DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements submitted on 12/7/2023 and 12/10/2025 were in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claim 13 is objected to because of the following informalities:
In claim 13 line 1, a colon should be inserted following “comprising.”
Appropriate correction is required.
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.
Claims 1-15 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
In claim 1 line 6, “a pollinator, such as a bumblebee” renders claim 1 indefinite because it is unclear from the language of the claim whether a “bumblebee” limits the scope of what constitutes a “pollinator” and, if so, in what manner (e.g., would a honeybee qualify as pollinator).
It appears that the characterization “such as a bumblebee” is intended not as a strict limitation on what constitutes a pollinator but is merely exemplary (i.e., one possible type of “pollinator”), which is how claim 1 is interpreted for purposes of examination.
Claims 2-11 and 14 depend from claim 1 and are likewise rejected for the same reason.
Dependent claim 3 further narrows “pollinators” as being “bees.” However, claim 3 also includes the characterization of “bees, such as bumblebees.” Similar to the interpretation of “such as bumblebees” in claim 1, the application is claim 3 is interpreted as merely exemplary.
Independent claim 12 recites a similar characterization of a pollinator as claim 1 and is rejected for the same reason.
Claims 13 and 15 depend from claim 12 and are likewise rejected for the same reason.
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.
Claims 1-3, 5-6, 12, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Jones (US 2022/0237481 A1) in view of Goethem et al., "An IoT solution for measuring bee pollination efficacy," 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 2019, pp. 837-841, as provided by Applicant, and in further view of Morgan et al. Floral Sonication is an Innate Behaviour in Bumblebees that can be Fine-Tuned with Experience in Manipulating Flowers. J Insect Behav 29, 233–241 (2016), as provided by Applicant.
As to claim 1, Jones teaches “[a] system for monitoring pollination of plants carrying one or more flowers (Abstract system for evaluating crop pollination; FIG. 1 pollination predictor system 100 including sensors 110; [0022] predictor system applied for estimating pollination state for insect-driven (e.g., bees) pollination of flowers), the system comprising:
a plurality of” [sensors] (FIG. 1 sensors 110) “for monitoring an area comprising said one or more flowers ([0018] sensors 110 may be incorporated into the environment such as land sub-region; [0022] monitored region includes plants and flowers), wherein each” [sensor] “out of the plurality of” [sensors] “is configured to monitor a sub-area of the monitored area ([0018] describing multiple sensors disposed on multiple, traveling vehicles such that each of the sensors (such as cameras or “other sensory equipment” would detect corresponding data corresponding to various portions of an overall region), wherein each” [sensor] “is suitable for recording” [sensed data] ([0018] sensors may be cameras (record optical image data)) “produced by a pollinator, such as a bumblebee ([0022] sensor data includes features such data indicating bee pollinating a flower), that is present in the” [sensor’s] “sub-area (sensed data, such as image data, indicating a bee pollinating a flower per [0022] would entail the bee being present in the sensing area), and is configured to output one or more signals indicative of recorded” [sensed data] ([0022] the sensed features are used in modeling and therefore have been output, which inherently entails recording of the sounds), “the system further comprising:
a data processing system (FIG. 1 pollination prediction system 100 including pollination prediction server 130 and smart device 120) comprising at least one input interface (FIG. 1 depicting communication interfaces including network 108 between pollination prediction server 130, smart device 120, and sensors 110) and at least one processor (each of pollination prediction server 130 and smart device 120 are computing devices that inherently process data; [0047]-[0048]) that is configured to:
receive, via the at least one interface, from each of the plurality of” [sensors] “said one or more signals indicative of recorded” [sensed data] (FIG. 1 pollination prediction server 130 and smart device 120 configured to receive sensor data; [0022] prediction models 132 models correlations based on features detected by sensors 110) “and to
determine, using the at least one processor, based on the one or more signals received from the plurality of” [sensors], “a value of a pollination quality parameter indicative of how well one or more flowers in the monitored area are pollinated ([0022] prediction models 132 determine pollination state in accordance with correlation to features detected by sensors 110 indicating occurrence of pollination events; [0035] evaluation of pollination state may be level of pollination),
wherein the value of the pollination quality parameter is determined based on a number of pollination events ([0022] features upon which the pollination state determined include features indicating pollination occurrence; [0030] and [0032] features for predicting pollination state may include pollination event rate (number of events over a period)) and/or based on a duration of each of the pollination events determined by the data processing system based on the one or more signals received from the plurality of” [sensors] ([0022], [0029], [0032] features for predicting pollination state may include duration of pollination events),
wherein each pollination event comprises a pollinator visiting a flower ([0022] pollination events include bee pollinating a flower) and wherein the data processing system is configured to determine a pollination event by recognizing from the one or more signals from the plurality of” [sensors], “a” [sensor data] “that is associated with a pollination event ([0022] prediction models 132 correlate features detected by sensors 110 that are associated with pollination events such as bee pollinating a flower) and/or a” [sensor data] “pattern that is associated with a pollination event ([0022] feature data may include patterns data such as insect types and sizes, and durations (multiple) of pollination events).
