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 Status
Claims 1-22 are pending for examination in the Application No. 18/970,076 filed December 5th, 2024.
Priority
Applicant’s claim for the benefit of prior-filed provisional U.S. Patent Application No. , filed on December 8th, 2023, under 35 U.S.C. 119(e) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/303,050 filed on February 3rd, 2022, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The prior-filled application does not disclose the subject matter of retro-reflective material including fabric and/or paint having micro glass beads as a retro-reflective element; a marker including a pattern comprising two strips and a gap separating the two stripes, each of the two stripes and the gap having configured thickness, a type of a golf club being identified based on determining a cross-ratio using four image points defining thicknesses of the two stripes; wherein the thickness of the two stripes and the gap are divided into a specified number of bines, the pattern being formed based on the number of bins, into which the two stripes and the gap fall; and creating a template that maps cross-ratios and golf club types based on using combinations of thicknesses of the two stripes and the gap corresponding to respective golf club types, wherein the cross-ratio is matched with one of the cross-ratios specified in the template in identifying the golf club type. Accordingly, claims 8-9, 12-16, 20, and 22 are not entitled to the benefit of the prior application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 5th, 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered and attached by the examiner.
Claim Objections
Claims 1, 13, 17, and 21 are objected to because of the following informalities:
In claims 1, 13, 17, and 21, the examiner respectfully suggests amending the phrase “the marker for representing a specific type of golf club” to recite “the marker [[for ]]representing a specific type of club” to prevent confusion regarding whether the phrase is interpreted as a functional limitation of the claim or as an intended use/result of the claim.
Appropriate correction is required.
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.
It is noted that claims 21-22 are considered eligible subject matter. Although claims 21-22 recite a “computer readable storage medium”, the instant specification disavowals the claimed computer readable storage medium from including transitory propagating signals per se (e.g., paragraph [0048] recites “…A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se…”). Therefore, the claims cover statutory subject matter and are eligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-6, 11, 17-19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Beach et al. (Beach; US 2021/0069548 A1) in view of Amarant et al. (Amarant; US 2019/0344138 A1).
Regarding claim 1, Beach discloses a system comprising:
at least one memory device (para(s). [0047], recite(s)
[0047] “The illustrated computing device 200 can include memory 220. The memory 220 can include non-removable memory 222 and/or removable memory 224. The non-removable memory 222 can include RAM, ROM, flash memory, a hard disk, or other well-known memory storage technologies. …”
); and
at least one processor coupled with the memory device (para(s). [0052], recite(s)
[0052] “With reference to FIG. 3, the server computer 300 includes one or more processing units 310, 315 and memory 320, 325. …The processing units 310, 315 execute computer-executable instructions. …The memory 320, 325 stores software 380 implementing one or more innovations described herein, in the form of computer-executable instructions suitable for execution by the processing unit(s).”
)the at least one processor configured to at least:
receive a series of images of a golf club captured during a golf swing (para(s). [0020], [0031], and [0076], recite(s)
[0020] “Some launch monitors use high speed cameras to capture measurements during a golf swing. Camera-based launch monitors are often referred to as optical launch monitors, and may use multiple cameras to capture the measurements during the golf swing. Multi-camera systems may measure the golf ball, the golf club, or a combination thereof. …”
[0031] “Video has also been long used to evaluate the golf swing, and many technologies integrate optical systems capable of capturing video. …”
[0076] “…the fitting system may deploy a camera and image recognition software to identify the golf club specifications. …”
, where the images captured “during a golf swing” by “high speed cameras” are a series of images (e.g., “video”) of a golf club captured during a golf swing);
detect from the series of images(para(s). [0076], recite(s)
[0076] “… Each golf club shaft, head, or golf club configuration may be tagged to identify the golf club specifications in the fitting system. The golf club specifications may also include club head, shaft, flex, length, lie, loft, weight configuration(s), adapter configuration(s) (e.g., flight control technology (FCT) by TaylorMade Golf), and other specifications. In an embodiment, different tags are provided for the golf club head, club shaft, and/or other components to identify each component separately. The golf clubs may be tagged using Bluetooth tags, RFID tags, bar codes, or other tags. For example, the Bluetooth tags may be provided as Bluetooth stickers, Bluetooth screws, or other types of Bluetooth tags. … the fitting system may deploy a camera and image recognition software to identify the golf club specifications. …”
, where identifying “Bluetooth tags, RFID tags, bar codes, or other tags” is detecting one or more sticker labels (i.e., the tags include “stickers”) placed on the golf club); and
classify(para(s). [0076], recite(s)
[0076] “…the fitting system may deploy a camera and image recognition software to identify the golf club specifications. When running the software application on the fitting system, the user may wave the club and/or tag in front of a sensor to automatically capture the club specifications. In an embodiment, the club tags are also motion sensors for capturing additional swing data, and may work in conjunction with a video camera, launch monitor, or another device.”
