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 .
Claim Objections
Claims 1 and 17 are objected to because of the following informalities:
Claim 1, line 16, “machine learning” should be changed to --machine learning (ML)--.
Claim 1, line 38, “theideal return” should be changed to --return--.
Claim 17, lines 18-19, “wherein the smart pollination apparatus is configured to: receiving instructions corresponding to an ideal condition” should be changed to --receiving instructions corresponding to an ideal condition by the smart pollination apparatus--.
Claim 17, line 35, “ideal returning” should be changed to --returning--.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “profiling unit for profiling types of pollens stored within the storage unit” in claims 1 and 17.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2, 5, and 9-17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 17 recite “a profiling unit for profiling types of pollens stored within the storage unit”. The specification mentions a profiling unit, however, does not disclose how or what does the profiling within the unit. The specification fails to provide written description that shows the inventor possessed the invention as recited in claims 1 and 17.
Claims 2, 5, and 9-16 are rejected for being dependent upon a rejected claim.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 5, and 9-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “identify an exact location of a female reproductive part of the target host based on 360-degree 3D mapping of the target host” in lines 31-32. It is unclear since the insertion of the stored pollens is to be into the female reproductive part of the identified recipient, but the target host, which appears to be misrepresentative of the disclosed invention.
Claim 1 recites the limitation "the fertility cycle" in line 35. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation "the fertility cycle" in line 32. There is insufficient antecedent basis for this limitation in the claim.
Claim limitation “profiling unit for profiling types of pollens stored within the storage unit” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The specification recites in paragraph [0022] that the storage unit may be connected to a weighing unit and a profiling unit (120). The pollens collected from the host plants may be weighed and profiled for their types with the relevant data shared with the central network system 200. Figure 1 shows the profiling unit within the storage unit (104), which equates to a contamination free storage unit as described in paragraph [0022] of the specification. The written description fails to disclose the corresponding structure, material, or act for performing the profiling that occurs to determine the types of pollen gathered within the storage unit. Although paragraph [0022] recites that the data of the pollens collected from the host plants that are weighed and profiled are shared with the central network system, there is no actual disclosure of what is performing the profiling that occurs to determine the types of pollen gathered. Further, the specification also recites cameras and image processing in paragraph [0030]. The cameras (112) however, are separate, distinct and independent from the profiling unit as shown in Fig. 1 and also perform a different function, wherein the cameras (112) are utilized for capturing patterns on flowers of plants for identifying the target host and recipient based on the captured patterns. Accordingly, the cameras disclosed in the specification fail to clearly link to the function of profiling the pollen type that is within the storage unit (104). No drawings, flow charts or description of the profiling has been provided for neither the process nor the apparatus.
Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 2, 5, and 9-16 are rejected for being dependent upon a rejected claim.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 5, and 9-17 are rejected under 35 U.S.C. 103 as being unpatentable over Cantrell et al. (US 2018/0065749) in view of Calleija et al. (WO 2016/090414), Atkinson et al. (US 4922651), Caldeira (US 2016/0353661), and Cope et al. (US 2016/0374280).
Regarding claim 1, Cantrell et al. discloses a smart pollination apparatus (120) for carrying out an artificial pollination, wherein the smart pollination apparatus (120) comprises: a storage unit ((124),(127)), for storing the pollens collected from the target host (paragraphs [0015], [0024], ((124),(127)) configured to collect pollen (180) from flower (190a)); the smart pollination apparatus is to identify a recipient near the target host (paragraph [0021], camera configured to optically observe the flowers and/or the presence of pollen (180) applied onto the flowers); and an inserter (124) connected to the storage unit for depositing the stored pollens into the identified recipient (paragraph [0024] deposit the collected pollen (180) into a receptacle internal to the UAV (120)); wherein the smart pollination apparatus (120) is configured to: receive instructions corresponding to an ideal condition, based on one or more of knowledge base of hosts, knowledge base of recipients, weather (paragraph [0029] notes at least one sensor (122) that measures speed and direction of wind that facilitates the controls ((140) or control circuit of (120)) to determine optimal location for the UAV to be moved to for pollination), seasonal trends, air quality, soil quality, and suitable conditions required for pollination (at least one sensor (122) configured to detect the presence of pollen (180)); and when the received instructions indicate the ideal condition is met, the smart pollination apparatus is configured to: fly to the identified recipient for initiating artificial pollination (paragraph [0018], actions of (120) including detecting, flying, pollinating) into the identified recipient (paragraph [0015], apply the pollen from flower (190a) onto a flower (190b)).
