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 with respect to claim(s) 1 (and by extension the dependent claims) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
An updated search and consideration was performed in light of the amendments to claim 1, upon such a piece of prior art was found US 20160232621 A1, “METHODS AND SYSTEMS FOR RECOMMENDING AGRICULTURAL ACTIVITIES”, Ethington et al was found which teaches using aerial images in order to determine crop growth conditions which in turn is used to determining the timing of agricultural activities (including nitrogen application , i.e. fertilizing) for a given field. ([0137]-[0138]; [0170]).
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) 1-2, 4-5 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP 2016076123 A, Isemura et al and further in view of US 20150302305 A1, Rupp et al and further in view of US 20160232621 A1, “METHODS AND SYSTEMS FOR RECOMMENDING AGRICULTURAL ACTIVITIES”, Ethington et al.
Regarding Claim 1, Isemura teaches “A work map provision server comprising: a memory configured to store first processor-executable code, a plurality of(“(Work Plan Registration Unit) The work plan registration unit 151 of the memory unit 135 stores work plans (information linking work date and time with work content) for all fields owned by the owner, with each field associated with it.” There are multiple work plans (maps));”and respective attribute information for each ([0107] When the work target value selection unit 146 receives date and time information and location information related to the work area from the remote monitoring terminal device 200 or the mobile terminal device 180, it selects a work target value from the work target value registration unit 157 that corresponds to the work plan selected by the work plan selection unit 142.” Work plans (maps) include target values);” the respective attribute information for each of the ([0079] (Work Plan Registration Unit) The work plan registration unit 151 of the memory unit 135 stores work plans (information linking work date and time with work content) for all fields owned by the owner, with each field associated with it.
In other words, when owning multiple fields, the crops produced in each field may differ, and the agricultural work required in each field (for example, plowing, rice planting, pesticide application, harvesting, etc.) may also differ. Therefore, a work plan is individually created for each field according to the crops and required agricultural work, and this information is registered in the work plan registration unit 151.
Specifically, since the same agricultural work on a single field is often carried out over several days, the work plan is registered by linking the content of the agricultural work with the period during which that work will be carried out.” Here Isemura teaches that a work plan links the content (specific work to be carried out) in the field with the timeframe of the overall plan (i.e. beginning at the creation/start of the plan the corresponding work is linked with time));” and is based on a growth condition of crops in the farm field associated with the corresponding fertilizer map.”(Isemura [0081] “Here, a production management model is generated by automatically correcting the necessary agricultural work based on weather conditions such as rainfall, sunshine hours, and temperature from several days prior to the present, onto a standard work plan (standard production management model) that pre-specifies the relationship between dates and agricultural work according to the type of crop, etc.
In other words, when storing the work plan generated by the production management model in the work plan registration unit 151, the work plan, which is updated sequentially according to weather conditions as described above, is stored corresponding to each field.” The corresponding time period/amount of work is corrected based on the growth conditions (sunshine hours, temperature, rainfall, etc) of the crops in the intervening time.)
“and each vehicle, controls the work vehicle to apply a target amount of ([0106]-[0107] “(Work Target Value Registration Unit) The work target value registration unit 157 has work target values for each work plan pre-registered.
For example, work target values such as working time, yield, and fuel consumption of agricultural machinery 110 are pre-registered for each work plan and for each field.
These work target values can be entered as arbitrary values by the field owner by operating a fixed terminal 160 or a portable terminal 170.