Jones discloses that a variety of types of sensors may be used for collecting the feature data for determining pollination states [0018], but does not expressly teach that acoustic sensors may be included among the sensors, and therefore does not teach use of “microphones” for sensor data collection and consequently does not teach that the sensor data processed by the data processing system includes “sounds.”
Prior to the effective filing date, it was known in the art to use acoustic sensors such as microphones to monitor bee activity associated with pollination. For example, Goethem discloses a system/method for measuring bee pollination efficacy using acoustic data (Abstract and page 838, III. A Practical Solution, paragraph beginning with “A proposition for a product-service …” explaining that acoustic data (sound) may be used to determine pollination-related bee activity in the field (i.e., at locations at which pollination occurs); page 839, III. A Practical Solution, paragraph beginning with “The main advantage …” explaining that the sensor data recorded in the field including whether and how well pollination is occurring) that is collected using multiple distributed microphones (page 838, III. A Practical Solution, paragraph beginning with “By spreading these sensors …” describing distribution of the sensors on a field; FIG. 2 depicting distribution of sensors in a field; page 838, III. A Practical Solution, paragraph beginning with “As seen in figure 1 …” explaining the sensor is a microphone).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Goethem’s teaching of using acoustic monitoring of pollination-related activities (e.g., presence and/or types of pollinator insects) to the system taught by Jones such that acoustic sensors such as microphones are used as an alternative or in addition to the variety of sensors disclosed by Goethem to monitor pollination activity, such that in combination the system includes a plurality of “microphones” for monitoring an area, wherein each “microphone” is configured to monitor a sub-area of the monitored area, wherein each “microphone” is suitable for recording “sounds” produced by a pollinator that is present in the “microphone's” sub-area and is configured to output one or more signals indicative of recorded “sounds,” receive, via the at least one interface, from each of the plurality of “microphones,” said one or more signals indicative of recorded “sounds,” and to determine, using the at least one processor, based on the one or more signals received from the plurality of “microphones,” a value of a pollination quality parameter.
The motivation would have been to further expand the scope of input sensor information related to pollination activity that may be useful for accurately assessing such activity. Furthermore, such a combination would amount to selecting a known design option for monitoring pollinator activity to achieve predictable results.
Prior to the effective filing date, it was further known that acoustic monitoring may be used to detect acoustic signatures that are specific to pollination events (events that may be counted and/or for which durations may be determined). For example, Morgan discloses a method/system for analyzing acoustic signatures (types of buzzes) emitted by bees in relation to pollination activity (Abstract) that includes collecting acoustic/sound data indicating pollination events including floral “visits” and durations of such visits (page 236, Methods, paragraph beginning with “We recorded flight and feeding sonication …” describing recordation of sounds indicating arrivals and departures of bees to and from flowers (entails recordation of one or more pollination events) and further recording times of arrivals and departures (recording the arrival and departure temporal delimiters constitutes an effective recordation of duration)).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Morgan’s teaching of collecting and using sounds to identify pollination events such as occurrences of pollination and durations of such occurrences to the system taught by Jones as modified by Goethem in which distributed microphones are used to monitor pollination activity by pollinators, such that in combination the system includes recognizing from the one or more signals from the plurality of microphones, “a sound that is associated with a pollination event” and/or a “sound pattern that is associated with a pollination event.”
The motivation would have been to leverage known relations between acoustic signatures (e.g., sonication and flight buzzes) and pollination activities to further utilize the acoustic sensing taught by the combination of Jones and Goethem to identify/monitor more specific aspects of pollination in a more accurate manner.
