DETAILED ACTION
In Applicant’s Response dated 5/29/2026, Applicant amended claims 1 to 6, 8, 10; and argued against all rejections previously set forth in the Office action dated 3/10/2026.
Response to Argument
Applicant’s arguments were considered, but are moot in view of the new ground(s) of rejection.
Allowable Subject Matter
Claim 2, 3, 4, 5 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
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.
Claim 1-6, 8, 10 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.
With regard to claim 1, Applicant claim the limitations of perform inference on the target included in the configuration data using a trained configuration data inference model as part of a convolutional neural network (CNN); perform inference on the target based on a result of performing inference on the target included in the data as inference on the target included in the data for which inference on the target included in the configuration data has failed, using a trained data inference model as part of the
CNN.
These limitations are not specifically taught by the specification or the original claims. More specifically the specification does not teach aspect of using a trained configuration data inference model as part of a convolutional neural network. The aspect of convolutional neural network is barely mentioned in the specification. Claims 8 and 10 are rejected for the same reason.
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.
Claim 1-6, 8, 10 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.
With regard to claim 1 applicant claims the limitation of “perform inference on the target based on a result of performing inference on the target included in the data as inference on the target included in the data for which inference on the target included in the configuration data has failed, using a trained data inference model as part of the
CNN.” It is unclear what is “using a trained data inference model as part of the
CNN”. For the purpose of a compact prosecution, it is interpreted that inference on the target uses data inference model as part of the CNN.
Claims 8 and 10 are rejected for the same reason.
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, 6, 8, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cai, Pub. No.: CN 112883973 A., and in view of Gabriel, Pub. No.: 2020/0257960A1.
With regard to claim 1:
Cai discloses an information processing device comprising: a memory configured to store instructions; and one or more processors configured to execute the instructions to: generate configuration data configured using a plurality of pieces of data respectively acquired, at least a portion of which including an inference target(see fig. 3 and 4 wherein the inference targets are license plates from a plurality of license plate images, “In some embodiments, license plate text image comprises a plurality of each license plate text image corresponding to a detection frame, for example, for the license plate image shown in FIG. 4, using the neural network for processing, can obtain two detection frame shown in FIG. 5, in FIG. 5; the second detection frame 501 and the third detection frame 502 in the image is two license plate text, the content of the second detection frame 501 is digital " 7356 ", the content of the third detection frame 502 is letter " TNJ ". In some embodiments, the license plate text image comprises a plurality of license plate text images in the same row and are arranged at intervals, each license plate text image corresponding to a detection frame, for example, in FIG. 5, the second detection frame 501 and the third detection frame 502 in the image representation in the two license plate text in the same row; the second detection frame 501 and the third detection frame 502 are arranged at intervals, namely the second detection frame 501 and the third detection frame 502 is provided with a second region of the image 503. Therefore, the present disclosure can identify the license plate number in the same row of different positions.”); perform inference on the target included in the configuration data (the system performs inference on the target such as license plate in the configuration data “In some embodiments of the present disclosure, the license plate text image comprises a plurality of condition, can be text recognition to a plurality of license plate text respectively, obtaining a plurality of license plate text corresponding to the text recognition result, then the plurality of license plate text corresponding to the text recognition result to combine to obtain the license plate text. In some embodiments of the present disclosure, the license plate text image comprises a plurality of, and a plurality of license plate text images are in the same row and the interval is set under the condition that the plurality of license plate text respectively in the same row for text recognition, obtaining a plurality of license plate text corresponding to the text recognition result; For example, referring to FIG. 5, the second detection frame 501 and the third detection frame 502 represented by two license plate text image in the same row, and the two license plate text images are set at intervals, at this time, can the second detection frame 501 and the third detection frame 502 represents the two license plate text to text recognition, to obtain the digital " 7356 " and letter " TNJ ", then, the number " 7356 " and letter " TNJ " to combine, obtaining the license plate text " 7356TNJ ".”) using a trained configuration data inference model (The detection or inference model is trained in advance, “Illustratively, the vehicle detection model can be trained in advance; the vehicle detection model is used for detecting the target of the vehicle from the image; after obtaining the training completion of the vehicle detection model, the collected image or collected video image is input to the vehicle detection model, using the vehicle detection model to process the collected image or collected image in the video to obtain the image corresponding to the vehicle detection frame or cut the whole vehicle image; The image in the vehicle detection frame is the vehicle image.”); determine whether inference on the target included in the configuration data has succeeded or failed for each piece of the data constituting the configuration data (see fig. 6 wherein the system determines whether the inference of the configuration data is successful or not, “Step 604: judging whether the angle detection model after adjusting the network parameter value satisfies the first training end condition, if not, then re-executing step 601 to step 604; if yes, executing step 605. In the embodiment of the present disclosure, the first training end condition can be the iteration times when the training angle point detection model reaches the first set times, or loss of the network parameter value after adjusting the corner of the detection model is less than the first set loss; Here, the first setting times and the first setting loss can be preset.”); perform inference on the target based on the configuration data and a result of performing inference on the target included in the configuration data as inference on the target included in the data for which inference on the target included in the configuration data has succeeded (the inference is done by adjusting the learning model, “Step 604: judging whether the angle detection model after adjusting the network parameter value satisfies the first training end condition, if not, then re-executing step 601 to step 604; if yes, executing step 605. In the embodiment of the present disclosure, the first training end condition can be the iteration times when the training angle point detection model reaches the first set times, or loss of the network parameter value after adjusting the corner of the