, where “identify[ing] the golf club specifications” is classifying a golf club type based on recognizing a marker (e.g., “tag”) coded on the one or more sticker labels).
Where Beach does not specifically disclose
detect from the series of images, using a machine learning model, one or more sticker labels placed on the golf club; and
classify, using a machine learning model, a golf club type of the golf club based on recognizing a marker coded on the one or more sticker labels, the marker for representing a specific type of golf club;
Amarant teaches in the same field of endeavor of detecting one or more sticker labels placed on a golf club
detect from the series of images, using a machine learning model, one or more sticker labels placed on the golf club (para(s). [0038] and [0047], recite(s)
[0038] “The data and image processing components 110 may also identify different configurations of the golf club through image analysis techniques as well. For example, some physical segments of an adjustable system may be labeled with optical code identifiers… Those optical code identifiers may be identified in an image of the adjustment system. Once the optical code identifiers are identified through image analysis, the configuration state of the adjustment system may be determined. Other optical code identifiers other than numbers or letters may also be used, such as dot or line patterns. In other examples, the configurable component may have a different two-dimensional or three-dimensional shape for each of its configuration states. In such examples, the image analysis techniques may be used to identify those shapes. The image analysis techniques may also be based on machine learning techniques, such as neural networks, deep learning algorithms, statistical analysis techniques, enhanced contrast techniques, blob analysis, optical character recognition, or other pattern recognition or matching techniques that are trained based on the shape of the adjustment system or the optical code identifiers of the adjustment system. For instance, a plurality of images may be captured for each configuration state of an adjustment system. Those images may then be used as a training set of for a machine learning image analysis algorithm. The image data received from the configuration detection devices 102 may subsequently be provided as an input into the trained image analysis algorithm to determine a current configuration state of the adjustment system being analyzed. The output from the trained image analysis algorithm may be configured to directly provide details of the configuration state of the adjustment system or a unique ID that can be compared against data in a database, such as club head and shaft database 112.”
[0047] “The configuration identifiers, such as the club head identifier 212, the shaft identifier 214, and the hosel identifier 216 may be attached to the golf club 200 via an adhesive or a shrink wrap. For example, the identifiers for the golf club 200 may be provided as a set or kit for attaching to the golf club 200 or a set of golf clubs (or golf club components). …”
, where identifying “optical code identifiers” using “image analysis techniques” including “machine learning techniques” like “neural networks” is detecting one or more sticker labels (e.g., “configuration identifiers”) using at least a machine learning model (e.g., “machine learning techniques, such as neural networks…”)); and
classify, using a machine learning model, a golf club type of the golf club based on recognizing a marker coded on the one or more sticker labels, the marker for representing a specific type of golf club (para(s). [0038] and [0047]—see citation above—, where “determin[ing] a current configuration state” based on the “optical identifiers” using at least “machine learning techniques, such as neural networks” includes at least classifying a golf club type (e.g., identifying “type of shaft or club”) of the golf club based on recognizing a marker (e.g., “optical identifier”) coded on the one or more sticker labels (e.g., “configuration identifiers”) as recited in para(s). [0046], [0061], and [0084] below:
[0046] “FIG. 2A depicts an example golf club 200 having multiple configuration identifiers. The golf club 200 includes a golf club head 202 attached to a shaft 204 via a hosel 210 of a shaft connection system. The shaft 204 also includes a grip 206 and the bottom of the golf club head 202 is referred to as the sole 208. The golf club head 202 includes a club head identifier 212 and the shaft 204 includes a shaft identifier 214. The club head identifier 212 may be an optical code identifier, such as a barcode or image, or an RFID identifier, such as a passive RFID tag. …”
[0061] “…Additional configuration data for the golf club head or a shaft may also be captured in method 300A. For instance, an image, RFID tag, or a barcode of a shaft or a club head of the golf club may be captured. The type of shaft or club head may be determined by performing similar operations as included in method 300A.”