As to claim 2, the combination of Jones, Geothem, and Morgan teaches “[t]he system according to claim 1, wherein the data processing system is configured to:
for each region out of a plurality of regions in the monitored area (Jones: [0018] describing multiple sensors disposed to multiple, traveling vehicles such that each of the sensors (such as cameras or “other sensory equipment”) detect corresponding data corresponding to various portions/regions of an overall area; [0035]-[0036] pollination state/level mapped, such as by color coding across regions of overall area (e.g., farm, orchard)), determine, based on the one or more signals received from the plurality of microphones (Jones as combined with Goethem for claim 1 teaches that the distributed sensors may be microphones), a number of pollination events in the region (Jones as modified by Goethem and Morgan for claim 1 teaches that region-based microphone signals (signals from sensors such as cameras in Jones or microphones in Goethem that inherently sense data in some proximity to the sensor) may be used to determine a number (one or more) pollination events based on the microphone signals) and/or a duration of each of the pollination events in the region, each pollination event comprising a pollinator visiting a flower (Jones as modified by Goethem and Morgan for claim 1 teaches that region-based microphone signals may be used to determine a number and/or duration of pollination events in which the pollination events constitute a pollinator visiting a flower), and to
for each region, based on the determined number of pollination events in the region and/or based on the determined durations of the pollination events in the region, determine a value of a pollination quality parameter indicative of how well one or more flowers in the region are pollinated (Jones as modified by Goethem and Morgan for claim 1 teaches that the pollination quality parameter (the pollination “state” disclosed by Jones) is determined based on the determined number and/or duration of pollination events in each region per the region based sensing itself (signals from sensors such as cameras in Jones or microphones in Goethem that inherently sense data in some proximity to the sensor); furthermore, Jones expressly teaches application/assignment of the pollination states/level to particular regions [0035]-[0036] pollination state/level mapped, such as by color coding across regions of overall area (e.g., farm, orchard)).”
As to claim 3, the combination of Jones, Goethem, and Morgan teaches “[t]he system according to claim 2,”
“wherein the pollinators are bees, such as bumblebees (Jones: [0022] sensor data includes features such data indicating bee pollinating a flower),” and Morgan further teaches “wherein the sound associated with a pollination event is a sonication sound (Abstract pollen removal determined by sonication characteristics that are analyzed; page 236, Methods, paragraph beginning with “We recorded flight and feeding sonication …” describing recordation of sonication sounds indicating arrivals and departures of bees to and from flowers (entails recordation of one or more pollination events) and further recording times of arrivals and departures).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Morgan’s teaching of monitoring/using sonication sounds as the sound associated with a pollination event to the system taught by Jones as modified by Goethem and Morgan, such that in combination the system is configured such that the sound associated with a pollination event is a sonication sound.
The motivation would have been to use a type of sound that is known to be associated with pollination events to more accurately determine pollination events as disclosed by Morgan.
Examiner notes that “wherein the sound pattern” is a time-lapsed sound pattern and “comprises a first time period comprising a sound associated with flying of a pollinator, a subsequent second time period substantially without sounds associated with flying of a pollinator and a subsequent third time period comprising a sound associated with flying of a pollinator” and “wherein the sound pattern associated with a pollination event is a sonication sound pattern” per the terms of claim 1 and claim 3 are alternative elements (and/or) that do not strictly limit the scope of claim 3.
As to claim 5, the combination of Jones, Goethem, and Morgan teaches “[t]he system according to claim 1, wherein the data processing system is further configured to perform a machine learning algorithm (Jones: [0015] machine learning model deployed by system to determine pollination state; [0022]) for improving the data processing system's capability to determine the value of the pollination quality parameter (Jones: [0015] machine learning model deployed by system to determine pollination state; [0022] models 132 model correlation between features detected by sensors 110 and pollination state), wherein performing the machine learning algorithm comprises:
receiving training data (Jones: FIG. 2 step 204, [0204] collect/receive training data), the training data comprising a plurality of sets of recorded sounds for respective batches of plants (Jones: [0025] training data includes the sensed imaging data of multiple pollination events for flowers. As combined with Goethem for claim 1, the sensor data would be sounds), wherein each set of recorded sounds is associated in the training data with an actual value for a pollination quality parameter indicative of how well flowers in the associated batch were pollinated (Jones: [0029] features related to pollination extracted from training data (sensed measurements); [0032] features are associated with pollination evaluation/prediction), and
building a pollination quality parameter estimation model based on the training data (Jones: [0032] models 132 are trained using the training data).”
As to claim 6, the combination of Jones, Goethem, and Morgan teaches “[t]he system according to claim 2, wherein the data processing system is configured to:
based on the determined value of the pollination quality parameter for each region of the plurality of regions (Jones: [0040]-[0041] pollination state determined based on sensed data that is labelled/tracked by respective location; [0035] pollination state/level mapped via color coding (i.e., distinct regions differentiated)), determine one or more regions of concern out of the plurality of regions (Jones: [0042] pollination predictor 134 used model to determine differentiations among multiple crop regions including determining relatively low levels of pollination for some regions), each region of concern having a value for the associated pollination quality parameters that is lower than a threshold value (Jones: [0042] model determines unsatisfactory levels of pollination (“only 50%” and “only 25%”) entailing levels insufficient (below a sufficient level) and therefore warranting potential remedial action (e.g., relocating beehives)).
