DETAILED ACTION
Status of Claims
This final Office action is responsive to Applicant's amendment filed 5/18/26. Claims 1, 3, 8, 10, 15, and 17 have been amended. Claims 2, 4-5, 9, 11-12, 16, 18-19 have been canceled. No claims have been added. Claims 1, 3, 6-8, 10, 13-15, 17, and 20 have been considered as follows.
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 the rejection of the pending claims under 35 U.S.C. § 103 have been considered but are moot in view of the new ground(s) of rejection. Regarding Applicant's argument that the cited references do not teach of the newly added limitations drawn to calculating the grazing pattern and determining an average grazing rate, Biffert et al. (US 2022/0200519 A1, herein Biffert) has been brought in, necessitated by amendment, to illustrate this aspect (see claim 1 rejection below).
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, 6-8, 10, 13-15, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Castelli et al. (US 2018/0049407 A1, herein Castelli) in view of Biffert et al. (US 2022/0200519 A1, herein Biffert) in further view of Houston et al. (US 2016/0144404 A1, herein Houston).
As per claim 1, Castelli teaches of a method comprising:
generating a knowledge corpus of a grazing pattern of a livestock group in a preconfigured area (pg. 4, [0038] which describes how the camera may be configured to detect one or more animals and track locations of one or more animals to avoid overgrazing of herds by periodically capturing images of animal movement and mapping such geolocations; and pg. 8, [0073] which describes how the database may store information related to past grazing areas and/or types of animals that have grazed in particular foraging areas, where the mapping device may appropriately select a geolocation foraging zone such that overgrazing in particular zones are avoided);
generating a knowledge corpus of a growing pattern of flora for each of a plurality of sections in the preconfigured area based on a flora type and weather conditions within a preconfigured time period in the preconfigured area (pg. 2, [0017] which describes unmanned aerial vehicles for determining geolocation foraging zones for animals, including monitoring vegetation characteristics of particular geolocations; and pg. 3, [0034] which describes how the unmanned aerial vehicle may include at least one camera configured to provide visual feedback and may capture/provide images of geolocations corresponding to vegetation/foliage to identify geolocation foraging zones, where the unmanned aerial vehicle may track and/or identify vegetation characteristics (e.g., type of vegetation, quality of vegetation, quantity of vegetation, etc.) and may provide corresponding geolocation information to generate a virtual map indicating foraging zones (e.g., areas in which animals may forage and/or where grazing may be beneficial));
generating a prediction of elevated risk of a natural disaster related to flora overgrowth based on the knowledge corpuses (pg. 1, [0002-0003] which describes how overgrazing in a particular location for an extended amount of time without sufficient recovery periods may be detrimental to the land, wildlife, and livestock, where grazing systems have been employed to minimize the effects of overgrazing, where appropriate levels of grazing may be effective in at least reducing fire hazards due to forage buildup; and pg. 1, [0004] which describes a risk analysis device to evaluate a level of risk associated with each of the plurality of geolocations; and pg. 2, [0017] which describes enabling the unmanned aerial vehicle to determine a level of risk associated with foraging zones; and pg. 8, [0075-0076] which describes how the risk analysis device may be configured to evaluate a level of risk associated with a geolocation foraging zone, where other factors such as environmental factors may be employed to determine a level of risk associated with the geolocation foraging zone, including at least weather predictions, weather analysis information, and historical data such as viability of vegetation);
transmitting instructions to an autonomous robotic device to traverse the preconfigured area to a current location of the livestock group and to relocate the livestock group from the current location to a new location at a lower risk of the natural disaster than the current location based on the prediction; and directing, by the autonomous robotic device, the livestock group to the new location using prerecorded audio cues (pg. 1, [0004] which describes a mapping device coupled to the monitoring device to select at least one geolocation foraging zone when the level of risk associated with the at least one geolocation foraging zone is below a predetermined threshold value; and pg. 2, [0017] which describes enabling the unmanned aerial vehicle to determine a level of risk associated with foraging zones and provide deterrence action such that the animals avoid particularly dangerous foraging zones; and pg. 3, [0033] which describes providing movement for the unmanned aerial vehicle to determine geolocation information for foraging zones and/or guiding one or more animals to a selected foraging zone; and pg. 5, [0048] which describes how the unmanned aerial vehicle may include a guidance generator that may perform one or more functions to guide/lead the detected animals to a selected geolocation foraging zone, such as generating a sound and/or verbal instructions/commands via a speaker and/or activating the movement mechanisms such that movement from the unmanned aerial vehicle induces the animals to relocate).