[0084] “…the training platform may deploy a camera and image recognition software to identify the golf clubs.”
).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Beach to incorporate detecting from the series of images one or more sticker labels placed on the golf club using a machine learning model and classifying a golf club type of the golf club based on recognizing a marker coded on the one or more sticker labels, the marker for representing a specific type of golf club, to improve golf swing performance analysis by classifying the type of golf club based on the marker coded on the one or more sticker labels as taught by Amarant (e.g., para(s). [0070], recite(s)
[0070] “FIG. 4 depicts an example method 400 for detecting a golf club configuration and storing swing and ball flight data. Method 400 generally allows for measuring performance data and statistics to be tracked for different configuration states of a golf club. For instance, a first golf club configuration state may be detected by a golf club configuration detection system. The system may then record performance data for multiple golf shots with the golf club in the first configuration state, and that performance data is correlated with the first configuration state and stored. When the golf club is altered to be in a new configuration state, the golf club can be rescanned and the new configuration state is detected. Performance data for golf shots with the golf club may then be recorded or tracked. That performance data is correlated with the new configuration state and stored. The process continues for all the desired configuration states that the fitting specialist or player desires to test. …”
).
Regarding claim 2, Beach in view of Amarant discloses the system of claim 1, wherein Beach further discloses they system of claim 1 further including at least a camera coupled with the at least one processor, the camera configured to capture the series of images of the golf club during the golf swing (a “camera” and/or “high speed cameras” as detailed in para(s). [0020], [0031], and [0076]—see citations in claim 1 limitation “receive a series of images…” above).
Regarding claim 3, Beach in view of Amarant discloses the system of claim 1, wherein Amarant further teaches prior to using the machine learning model in detecting and classifying, the at least one processor is further configured to train the machine learning model using a plurality of images of golf clubs captured during a plurality of sessions of golf swings, as training data, to detect sticker labels placed on golf clubs and to classify golf club types based on recognizing markers assigned to different types of golf clubs coded on the sticker labels (para(s). [0038] and [0047]—see citation in claim 1 limitation “detect from the series of images…” above—, where para(s). [0039] further recite(s):
[0039] “The golf club configuration detection system 100 may also include performance tracking devices 118, such as a ball flight tracker 120 and a swing tracker 122. The performance tracking devices 118 track the performance of a ball strike from a golf club in a detected configuration state. In an example, once the configuration of the golf club is detected, each ball strike may be tracked by the performance tracking devices 118. For instance, the ball flight tracker 120 tracks the flight characteristics of a golf ball struck by the golf club in the detected configuration state. The flight characteristics may include ball speed, trajectory, spin, carry, roll, total distance, and other ball flight characteristics. The swing tracker 122 tracks swing characteristics of the golf club, such as swing path, face angle, club head speed, loft, and other swing characteristics. …”
, where the “machine learning techniques, such as neural networks,” are trained on a “training set” of a “plurality of images… captured for each configuration state of an adjustment system” as disclosed previously in para(s). [0038]—wherein the configuration state includes during a golf “swing” as disclosed in para(s). [0039] above—is training the machine learning model using training data (e.g., “training set”) comprising of a plurality of images of golf clubs captured during a plurality of sessions of golf swings (e.g., “plurality of images… captured for each configuration state” including each the configuration states of a golf “swing” and/or “ball strike”) to perform the intended use/result of detecting sticker labels placed on golf clubs and to classify golf club types based on recognizing markers assigned to different types of golf clubs coded on the sticker labels (see the teachings of Amarant in the rejection of claims “detect from the series of images…” and “classify…” in claim 1 above)).