Cantrell et al. does not explicitly disclose that the smart pollination apparatus is machine learned and uses artificial intelligence for the artificial pollination, wherein the smart pollination apparatus comprises: a suction unit having an aspiration pump, for collecting pollens from a target host; the storage unit detachably connected to the suction unit, wherein the storage unit further comprises: a profiling unit for profiling types of the pollens stored within the storage unit; wherein based on the profiled types of the pollens, the smart pollination apparatus is to identify a recipient near the target host based on machine learning and artificial intelligence; wherein the ideal condition is determined, by a global communications system (GCS) using AI/ML, identify an exact location of a female reproductive part of the target host based on 360-degree 3D mapping of the target host; and insert a predetermined quantity of the stored pollens into the female reproductive part based on the fertility cycle; when the received instructions indicate the ideal condition is unmet, the smart pollination apparatus is configured to: return the stored pollens to a warehouse associated with the smart pollination apparatus, wherein the storage unit is to be used as a storage container for storing the pollens in the warehouse under ideal environmental conditions, until a suitable recipient for depositing the stored pollens is identified.
Calleija et al. teaches a smart pollination apparatus (1) that is machine learned and uses artificial intelligence for the artificial pollination (paragraph [0055] teaches machine learning and algorithms (i.e. Artificial Intelligence) to determine targets, and overall control strategy for the apparatus), wherein identification of the ideal condition and a recipient near the target host is based on machine learning and artificial intelligence and determined by a global communications system (paragraph [0055] teaches machine learning and algorithms (i.e. Artificial Intelligence) to determine targets, and overall control strategy for the apparatus, paragraph [0125] teaches the use of a GCS).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of Cantrell to include the smart pollination apparatus that uses artificial intelligence for the artificial pollination, identification of the ideal condition and a recipient near the target host based on machine learning and artificial intelligence determined by a global communications system as taught by Calleija et al., with a reasonable expectation of success, because doing so would have provided a flying device designed to identify a type of flower or plant and perform pollination based on machine learning and artificial intelligence to autonomously determine a flight path by identifying a pollination target with improved reliability, precision, and accuracy through a system that is global.
Atkinson teaches an apparatus for improving pollination of plants that includes a suction unit having an aspiration pump (6), for collecting pollens from the target host (Fig. 9, col. 3, lines 5-6, fan 6 operates to suck pollen grain from male plants; pump 6 is positioned between the cyclone 5 and the nozzles 15; col. 4, lines 45- 47, pump 6 is connected via branch 24 to a dispersal device generally indicated by arrow 53 which includes a splitter 54 and nozzles 14; col. 4, lines 51-52, the cyclone also has an upper outlet 8 that leads to the air pump 6) and a storage unit (52) detachably connected to the suction unit, for storing the pollens collected from the target host (Fig. 9, col. 3, lines 5-6, fan 6 operates to suck pollen grain from male plants, col. 4, lines 39-45, stored from the cyclone and into the collection bottle (52) which is detachable), wherein the storage unit (52) is to be used as a storage container for storing the pollens (collection bottle (52) which is detachable).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of Cantrell et al. modified by Calleija et al. to include a suction unit having an aspiration pump for collecting pollens from the target host and a storage unit detachably connected to the suction unit for storing the pollens collected from the target host as taught by Atkinson, with a reasonable expectation of success, because doing so would have provided an air suction device for sucking pollen from plants to collect and transfer pollen to a holding device for improved pollination of plants by increasing the amount collected at one time.