However, Castelli fails to explicitly teach of calculating the grazing pattern, determining an average grazing rate, and directing livestock using haptic sensations. Biffert teaches of a livestock management system, including:
wherein the generating comprises: calculating the grazing pattern using a location of each member of the livestock group, a length of time each member remained in a specific location, and an activity each member is engaged in while present at the specific location (abstract which describes tags and sensors attached to and implanted in a plurality of livestock that maintains data regarding the location, activity and physical parameters of a livestock to which it is attached, where the remote computer uses the tag data to generate and update livestock behavior and condition models for download to the tags; and pg. 1, [0003, 0006] which describes a livestock management system for detecting, tracking, and responding to livestock location and activity, where each tag locally and autonomously receives and/or acquires data regarding the location, orientation, movement of the livestock and determines certain activities and behaviors of the livestock, e.g. eating, ruminating, ambulating, where the tag can make predictions and determinations about livestock activity and physical condition by applying one or more AI models and/or other detection algorithms to the received and acquired data; and pg. 14, [0125-0128] which describes how the tag can detect, determine, and monitor certain physical parameters, activities, and behaviors of the livestock, such as the absolute location of the livestock and whether the livestock is within a designated pasture area, the amount of time the livestock has been present in the particular designated pasture area, along with data regarding certain livestock activity, e.g. eating, where and when the livestock has eaten and perhaps how long it ate; and pg. 33, [0262] which describes how the management system platform can identify the livestock having locations within the boundary coordinates of each designated grazing area at any given time, determine the amount of time they spent there engaged in eating activity); and
determining an average grazing rate for each member based on a species of the member (pg. 4-5, [0051, 0056] which describes how the management system platform can track the locations and movements of individual livestock and the herd under management in general and can use models and/or other algorithms to determine certain activities and behaviors and can also process the data and determinations from the tags that include both individual and herd level data and information, such as genetic and family lineage, history of physical characteristics such as weight, health and medical history, etc. and herd demographics and statistics, herd location relative to pasturage, and the status of use and remaining inventories of pasturage, feed, and/or other consumables; and pg. 17, [0147] which describes how the tag has in memory the data whether the livestock to which it is attached is a cow or bull; and pg. 19, [0162] which describes how the tag can autonomously and automatically determine the above-described physical parameters, activities and behaviors of the livestock from data acquired from the sensors, accelerometers, gyroscope, compass, altimeter, barometer, etc., where the Inertial Measurement Unit can measure the motion of a livestock, which may include measuring step count, high acceleration motion, reduction in motion, and average motion, where the tag can use data from the GNSS receiver and inertial measurements from the accelerometer and gyroscope to determine that the livestock is eating, which is typically indicated by low GPS speed, and low acceleration and movement related to eating; and pg. 31, [0247] which describes how the management system platform can be adapted and configured to receive characteristic data for each individual livestock as input data, including birth date, age, sex, breed, coloring, etc. of the livestock; and pg. 33, [0262] which describes how the management system platform can identify the livestock having locations within the boundary coordinates of each designated grazing area at any given time, determine the amount of time they spent there engaged in eating activity, and apply a predetermined rate of consumption value to estimate the amount of grazing material consumed).