Regarding claim 4, Beach in view of Amarant discloses the system of claim 1, wherein Beach further discloses the one or more sticker labels are detected from club head of the golf club (para(s). [0076]—see citation in claim 1 limitation “detect from the series of images…” above—, where the one or more sticker labels (e.g., “tags”) is detected from at least a club head of the golf club (e.g., the “golf club… head… tagged to identify the golf club specifications”)).
Regarding claim 5, Beach in view of Amarant discloses the system of claim 1, wherein Beach further discloses the one or more sticker labels are detected from golf shaft of the golf club (para(s). [0076]—see citation in claim 1 limitation “detect from the series of images…” above—, where the one or more sticker labels (e.g., “tags”) is detected from at least a golf shaft of the golf club (e.g., the “golf club shaft… tagged to identify the golf club specifications”)).
Regarding claim 6, Beach in view of Amarant discloses the system of claim 1, wherein Amarant further teaches the marker includes a combination of symbols coded on the one or more sticker labels (para(s). [0038]—see citation in claim 1 limitation “detect from the series of images…” above—, where the “optical identifiers” are markers including a combination of symbols (e.g., “dot or line patterns”) on the one or more sticker labels (e.g., “configuration identifiers”)).
Regarding claim 11, Beach in view of Amarant discloses the system of claim 1, wherein Beach further discloses the at least one processor is further configured to use information of the golf club type with motion properties of the golf club in analyzing performance of the golf swing (para(s). [0070]—see citation in the motivation to combine Beach and Amarant paragraph in claim 1 above—, where para(s). [0084] further recite(s):
[0084] “At 620, the launch monitor and/or sensor device captures user data during a golf swing and transmits the user data to the training platform. In some embodiments, the golf club is tagged so that the training platform automatically receives information on what club, or what club specifications, are being used during the golf swing ….”
, where “detecting a golf club configuration” includes determining “what club, or what club specifications, are being used during the golf swing” to measure “performance data and statistics to be tracked for different configuration states of a golf club”, such as during the “golf swing”, is using information of the golf club type (e.g., “what club”) with motion properties of the golf club (e.g., “swing… data”) in analyzing performance of the golf swing (e.g., “measuring performance data and statistics”)).
Regarding claim 17, the claim recites similar limitations to claim 1 but in the form of a method. Therefore, claim 17 is rejected for similar rationale and reasoning as claim 1 (see the analysis for claim 1 above).
Regarding claim 18, the claim recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Regarding claim 19, the claim recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 above).
Regarding claim 21, the claim recites similar limitations to claim 1 but in the form of a computer readable storage medium. Therefore, claim 21 is rejected for similar rationale and reasoning as claim 1 (see the analysis for claim 1 above).
Claims 7 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Beach in view of Amarant as applied to claim 1 above, and further in view of Greaney et al. (Greaney; US 2023/0356039 A1).
Regarding claim 7, Beach in view of Amarant discloses the system of claim 1, wherein Greaney teaches in the same field of endeavor of one or more sticker labels placed on a golf club the one or more sticker labels include retro-reflective material (para(s). [0003], [0017], and [0079] recite(s)
[0003] “For some launch monitors, which detect head presentation parameters of a golf club head during a golf swing, tracking markers (or stickers) are manually and temporarily adhered to the face of a finished golf club head. …”
[0017] “The at least one tracking marker comprises a sticker having an adhesive layer and a retroreflective layer. …”
[0079] “The tracking markers 160 are more retroreflective than other portions of the golf club head 110. Accordingly, the identification of the position of the tracking markers 160 in the digital images is made easier by the increased retroreflectively of light off of the tracking markers 160 relative to other portions of the golf club head 110. …”
, where the “stickers” are one or more sticker labels including retro-reflective material (e.g., “retroreflective layer”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Beach in view of Amarant to incorporate retro-reflective material in the one or more sticker labels to improve detection of the one or more sticker labels placed on the golf club during a golf swing from the series of images as taught by Greaney above.
