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 Rejections - 35 USC § 112
Claims 1, 17, and 22 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.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “depth” in claims 1,17,and 22 is used by the claim to mean “distance,” while the accepted meaning is “the distance from the top or surface to the bottom of something.” The term is indefinite because the specification does not clearly redefine the term.
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.
5. Claims 1-9, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over McCann (US 20230224436 A1) in view of Lyren (US 20160377381 A1).
Regarding claim 1, McCann teaches a method for evaluating hits on a target (Abstract), comprising: capturing frames (paragraphs 13-14; buffer frames-171; Figures 1-3) of the target (101) by a camera (102); detecting a target in a captured frame, by a processing device (recognition module-104) (paragraphs 99 and 111; paragraph 105, last 2 sentences; Figures 3 and 4); classifying the target in the captured frame as a target type, by a processing device (104) (paragraph 76, first 3 sentences; paragraph 97, last sentence; paragraph 99; paragraph 105, last 2 sentences); identifying a hit on the target (paragraph 105, last sentence), by a processing device (104); and scoring the hit, by a processing device (compute module-142; paragraph 99, last sentence; paragraph 101, first sentence). McCann does not teach determining a depth of the target from a user. As noted above, the examiner is interpreting “depth” to read on distance as it appears that the distance is being determined from the target to the user. However, Lyren teaches a processing device that displays a video of the target and “determines a depth of the target from the user” (paragraph 46; Figure 3A). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instantly claimed invention to have combined the teachings of McCann and Lyren by adding the feature of calculating the distance from the user to the target, as taught by Lyren, to the processing device of McCann to obtain a method that includes calculating scores for determining performance based on distances from the target during shooting practice.
Regarding claim 2, McCann teaches detecting the target (performed by recognition module-141), classifying the target (paragraph 98, last 4 sentences; paragraph 99; paragraph 105, last 2 sentences), and/or identifying a hit on the target by a respective machine learning model run by a processing device (paragraph 98, last 4 sentences).
Regarding claim 3, McCann teaches comprising: classifying the target as a known target by a machine learning model trained on known targets (paragraph 98, last 4 sentences), the known target having known target area data stored in a storage device (105) (paragraph 22); the method further comprising: retrieving target area data stored in the storage device, by the processing device (paragraph 36); and scoring a hit on the target based, at least in part, on the target area data (paragraph 82, last three sentences).
Regarding claim 4, McCann teaches wherein the target area data for a target includes a valid hit area (paragraph 18; paragraph 99, “bounds and center of the target”), the method comprising: scoring the hit on the target based, at least in part, on whether the hit is within the valid hit area for the target (paragraph 18; paragraph 99).
Regarding claim 5, McCann teaches, scoring the hit based, at least in part, on a distance between the hit and a center of mass of a valid target hit area (paragraph 101, sentence 3 “distance from hit locations to the target center”). Since module 142 is calculating and saving the distances from the target center, the examiner is interpreting that the calculating/scoring is based at least in part on a distance between the hit and target center.
Regarding claim 6, McCann teaches, wherein the target area data includes an invalid hit area (paragraph 18, invalid hit area would inevitably be outside of “bounding box”), the method comprising: scoring the hit on the target based, at least in part, on whether the hit is within the valid hit area for the target (paragraph 99, last three sentences).
Regarding claim 7, McCann, teaches determining whether the target is a known target by a machine learning model trained on a training set including known targets (paragraph 98, last 4 sentences).
Regarding claim 8, . As shown in fig 14, McCann shows a synthetically generated target based on actual targets (bulls-eye). McCann teaches that the target “can be any object placed as the aim of a shooter” that can be composed of “paper, rubber, metal, straw, etc.” (paragraph 75). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instantly claimed invention to modify the texture of a target, as taught by McCann, to provide for different textures to accommodate different projectiles (i.e. metal texture provides sufficient structure for high velocity projectiles versus rubber texture to accommodate slow velocity projectiles).
Regarding claim 9, McCann teaches the method of claim 3, wherein the known target type is a bullseye target type (element 101, Figure 1), silhouette target type, or a hostage target type. McCann teaches the bullseye target, which reads on this claim.
Regarding claim 17, McCann teaches a system for evaluating bullet hits on a target (Abstract), comprising: a camera (102) to capture frames of a target (101) (Figures 1-3; paragraphs 13-14); at least one processing device (recognition module-104) (paragraphs 99 and 111; paragraph 105, last 2 sentences; Figures 3 and 4); and storage (element 105) (paragraph 22); the at least one processing device (104) configured to: detect a target in a captured frame (paragraph 76, first 3 sentences; paragraph 97, last sentence; paragraph 99; paragraph 105, last 2 sentences); classify the target in the captured frame as a target type (paragraph 76, first 3 sentences; paragraph 97, last sentence; paragraph 99; paragraph 105, last 2 sentences); identify a hit on the target (paragraph 105, last sentence), by running a machine learning model (paragraph 99, last 4 sentences); and score the hit (using compute module-142; paragraph 99, last sentence; paragraph 101, first sentence). McCann does not teach determining a depth of the target from a user. As stated supra, the examiner is interpreting “depth” to be ---distance---.However, Lyren teaches a processing device that displays a video of the target and “determines a depth of the target from the user” (paragraph 46; Figures 1 and 3A). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instantly claimed invention to have combined the teachings of McCann and Lyren by adding the feature of calculating the distance from the user to the target, as taught by Lyren, to the processing device of McCann to obtain a method that includes calculating scores for determining performance based on the distances away from a target during shooting practice.