Castelli teaches of unmanned aerial vehicles for determining geolocation foraging zones. Biffert teaches of a livestock management system, specifically including calculating the grazing pattern and determining an average grazing rate as claimed. Both references are drawn to tracking and managing livestock. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Castelli with the calculated grazing pattern and determined average grazing rate as taught by Biffert for the purpose of autonomously and automatically without the need for a rancher, herd manager, or other person to physically observe the livestock, using the tag to improve the overall health, well-being, and productivity of the herd (Biffert, pg. 19, [0158]). By doing so, one would reasonably expect the overall appeal of the invention to improve in efficiency in the managing and tracking of livestock by autonomously to minimize human error and intervention.
However, Castelli in view of Biffert still fails to explicitly teach of directing livestock using haptic sensations. Houston teaches of synchronized array of vibration actuators in an integrated module, specifically including directing the livestock group to a location identified in the generated prediction using prerecorded audio cues and haptic sensations produced through focused pressure fields created in mid-air by an array of ultrasound transducers (abstract which describes the synchronized array of vibration actuators; and pg. 49, [0688] which describes using beat patterns in haptic navigation; and pg. 50, [0689] which describes how the vibration beat patterns may be simultaneously enhanced with relevant visual stimuli, relevant audio stimuli, or both, where these types of devices may be used to gently and humanely guide animals away from certain areas via vibration collars, particularly if there were certain frequencies that produced sharp responses in the given animal).
Castelli in view of Biffert teaches of unmanned aerial vehicles for determining geolocation foraging zones including calculating a grazing pattern and determining an average grazing rate. Houston teaches of synchronized array of vibration actuators in an integrated module, specifically including directing livestock to a specific location using prerecorded audio cues and haptic sensations. Both references are drawn to tracking and managing livestock. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Castelli in view of Biffert with the prerecorded audio cues and haptic sensations as taught by Houston for the purpose of gently and humanely guiding animals away from certain areas (Houston, pg. 50, [0689]). By doing so, one would reasonably expect the overall appeal of the invention to improve in efficiency in the managing and tracking of livestock by providing additional means to direct and manage the livestock.
As per claim 8, it refers to a computer system for performing the above steps. It recites limitations already addressed by claim 1 above, and is therefore rejected under the same art and rationale. Furthermore Castelli et al. (US 2018/0049407 A1, herein Castelli) discloses the steps are performed on a system, the computer system/server operational with numerous other general purpose or special purpose computing system environments or configurations (pg. 2, [0019]; and pg. 10, [0105]).
As per claim 15, it refers to a computer program product for performing the above steps. It recites limitations already addressed by claim 1 above, and is therefore rejected under the same art and rationale. Furthermore Castelli et al. (US 2018/0049407 A1, herein Castelli) discloses the steps are performed using a non-transitory computer readable storage medium and/or a computer program product (pg. 1, [0005]; and pg. 2, [0019]).
As per claim 3, Castelli in view of Biffert in further view of Houston discloses all the elements of claim 1, and Castelli further teaches wherein the activity of each animal is identified using a plurality of sensors affixed to each animal or nearby the livestock group (pg. 5, [0051] which describes how one or more animals may be equipped with a tracking device that may communicate with each other and/or the unmanned aerial vehicle to communicate location information and/or movement information for each animal, monitoring information associated with a respective animal, such as location of the animal (e.g., movement of the animal) and/or health characteristics of the animal (e.g., temperature, blood information, pulse rate, oxygen content etc.)).
As per claim 10, it refers to the system of claim 9 used for performing the above steps. It recites limitations already addressed by claim 3 above, and is therefore rejected under the same art and rationale.
As per claim 17, it refers to the computer program product of claim 16 used for performing the above steps. It recites limitations already addressed by claim 3 above, and is therefore rejected under the same art and rationale.