Caldeira teaches a system for pollination that includes a storage unit (paragraph [0029], Robotic device 200 may include stores for storing multiple pollens 450 of multiple pollen types) comprises a profiling unit (paragraphs [0051] and [0068], sensors 520 acquire plant data regarding plants (100) and flowers (120); plant data includes pollen, type of pollen) for profiling types of the pollens stored within the storage unit (paragraph [0052], a pollen type could describe characteristics of pollen 450 such as height, width, depth, volume, mass, density, color, surface roughness, hardness, longevity, response to temperature or moisture changes, genetic information including relationship to other plants 100 and pollen 450, or any other such characteristics); for depositing the stored pollens, identify an exact location of a female reproductive part based on monitoring analytics of the recipient (Fig. 4C, paragraphs [0036], [0043]-[0045] teach the gathering of the analytics of the recipient and exact location of the female reproductive part (stigma, pistil, etc.) for insertion), to insert the stored pollens into the female reproductive part (Fig. 4C, paragraphs [0036] and [0045], “entry of pollen (450) into stigma (410)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of Cantrell et al. modified by Calleija et al. and Atkinson to include a profiling unit for profiling types of the pollens stored within the storage unit and identify an exact location of a female reproductive part based on monitoring analytics of the recipient, to insert the stored pollens into the female reproductive part as taught by Caldeira, with a reasonable expectation of success, because doing so would have provided an airborne robotic pollen collection device configured to collect and store multiple pollen types with sensors configured to acquire characteristics of pollen to selectively pollinate flowers of a plant based on the characteristics of pollen. Please note in the combination of Cantrell et al., Calleija et al., Atkinson, and Caldeira, identifying a recipient near the target host is based on the profiled types of the pollens, machine learning, and artificial intelligence.
Cope et al. teaches a smart pollination apparatus that carries pollen from a target host to a recipient, identify an exact location of a female reproductive part of the target host (paragraphs [0036] and [0040] teach the pollen is placed on the exact location of the female reproductive part, silk is a functional stigma for a corn plant) based on 360-degree 3D mapping of the target host (paragraphs [0036] and [0040] teach the use of GPS, which is a 3D mapping of the area including target host); and inserting a predetermined quantity of the stored pollens into the female reproductive part (paragraph [0040] teaches the successful pollination amount needed, i.e. a predetermined quantity, and adjusted as necessary) based on the fertility cycle (paragraph [0038] teaches the delivering of the pollen during a plant’s fertility window, i.e. based on the fertility cycle); and when the ideal condition is not met (paragraph [0043], such as caused by a drought), the apparatus is to return the content, to a warehouse associated with the apparatus (paragraph [0043], pollen storage back or controlled environment), and store in the warehouse under ideal environmental conditions, until a suitable recipient for depositing the stored content is identified (paragraph [0043], stored until females are available for pollination).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of Cantrell et al. modified by Calleija et al., Atkinson, and Caldeira to insert predetermined quantities of stored pollen, and return the stored content to a warehouse until a suitable recipient is identified as taught by Cope et al., with a reasonable expectation of success, in order to secure the stored content while unable to deposit, due to bad weather or the like in order to avoid damage to the apparatus or the loss of the stored content.
Regarding claim 2, Cantrell et al. as modified by Calleija et al., Atkinson, Caldeira, and Cope et al. teaches the apparatus of claim 1, and teaches (references to Cantrell et al. unless otherwise noted) wherein the smart pollination apparatus comprises a robotic arm (claim 3 lists at least one arm extending outwardly from the body), and wherein the robotic arm is guided by machine learning and artificial intelligence to enable release of the pollens within the target host (Calleija et al.: paragraph [0055] teaches machine learning and algorithms (i.e. Artificial Intelligence) to determine targets, and overall control strategy for the apparatus). Cantrell et al. as modified by Calleija et al., Atkinson, Caldeira, and Cope et al., as discussed so far, does not specifically teach the robotic arm is for latching onto the target host and causing vibrations for shedding the pollens within the target host.
