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 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 4-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation "the model control unit" in lines 1 and 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 5 recites the limitation "the model control unit" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 is rejected due to its dependence on claim 5.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2 and 11-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fukuya et al. (United States Patent Application Publication 2019/0253615), hereinafter referenced as Fukuya.
Regarding claim 1, Fukuya discloses a detection device comprising: processing circuitry (figure 1 exhibits inference engine 12 as disclosed at paragraph 28) to execute a detection process of using a learned model selected from a plurality of learned models, using an image as an input to the selected learned model, and obtaining a detection result, as a result of detecting an object in the image, as an output from the selected learned model (figure 6A exhibits step S7; paragraph 97 teaches performing object detection using a plurality of models and outputting a detection result; paragraph 30 teaches that the models may be for object detection); and to execute a determination process of having the detection process executed in regard to each of the plurality of learned models, calculating accuracy of the detection result in regard to each of the plurality of learned models, and determining a recommended learned model out of the plurality of learned models based on the accuracy (figure 6A exhibits step S7; paragraph 97 teaches determining the reliability of each result and selecting a model with the highest reliability), wherein the detection process after the determination process is executed by using the recommended learned model (figure 6A exhibits step S9 in which the selected model is used as disclosed at paragraph 98).
Regarding claim 2, Fukuya discloses the detection device according to claim 1, wherein the processing circuitry executes the detection process and outputs the detection result in regard to each frame in the image (the image used for selection is a single image, therefore the detection process is performed for each frame in the image), and calculates the accuracy in regard to each of the plurality of learned models and in regard to each frame (the image used for selection is a single image, therefore the reliability calculation is performed for each frame in the image).
Regarding claim 11, Fukuya discloses a camera system comprising: the detection device according to claim 1 (see claim 1, above); and a camera to capture the image (figure 1 exhibits imaging section 11 as disclosed at paragraph 27).
Claim 12, a method, corresponds to and is analyzed the same as the device of claim 1.
Claim 13, a non-transitory computer-readable record medium, corresponds to and is analyzed the same as the device of claim 1 (paragraph 49 teaches storing a program in memory).
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.
Claims 3-8 are rejected under 35 U.S.C. 103 as being unpatentable over Fukuya in view of Tomioka et al. (United States Patent Application Publication 2019/0130216), hereinafter referenced as Tomioka.
Regarding claim 3, Fukuya discloses the detection device according to claim 2, however, Fukuya fails to disclose wherein the processing circuitry calculates a statistical value of the accuracy in regard to each of the plurality of learned models and determines the recommended learned model out of the plurality of learned models based on the statistical value.
Tomioka is a similar or analogous system to the claimed invention as evidenced Tomioka teaches a method for selecting a model wherein the motivation of improving the selection process by accounting for changes in the scene would have prompted a predictable variation of Fukuya by applying Tomioka’s known principal of using a plurality of training images and calculating an average value of the evaluation values of each image (paragraph 66 teaches calculating an average value of the evaluation value for each of the images analyzed; an average is a statistical value). When applying this known technique to Fukuya, it would have been obvious to test each inference model against a plurality of input images and then calculate an average of the reliability of the result obtained from each of the images and to select a model with the highest average reliability.
In view of the motivations such as improving the selection process by accounting for changes in the scene one of ordinary skill in the art would have implemented the claimed variation of the prior art system of Fukuya.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 4, Fukuya in view of Tomioka discloses the detection device according to claim 3, in addition, Tomioka discloses wherein the model control unit determines a learned model having the highest statistical value among the plurality of learned models as the recommended learned model (paragraph 66 teaches performing analysis using a predetermined number of frames, calculating an average value of the accuracy of each of the frames).
Regarding claim 5, Fukuya discloses the detection device according to claim 2, however, Fukuya fails to disclose wherein the model control unit executes a determination process of calculating the accuracy of the detection result in regard to each of the plurality of learned models after the detection process is executed for a predetermined first frame number of frames and determining the recommended learned model out of the plurality of learned models based on the accuracy.
Tomioka is a similar or analogous system to the claimed invention as evidenced Tomioka teaches a method for selecting a model wherein the motivation of improving the selection process by accounting for changes in the scene would have prompted a predictable variation of Fukuya by applying Tomioka’s known principal of calculating the accuracy of the detection result in regard to each of the plurality of learned models after the detection process is executed for a predetermined first frame number of frames (paragraph 66 teaches performing analysis using a predetermined number of frames, calculating an average value of the accuracy of each of the frames) and determining the recommended learned model out of the plurality of learned models based on the accuracy (paragraph 66 teaches performing analysis using a predetermined number of frames, calculating an average value of the accuracy of each of the frames). When applying this known technique to Fukuya, it would have been obvious to test each inference model against a plurality of input images and then calculate an average of the reliability of the result obtained from each of the images and to select a model with the highest average reliability.
In view of the motivations such as improving the selection process by accounting for changes in the scene one of ordinary skill in the art would have implemented the claimed variation of the prior art system of Fukuya.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 6, Fukuya in view of Tomioka discloses the detection device according to claim 5, in addition, Fukuya discloses wherein the processing circuitry executes the determination process of calculating the accuracy of the detection result and determining the recommended learned model based on the accuracy again after the detection process using the recommended learned model is executed for a predetermined second frame number of frames (paragraph 39 teaches repeating the model selection process if a number of frames with low reliability exceeds a threshold number).
Regarding claim 7, Fukuya discloses the detection device according to claim 2, in addition, Fukuya discloses wherein the processing circuitry calculates the accuracy in regard to each of the plurality of learned models. However, Fukuya fails to disclose wherein the processing circuitry determines a learned model having the accuracy exceeding a predetermined threshold value as the recommended learned model.
