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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17,19-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a processor. This judicial exception is not integrated into a practical application because the claims merely recite data input to a processor, and processed by an algorithm, without a result of significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not recite an output that provides a significant result or benefit.
Claims 1-17,19-21 Analysis:
Broadest reasonable interpretation:
Step 2A Prong One Analysis:
Claim 1 recites an information processing apparatus comprising at least one processor, which generates feature map, series generation, and feature information. Given its broadest reasonable interpretation, the claimed subject matter is directed to an apparatus, which would falls under one of the four statutory categories.
Step 2A Prong Two Analysis:
Claim 1 fails the Prong 2 analysis, because it is directed to a judicial exception in the form of an Abstract Idea. The claim applies mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, without adding any significant extra solution activity.
Step 2B Analysis:
Does the claims provide an inventive concept, i.e., does the claims recite additional elements or a combination of elements that mount to significantly more than the judicial exception in the claim? A review of the claims do not provide an inventive concept significantly more than the abstract idea. The processor and recursive model are at best the equivalent of merely adding the words “apply it” to the judicial exception. The limitations are mere data gathering and output recited at a high level of generality. The limitations remain insignificant extra solution activity even upon reconsideration.
ART REJECTION:
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1,4,6,8,11,13,15, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jiang et al(USPGPUB 2018/0211130 A1).
-- In considering claim 1, the claimed subject matter that is met by Jiang includes:
1) at least one processor is met by the processor(310), which runs a Convolutional Neural Network(see: Jiang, sec[0064]);
2) at least one processor carrying out:
i) a feature map generation process of generating a plurality of scale specific feature maps from input data is met by the Phase(610), which provides input data items to a first convolutional layer off an artificial neural network, such that the data is processed, and feature maps output from the convolutional layers(see: Jian, sec[0078]);
ii) a feature series generation process of generating a feature series from the plurality of scale-specific feature maps is met by the generation of feature map patches having different dimensions(see: Jiang, secs[0074-0075]);
iii) a feature information generation process of generating feature information by inputting the feature series into a recursive model is met by the input window running feature map patches having different dimensions, into convolutional layers of a convolutional neural network(see: Jiang, se[0074]).
-- With regards to claim 4,
1) in the feature map generation process, the at least one processor causes a plurality of convolutional layers to act on the input data in series, to generate the plurality of scale-specific feature maps is met by the feature map patches being input to a set of convolutional layers, to define a feature map output(see: Jiang, secs[0009; 0051]).
-- With regards to claim 6,
1) in the feature series generation process, the at least one processor arranges a plurality of feature data outputted from the respective fully connected layers, in order of scale corresponding to the plurality of feature data, to generate the feature series is met by the first, second, and third convolutional layers, each producing respective first, second, and third layer feature maps(see: Jiang, sec[0051]).
-- Claim 8 recites a method that substantially corresponds to the subject matter of claim 1, and therefore, is met for the reasons as discussed in the rejection of claim 1 above.
-- Claim 11 depends from claim 8, and recites a method that substantially corresponds to the subject matter of claim 4. Therefore, claim 11 is met for the reasons as discussed in the rejection of claims 4 and 8 above
-- Claim 13 depends from claim 12, and recites a method that substantially corresponds to the subject matter of claim 6. Therefore, claim 13 is met for the reasons as discussed in the rejection of claims 6 and 12 above.
-- Claim 15 recites essentially the same subject matter that is met as discussed in the rejection of claim 1 above, as well as: 1) a non-transitory storage medium storing a program for a computer is met by the non-transitory computer readable medium having stored thereon a set of computer readable instructions executed by the at least one processor(see: Jiang, sec[0038]).
-- Claim 20 depends from claim 19, and recites subject matter that substantially corresponds to the subject matter of claim 6. Therefore, claim 20 is met for the reasons as discussed in the rejection of claims 6 and 19 above.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2-3,5,7,9-10,12,14,16-17,19, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiang et al(Jiang) in view of Syeda Mahmood et al(USPUB 2024/0071049 A1).
-- With regards to claim 2, Jiang does not teach:
1) the at least one processor carries out a maximum scale calculation process of calculating a maximum scale
2) in the feature series generation process, the at least one processor generates a feature series having a length in accordance with the maximum scale.