As per claim 6, Castelli in view of Biffert in further view of Houston discloses all the elements of claim 1, and Castelli further teaches wherein the prediction further comprises identifying an elevated risk of a natural disaster is present in a section of the preconfigured area and a location towards which the livestock group is to be directed so as to prevent or mitigate the elevated risk (pg. 1, [0002-0004] which describes how overgrazing in a particular location for an extended amount of time without sufficient recovery periods may be detrimental to the land, wildlife, and livestock, where grazing systems have been employed to minimize the effects of overgrazing, where appropriate levels of grazing may be effective in at least reducing fire hazards due to forage buildup, where the unmanned aerial vehicle includes a risk analysis device to evaluate a level of risk associated with each of the plurality of geolocations and a mapping device coupled to the monitoring device to select at least one geolocation foraging zone when the level of risk associated with the at least one geolocation foraging zone is below a predetermined threshold value; and pg. 2, [0017] which describes enabling the unmanned aerial vehicle to determine a level of risk associated with foraging zones and provide deterrence action such that the animals avoid particularly dangerous foraging zones; and pg. 3-4, [0034-0035] which describes how the unmanned aerial vehicle may include at least one camera configured to provide visual feedback and may capture/provide images of geolocations corresponding to vegetation/foliage to identify geolocation foraging zones, where the unmanned aerial vehicle may track and/or identify vegetation characteristics (e.g., type of vegetation, quality of vegetation, quantity of vegetation, etc.) and may provide corresponding geolocation information to generate a virtual map indicating foraging zones (e.g., areas in which animals may forage and/or where grazing may be beneficial)); and pg. 8, [0075-0076] which describes how the risk analysis device may be configured to evaluate a level of risk associated with a geolocation foraging zone, where other factors such as environmental factors may be employed to determine a level of risk associated with the geolocation foraging zone, including at least weather predictions, weather analysis information, and historical data such as viability of vegetation).
As per claim 13, it refers to the system of claim 8 used for performing the above steps. It recites limitations already addressed by claim 6 above, and is therefore rejected under the same art and rationale.
As per claim 20, it refers to the computer program product of claim 15 used for performing the above steps. It recites limitations already addressed by claim 6 above, and is therefore rejected under the same art and rationale.
As per claim 7, Castelli in view of Biffert in further view of Houston discloses all the elements of claim 1, and Castelli further teaches wherein the growing pattern comprises flora growing rates, soil requirements, flora water requirements, and recent weather conditions for each section (pg. 2, [0017] which describes unmanned aerial vehicles for determining geolocation foraging zones for animals, including monitoring vegetation characteristics of particular geolocations; and pg. 3-4, [0034-0035] which describes how the unmanned aerial vehicle may include at least one camera configured to provide visual feedback and may capture/provide images of geolocations corresponding to vegetation/foliage to identify geolocation foraging zones, where the unmanned aerial vehicle may track and/or identify vegetation characteristics (e.g., type of vegetation, quality of vegetation, quantity of vegetation, etc.) and may provide corresponding geolocation information to generate a virtual map indicating foraging zones (e.g., areas in which animals may forage and/or where grazing may be beneficial), where the camera may also acquire information associated with a particular geolocation, such as pasture biophysical properties (e.g., biomass, leaf area index, nitrogen content, chlorophyll content, grass density, canopy height, etc.); and pg. 4-5, [0043-0045] which describes the sensor for detecting information associated with a particular geolocation, including biophysical characteristics of a geolocation, vegetation characteristics of a geolocation, environmental variables in a particular geolocation).
As per claim 14, it refers to the system of claim 8 used for performing the above steps. It recites limitations already addressed by claim 6 above, and is therefore rejected under the same art and rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hempstead et al. (US 2012/0012069 A1) teaches of a method and apparatus for improved monitoring and managing of livestock.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEY Y YOUNG whose telephone number is (571)270-5294. The examiner can normally be reached Mondays, Tuesdays, and Thursdays, 9:00a-3:00p, EST.
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/ASHLEY Y YOUNG/Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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