Regarding claim 10, Beach in view of Amarant discloses the system of claim 1, wherein Greaney teaches in the same field of endeavor of one or more sticker labels placed on a golf club the one or more sticker labels are one or more of: vinyl stickers, vinyl stickers with laminate, polyester stickers, polypropylene stickers, synthetic paper stickers, laminated stickers, stickers with ultra-violet (UV) light resistant inks, permanent adhesives, heat-releasable stickers, see-through background stickers, or any combinations thereof (para(s). [0103]—see citation in claim 8 above—, where the “tracking marker” and/or “sticker” is at least one or more sticker labels that are at least see-through background stickers (e.g., a “sticker” comprising of a “polymeric cover” that is “transparent such that light is transmissible through the polymeric cover 269 to and from the pattern of retroreflective elements…”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Beach in view of Amarant to incorporate at least see-through background stickers as the one or more sticker labels to improve detection of the marker (e.g., pattern) coded on the one or more sticker labels as taught by Greaney above (e.g., para(s). [0079]—see citation in claim 7 above).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Beach, as modified by Amarant and Greaney, as applied to claim 7 above, and further in view of Holliday et al. (Holliday; US 2019/0392605 A1).
Regarding claim 8, Beach, as modified by Amarant and Greaney, discloses the system of claim 7, wherein Greaney further teaches the retro-reflective material includes(para(s). [0102-0103], recite(s)
[0102] “…Generally, the retroreflective surface of the tracking marker 260 is defined by a pattern of retroreflective elements, such as spherical beads (with reflective back surfaces), microprisms, or corner reflectors.”
[0103] “…the tracking marker 260 is a sticker 262 that includes multiple layers. The sticker 262 includes a retroreflective layer 267, an adhesive layer 275 fixed to the retroreflective layer 267, and a polymeric cover 269 fixed to the retroreflective layer 267. …In some examples, each retroreflective element 277 is made of an at least partially transparent material, such as glass, and has a reflective rear surface (e.g., retroreflective beads) or a particular shape (e.g., microprisms) so that light entering a retroreflective element is redirected back out of the retroreflective element in a direction that is approximately opposite the direction of the light when it entered the retroreflective element. …The retroreflective layer 267 can further include a substrate to which the pattern of retroreflective elements 277 are fixed. The polymeric cover 269 is transparent such that light is transmissible through the polymeric cover 269 to and from the pattern of retroreflective elements 277. The polymeric cover 269 is fixed relative to the pattern of retroreflective elements 277 such that an air gap 265 is situated between the polymeric cover 269 and the pattern of retroreflective elements 277. The air gap 265 promotes transmission of light into and out from the pattern of retroreflective elements 277. In some examples, an air gap is located between the pattern of retroreflective elements 277 and the adhesive layer 275.”
, where the “spherical beads” or “retroreflective beads” are at least micro glass beads).
Where Beach, as modified by Amarant and Greaney, does not specifically disclose
the retro-reflective material includes fabric having micro glass beads as a retro-reflective element;
Holliday teaches in the same field of endeavor of retro-reflective materials
the retro-reflective material includes fabric having micro glass beads as a retro-reflective element (para(s). [0033], recite(s)
[0033] “In certain aspects, the retroreflective tag 130 can be coated to have desired spectral characteristics (e.g., reflectivity in selected wavelength ranges). In one embodiment, the retroreflective tag 130 may include retroreflective beads, such as glass spheres (or microspheres) coated (or metallized) with a metal coating (e.g., aluminum) to achieve high reflectivity. …Accordingly, as a result of strong directional reflectivity of the retroreflective material, systems and methods disclosed herein can clearly distinguish electromagnetic radiation reflected by the retroreflective tag 130 from background electromagnetic radiation. Alternative materials such as fabrics, pigments or dyes that provide high reflectivity at wavelength ranges of interest (e.g., infrared pass filters) can be used to form the retroreflective tag 130. Several commercially available fabrics can be suitable for this purpose, and may be reflective in one or more wavelength ranges (visible, infrared) and/or transparent in one or more wavelength ranges (infrared, visible, respectively). For example, retroreflective tag 130 can include, but may not be limited to badges and labels manufactured by OscarDelta Limited, United Kingdom.”