However, Syeda Mahmood et al(Mahmood) teaches deep learning neural networks, which includes dilated convolution, such that data is output as dilated blocks with different feature channels that are cascaded with max pooling to learn more abstract features(see: Mahmood, sec[0056]). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the max pooling as discussed in Mahmood, into the system of Jiang, since this would have provided a more efficient means of processing feature data in a neural network system.
-- With regards to claim 3,
1) in the maximum scale calculation process, the at least one processor refers to the input data or relevant information associated with the input data, to calculate the maximum scale is met by the dilated convolutions for multi-scale features, and the output including dilated blocks with different feature channels cascaded with max pooling(see: Mahmood, sec[0056]).
-- With regards to claim 5, Jiang does not teach:
1) in the feature series generation process, the at least one processor carries out, for each of the plurality of convolutional layers:
i) a process of causing a global pooling layer to act on a scale specific feature map outputted from the convolutional layer
ii) a process of causing a fully connected layer to act on output of the global pooling layer.
However, Syeda Mahmood et al(Mahmood) teaches deep learning neural networks, which includes dilated convolution, such that data is output as dilated blocks with different feature channels that are cascaded with max pooling to learn more abstract features(see: Mahmood, sec[0056]). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the max pooling as discussed in Mahmood, into the system of Jiang, since this would have provided a more efficient means of processing feature data in a neural network system.
-- With regards to claim 7, Jiang does not teach:
1) the at least one processor carries out:
i) an input data generation process of generating a plurality of input data by cutting target data into a plurality of lengths
ii) a recommendation process of determining a recommended length from among the plurality of lengths, with reference to feature information corresponding to each of the plurality of the input data.
As discussed above, Mahmood teaches deep learning neural networks, which utilizes processors which execute neural networks composed of dilated convolutions for multi-scale features. Such that dilated blocks with different feature channels are output and cascaded with max pooling, so as to learn about abstract features(see: Mahmood, sec0056]). This would have read on the subject matter of claim 7, because the multi scale features would have constituted the plurality of input data being cut into a plurality of lengths. As well, the max pooling by dilated blocks with different feature channels would have constituted determination of recommendations, since the max pooling allows more to be learned about abstract features, which is a form of recommendation.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the deep neural network of Mahmood, into the system of Jiang, since this would have provided a more efficient manner of processing the input data of Jiang, thereby providing desired results of the output of the feature data.
-- Claim 9 depends from claim 8, and recites a method that substantially corresponds to the subject matter of claim 2. Therefore, claim 9 is met for the reasons as discussed in the rejection of claims 2 and 8 above.
-- Claim 10 depends from claim 9, and recites a method that substantially corresponds to the subject matter of claim 3. Therefore, claim 10 is met for the reasons as discussed in the rejection of claims 3 and 9 above.
-- Claim 12 depends from claim 11, and recites a method that substantially corresponds to the subject matter of claim 5. Therefore, claim 12 is met for the reasons as discussed in the rejection of claims 5 and 11 above.
-- Claim 14 depends from claim 8, and recites a method that substantially corresponds to the subject matter of claim 7. Therefore, claim 14 is met for the reasons as discussed in the rejection of claims 7 and 8 above.
-- Claim 16 depends from claim 15, and recites subject matter that substantially corresponds to the subject matter of claim 2. Therefore, claim 16 is met for the reasons as discussed in the rejection of claims 2 and 15 above.
-- Claim 17 depends from claim 16, and recites subject matter that substantially corresponds to the subject matter of claim 3. Therefore, claim 17 is met for the reasons as discussed in the rejection of claims 3 and 16 above.
-- Claim 19 depends from claim 15, and recites subject matter that substantially corresponds to the subject matter of claims 4 and 5. Therefore, claim 19 is met for the reasons as discussed in the rejection of claims 4,5, and 15 above.
-- Claim 21 depends from claim 15, and recites subject matter that substantially corresponds to the subject matter of 7. Therefore, claim 21 is met for the reasons as discussed in the rejection of claims 7 and 21 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARYL C POPE whose telephone number is (571)272-2959. The examiner can normally be reached 9AM - 5PM M-F.
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/DARYL C POPE/Primary Examiner, Art Unit 2686
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