Tomioka is a similar or analogous system to the claimed invention as evidenced Tomioka teaches a method for selecting a model wherein the motivation of ensuring that a model has a minimum reliability would have prompted a predictable variation of Fukuya by applying Tomioka’s known principal of providing a predetermined first threshold value (paragraph 123 discloses a predetermined threshold which a model accuracy needs to exceed in order to be selected) and when there occurs a learned model having the accuracy exceeding the first threshold value, determines one or more learned models as the recommended learned models (paragraph 123 teaches that a model with the highest evaluation value which is also greater than a predetermined threshold is selected).
In view of the motivations such as ensuring that a model has a minimum reliability one of ordinary skill in the art would have implemented the claimed variation of the prior art system of Fukuya.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 8, Fukuya discloses the detection device according to claim 2, wherein the processing circuitry has a predetermined second threshold value (paragraph 50 discloses maintaining a selected model as long as the reliability stays above a specified value), and calculates the accuracy in regard to each of the plurality of learned models (figure 6A exhibits step S7; paragraph 97 teaches determining the reliability of each result and selecting a model with the highest reliability). However, Fukuya fails to disclose a predetermined first threshold value, the second threshold being lower than the first threshold value; and when there occurs a learned model having the accuracy exceeding the first threshold value, determines one or more learned models having the accuracy exceeding the second threshold value as the recommended learned models (figure 6A exhibits step S7; paragraph 97 teaches determining the reliability of each result and selecting a model with the highest reliability).
Tomioka is a similar or analogous system to the claimed invention as evidenced Tomioka teaches a method for selecting a model wherein the motivation of ensuring that a model has a minimum reliability would have prompted a predictable variation of Fukuya by applying Tomioka’s known principal of providing a predetermined first threshold value (paragraph 123 discloses a predetermined threshold which a model accuracy needs to exceed in order to be selected) and when there occurs a learned model having the accuracy exceeding the first threshold value, determines one or more learned models as the recommended learned models (paragraph 123 teaches that a model with the highest evaluation value which is also greater than a predetermined threshold is selected).
In view of the motivations such as ensuring that a model has a minimum reliability one of ordinary skill in the art would have implemented the claimed variation of the prior art system of Fukuya.
However, Fukuya in view of Tomioka fails to disclose that the second threshold value is lower than the first threshold value.
At the time of filing, there was a recognized problem or need in the art to determine values for the first and second thresholds so that a model could be initially picked and later maintained or changed. There were a finite number of identified and predictable potential solutions to the recognized need or problem which were:
Set the first threshold value to be greater than the second threshold value;
Set the first threshold value to be equal to the second threshold value; or
Set the first threshold value to be less than the second threshold value.
One of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success since all the solutions allow for a model to be selected and to decide whether a selected model is still sufficient. Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
When setting the second threshold to be less than the threshold which is used to select a model, then when there occurs a learned model having the accuracy exceeding the first threshold value, determines one or more learned models having the accuracy exceeding the second threshold value as the recommended learned models because the model which is greater than the first threshold is also greater than the second threshold.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Fukuya in view of Choi et al. (United States Patent Application Publication 2023/0276140), hereinafter referenced as Choi.
Regarding claim 9, Fukuya discloses the detection device according to claim 1, however, Fukuya fails to explicitly disclose a working memory in which one or more learned models out of the plurality of learned models are deployed, wherein the processing circuitry executes the detection process by using a learned model deployed in the working memory.
Choi is a similar or analogous system to the claimed invention as evidenced Choi teaches a system with a plurality of AI models wherein the motivation of minimizing the size of working memory would have prompted a predictable variation of Fukuya by applying Choi’s known principal of providing a working memory in which one or more learned models out of the plurality of learned models are deployed (paragraph 78 teaches including a working memory which can be used by the processor and paragraph 84 teaches that the working memory can receive various models), wherein the processing circuitry executes the detection process by using a learned model deployed in the working memory (paragraph 85 teaches loading a model to be used into the working memory and using said model).
In view of the motivations such as minimizing the size of working memory one of ordinary skill in the art would have implemented the claimed variation of the prior art system of Fukuya.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 10, Fukuya in view of Choi discloses the detection device according to claim 9, however, Fukuya fails to disclose wherein the processing circuitry releases a storage area by deleting the learned model deployed in the working memory; and deploys one of the plurality of learned models in the working memory having the released storage area.
Choi is a similar or analogous system to the claimed invention as evidenced Choi teaches a system with a plurality of AI models wherein the motivation of minimizing the size of working memory would have prompted a predictable variation of Fukuya by applying Choi’s known principal of releasing a storage area by deleting the learned model deployed in the working memory; and deploying one of the plurality of learned models in the working memory having the released storage area (paragraph 85 teaches deleting models from working memory when they are no longer needed and loading models into working memory as they are needed).
In view of the motivations such as minimizing the size of working memory one of ordinary skill in the art would have implemented the claimed variation of the prior art system of Fukuya.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tanabe (United States Patent 11,516,372) discloses a method for selecting a learned model.
Sakane (United States Patent 11,373,422) discloses a method for selecting a learned model.
Nonaka et al. (United States Patent Application Publication 2019/0332952) discloses a method for selecting a learned model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON A FLOHRE whose telephone number is (571)270-7238. The examiner can normally be reached Mon-Fri 8:00-3:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sinh Tran can be reached at 571-272-7564. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JASON A. FLOHRE
Patent Examiner
Art Unit 2637
/JASON A FLOHRE/ Patent Examiner, Art Unit 2637