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 .
Response to Arguments
Applicant's arguments filed 6/24/2026 have been fully considered but they are not persuasive.
Applicant’s argument (pg. 12) is not persuasive. Applicant did not correct claims 5, 12 and 19.
Applicant’s argument (pg. 16-24) are not persuasive. The Examiner agrees that paragraph 2 discuss a problem. Eligibility is based on what the claim recites, not what the Specification says is the problem. The additional elements in the claim are generic processors (MPEP 2106.05(f)), receiving data (MPEP 2106.05(g)) a machine learning model with no algorithm, architecture or training method, which is claimed purely by its result (MPEP 2106.05(f)) , and a storing operation (MPEP 2106.05(g)). The claim recites two abstract ideas (averaging or clustering) – mathematical concept and a mental process of determining if an image has enough identifying information, both of which can be done by a person mentally.
MPEP 2106.05(a) states “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement…..Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.” And “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.”
Applicant’s arguments concerning the 35 USC 103 rejection are persuasive, but are moot in view of new grounds of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim(s) 1 recite(s):
“one or more processors; and one or more computer-readable media storing instructions executable to configure the one or more processors to perform operations including: “, which is directed to an additional element recited at a high level of generality. These amount to merely reciting the words “apply it” with the judicial exception. See MPEP 2106.05(g)
“receiving, by the one or more processors, over time, from one or more agent devices and in association with a delivery location, a plurality of images and associated respective location data, the respective location data associated with at least one of the images of the plurality of images differing from the respective location data associated with at least one other one of the images of the plurality of images;”, which is an additional element directed to insignificant extra-solution activity. See MPEP 2015.05(g).
“providing the plurality of images as inputs to a machine-learning model that is trained to determine whether individual images of the plurality of images include a threshold amount of information, the machine-learning model determining for each of the individual images, whether the threshold amount of information is satisfied based solely on content of the individual image;;” wherein the “machine learning model” is akin to using a computer as a tool to implement the abstract idea. The ML model is recited at a high level of generality without any specificity. These amount to merely reciting the words “apply it” with the judicial exception. See MPEP 2106.05(g); The determining step is directed to a mental process, for example a person could look at an image and determine if there is sufficient information in the image, such as the door, delivered item and a legible number.
“based at least in part on the machine-learning model indicating that the individual images of the plurality of received images satisfy the threshold amount of information, ” which is directed to a mental process of visually inspecting each image and determining if it contains sufficient features such as a door number;
“ determining, based on at least one of averaging or clustering of the respective location information associated with the plurality of images, a consensus location, including a consensus latitutde or longitude for the delivery location;” Averaging directed to a mathematical concept and clustering is directed to a mental process of determining if the location information is close together or not. Determining a consensus location for the delivery location is also a mental process. For example, a person could mentally determine if the location information was close to each other then set a consensus location based on the cluster analysis.
“storing in the mapping information database the consensus latitude and longitude as mapping information associated with the delivery location.”, which is directed to insignificant post-solution activity. See MPEP 2106.05(g)
This judicial exception is not integrated into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 2 is directed to data gathering and does not add a practical application or significantly more.
Claim 3-5 is directed to data gathering and error checking and does not add a practical application or significantly more.
Claim 6 is directed to field of use and is not significantly more. See MPEP 2106.05(h)
Claim 7 is directed to the mental process and is significantly more.
Claims 8-14 fand 15-20 are rejected under similar grounds as claims 1-7 above.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5,12,19 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.
Claims 5,12 and 19 recites “the other image”. “the other image” lacks antecedent basis. Most likely this should refer back to the “the another image”.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mishra (10,219,112) in view of Gil (2020/0317340).
1. (Currently Amended) A system comprising:one or more processors; andone or more computer-readable media storing instructions executable to configure the one or more processors to perform operations including:
receiving, by the one or more processors, over time, from one or more agent devices and in association with a delivery location(UAVs with cameras and GPS, see paragraph 171) , a plurality of images and associated respective location data, the respective location data associated with at least one of the images of the plurality of images differing from the respective location data associated with at least one other one of the images of the plurality of images; (Gil, Fig. 68, #6806-6808, paragraph 455 “The method 6800 further comprises receiving one or more of a photo or video captured by the camera of the UAV, the one or more of the photo or the video indicative of the release of the parcel at the serviceable point, as shown at block 6806. The photo and/or video may be captured concurrently with or subsequent to the notification that the parcel is dis-engaged …”; “[0457] At block 6808, the method 6800 comprises communicating a confirmation of delivery of the parcel to a user computing entity based on the notification that the parcel is disengaged, wherein the confirmation includes the one or more of the photo or the video.” “[0458] Generally, metadata is generated when the photo and/or video are captured and digitally converted into an image. With regard to digital images, metadata provides information in addition to the image data itself, such that the additional data travels with the image data. The metadata may include location data, address data, unique identifiers, file type, data type, file size, data quality, a source of the data, a caption, a tag, a date and time of creation, one or more properties, a name, a color depth, an image resolution, an image size, and the like….”).