In addition to the above, Caldeira teaches a robotic arm for latching onto the target host and causing vibrations for shedding the pollens within the target host (paragraph [0038] teaches airborne robotic device 200 may include a gripping member configured to reversibly attach airborne robotic device 200 to the plant 100 (e.g., while performing a pollination operation to a flower of the plant). Please note this is an intended use limitation and the arm is capable of latching onto the target host and causing vibrations for shedding the pollens).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of Cantrell modified by Calleija et al., Atkinson, Caldeira, and Cope et al. to include a robotic arm for latching onto the target host and causing vibrations for shedding the pollens within the target host as taught by Caldeira with a reasonable expectation of success because doing so would have provided an airborne robotic pollen collection device with a gripping member configured to reversibly attach the airborne robotic device to a plant while performing a pollination operation to a flower of the plant allowing for further control of the operation.
Regarding claim 5, Cantrell et al. as modified by Calleija et al., Atkinson, Caldeira, and Cope et al. teaches the apparatus of claim 1 and teaches (references to Atkinson unless otherwise noted) wherein the storage unit ((52), claim 1, abstract, pollen particles stored in said cyclonic separator, a cyclone where the pollen may be stored), detachably connected to the suction unit (col. 3, lines 5-6, fan 6 operates to suck pollen grain from male plants) is for storing the collected pollens at prespecified controlled conditions to maintain properties of the collected pollens (Cantrell et al.: paragraph [0024], to collect pollen (180) from the flower (190a) and either deposit the collected pollen (180) into a receptacle internal to the UAV (120) or otherwise securely retain the collected pollen (180)).
Regarding claim 9, Cantrell et al. as modified by Calleija et al., Atkinson, Caldeira, and Cope et al. teaches the apparatus of claim 5, and teaches (references to Cantrell et al.) wherein the smart pollination apparatus comprises a spray unit (127), connected to the storage unit, for spraying the stored pollens over the identified recipient with multiple female reproductive parts (paragraph [0030], (127) is coupled to the flow generating component (spray nozzle) generating air flow to cause the pollen (180) to be blown off the bristles (129) and directed toward the recipient flower).
Regarding claim 10, Cantrell et al. as modified by Calleija et al., Atkinson, Caldeira, and Cope et al. teaches the apparatus of claim 1, and teaches (references to Cantrell et al.) wherein the smart pollination apparatus (120) is to scan a global geographic landscape based on prespecified parameters (paragraph [0070], GPS tracking device and identification of the location of the UAV (120)) for identifying the target host and the recipient (paragraph [0017], UAV 120 is configured to fly above ground through a space overlying the crop-containing area (110), to collect pollen (180) from a flower (190 a) of a first crop (192 a) and to apply the pollen 180 collected from the flower (190 a) of the first crop (192 a) onto a flower (190 b) of a second crop (192 b)).
Regarding claim 11, Cantrell et al. as modified by Calleija et al., Atkinson, Caldeira, and Cope et al. teaches the apparatus of claim 1, and teaches (references to Cantrell et al.) wherein the smart pollination apparatus comprises a plurality of cameras (122) for capturing patterns on flowers of plants for identifying the target host and the recipient based on the captured patterns (paragraphs [0021]-[0022], sensors (120) include a video camera configured to optically observe the flowers of the crops and/or the presence of pollen (180) applied onto the flowers of the crops; cameras capable of providing a visual of the pollen (180) as it appears on the crops (e.g., on leaves, flowers, fruits, or stalks); after receiving pollen detection data indicating the detection of pollen (180) computing device (140) is configured to send a control signal to the UAV (120) to instruct the UAV (120) to further pollinate the crops in that section of the crop-containing area).