Regarding claim 20, McCann teaches the system of claim 17, wherein the at least one processing device (104) is configured to: classify the target (101) in the captured frame as a target type by running a machine learning model (paragraph 98, last 4 sentences; paragraph 99; paragraph 105, last 2 sentences).
Claims 10,11,14-16 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over McCann in view of Lyren and further in view of Goldstein (US 20220057519 A1).
Regarding claim 10, McCann does not directly teach wherein, if a known target is not identified based on the machine learning model, the method further comprises: running a second machine learning model different from the first machine learning model, the second machine learning model including multiple, different machine learning models for identifying different characteristics of the target; and resolving the outputs of the multiple, different machine learning models to define target area data for the target. However, McCann teaches the use of machine learning models to recognize hit locations and providing feedback by a user to train a machine learning model to recognize a hit location of any target (paragraph 98, last sentence). McCann also teaches a recognition module (141) to determine the boundary and center of any target via edge detection and shape recognition (paragraph 99, first three sentences). Furthermore, Goldstein teaches multiple machine learning models for identifying different characteristics of a target (Figure 4, object classifier 436, face recognition classifier 452, among others). Since Goldstein teaches multiple machine learning models that are identifying different characteristics, it is the examiner’s position that the different machine learning models are being applied to different conditions as claimed. Furthermore, running a second machine learning model only if the conditions of a first model is met is not a novel concept and commonly applied in machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instantly claimed invention to combine the teachings of McCann, Lyren and Goldstein by adding the machine learning models of Goldstein to the machine learning model of McCann to train the models to recognize the hit locations of a target based on the identity of the target.
Regarding claim 11, McCann does not clearly teach wherein the multiple machine learning models comprise a segmentation machine learning model, a circle detection machine learning model, a face detection machine learning model, and/or a sentiment detection machine learning model. However, McCann teaches a machine learning model to determine the hit locations of any target (element 101, Figure 1), but does not specify the type of machine learning model used. Furthermore, Goldstein teaches a face detection machine learning algorithm (paragraph 124, “facial recognition”; Figures 4 and 5) to detect a target. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instantly claimed invention to combine the teachings of McCann, Lyren and Goldstein to apply any one of the machine learning models as claimed by the applicant, such as the face detection machine learning model of Goldstein, to the machine learning models of modified McCann to classify the target type and shape and determine the target hit area.
Regarding claim 14, modified McCann teaches the method of claim 10, wherein the target area data includes a valid hit area (paragraph 18; paragraph 99, “bounds and center of the target”), the method comprising: scoring the hit on the target based, at least in part, on whether the hit is within the valid hit area for the target (paragraph 18; paragraph 99).
Regarding claim 15, modified McCann teaches the method of claim 14, wherein the target area data for a target includes an invalid hit area (paragraph 18, invalid hit area would inevitably be outside of “bounding box”), the method comprising: scoring the hit on the target based, at least in part, on whether the hit is within the invalid hit area for the target (paragraph 99, last 3 sentences).
Regarding claim 16, modified McCann teaches the method of claim 10, comprising scoring the hit based, at least in part, on a center of mass of an invalid target hit area (paragraph 101, sentence 3 “distance from hit locations to the target center”).
Regarding claim 21, modified McCann teaches a method for evaluating bullet hits on a target and scoring the hit (by compute module-142; paragraph 99, last sentence; paragraph 101, first sentence). McCann does not clearly teach classifying the target in the captured frame as a target type by running a first machine learning model; identifying a hit on the target by running a second machine learning model different from the first machine learning model. However, McCann teaches the use of machine learning models to recognize hit locations and providing feedback to the model by a user to train a machine learning model to recognize a hit location of any target (paragraph 98, last 4 sentences). McCann also teaches a recognition module (141) to determine the boundary and center of any target via edge detection and shape recognition (paragraph 99, first three sentences). Furthermore, Goldstein teaches multiple machine learning models for identifying different characteristics of a target (Figure 4, object classifier 436, face recognition classifier 452, among others). Furthermore, running multiple machine learning models based on the condition of a prior machine learning model is not a novel concept and commonly applied in machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instantly claimed invention to combine the teachings of McCann, Lyren and Goldstein by adding the machine learning models of Goldstein to the machine learning model of McCann to train the models to recognize the hit locations of a target based on the identity of the target and keep track of score.