As to claim 12, Jones teaches “[a] method for monitoring pollination of plants carrying one or more flowers (Abstract method for evaluating crop pollination; method implemented by FIG. 1 pollination predictor system 100 including sensors 110; [0022] predictor system applied for estimating pollination state for insect-driven (e.g., bees) pollination of flowers; FIG. 2), the method comprising:
receiving, from each of a plurality of” [sensors] (FIG. 1 sensors 110) “for monitoring an area comprising said one or more flowers ([0018] sensors 110 may be incorporated into the environment such as land sub-region; [0022] monitored region includes plants and flowers), one or more signals indicative of recorded” [sensed data] (FIG. 1 pollination prediction server 130 and smart device 120 configured to receive sensor data; [0022] prediction models 132 models correlations based on features detected by sensors 110), “wherein
each” [sensor] “out of the plurality of” [sensors] “is configured to monitor a sub-area of the monitored area ([0018] describing multiple sensors disposed on multiple, traveling vehicles such that each of the sensors (such as cameras or “other sensory equipment” would detect corresponding data corresponding to various portions of an overall region), wherein each” [sensor] “is suitable for recording” [sensed data] ([0018] sensors may be cameras (record optical image data)) “produced by a pollinator, such as a bumblebee ([0022] sensor data includes features such data indicating bee pollinating a flower), that is present in the microphone's sub-area (sensed data, such as image data, indicating a bee pollinating a flower per [0022] would entail the bee being present in the sensing area),
determining, based on the one or more signals received from the plurality of” [sensors], “a number of pollination events ([0022] features upon which the pollination state determined include features indicating pollination occurrence; [0030] and [0032] features for predicting pollination state may include pollination event rate (number of events over a period)) and/or a duration of each of the pollination events ([0022], [0029], [0032] features for predicting pollination state may include duration of pollination events) wherein each pollination event comprises a pollinator visiting a flower ([0022] pollination events include bee pollinating a flower), and
determining, based on said number of pollination events and/or a duration of each of the pollination events, a value of a pollination quality parameter indicative of how well one or more flowers in the monitored area are pollinated ([0022] prediction models 132 determine pollination state in accordance with correlation to features detected by sensors 110 indicating occurrence of pollination events; [0035] evaluation of pollination state may be level of pollination).
Jones discloses that a variety of types of sensors may be used for collecting the feature data for determining pollination states [0018], but does not expressly teach that acoustic sensors may be included among the sensors, and therefore does not teach use of “microphones” for sensor data collection and consequently does not teach that the sensor data processed by the data processing system includes “sounds.”
Prior to the effective filing date, it was known in the art to use acoustic sensors such as microphones to monitor bee activity associated with pollination. For example, Goethem discloses a system/method for measuring bee pollination efficacy using acoustic data (Abstract and page 838, III. A Practical Solution, paragraph beginning with “A proposition for a product-service …” explaining that acoustic data (sound) may be used to determine pollination-related bee activity in the field (i.e., at locations at which pollination occurs); page 839, III. A Practical Solution, paragraph beginning with “The main advantage …” explaining that the sensor data recorded in the field including whether and how well pollination is occurring) that is collected using multiple distributed microphones (page 838, III. A Practical Solution, paragraph beginning with “By spreading these sensors …” describing distribution of the sensors on a field; FIG. 2 depicting distribution of sensors in a field; page 838, III. A Practical Solution, paragraph beginning with “As seen in figure 1 …” explaining the sensor is a microphone).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Goethem’s teaching of using acoustic monitoring of pollination-related activities (e.g., presence and/or types of pollinator insects) to the method taught by Jones such that acoustic sensors such as microphones are used as an alternative or in addition to the variety of sensors disclosed by Goethem to monitor pollination activity, such that in combination the method includes receiving, from each of a plurality of “microphones” for monitoring an area comprising said one or more flowers, one or more signals indicative of recorded “sounds,” each “microphone” out of the plurality of “microphones” is configured to monitor a sub-area of the monitored area, wherein each “microphone” is suitable for recording “sounds” produced by a pollinator for ultimately determining a quality parameter.
The motivation would have been to further expand the scope of input sensor information related to pollination activity that may be useful for accurately assessing such activity. Furthermore, such a combination would amount to selecting a known design option for monitoring pollinator activity to achieve predictable results.
