant (see at least [0009], [0015], [0038] and 0039], where “target harvest date”).
Nguyen does not explicitly disclose robotic arm that sow seeds. However, in [0076], Nguyen teaches that the robotic arm is moving, harvesting and packaging plants. Thus, it would have been obvious to a person of ordinary skill in the art to modify Nguyen with also having the robotic arm planting or sowing the seeds for increasing the efficiency by utilizing the robotic arm for another process of farming.
Regarding claim 2 (and similarly claim 13 and 20), Nguyen further discloses a system comprising: wherein there are sensors throughout the vertical farm (see at least [0015]);
wherein there are sensors throughout the hydroponics greenhouse (see at least [0015] and [0067]);
wherein the sensors throughout the vertical farm and the sensors throughout the hydroponics greenhouse provide feedback to the artificial intelligence optimization software (see at least [0012], where “The AI algorithm may be any program known in the field, such as a machine learning algorithm and the like. Thermal, electronic, moisture, nutrient, and temperature sensors, may feed data to the control system.”; see also [0014]).
Regarding claim 3 (and similarly claim 14), Nguyen further discloses a system comprising: wherein data from the sensors of the vertical farm and the hydroponics greenhouse work together is analyzed together by the artificial intelligence optimization software (see at least [0067-68]).
Regarding claim 7, Nguyen further discloses a system comprising: wherein a plant medium for plants growing in the hydroponics greenhouse can be any of the following: deep water culture or nutrient film technique (“NFT”), or ebb & flow, or rockwool slab, or Dutch bucket (see at least [0053], where “a deep-water agriculture location”).
Regarding claim 10, Nguyen further discloses a system comprising: wherein the artificial intelligence optimization software utilizes machine learning (see at least [0012], where “The AI algorithm may be any program known in the field, such as a machine learning algorithm”).
Regarding claim 11, Nguyen further discloses a system comprising: wherein the artificial intelligence optimization software utilizes deep learning (see at least [0012], where “AI may implement machine learning, for example, based on an algorithmic driven regression formula including but not limited to cluster analysis, bootstrap sampling, and/or extreme gradient boosting that can feed into a convolutional neural network (CNN) or the like.”).
Regarding claim 12, Nguyen further discloses a system comprising: wherein the artificial intelligence optimization software utilizes neural networks (see at least [0012], where “AI may implement machine learning, for example, based on an algorithmic driven regression formula including but not limited to cluster analysis, bootstrap sampling, and/or extreme gradient boosting that can feed into a convolutional neural network (CNN) or the like.”).
Regarding claim 21, as best understood in view of indefiniteness rejection explained above, Nguyen further discloses a method comprising: wherein the artificial intelligence optimization software is configured to: instruct a robotic arm in the hydroponics greenhouse to place a seedling in an optimal location for maximum growth of the seedling (see at least [0076], where “A robotic arm may move hydroponic plant vessels via transport channels in the final hydroponic system seeding phase”; see also [0064]).
Regarding claim 22, Nguyen further discloses a method comprising: wherein the artificial intelligence optimization software is configured to: determine an optimal time to harvest each plant (see at least [0009], [0015], [0038] and 0039], where “target harvest date”) and instruct a robotic arm to harvest each plant at the optimal time (see at least [0076]).
Claim(s) 4, 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0143014 (“Nguyen”), as applied to claim 1 and 13 above, and further in view of IEEE publication title “System Integration of an Intelligent Lighting Control System for Greenhouses with a High Proportion of Local Renewable Energy”, by (“Solis”).
Regarding claim 4 (and similarly claim 15), Nguyen further discloses a system comprising: wherein the vertical farm and hydroponics greenhouse, see citation above.
Nguyen does not disclose the following limitations:
receive power through solar power from a grid of solar panels; and
wherein the artificial intelligence optimization software optimizes the distribution of the power from solar power.
However, Solis discloses a system wherein receive power through solar power from a grid of solar panels (see at least fig 1 and fig 4); and
wherein the artificial intelligence optimization software optimizes the distribution of the power from solar power (see at least abstract, where “an intelligent control system for optimizing the operation of lighting systems in greenhouses with a high proportion of local renewable energy using adaptive control methods, artificial intelligence algorithms and optimization of built-in lighting control.”; see also section B).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Nguyen to incorporate the teachings of Solis by including the above feature for providing environment friendly power for framing.
Regarding claim 5 (and similarly claim 15), Solis further discloses a system comprising: wherein there is a solar battery storing power from the grid of solar panels (see at least fig 1, where battery storage system is shown).
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0143014 (“Nguyen”), as applied to claim 1 and 13 above, and in view of IEEE publication title “System Integration of an Intelligent Lighting Control System for Greenhouses with a High Proportion of Local Renewable Energy”, by (“Solis”), as applied to claim 5 and 15 above, and further in view of US 2024/0181927 (“Cronin”).
Regarding claim 6 (and similarly claim 16), Nguyen in view of Solis does not disclose claim 6. However, Cronin discloses a system comprising: wherein the Artificial intelligence optimization software balances electric load such that power goes directly from the grid of solar panels to heating, cooling and pumps, and is balanced with charging the solar battery (see at least fig 5, [0083], [0088] and [0099]); and
wherein the solar battery sends battery percentage data to the artificial intelligence optimization software (see at least fig 2 and fig 4); and
wherein battery percentage data includes information on remaining amount of power in the solar battery (see at least [0082], where “remaining charge”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Nguyen in view of Solis to incorporate the teachings of Cronin by including the above feature for increasing power consumption efficiency by balancing power.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0143014 (“Nguyen”), as applied to claim 1 above, and further in view of US 2024/0181927 (“Cronin”).