Regarding claim 12, Cantrell et al. as modified by Calleija et al., Atkinson, Caldeira, and Cope et al. teaches the apparatus of claim 1, and teaches (references to Cantrell et al.) wherein the smart pollination apparatus (120) is an unmanned aerial vehicle (paragraph [0020], UAV (120) shown in Fig. 1).
Regarding claim 13, Cantrell et al. as modified by Calleija et al., Atkinson, Caldeira, and Cope et al. teaches the apparatus of claim 1, and teaches (references to Cantrell et al.) wherein the smart pollination apparatus is controlled by a remote source (140) in communication with the smart pollination apparatus (paragraph [0018], UAV (120) deployed in the exemplary system 100 does not require physical operation by a human operator and wirelessly communicates with, and is wholly or largely controlled by, the computing device (140). In particular, in some embodiments, the computing device (140) is configured to control directional movement and actions of the UAV (120) (e.g., flying, hovering, landing, taking off, moving while on the ground, pollinating the crops, detecting pollen 180 on the crops, etc.) based on a variety of inputs).
Regarding claim 14, Cantrell et al. as modified by Calleija et al., Atkinson, Caldeira, and Cope et al. teaches the apparatus of claim 1, and teaches (references to Cantrell et al.) wherein the smart pollination apparatus is automatically controlled (paragraph [0018], UAV (120) deployed in the exemplary system 100 does not require physical operation by a human operator and wirelessly communicates with, and is wholly or largely controlled by, the computing device (140)).
Regarding claim 15, Cantrell et al. as modified by Calleija et al., Atkinson, Caldeira, and Cope et al. teaches the apparatus of claim 1, and teaches (references to Cantrell et al.) wherein the smart pollination apparatus comprises a power supply to power the smart pollination apparatus (paragraph [0047], UAV (120) includes a rechargeable power source (220) such as one or more batteries).
Regarding claim 16, Cantrell et al. as modified by Calleija et al., Atkinson, Caldeira, and Cope et al. teaches the apparatus of claim 15, and teaches (references to Cantrell et al.) wherein the power supply is a wireless power supply (paragraph [0047], UAV (120) includes a rechargeable power source (220) such as one or more batteries, and may include one or more solar charging panels).
Regarding claim 17, Cantrell et al. discloses a method for carrying out an artificial pollination using a smart pollination apparatus (120), the method comprising: storing, by a storage unit ((124),(127)), the pollens collected from the target host (paragraphs [0015], [0024], ((124),(127)) configured to collect pollen (180) from flower (190a)); wherein identifying a recipient near the target host (paragraph [0021], camera configured to optically observe the flowers and/or the presence of pollen (180) applied onto the flowers); an inserter (124) connected to the storage unit for depositing the stored pollens into the identified recipient (paragraph [0024] deposit the collected pollen (180) into a receptacle internal to the UAV (120)); wherein the smart pollination apparatus (120) is configured to: receiving instructions corresponding to an ideal condition, based on one or more of knowledge base of hosts, knowledge base of recipients, weather (paragraph [0029] notes at least one sensor (122) that measures speed and direction of wind that facilitates the controls ((140) or control circuit of (120)) to determine optimal location for the UAV to be moved to for pollination), seasonal trends, air quality, soil quality, and suitable conditions required for pollination (at least one sensor (122) configured to detect the presence of pollen (180)); and when the received instructions indicate the ideal condition is met, the smart pollination apparatus is configured to: flying to the identified recipient for initiating artificial pollination (paragraph [0018], actions of (120) including detecting, flying, pollinating) into the identified recipient (paragraph [0015], apply the pollen from flower (190a) onto a flower (190b)).