Prior to the effective filing date, it was further known that acoustic monitoring may be used to detect acoustic signatures that are specific to pollination events (events that may be counted and/or for which durations may be determined). For example, Morgan discloses a method/system for analyzing acoustic signatures (types of buzzes) emitted by bees in relation to pollination activity (Abstract) that includes collecting acoustic/sound data indicating pollination events including floral “visits” and durations of such visits (page 236, Methods, paragraph beginning with “We recorded flight and feeding sonication …” describing recordation of sounds indicating arrivals and departures of bees to and from flowers (entails recordation of one or more pollination events) and further recording times of arrivals and departures (recording the arrival and departure temporal delimiters constitutes an effective recordation of duration)).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Morgan’s teaching of collecting and using sounds to identify pollination events such as occurrences of pollination and durations of such occurrences to the method taught by Jones as modified by Goethem in which distributed microphones are used to monitor pollination activity by pollinators, such that in combination the system includes determining, based on the one or more signals received from the plurality of microphones, a number of pollination events and/or a duration of each of the pollination events.
The motivation would have been to leverage known relations between acoustic signatures (e.g., sonication and flight buzzes) and pollination activities to further utilize the acoustic sensing taught by the combination of Jones and Goethem to identify/monitor more specific aspects of pollination in a more accurate manner.
As to claim 14, the combination of Jones, Goethem, and Morgan teaches “[a] data processing system for use in the system for monitoring pollination of plants according to claim 1 (Jones: FIG. 1 pollination prediction system 100 including pollination prediction server 130 and smart device 120), the data processing system comprising:
at least one input interface (Jones: FIG. 1 depicting communication interfaces including network 108 between pollination prediction server 130, smart device 120, and sensors 110) adapted to receive one or more signals indicative of recorded sounds from each of a plurality of microphones (Jones: FIG. 1 pollination prediction server 130 and smart device 120 configured to receive sensor data; [0022] prediction models 132 models correlations based on features detected by sensors 110. As combined with Goethem per claim 1 the sensors are microphones.), and
at least one processor (Jones: each of pollination prediction server 130 and smart device 120 are computing devices that inherently process data; [0047]-[0048]) adapted to determine, based on the one or more signals from the plurality of microphones, a value of a pollination quality parameter indicative of how well one or more flowers in the monitored area are pollinated (Jones: [0022] prediction models 132 determine pollination state in accordance with correlation to features detected by sensors 110 indicating occurrence of pollination events; [0035] evaluation of pollination state may be level of pollination), wherein the value of the pollination quality parameter is determined based on a number of pollination events (Jones: [0022] features upon which the pollination state determined include features indicating pollination occurrence; [0030] and [0032] features for predicting pollination state may include pollination event rate (number of events over a period)) and/or based on a duration of each of the pollination events determined by the data processing system based on the one or more signals received from the plurality of microphones (Jones: [0022], [0029], [0032] features for predicting pollination state may include duration of pollination events), wherein each pollination event comprises a pollinator visiting a flower (Jones: [0022] pollination events include bee pollinating a flower).
As to claim 15, the combination of Jones, Goethem, and Morgan teaches “[a] non-transitory computer readable medium comprising instructions which, when executed by at least one processor of the data processing system, cause the data processing system to perform the method according to claim 12 (Jones: Abstract computer program used for implementing method (inherently entails computer storage for storing instructions and processor-executable instructions); FIG. 3 depicting computer system for implementing method include memory 28 for storing instructions and processing unit 16; [0047]-[0048]).”
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Jones in view of Goethem and Morgan as applied to claim 1 above, and further in view of Marka (US 2019/0000059 A1).
As to claim 4, the combination of Jones, Goethem, and Morgan teaches “[t]he system according to claim 1, wherein the plurality of microphones comprises a first microphone configured to monitor a first sub-area of the monitored area (Jones (as combined with Goethem for claim 1 in which the sensors are microphones): [0018] describing multiple sensors distributed within an environment (e.g., attached to field fixtures and/or disposed on multiple, traveling vehicles) such that a first monitors a corresponding proximate sub-area) and a second microphone configured to monitor a second sub-area of the monitored area (Jones: [0018] describing multiple sensors distributed within an environment (e.g., attached to field fixtures and/or disposed on multiple, traveling vehicles) such that a second sensor monitors a proximate sub-area; Goethem: FIG. 2 depicting microphone sensors distributed on a field),” “and wherein the data processing system is configured to:
receive first one or more signals indicative of recorded sounds in the first sub-area from the first microphone and second one or more signals indicative of recorded sounds in the second sub-area from the second microphone (Jones: FIG. 1 pollination prediction server 130 and smart device configured to receive the signals from the sensors; [0022] prediction models 132 models correlations based on features detected by sensors 110), and to
determine, based on the first and the second one or more signals (Jones: [0022] values from sensors (multiple) used by algorithms to model correlation between features and pollination state), a value of a pollination quality parameter indicative of how well one or more flowers in a region of the monitored area” “are pollinated ([0022] prediction models 132 determine pollination state in accordance with correlation to features detected by sensors 110 indicating occurrence of pollination events; [0035] evaluation of pollination state may be level of pollination).”