Regarding claim 8, Nguyen further discloses a system comprising: wherein the artificial intelligence optimization software manages heating and cooling per type of plant and stage of growth (see at least [0036], where “identify a plant with a plant diameter that is below an optimal level, and may thus determine that the identified plant requires additional nutrients, water, and/or light based on the measurement and the plant type.”; see also [0059]).
Nguyen does not disclose the following limitations:
the Artificial intelligence optimization software balances electric load such that power goes directly from a grid of solar panels to heating, cooling and pumps, and is balanced with charging a solar battery;
wherein the solar battery sends battery percentage data to the artificial intelligence optimization software; and
wherein battery percentage data includes information on remaining amount of power in the solar battery.
However, Cronin further discloses a system wherein the Artificial intelligence optimization software balances electric load such that power goes directly from a grid of solar panels to heating, cooling and pumps, and is balanced with charging a solar battery (see at least fig 5, [0083], [0088] and [0099]); and
wherein the solar battery sends battery percentage data to the artificial intelligence optimization software (see at least fig 2 and fig 4); and
wherein battery percentage data includes information on remaining amount of power in the solar battery (see at least [0082], where “remaining charge”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Nguyen to incorporate the teachings of Cronin by including the above feature for increasing power consumption efficiency by balancing power.
Claim(s) 9 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0143014 (“Nguyen”), as applied to claim 1 and 20 above, and further in view of IEEE publication title “System Integration of an Intelligent Lighting Control System for Greenhouses with a High Proportion of Local Renewable Energy”, by (“Solis”).
Regarding claim 9, as best understood in view of indefiniteness rejection explained above, Nguyen further discloses a system comprising: wherein there is a combined cycle sensor, instrumentation and control (see at least [0012], where “monitor the lifecycle of a plant as well as environmental data, such as light, temperature, humidity, and the like.”; [0048] of PGPub of submitted specification describe combined cycle sensor);
wherein there is also an outdoor light measurement sensor and instrumentation (see at least [0012]);
wherein data from both the vertical farm and the hydroponic greenhouse is fed into the combined cycle sensor, instrumentation and control (see at least [0012], where “Thermal, electronic, moisture, nutrient, and temperature sensors, may feed data to the control system.”);
wherein Data from the outdoor light measurement sensor and instrumentation is fed into the artificial intelligence optimization software (see at least [0012]);
wherein the outdoor light measurement sensor and instrumentation measures an estimated photosynthetic (see at least [0068]);
wherein the outdoor light measurement sensor and instrumentation also measures photosynthetic active radiation (“PAR”) and overall flow of radiation (see at least [0012], where “light intensity (or photosynthetically available radiation, PAR), light spectrum”; see also [0068], where “net radiation”);
wherein data from the outdoor light measurement sensor and instrumentation will allow the artificial intelligence energy optimization software to determine how much light various plants in the hydroponic greenhouse needs (see at least [0012] and [0036]); and
wherein the vertical farm and hydroponics greenhouse receive power (see citation above).
Nguyen does not disclose the following limitations:
measures solar power generating yield;
receive power through solar power from a grid of solar panels;
wherein the artificial intelligence optimization software optimizes the distribution of the power from solar power;
wherein there is a solar battery storing some power from the grid of solar panels; and
wherein the outdoor light measurement sensor and instrumentation will also measure and estimate the conditions of solar power in the solar grid.
However, Solis further discloses a system wherein measures solar power generating yield (see at least [0068]);
receive power through solar power from a grid of solar panels (see at least fig 1);
wherein the artificial intelligence optimization software optimizes the distribution of the power from solar power (see at least abstract and section B);
wherein there is a solar battery storing some power from the grid of solar panels (see at least fig 1); and
wherein the outdoor light measurement sensor and instrumentation will also measure and estimate the conditions of solar power in the solar grid (see at least section IV). Same motivation of claim 6 applies.
Regarding claim 23, as best understood in view of indefiniteness rejection explained above, Nguyen further discloses a method comprising: wherein the artificial intelligence optimization software analyzes the input from the vertical farm, the hydroponic greenhouse, the energy (see at least [0037], [0045] and [0063-64]). Nguyen discloses a method wherein energy is optimized. Nguyen does not disclose solar grid and the solar battery. However, Solis further discloses a method where solar grid and solar battery used (see citation on claim 9).
Response to Arguments
Applicant’s arguments filled on 02/02/2026, with respect to claim 1-16 and 20, have been considered but they are not persuasive.
The Applicant contends that:
“The claimed invention solves a new problem: minimizing energy usage per unit yield by synchronizing biological growth with energy availability… In addition, the claimed system requires predictive coordination across: biological growth variability, solar generation uncertainty, battery degradation constraints and robotic timing precision.”
The Examiner disagrees:
The recited claim does not recite minimizing energy usage and predictive coordination across: biological growth variability, solar generation uncertainty, battery degradation constraints and robotic timing precision. The argument is considered irrelevant with respect to the recited claim limitation.
The Applicant also contends that:
“Nguyen Lacks Closed-Loop Control Between Computer Vision, Robotics, and Energy Allocation”
The Examiner also disagrees:
The recited claim does not recite closed control. It is not clear what is referred by closed control loop. It is recommended to present arguments based on recited claim limitation.
The Applicant also contends that:
“Nguyen Treats Energy, Robotics, and Growth Monitoring as Independent Subsystems… Nguyen Fails to Teach Energy Optimization Tied to Plant Growth Stages”
The Examiner also disagrees:
Claim 9 recites “energy optimization software”. [0036] of Nguyen discloses that AI is used to determine the amount of required light based on plant type and measurement.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOHANA TANJU KHAYER whose telephone number is (408)918-7597. The examiner can normally be reached on Monday - Thursday, 7 am-5.30 pm, PT.
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/SOHANA TANJU KHAYER/Primary Examiner, Art Unit 3657