(Work Target Value Selection Unit) When the work target value selection unit 146 receives date and time information and location information related to the work area from the remote monitoring terminal device 200 or the mobile terminal device 180, it selects a work target value from the work target value registration unit 157 that corresponds to the work plan selected by the work plan selection unit 142.” Isemura’s work plans include target values/amount for the corresponding work);”the processor configured to execute the first processor-executable code to perform operations including: receiving a map acquisition request at a receiving time and date, selecting, from among the plurality of ([0082] “The work plan selection unit 142 selects a work plan from among the multiple work plans registered in the work plan registration unit 151 or the multiple work plans generated by the production management model, based on the received date and time information and the specified work area (specified by the work area specification unit 141). “ + [0083] “ In other words, the work plan selection unit 142 selects (extracts) from among multiple work plans registered in the work plan registration unit 151 a work plan that matches the date and time specified by the received date and time information and matches the work area specified by the received location information.” Isemura selects a work plan which corresponds to the location and dat and time information from the remote terminal, and subsequently sends the plan to the terminal; (and subsequently the work would then control the vehicle to follow the plan, i.e. “for use by the work vehicle”)
Isemura however does not teach (1) that the work plans are/include fertilizing, while it teaches various examples of “agricultural work” ([0079] “In other words, when owning multiple fields, the crops produced in each field may differ, and the agricultural work required in each field (for example, plowing, rice planting, pesticide application, harvesting, etc.) may also differ.”) it does not explicitly teach fertilizing as one of those types of work. Additionally While Isemura discloses work plans corresponding to specific fields, it does not teach that the target work varies within the field, i.e. does not teach work values specific to “to each respective area of a plurality of areas in a farm field;” and (2) and that the growth condition (used to determined the predetermined periods) is based on images of the crops.
Rupp et al teaches a similar (agricultural work plan) transmission system which includes fertilizer plans/maps as a type of agricultural work ([0138] The method continues at step 136 where the host device sends the agriculture prescription to one or more of the agriculture equipment. For example, the host device transmits the agriculture prescription to a fleet of farming tractors. The sending may further include transmitting the agriculture prescription to one or more user devices associated with the geographic region. The method continues at step 138 where the one or more of the agriculture equipment executes at least a portion of the agriculture prescription. For example, the fleet of farming tractors executes steps of the agriculture prescription.); and that each plan includes a specific amount (pattern) of fertilizer application in the corresponding fields ([0086] Having produced the agricultural prescription 80, the application processing module 34 of the application unit 16 sends, via the network 24, one or more of the analysis summary 78 and the agricultural prescription 80 to the user device 14. The application processing module 34 of the application unit 16 may further send the agricultural prescription 80, via the network 24 and the wireless communication network 1, to the user device 1-1C for utilization in performing of one or more steps of the agricultural lifecycle in accordance with the agricultural prescription 80. For example, the user device 1-1C displays a portion of the agricultural prescription 80 and sends control information of the agricultural prescription, via wireless signals 42, to the user device 1-1A to automate a portion of the execution of at least some of the steps of the agricultural lifecycle. For the example, the user device 1-1A issues control information to a set of actuators to dispense fertilizer in accordance with the agricultural prescription 80. For instance, to control dispensing a specified volume of liquid fertilizer in a specified date range in a specified geometric pattern for at least a portion of the geographic region 1-1 as the user device 1-1A versus the drive path 1-1.” As can be seen in figure 2 below the geographic regions = respective areas in a field + [0217] “An example of operation, a user device associated with farming machinery traverses the series of drive paths. … As another specific example, the farming machinery enters the geographic region 1-1 via the encoded zone 1-1, produces the recovered data, extracts an agricultural prescription identifier from the recovered data, and facilitates a next agricultural lifecycle step in accordance with the agricultural prescription (e.g., automatically applies a desired amount of fertilizer across desired portions of the geographic region based on the agricultural prescription).” Here teaches that the plans include applying a desired amount of fertilizer per region; and that control of the vehicle is automatic)
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It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to modify Isemura to include the fertilizer application map(s)/plan as taught by Rupp as a form of “agricultural work” as taught in Isemura. One would be motivated to include the fertilizer application as part of the plans/maps in order to allow for increased crop yield.
The combination however would still lack teachings for “the growth condition of crops being determined from captured images of crops”
Ethington et al teaches a agricultural activities planning system which includes determining of field growth conditions based on aerial images of the field(([0137] Further, agricultural intelligence computer system may receive additional information from other data networks 140 to determine field-specific & environmental data 170 and field condition data 180. In the example, other data networks 140 receive inputs from aerial monitoring system 145 and satellite device 146. Such inputs 145 and 146 may provide field-specific & environmental data for a plurality of fields 120.