Cantrell et al. does not explicitly disclose that the smart pollination apparatus is machine learned and uses artificial intelligence for the artificial pollination; collecting, by a suction unit having an aspiration pump, pollens from a target host; wherein the storage unit is detachably connected to the suction unit, wherein the storage unit comprises: a profiling unit for profiling types of the pollens stored within the storage unit; the identifying of the recipient near the target host is based on the profiled types of the pollens and based on machine learning and artificial intelligence; wherein the ideal condition is determined, by a global communications system (GCS) using AI/ML, identifying an exact location of a female reproductive part based on monitoring analytics of the recipient and the 360-degree 3D mapping of the target host; and inserting a predetermined quantity of the stored pollens into the female reproductive part based on the fertility cycle; when the received instructions indicate the ideal condition is unmet, the smart pollination apparatus is configured to: returning the stored pollens to a warehouse associated with the smart pollination apparatus, wherein the storage unit is to be used as a storage container for storing the pollens in the warehouse under ideal environmental conditions, until a suitable recipient for depositing the stored pollens is identified.
Calleija et al. teaches a method for carrying out an artificial pollination using a smart pollination apparatus (1) that is machine learned and uses artificial intelligence for the artificial pollination (paragraph [0055] teaches machine learning and algorithms (i.e. Artificial Intelligence) to determine targets, and overall control strategy for the apparatus) wherein identification of the ideal condition and a recipient near the target host is based on machine learning and artificial intelligence and determined by a global communications system (paragraph [0055] teaches machine learning and algorithms (i.e. Artificial Intelligence) to determine targets, and overall control strategy for the apparatus, paragraph [0125] teaches the use of a GCS).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Cantrell to include the smart pollination apparatus that uses artificial intelligence for the artificial pollination, identification of the ideal condition and a recipient near the target host based on machine learning and artificial intelligence determined by a global communications system as taught by Calleija et al., with a reasonable expectation of success, because doing so would have provided a flying device designed to identify a type of flower or plant and perform pollination based on machine learning and artificial intelligence to autonomously determine a flight path by identifying a pollination target with improved reliability, precision, and accuracy through a system that is global.
Atkinson teaches a method for carrying out an artificial pollination using an apparatus for improving pollination of plants that includes a suction unit having an aspiration pump (6), for collecting collect pollens from the target host (Fig. 9, col. 3, lines 5-6, fan 6 operates to suck pollen grain from male plants; pump 6 is positioned between the cyclone 5 and the nozzles 15; col. 4, lines 45- 47, pump 6 is connected via branch 24 to a dispersal device generally indicated by arrow 53 which includes a splitter 54 and nozzles 14; col. 4, lines 51-52, the cyclone also has an upper outlet 8 that leads to the air pump 6) and a storage unit (52) detachably connected to the suction unit for storing store the pollens collected from the target host (Fig. 9, col. 3, lines 5-6, fan 6 operates to suck pollen grain from male plants, col. 4, lines 39-45, stored from the cyclone and into the collection bottle (52) which is detachable), wherein the storage unit (52) is to be used as a storage container for storing the pollens (collection bottle (52) which is detachable).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Cantrell et al. modified by Calleija et al. to include a suction unit having an aspiration pump for collecting collect pollens from the target host and a storage unit detachably connected to the suction unit for storing store the pollens collected from the target host as taught by Atkinson, with a reasonable expectation of success, because doing so would have provided an air suction device for sucking pollen from plants to collect and transfer pollen to a holding device for improved pollination of plants by increasing the amount collected at one time.