, determining, based on at least one of averaging or clustering of the respective location information associated with the plurality of images, a consensus location including a consensus latitude and longitude for the delivery location; and (Gil, paragraph 350 discloses determining a single consensus coordinate from multiple locations, “[0350] The geo coordinate samples can be provided to the geographic information/data database, which, after an appropriate number of geo coordinate samples associated with a primary/secondary delivery point, processes the sample geo coordinates and creates or updates the primary/secondary delivery point geo coordinate for the serviceable point 5901. For example, the geographic information/data database may be configured to require two, three, and/or more consistent sample geo coordinates associated with a primary/secondary delivery point 5902, 5904 before creating or updating a primary/secondary delivery point geo coordinate for the serviceable point 5901”)
But doesn’t expressly disclose “averaging” or “clustering”
Mishra discloses “averaging or clustering” (Mishra, Col. 7 lines 36-67, “The systems and methods of the present disclosure are directed to determining preferred points or regions at a given location, such as routing points and/or delivery points, for the performance of a given task. In some embodiments, routing points and/or delivery points may be defined based at least in part on geolocation estimation techniques which determine probability distributions, e.g., according to a Gaussian location hypothesis or other methods or techniques for modeling errors or uncertainty, of sensed positions at a location and any levels of uncertainty associated with the distributions, and group the sensed positions into one or more hypothetical location clusters, e.g., location hypotheses or areas of uncertainty.”)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to further use Mishra’s clustering technique in with the 2 or more points of Gil to determine the delivery point geo coordinate.
The suggestion/motivation for doing so would have been Mishra provides as well known method of solving the problem that Gil is required to solve (converting multiple points into a single point).
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
storing, in a mapping information database, the consensus latitude and longitude as mapping information associated with the delivery location. (Gili, paragraph 351,” [0351] In various embodiments, the information/data sets for the points need to be stored and accessed for route/path determination and optimization. In various embodiments, the primary/secondary delivery point 5902, 5904 information/data may be stored in a variety of ways-including as part of a user profile, parcel information/data, and/or a serviceable point 5901 profile.”)
Gil in view of Mishra discloses using an algorithm or a neural network to compare an individual image with reference images to determine if there is a match does not expressly disclose “providing the plurality of images as inputs to a machine-learning model that is trained to determine whether individual images of the plurality of images include a threshold amount of information, the machine-learning model determining, for each of the individual images, whether the threshold amount of information is satisfied based solely on content of the individual image; based at least in part on the machine-learning model indicating that each of the individual images of the plurality of received images satisfy the threshold amount of information”
Khadloya discloses “providing the plurality of images as inputs to a machine-learning model that is trained to determine whether individual images of the plurality of images include a threshold amount of information, the machine-learning model determining, for each of the individual images, whether the threshold amount of information is satisfied based solely on content of the individual image; based at least in part on the machine-learning model indicating that each of the individual images of the plurality of received images satisfy the threshold amount of information” (Khadloya, Col. 4, lines 52-67, “(23) Aspect 12 can include or use, or can optionally be combined with the subject matter of one or any combination of Aspects 1 through 11 to optionally include analyzing the image information from the camera using machine learning to validate the parcel delivery notification, including by applying one or more frames from the camera as an input to a first neural network and, in response, receiving an indication of a likelihood that the one or more frames includes at least a portion of a specified object. In an example, in Aspect 12, providing the control signal to control operation of the barrier door includes when the likelihood meets or exceeds a threshold likelihood criteria.
(24) Aspect 13 can include or use, or can optionally be combined with the subject matter of Aspect 12, to optionally include the specified object includes one or more of the parcel, delivery personnel, or a delivery vehicle.”)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to further to replace Gil’s reference image matching technique with Gil’s single-frame no-reference technique.