Jones discloses (e.g., [0022] and [0037]) a sensor distribution in which multiple sensor inputs are used for determining pollination events and state over a region in which the sensors are located such that sensor overlap (e.g., imaging) may occur at least incidentally. Similarly, Goethem discloses a distributed microphone monitoring context (e.g., FIG. 2) in which the distributed microphones appear apt to receive sound data from overlapping regions. However, neither Jones nor Goethem appear to expressly teach collecting sensor data in overlapping regions that is used for determining pollination events/state, and therefore neither teaches determine, based on the first and the second one or more signals, a value of a pollination quality parameter indicative of how well one or more flowers in a region of the monitored area, “said region comprising said at least partial overlap between the first and second sub-area.”
Marka discloses a system/method for detection of insect activity (Abstract) that uses multiple microphones for tracking insect location (FIG. 1 microphones 110a, 110b, and 110c monitoring swarm 190; [0110]) in which an array of multiple microphones monitoring overlapping regions ([0034] multiple microphones deployed with overlapping sensing ranges to determine an intersecting region indicating location of swarm).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Marka’s teaching of the potential use of multiple microphones having overlapping sensing regions for ascertaining insect activity to the system taught by Jones as modified by Goethem and Morgan, which teaches collecting pollination event/state sensor data from multiple microphones, such that in combination the system is configured to determine, based on the first and the second one or more signals, a value of a pollination quality parameter indicative of how well one or more flowers in a region of the monitored area, “said region comprising said at least partial overlap between the first and second sub-area.”
The motivation would have been to leverage the sensor data that may be obtained from the perspective of multiple microphones over an overlapped region to enhance the acoustic perception for more accurate assessment of insect activity, which per Jones and Geothem are relevant to pollination state, as suggested by Marka.
Claims 7-10 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Jones in view of Goethem and Morgan as applied to claims 6 and 12 above, and further in view of Aikala (US 2014/0373442 A1).
As to claim 7, the combination of Jones, Goethem, and Morgan teaches “[t]he system according to claim 6, further comprising:
a pollination control system (Jones: FIG. 1 prediction system 100 including prediction server 130/pollination predictor 134 communicatively coupled with smart device 120; [0043] pollination predictor 134 provides pollination state/level information to smart device 120; [0042] pollination level information used to control pollination via beehive relocation) configured to influence pollination in selected regions by controlling one or more environmental conditions (Jones: [0042]-[0043] in response to the model determining low levels of pollination, the system entails provisioning such information to implement remedial action altering the environmental conditions (e.g., relocating beehives) for influencing pollination to address the region with low pollination)” “wherein the data processing system is configured to:
based on the determination of the one or more regions of concern, control the pollination control system to improve the pollination in said one or more regions of concern by controlling an environmental condition (Jones: [0042]-[0043] in response to the model determining low levels of pollination, the system entails provisioning such information to implement remedial, pollination improvement action (e.g., relocating beehives) for influencing pollination to address the region with low pollination).”
None of Jones, Goethem, and Morgan appear to teach that the controlled environmental condition for influencing pollination is “selected from a lighting condition, a sound, a vibration, an air flow, a temperature and a humidity in the selected regions.”
Aikala discloses a system/method for controlling insect pollination (Abstract insect pollination efficiency increased using lighting device) that uses a lighting condition in selected regions for environmental control ([0011] lighting device providing particular emission peaks for enhancing pollination activity (bee recognition of flowers) (light/reflection area would constitute the region); [0042] light device may be deployed in greenhouse and directed to area occupied by plants); [0044] effect of the light is to enhance pollination activity).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Aikala’s teaching of using lighting as a means of environmental control to influence pollination activity to the system taught by Jones as modified by Goethem and Morgan, in which Jones teaches controlling pollination influencing environmental factors such as location of beehives based on determining regions of concern, such that in combination the system is configured to use a lighting condition in addition or as an alternative to hive relocation as an environment condition influencing pollination for regions of concern, and consequently to control, based on the determination disclosed in Jones of a region of concern, a lighting condition for the region of concern.
The motivation would have been to enable relatively precisely controllable influence of location-sensitive pollination activity as suggested by Aikala.