[0138] Using field-specific & environmental data 170 associated with each field 122 and 124 (as defined by field definition data 160), agricultural intelligence computer system determines field condition data 180 and/or at least one recommended agricultural activity 190, as described herein. Field condition data 180 substantially represents a response to a request from grower 110 for information related to field conditions of fields 120 including field weather conditions, field workability conditions, growth stage conditions, soil moisture, and precipitation conditions. Recommended agricultural activity 190 includes outputs from any of the plurality of services described herein including planting advisor, a nitrogen application advisor, a pest advisor, a field health advisor, a harvest advisor, and a revenue advisor. Accordingly, recommended agricultural activity 190 may include, for example, suggestions on planting, nitrogen application, pest response, field health remediation, harvesting, and sales and marketing of crops.” Here teaches recommended activities includes nitrogren application, i.e. fertilizer) + [0036] teaches that the aerial monitoring system is providing images specifically as data)) and then determining the timing of agricultural activities (i.e. determining the predetermined period) corresponding to the growth conditions.( [0170] As part of field condition data 180 provided, agricultural intelligence computer system 150 runs or executes field growth stage data module 413 (e.g., for corn, vegetative (VE-VT) and reproductive (R1-R6) growth stages). Field growth stage data module 413 receives and processes field-specific & environmental data 170 and user information input 402 to determine timings of key farming decisions. Agricultural intelligence computer system 150 computes crop progression for each crop through stages of growth (agronomic stages) by tracking the impact of weather on the phenomenological development of the crop from planting through harvest.” Here teaches that the timing (predetermined period) for activities (nitrogen application/fertilizing) is determined based on the data 170 which earlier in [0137] is known to be/include images of the crops)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to modify Isemura to include the aerial imaging and agricultural intelligence computer system and recommendations of Ethington as part of the plan creation and correcting system. One would be motivated to implement the images and AI system of Ethington to allow for site specific data and information improving farming strategy thereby increasing profit. ([0073] The agricultural intelligence computer system is further configured to generate a plurality of reports based on field condition data. Such reports may be used by the user to improve strategy and decision-making in farming.)
Regarding claim 2, modified Isemura teaches “The work map provision server according to claim 1, wherein the second processor-executable code causes the work vehicle to apply a first target amount of fertilizer to a first area of the farm field and a second target amount of fertilizer, that is different than the first target amount, to a second area of the farm field.”(Rupp “[0217] “An example of operation, a user device associated with farming machinery traverses the series of drive paths. … As another specific example, the farming machinery enters the geographic region 1-1 via the encoded zone 1-1, produces the recovered data, extracts an agricultural prescription identifier from the recovered data, and facilitates a next agricultural lifecycle step in accordance with the agricultural prescription (e.g., automatically applies a desired amount of fertilizer across desired portions of the geographic region based on the agricultural prescription).” Here teaches that the plans include applying a desired amount of fertilizer per region; and that control of the vehicle is automatic” + as seen in figure 2 above the amount of fertilizer changes based on the geographic region (individual area) of the field)
Regarding Claim 4, modified Isemura teaches “The work map provision server according to claim 1; wherein the operations further include: receiving one or more images of the farm field associated with the corresponding fertilizer application map; and determining the growth condition of crops based on the one or more images.”(Ethington [0137] Further, agricultural intelligence computer system may receive additional information from other data networks 140 to determine field-specific & environmental data 170 and field condition data 180. In the example, other data networks 140 receive inputs from aerial monitoring system 145 and satellite device 146. Such inputs 145 and 146 may provide field-specific & environmental data for a plurality of fields 120.