Caldeira teaches a method including a system for pollination that includes a storage unit (paragraph [0029], Robotic device 200 may include stores for storing multiple pollens 450 of multiple pollen types) comprises a profiling unit (paragraphs [0051] and [0068], sensors 520 acquire plant data regarding plants (100) and flowers (120); plant data includes pollen type of pollen) for profiling types of the pollens stored within the storage unit (paragraph [0052], a pollen type could describe characteristics of pollen 450 such as height, width, depth, volume, mass, density, color, surface roughness, hardness, longevity, response to temperature or moisture changes, genetic information including relationship to other plants 100 and pollen 450, or any other such characteristics); for depositing the stored pollens, identify an exact location of a female reproductive part based on monitoring analytics of the recipient (Fig. 4C, paragraphs [0036], [0043]-[0045] teach the gathering of the analytics of the recipient and exact location of the female reproductive part (stigma, pistil, etc.) for insertion), inserting the stored pollens into the female reproductive part (Fig. 4C, paragraphs [0036] and [0045], “entry of pollen (450) into stigma (410)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Cantrell et al. modified by Calleija et al. and Atkinson to include a profiling unit for profiling types of the pollens stored within the storage unit and identify an exact location of a female reproductive part based on monitoring analytics of the recipient, to insert the stored pollens into the female reproductive part as taught by Caldeira with a reasonable expectation of success because doing so would have provided an airborne robotic pollen collection device configured to collect and store multiple pollen types with sensors configured to acquire characteristics of pollen to selectively pollinate flowers of a plant based on the characteristics of pollen. Please note in the combination of Cantrell et ala, Calleija et al., Atkinson, and Caldeira, identifying a recipient near the target host is based on the profiled types of the pollens, machine learning, and artificial intelligence.
Cope et al. teaches a method that includes a smart pollination apparatus that carries pollen from a target host to a recipient, identify an exact location of a female reproductive part of the target host (paragraphs [0036] and [0040] teach the pollen is placed on the exact location of the female reproductive part, silk is a functional stigma for a corn plant) based on 360-degree 3D mapping of the target host (paragraphs [0036] and [0040] teach the use of GPS, which is a 3D mapping of the area including target host); and inserting a predetermined quantity of the stored pollens into the female reproductive part (paragraph [0040] teaches the successful pollination amount needed, i.e. a predetermined quantity, and adjusted as necessary) based on the fertility cycle (paragraph [0038] teaches the delivering of the pollen during a plant’s fertility window, i.e. based on the fertility cycle); and when the ideal condition is not met (paragraph [0043], such as caused by a drought), the apparatus is to return the content, to a warehouse associated with the apparatus (paragraph [0043], pollen storage back or controlled environment), and store in the warehouse under ideal environmental conditions, until a suitable recipient for depositing the stored content is identified (paragraph [0043], stored until females are available for pollination).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Cantrell et al. modified by Calleija et al., Atkinson, and Caldeira to insert predetermined quantities of stored pollen, and return the stored content to a warehouse until a suitable recipient is identified as taught by Cope et al., with a reasonable expectation of success, in order to secure the stored content while unable to deposit, due to bad weather or the like in order to avoid damage to the apparatus or the loss of the stored content.
Response to Arguments
Applicant's arguments entered 11/14/2025 have been fully considered but they are not persuasive.
With respect to claims 1 and 17, applicant argued that separately, Cantrell et al., Ajamian, Atkinson, Caldeira, and Carlson do not teach wherein the ideal condition is determined, by a global communications system (GCS) using AI/ML, and identifies an exact location of a female reproductive part of the target host based on 360-degree 3D mapping of the target host. Therefore, applicant argued that it would not be obvious to combine the prior art as suggested.
The examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
All arguments against Carlson are moot at this point based on the revised rejection above using Cope et al. (US 2016/0374280).
In response to applicant's argument regarding the modification of Cantrell et al., the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). Here, the primary reference is Cantrell et al., and Cantrell et al. can easily be modified to include further controls with respect to the ideal condition. Additionally, the machine learning and artificial intelligence taught by Calleija et al. teaches the use throughout the system and Cope et al. teaches the identification of an exact location of a female reproductive part through mapping. Therefore, the combined prior art as noted in the rejections above read on the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Cope et al. (US 2015/0324975) teaches imaging of crop plants. Cope et al. (US 2014/0223812) and Cope et al. (US 2013/0118066) teach an apparatus and method for dispensing grain of pollen.
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/CARLY W. LYNCH/Examiner, Art Unit 3643