As to claim 8, the combination of Jones, Goethem, Morgan, and Aikala teaches “[t]he system according to claim 7, wherein the data processing system is configured to, based on the determination of the one or more regions of concern,
control the pollination control system to improve the pollination in said one or more regions of concern by controlling an environmental condition in one or more of said selected regions other than said one or more regions of concern (Jones: [0042] relocation beehives (beehive relocation entailed with pollination control system) corresponding to determination of low pollination in certain regions, which clearly infers repositioning of bees from proximity of a higher pollinated area and placement closer to a lower pollinated area, to improve pollination of the region of concern results in a change in “environmental condition” in terms of the absence or greater distance of the relocated bees within the regions outside the region of concern) to deteriorate the pollination in said selected regions other than said one or more regions of concern (the hive relocation(s) taught by Jones results in pollination deterioration of the areas from which the hives are moved at least in terms of proximity of pollinators that have been relocated).”
As to claim 9, the combination of Jones, Goethem, Morgan, and Aikala teaches “[t]he system according to claim 7, wherein the data processing system is configured to, based on the determined value of the pollination quality parameter for each region of the plurality of regions (Jones: [0042] determination of pollination levels including low levels),
control the pollination control system to control the pollination in the plurality of regions (Jones: [0042] beehives relocated based on determined (via control system entailing communications to smart device per [0043]) dissimilarity in pollination between different regions (clearly inferred that relocation is performed with respect to proximate distances to monitored regions of crops)) to achieve a substantially uniform pollination across the plurality of regions in the monitored area (Jones: [0042] pollination levels determined as low for some regions and sufficient for other regions, resulting in beehive relocation action to remediate the disparity (i.e., bring the lower pollination regions higher) to reach sufficiency as for the regions already determined to be at sufficient level).”
As to claim 10, the combination of Jones, Goethem, Morgan, and Aikala teaches “[t]he system according to claim 9,” and as set forth in the grounds for rejecting claims 7 and 8 Jones teaches a control system for controlling the environment condition in regions of concern to influence pollination, and as combined with Aikala for claim 7 the controlled environment condition may be a lighting condition.
Aikala further teaches that the lighting device may be provided via “a horticulture illumination system that is configured to generate pollination light suitable for influencing pollination ([0042]-[0043] light device used in direct light emissions in horticulture environment (greenhouse and/or indoor environment or outdoors) to enhance (control) pollinator activities; FIG. 7, [0072] depicting and describing greenhouse in which lighting device is deployed),”
“control the horticulture illumination system to provide pollination light in” “one or more regions” “for improving the pollination in” “one or more regions ([0042]-[0043] light device used in direct light emissions in horticulture environment (greenhouse and/or indoor environment or outdoors) to enhance pollination activities)”
“wherein the pollination light comprises wavelengths of blue ([0011] lighting device providing particular emission peaks for enhancing pollination activity (bee recognition of flowers); [0012] wavelengths typically suited as emission peaks include 435 nm; [0038]) and/or long UVA ([0011] lighting device providing particular emission peaks for enhancing pollination activity (bee recognition of flowers); [0012] wavelengths typically suited as emission peaks include 348 nm and 375 nm; [0038]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Aikala’s teaching of using an environmental control lighting device configured as a horticulture illumination system that is configured to generate pollination light suitable for influencing pollination, and using the illumination system to control the horticulture illumination system to provide pollination light in one or more regions for improving the pollination in said one or more regions, wherein the pollination light comprises wavelengths of blue and/or long UVA to the system taught by Jones as modified by Goethem, Morgan, and Aikala, which teaches a control system for controlling the environment condition based on determined regions of concern to influence pollination, in which the controlled environment condition may be a lighting condition, such that in combination the system is configured that that the control system includes a horticulture illumination system that is controlled by the data processing system in response to determining regions of concern (e.g., networked system depicted in Jones FIG. 1 in which pollination predictor communicates with other devices and effectuated for regions of concern per [0042]-[0043]) to provide pollination light in said one or more regions “of concern” for improving the pollination in said one or more regions “of concern, “wherein the pollination light comprises wavelengths of blue and/or long UVA.”
The motivation for such combination would be to leverage the efficiently and precisely controllable lighting as an efficient and highly controllable pollination influencing factor, which is provided at a wavelength design option known to enhance pollination activity, to improve efficiency and accuracy of pollination remedial activity.