[0138] Using field-specific & environmental data 170 associated with each field 122 and 124 (as defined by field definition data 160), agricultural intelligence computer system determines field condition data 180 and/or at least one recommended agricultural activity 190, as described herein. Field condition data 180 substantially represents a response to a request from grower 110 for information related to field conditions of fields 120 including field weather conditions, field workability conditions, growth stage conditions, soil moisture, and precipitation conditions.” + [0036] teaches that the aerial monitoring system is providing images specifically as data)
Regarding Claim 5, modified Isemura teaches “The work map provision server according to claim 4, wherein the one or more images of the farm field are aerial images of the farm field.”( Ethington [0137] Further, agricultural intelligence computer system may receive additional information from other data networks 140 to determine field-specific & environmental data 170 and field condition data 180. In the example, other data networks 140 receive inputs from aerial monitoring system 145 and satellite device 146. Such inputs 145 and 146 may provide field-specific & environmental data for a plurality of fields 120.
[0138] Using field-specific & environmental data 170 associated with each field 122 and 124 (as defined by field definition data 160), agricultural intelligence computer system determines field condition data 180 and/or at least one recommended agricultural activity 190, as described herein. Field condition data 180 substantially represents a response to a request from grower 110 for information related to field conditions of fields 120 including field weather conditions, field workability conditions, growth stage conditions, soil moisture, and precipitation conditions.” + [0036] teaches that the aerial monitoring system is providing images specifically as data)
Regarding Claim 9, modified Isemura teaches “The work map provision server according to claim 1, wherein the one or more fertilizer application maps are transmitted to the work vehicle.”(Rupp [0050] + [0217] Rupp teaches that the user device can be embedded in the farm equipment (tractor/work vehicle) and includes automatically following along for fertilizer spreading)
Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over modified Isemura (Isemura + Rupp + Ethington) as applied to claim 1 above, and further in view of US 20190116725 A1, Hanya et al.
Regarding Claim 6, modified Isemura, while teaching various fertilizer maps (“additional” fertilizer maps),(Rupp “[0086] Having produced the agricultural prescription 80, the application processing module 34 of the application unit 16 sends, via the network 24, one or more of the analysis summary 78 and the agricultural prescription 80 to the user device 14. The application processing module 34 of the application unit 16 may further send the agricultural prescription 80, via the network 24 and the wireless communication network 1, to the user device 1-1C for utilization in performing of one or more steps of the agricultural lifecycle in accordance with the agricultural prescription 80. For example, the user device 1-1C displays a portion of the agricultural prescription 80 and sends control information of the agricultural prescription, via wireless signals 42, to the user device 1-1A to automate a portion of the execution of at least some of the steps of the agricultural lifecycle.” Here teaches fertilization within the lifecycle (i.e. additional fertilization) + Rupp [0171] “This spray information is then correlated with the plant yield or crop yield data for each specific location that was gathered and uploaded for offline analysis. The correlations with the crop yield help determine best practices for the next crop cycle (e.g. whether to reduce or increase the amount of herbicide, fertilizer, and so on) for each location in the crop field.” Here teaches adjusting fertilizer through each part of the cycle (i.e. “additional” fertilizer maps)), however it does not explicitly teach “basal” fertilizer application/maps thereof
Hanya et al teaches a agricultural work plan creation method for fertilizer spraying which specifically includes determining a basal fertilizer work plan which determines specific amounts of basal fertilizer application at specific areas of the field. ([0057] In Step 14, the basal fertilization plan map MP shown in FIG. 6 is displayed on the display 40 and is stored in a not-shown memory.
[0058] The basal fertilization plan map MP displayed on the display device 40 shows an area having good growth and an area having poor growth in the field E, and also shows an area having considerably large growth fluctuation depending on year and season and an area having significantly poor growth. The map MP also shows the amount of fertilization for each area. Accordingly, the condition of the field E can be confirmed even before the field is seeded.
[0059] The data of the basal fertilization plan map MP stored in a memory is sent to the tractor 20 by the transmission/reception part 36. The data may be wirelessly sent or may be sent with signal lines.