The suggestion/motivation for doing so would have been Khodloya solves the same problem (verifying that delivery location capture is reliable for parcel delivery) with the benefit of
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Gil in view of Mishra in view of Khodloya discloses 2. The system as recited in claim 1, the operations further comprising: receiving a request for an item for delivery to the delivery location;(Mishra, Fig. 7 #720, “
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”) and retrieving the consensus location as the mapping information associated with the delivery location for use in generating a map to present on an agent device for delivery of the item to the delivery location.(Mishra, Fig. 7, #780-790,
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, where the consensus locations are determined and stored in 710
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Gil in view of Mishra in view of Khodloya discloses 3. The system as recited in claim 1, the operations further comprising: receiving feedback indicating that an item associated with one of the received images of the plurality of images was not received or was in a wrong location; and removing the associated respective location data from being associated with the delivery location when determining the consensus location. (Mishra, Fig. 3, #310-350 discloses disregarding coordinates when coordinates are not validated for a task
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Gil in view of Mishra in view of Khodloya discloses 4. The system as recited in claim 1, the operations further comprising: receiving another image and associated respective location data for a different delivery location; providing the other image as input to the machine-learning model; and based at least in part on an output of the machine-learning model indicating that the other image fails to satisfy the threshold amount of information, sending, by the one or more processors, to an agent device that sent the other image, an instruction to capture an additional image corresponding to the different delivery location.(Gil, Fig. 70,
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, discloses acquiring an image, comparing the image to determine if it was delivered correctly (adverse delivery event, see claim 1 for neural network) and in the event that the package was misdelivererd sending the/another uav to retrieve the package which includes photographing the delivery location . See Fig. 71
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Gil in view of Mishra in view of Khodloya discloses 5. The system as recited in claim 1, the operations further comprising: receiving another image and associated respective location data for a different delivery location; providing the other image as input to the machine-learning model; and based at least in part on an output of the machine-learning model indicating that the other image fails to satisfy the threshold amount of information, excluding the respective location data associated with the other received image from being associated with mapping information for the different delivery location. (see claim 1 and claim 3, where the process of claim 1 is repeated for numerous packages)
Gil in view of Mishra in view of Khodloya discloses 6. The system as recited in claim 1, the operations further comprising training the machine learning model using a plurality of images of past delivery locations for a plurality of past deliveries to densely populated structures. (Gil, paragraph 485, “[0485] Additionally or alternatively, a convoluted neural network may be leveraged to account for changes to the appearance of the serviceable point, for example, when determining whether the captured photo and/or video are a match to a stored image. For example, regarding a serviceable point, the colors of exterior paint color a house or apartment building, the colors of architectural features of a house or apartment building (e.g., shutters, front door, roof shingles, trim), and/or the colors of landscaping features at the serviceable point (e.g., lack of foliage in winter, autumn leaf color changes, spring flowering) may be changed from time to time, or from season to season. Further, regarding a serviceable point, the colors of a house or apartment building, architectural features, and/or landscaping features may appear to be visually different depending on lighting changes or lighting fluctuations resulting from weather conditions (e.g., sunny conditions, overcast or cloudy conditions, stormy and rainy conditions, foggy conditions), time of day or night, and seasons (e.g., time of sunrise and sunset being affected by seasonality). Even further, regarding a serviceable point, the appearance of the house or apartment building may fluctuate and/or be obscured by the addition of a mailbox, the addition or removal of fencing, and/or holiday decorations, for example.”)
Gil in view of Mishra in view of Khodloya discloses 7. The system as recited in claim 1, wherein the threshold amount of information includes a delivered item and at least one of an entrance portion, a door portion, or a unit number. (Gil, paragraph 485, “[0485] Additionally or alternatively, a convoluted neural network may be leveraged to account for changes to the appearance of the serviceable point, for example, when determining whether the captured photo and/or video are a match to a stored image. For example, regarding a serviceable point, the colors of exterior paint color a house or apartment building, the colors of architectural features of a house or apartment building (e.g., shutters, front door, roof shingles, trim), and/or the colors of landscaping features at the serviceable point (e.g., lack of foliage in winter, autumn leaf color changes, spring flowering) may be changed from time to time, or from season to season. Further, regarding a serviceable point, the colors of a house or apartment building, architectural features, and/or landscaping features may appear to be visually different depending on lighting changes or lighting fluctuations resulting from weather conditions (e.g., sunny conditions, overcast or cloudy conditions, stormy and rainy conditions, foggy conditions), time of day or night, and seasons (e.g., time of sunrise and sunset being affected by seasonality). Even further, regarding a serviceable point, the appearance of the house or apartment building may fluctuate and/or be obscured by the addition of a mailbox, the addition or removal of fencing, and/or holiday decorations, for example.”)
Claims 8-14 and 15-20 are rejected under similar grounds as claims 1-7 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GANDHI THIRUGNANAM whose telephone number is (571)270-3261. The examiner can normally be reached M-F 8:30-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached at 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GANDHI THIRUGNANAM/ Primary Examiner, Art Unit 2672 +
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