As to claim 13, the combination of Jones, Goethem, and Morgan teaches “[t]he method according to claim 12, further comprising
determining, based on the one or more signals received from the plurality of microphones (Jones as combined with Goethem per claim 12 teaches use of microphones), for each region out of a plurality of regions in the monitored area, a pollination quality parameter indicative of how well one or more flowers in the region are pollinated (Jones: [0022] prediction models 132 determine pollination state in accordance with correlation to features detected by sensors 110 indicating occurrence of pollination events; [0035] evaluation of pollination state may be level of pollination), and
based on the determined value of the pollination quality parameter for each region of the plurality of regions (Jones: [0040]-[0041] pollination state determined based on sensed data that is labelled/tracked by respective location; [0035] pollination state/level mapped via color coding (i.e., distinct regions differentiated)), determining one or more regions of concern out of the plurality of regions (Jones: [0042] pollination predictor 134 used model to determine differentiations among multiple crop regions including determining relatively low levels of pollination for some regions), each region of concern having a value for the associated pollination quality parameters that is lower than a threshold value (Jones: [0042] model determines unsatisfactory levels of pollination (“only 50%” and “only 25%”) entailing levels insufficient (below a sufficient level) and therefore warranting potential remedial action (e.g., relocating beehives)), and
based on the determination of the one or more regions of concern, controlling a pollination control system (Jones: FIG. 1 prediction system 100 including prediction server 130/pollination predictor 134 communicatively coupled with smart device 120; [0043] pollination predictor 134 provides pollination state/level information to smart device 120; [0042] pollination level information used to control pollination via beehive relocation as remediation for determined low pollination levels), configured to influence pollination in selected regions by controlling an environmental condition” “in the selected regions, to improve the pollination in said one or more regions of concern (Jones: [0042]-[0043] in response to the model determining low levels of pollination, the system entails provisioning such information to implement remedial action altering the environmental conditions (e.g., relocating beehives) for influencing pollination to address the region with low pollination).”
None of Jones, Goethem, and Morgan appear to teach that the controlled environmental condition for influencing pollination is “selected from a lighting condition, a sound, a vibration, an air flow, a temperature and a humidity in the selected regions.”
Aikala discloses a system/method for controlling insect pollination (Abstract insect pollination efficiency increased using lighting device) that uses a lighting condition in selected regions for environmental control ([0011] lighting device providing particular emission peaks for enhancing pollination activity (bee recognition of flowers) (light/reflection area would constitute the region); [0042] light device may be deployed in greenhouse and directed to area occupied by plants); [0044] effect of the light is to enhance pollination activity).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Aikala’s teaching of using lighting as a means of environmental control to influence pollination activity to the method taught by Jones as modified by Goethem and Morgan, in which Jones teaches controlling pollination influencing environmental factors such as location of beehives based on determining regions of concern, such that in combination the method is configured to use a lighting condition in addition or as an alternative to hive relocation as an environment condition influencing pollination for regions of concern, and consequently to control, based on the determination disclosed in Jones of a region of concern, a lighting condition for the region of concern.
The motivation would have been to enable relatively precisely controllable influence of location-sensitive pollination activity as suggested by Aikala.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Jones in view of Goethem, Morgan, and Aikala as applied to claim 9 above, and further in view of Hasegawa et al., “How do honeybees attract nestmates using waggle dances in dark and noisy hives?” PLoS One. 2011.
As to claim 11, the combination of Jones, Goethem, Morgan, and Aikala teaches “[t]he system according to claim 9,” but none of Jones, Goethem, Morgan, and Aikala appear to teach “wherein the pollination control system comprises a sound producing system configured to produce acoustic signals suitable for influencing pollination, wherein
the data processing system is configured to, based on the determination of the one or more regions of concern, control the sound producing system to provide acoustic signals in said one or more regions of concern for improving pollination in said one or more regions of concern,
wherein the acoustic sound signals comprise a sound of a pollinator flying or a sonication sound.”
Hasegawa discloses a system/method for implementing and test sound-based bee attraction (Abstract explaining that honeybees share information related to food source using dance sounds in the 250-300 Hz range the is the same as honeybee flight sounds and that the proposed system is configured to generate sounds preferred by honeybee of around 265 Hz) in which flight sounds at 250 Hz carrier frequence and dance sounds similar to flight sounds at 265 Hz used to determine bee attraction and found to have a positive attractive effect (page 2, Results, paragraph beginning with “In order to verify the performance of sound source localization…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Hasegawa’s teaching of using an artificial sound source for producing sounds, including flight sounds of bees, to attract bees to the system taught by Jones as modified by Goethem, Morgan, and Aikala, which teaches using a control system to remediate areas of concern in which as disclosed by Jones proximity of bees is an indicator and influencer of pollination ([0042] beehive relation to remediate uneven pollination of crops), such that in combination the system is configured such that the control system produces acoustic signals for influencing pollination, wherein the data processing system is configured to, based on the determination of the one or more regions of concern, control the sound producing system to provide acoustic signals in said one or more regions of concern for improving pollination in said one or more regions of concern, wherein the acoustic sound signals comprise a sound of a pollinator flying.
The motivation would have been to enhance pollination remediation in addition to or as an alternative to the beehive relocation and or light-emitting pollination enhancements to achieve more effective pollination remediation.
Conclusion
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/MATTHEW W. BACA/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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