Regarding claim 22 (note the examiner is interpreting claim 22 to be dependent on claim 21), McCann does not teach determining a depth of the target from a user by a third machine learning model different from the first and second machine learning model. However, Lyren teaches determining a depth of the target from the user (paragraph 46; Figure 3A) such that the scores can depend on the distance (paragraph 78, first sentence). Furthermore, although McCann does not specifically teach determining a depth of the target from a user using machine learning, McCann does teach using machine learning to recognize hit locations (paragraph 98, last four sentences). Furthermore, regarding the third machine learning model being different than the first and second, this is not a novel concept as multiple different machine learning models can be used for training purposes. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instantly claimed invention to have combined the teachings of McCann, Lyren and Goldstein to obtain the depth of the target from the user, as taught by Lyren, by using machine learning as taught by McCann and Goldstein to calculate the scores upon hitting a target.
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over McCann in view of Lyren further in view of Goldstein and further in view of Pederson (US 20210334522 A1).
Regarding claim 12, McCann does not teach comprising running the sentiment detection machine learning model only if at least one face is identified by the face detection machine learning model. However, Pederson teaches a sentiment detection device and system processor. Furthermore, Goldstein teaches an object classifier (element 436, Figure 4; paragraph 118) and face recognition classifier (element 452, Figure 4; paragraph 129), which can be used for identifying a face. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instantly claimed invention to combine the teachings of McCann, Lyren, Goldstein, and Pederson by combining the machine learning model of McCann (while considering the distance of the user from the target as taught by Lyren) with a sentiment detection model as taught by combined Pederson and Goldstein to run a sentiment detection machine learning model only if the target was classified as having a face, to properly classify the target and hit area for improved aiming while shooting.
Regarding claim 13, modified McCann does not teach wherein the sentiment detection machine learning model is trained on a training set including faces expressing anger, disgust, fear, sadness and/or surprise. However, Pederson teaches a sentiment detection device and system processor. Furthermore, Goldstein teaches a face recognition classifier (element 452, Figure 4) and behavior classifier (element 448, Figure 4; paragraph 128, last 2 sentences). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instantly claimed invention to combine the teachings of McCann, Lyren, Goldstein and Pederson to add a sentiment detection machine learning algorithm, as taught by combined Goldstein and Pederson, to the machine learning algorithm of McCann, to accordingly classify the target and hit area for improved aiming while shooting.
Claims 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over McCann in view of Lyren further in view of Chirokov (US 20230226454 A1).
Regarding claim 18, McCann teaches the system of claim 17, further comprising: a casing (Figure 1, encasing of camera unit 102; paragraph 78, first sentence) having an opening wherein the camera (102; Figure 1) has a camera lens proximate the opening to capture frames down range of the casing. Although McCann teaches the camera (102), the processing device (104), and the storage (105), McCann does not teach that the camera, the processing device, and the storage are all contained within a portable casing; and that the system is self-contained and portable. However, containing the camera, the processing device, and the storage components, which are components also taught by McCann, in a casing is a simple matter of design choice. See In reLarson, 340 F.2d 965, 968, 144 USPQ 347, 349 (CCPA 1965) , where the court affirmed “that the use of a one piece construction instead of the structure disclosed in [the prior art] would be merely a matter of obvious engineering choice. Furthermore, regarding making portable, see, In reLindberg, 194 F.2d 732, 93 USPQ 23 (CCPA 1952) (Fact that a claimed device is portable or movable is not sufficient by itself to patentably distinguish over an otherwise old device unless there are new or unexpected results.). Furthermore, Chirokov teaches a portable encasing consisting of a camera where “embedded Al technology captures each shot with near real-time precision for immediate shot score and shot tracking” (paragraph 76; Figure 10). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instantly claimed invention to combine the teachings of McCann and Chirokov such that the components taught by McCann (camera, the processing device, and the storage) are enclosed in an encasing and made portable to conveniently and simultaneously move the consolidated encasing with the camera, processing, and storage components to a variety of shooting ranges and areas.
Regarding claim 19 (note the examiner is interpreting claim 19 to be dependent on claim 17), McCann does not teach wherein the casing has a second opening different from the first opening, and contains a second camera different from the first camera, the second opening and the second camera being configured to image at least a user's shooting hand during use. However, McCann teaches the use of multiple cameras in separate encasings (paragraph 7, first sentence; Fig. 1). Furthermore, Chirokov teaches a portable encasing with a single camera (paragraph 76, Figure 10). The second camera and second opening are a matter of duplication of parts with reference to the first camera and first opening. See In reHarza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960), where the court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.) Regarding “configured to image at least a user's shooting hand during use” as claimed by the applicant, that is intended-use and does not structurally limit the claim. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instantly claimed invention to have combined the teachings of McCann, Lyren, and Chirokov by placing a second camera of McCann in a portable encasing as that taught by Chirokov to obtain a single encasing with two cameras to record or detect more than one feature while shooting the target and to assist with the target scoring process.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SABA ALI whose telephone number is (571)272-0268. The examiner can normally be reached 8:00 a.m. - 5 p.m..
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Eugene Kim can be reached at 571-272-4463. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SABA N. ALI/Patent Examiner, Art Unit 3711 /EUGENE L KIM/Supervisory Patent Examiner, Art Unit 3711