detection model is less than the first set loss; Here, the first setting times and the first setting loss can be preset. Step 605: and taking the angle detection model after adjusting the network parameter value as the corner detection model finished by training. In the actual application, step 601 to step 605 can be realized by the processor in the electronic device, the processor can be an ASIC, DSP, DSPD, PLD, FPGA, CPU, controller, microcontroller, microprocessor is at least one. It can be seen that in the embodiment of the present disclosure, by pre-training the angular point detection model, can make the training corner of the detection model can more accurately corner the information of the license plate, so as to correct the initial image of the license plate. In some embodiments of the present disclosure, the neural network is based on the license plate of the second sample image set and the second sample image set of the marked license plate text image training; the marked license plate text image of the second sample image set is the marked license plate text image of each sample image in the second sample image set. In the embodiment of the present disclosure, the second sample image set may include a plurality of sample images, in some embodiments, for each sample image in the second sample image set, can be pre-marked the real license plate text line area, namely obtaining the sample image of the marked license plate text. Hereinafter, the training process of the neural network is illustrated by the accompanying drawings.”.) and perform inference on the target based on a result of performing inference on the target included in the data as inference on the target included in the data for which inference on the target included in the configuration data has failed (the inference is done by reperform the training steps when the inference on the target included in the configuration data has failed, “Step 604: judging whether the angle detection model after adjusting the network parameter value satisfies the first training end condition, if not, then re-executing step 601 to step 604; if yes, executing step 605. In the embodiment of the present disclosure, the first training end condition can be the iteration times when the training angle point detection model reaches the first set times, or loss of the network parameter value after adjusting the corner of the detection model is less than the first set loss; Here, the first setting times and the first setting loss can be preset.”) using a trained data inference model (The detection or inference model is trained in advance, “Illustratively, the vehicle detection model can be trained in advance; the vehicle detection model is used for detecting the target of the vehicle from the image; after obtaining the training completion of the vehicle detection model, the collected image or collected video image is input to the vehicle detection model, using the vehicle detection model to process the collected image or collected image in the video to obtain the image corresponding to the vehicle detection frame or cut the whole vehicle image; The image in the vehicle detection frame is the vehicle image.”).
Cai does not disclose. the aspect wherein the trained configuration data inference model is part of a convolutional neural network (CNN).
However Gabriel discloses the aspect wherein the trained configuration data model is part of a convolutional neural network (CNN) (See paragraph 37 wherein configuration memory 920 represents a non-volatile memory device containing configuration data for the CNN model deployed on FPGA 920 and may contain configuration data other components of electronic device 140. Configuration data may include the quantized weights and other parameters associated with the various layers of the trained CNN model). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Gabriel to Cai so the inference model is trained using a convolutional neural network for automatic feature extraction which makes it fast and accurate.
With regard to claim 6:
Cai and Gabriel disclose The information processing device according to claim 1, wherein the data is an image of two-dimensional data, and the one or more processors are further configured to execute the instructions to: generate a configuration image in which a plurality of images are arranged on a two-dimensional plane(Cai see fig. 2 to 4 wherein a plurality of images are arranged on a two dimensional plane: “In some embodiments, the license plate text image comprises a plurality of license plate text images in the same row and are arranged at intervals, each license plate text image corresponding to a detection frame, for example, in FIG. 5, the second detection frame 501 and the third detection frame 502 in the image representation in the two license plate text in the same row; the second detection frame 501 and the third detection frame 502 are arranged at intervals, namely the second detection frame 501 and the third detection frame 502 is provided with a second region of the image 503. Therefore, the present disclosure can identify the license plate number in the same row of different positions.”); execute an object detection task as inference on the target included in a configuration image (Cai the system performs inference on the target such as license plate in the configuration data “In some embodiments of the present disclosure, the license plate text image comprises a plurality of condition, can be text recognition to a plurality of license plate text respectively, obtaining a plurality of license plate text corresponding to the text recognition result, then the plurality of license plate text corresponding to the text recognition result to combine to obtain the license plate text. In some embodiments of the present disclosure, the license plate text image comprises a plurality of, and a plurality of license plate text images are in the same row and the interval is set under the condition that the plurality of license plate text respectively in the same row for text recognition, obtaining a plurality of license plate text corresponding to the text recognition result; For example, referring to FIG. 5, the second detection frame 501 and the third detection frame 502 represented by two license plate text image in the same row, and the two license plate text images are set at intervals, at this time, can the second detection frame 501 and the third detection frame 502 represents the two license plate text to text recognition, to obtain the digital " 7356 " and letter " TNJ ", then, the number " 7356 " and letter " TNJ " to combine, obtaining the license plate text " 7356TNJ ".”); and execute an image classification task as inference on the target included in an image (Cai “Exemplary, when the vehicle detection model is a target detection model based on the deep learning, the vehicle detection task of the vehicle detection model can be divided into the following two sub-tasks: target classification and target location. the target classification task is responsible for judging whether there is vehicle in the input image or the selected image area (Provision); outputting a series of label with score indicating the possibility of the vehicle appearing in the image or the selected image area. The target positioning task is responsible for determining the position and range of the vehicle in the image or the selected image area, the surrounding box of the output object, or the object centre, or the closed boundary of the object, generally using a square detection frame to represent the position information of the vehicle.”).
Claim 8 is rejected for the same reason as claim 1.
Claim 10 is rejected for the same reason as claim 1.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/DI XIAO/Primary Examiner, Art Unit 2178
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