[0060] When the transmission/reception part 26 of the tractor 20 receives the data of the basal fertilization plan map MP, the control part 25 stores the data of the basal fertilization plan map MP in a not-shown memory.
[0061] The basal fertilization plan map MP is displayed on the display part 24 based on the data of the basal fertilization plan map MP stored in the memory. The position of the tractor 20 is shown on the basal fertilization plan map MP of the display part 24 based on the positional information measured by the GPS device 22. An operator confirms the position of the tractor 20 on the basal fertilization plan map MP displayed on the display part 24, so as to recognize the amount of basal fertilizer for basal fertilization before seeding the area in that position. The operator thereby fertilizes that area as much as necessary.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application to modify Isemura to include the Basal fertilizer application plan/map creation and use as taught by Hanya as part of the agricultural work plans taught in Isemura. One would be motivated to implement basal fertilizer work plans to allow for the system to identify areas of good or poor growth earlier in the planting cycle allowing for more efficient fertilizer application spread out through the full crop lifecycle as opposed to responding to only current growing conditions. (Hanya
[0062] Namely, even in the field E before being seeded, the area having good growth and the area having poor growth are clearly distinguished, and an area having considerably large growth fluctuation depending on year and season and an area having significantly poor growth are also clearly distinguished. Moreover, the appropriate amount of fertilization can be obtained in each area in the field E. The field E is thereby prevented from being excessively fertilized. As a result, the fertilizer can be saved.”)
Regarding Claim 7, modified Isemura teaches “The work map provision server according to claim 6, wherein the basal fertilizer map includes a target amount of fertilizer for application to each respective area of a plurality of areas in a farm field prior to sowing or planting,”(Hanya [0060] When the transmission/reception part 26 of the tractor 20 receives the data of the basal fertilization plan map MP, the control part 25 stores the data of the basal fertilization plan map MP in a not-shown memory.
[0061] The basal fertilization plan map MP is displayed on the display part 24 based on the data of the basal fertilization plan map MP stored in the memory. The position of the tractor 20 is shown on the basal fertilization plan map MP of the display part 24 based on the positional information measured by the GPS device 22. An operator confirms the position of the tractor 20 on the basal fertilization plan map MP displayed on the display part 24, so as to recognize the amount of basal fertilizer for basal fertilization before seeding the area in that position. The operator thereby fertilizes that area as much as necessary.);” and wherein the additional fertilizer map includes a target amount of fertilizer for application to each respective area of the plurality of areas in a farm field subsequent to sowing or planting.”( Rupp [0171] “This spray information is then correlated with the plant yield or crop yield data for each specific location that was gathered and uploaded for offline analysis. The correlations with the crop yield help determine best practices for the next crop cycle (e.g. whether to reduce or increase the amount of herbicide, fertilizer, and so on) for each location in the crop field.” Here Rupp teaches adjusting fertilizer through each part of the cycle (i.e. “additional” fertilizer maps) )
Regarding Claim 8, modified Isemura (Isemura + Rupp + Ethington + Hanya) “The work map provision server according to claim 6, wherein the additional fertilizer map is determined based on an aerial image of at least a portion of the farm field.”(Ethington [0137] Further, agricultural intelligence computer system may receive additional information from other data networks 140 to determine field-specific & environmental data 170 and field condition data 180. In the example, other data networks 140 receive inputs from aerial monitoring system 145 and satellite device 146. Such inputs 145 and 146 may provide field-specific & environmental data for a plurality of fields 120.
[0138] Using field-specific & environmental data 170 associated with each field 122 and 124 (as defined by field definition data 160), agricultural intelligence computer system determines field condition data 180 and/or at least one recommended agricultural activity 190, as described herein. Field condition data 180 substantially represents a response to a request from grower 110 for information related to field conditions of fields 120 including field weather conditions, field workability conditions, growth stage conditions, soil moisture, and precipitation conditions.
+ [0036] Teaches that the aerial monitoring system is obtaining images )
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 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.
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/KENNETH M DUNNE/Primary Examiner, Art Unit 3669
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