, where the “retroreflective material” having “glass spheres” includes “fabrics” is the retro-reflective material includes fabric having micro glass beads as a retro-reflective element).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Beach, as modified by Amarant and Greaney, to incorporate fabric having micro glass beads as a retro-reflective element in the retro-reflective material to improve detection of the one or more sticker labels (e.g., a tag) for object classification as taught by Holliday (e.g., para(s). [0047], recite(s)
[0047] “FIG. 9 illustrates one such algorithm 900 for identifying unique individuals, unique objects, unique group of individuals or unique group of objects. The image processor 140 can receive signals (or images) from the detector 120 at step 902. At step 904, upon receipt of signals (or images) corresponding to reflected electromagnetic radiation from the retroreflective tag 130, the image processor 140 can convert the signals or images into a unique code (e.g., barcode or QR code). The image processor 140 can read the unique code because the barcode and/or the QR code may have discrete bands of opaque and retroreflective stripes. The detected signals (or captured image), in such cases, would correspond to similarly discrete bands of dark and retroreflective stripes, which can be associated with a unique code.”
).
Regarding claim 9, Beach, as modified by Amarant and Greaney, discloses the system of claim 7, wherein Greaney further teaches the retro-reflective material includes(para(s). [0102-0103]—see similar limitation in claim 8 above—, where the “spherical beads” or “retroreflective beads” are at least micro glass beads).
Where Beach, as modified by Amarant and Greaney, does not specifically disclose
the retro-reflective material includes paint having micro glass beads as a retro-reflective element;
Holliday teaches in the same field of endeavor of retro-reflective materials
the retro-reflective material includes paint having micro glass beads as a retro-reflective element (para(s). [0033], recite(s)
[0033] “In certain aspects, the retroreflective tag 130 can be coated to have desired spectral characteristics (e.g., reflectivity in selected wavelength ranges). In one embodiment, the retroreflective tag 130 may include retroreflective beads, such as glass spheres (or microspheres) coated (or metallized) with a metal coating (e.g., aluminum) to achieve high reflectivity. …Accordingly, as a result of strong directional reflectivity of the retroreflective material, systems and methods disclosed herein can clearly distinguish electromagnetic radiation reflected by the retroreflective tag 130 from background electromagnetic radiation. Alternative materials such as fabrics, pigments or dyes that provide high reflectivity at wavelength ranges of interest (e.g., infrared pass filters) can be used to form the retroreflective tag 130. Several commercially available fabrics can be suitable for this purpose, and may be reflective in one or more wavelength ranges (visible, infrared) and/or transparent in one or more wavelength ranges (infrared, visible, respectively). For example, retroreflective tag 130 can include, but may not be limited to badges and labels manufactured by OscarDelta Limited, United Kingdom.”
, where the “retroreflective material” having “glass spheres” includes “pigments or dyes” is the retro-reflective material includes paint having micro glass beads as a retro-reflective element).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Beach, as modified by Amarant and Greaney, to incorporate paint having micro glass beads as a retro-reflective element in the retro-reflective material to improve detection of the one or more sticker labels (e.g., a tag) for object classification as taught by Holliday (e.g., para(s). [0047], recite(s)
[0047] “FIG. 9 illustrates one such algorithm 900 for identifying unique individuals, unique objects, unique group of individuals or unique group of objects. The image processor 140 can receive signals (or images) from the detector 120 at step 902. At step 904, upon receipt of signals (or images) corresponding to reflected electromagnetic radiation from the retroreflective tag 130, the image processor 140 can convert the signals or images into a unique code (e.g., barcode or QR code). The image processor 140 can read the unique code because the barcode and/or the QR code may have discrete bands of opaque and retroreflective stripes. The detected signals (or captured image), in such cases, would correspond to similarly discrete bands of dark and retroreflective stripes, which can be associated with a unique code.”
).
Allowable Subject Matter
Claims 12, 20, and 22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 13-16 are objected to because of informalities recited in the claims, but would be allowable if rewritten or amended to overcome the claim objections set forth in this Office action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JULIA Z YAO whose telephone number is (571)272-2870. The examiner can normally be reached Monday - Friday (8:30AM - 5PM).
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/J.Z.Y./Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
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