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 .
Status of Claims
Claims 1-24 are pending.
Claims 11-12, 14-17, 19 and 22 are withdrawn.
Claims 13, 18 and 20 are cancelled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/12/2026 has been entered.
Response to Arguments
Applicant’s amendments filed 01/12/2026 have been considered and entered, however, in view of applicant’s amendments, the previous rejection(s) have been withdrawn and upon further consideration, a new ground(s) of rejection is made now in view of just Tang and Li.
Applicant argues, see remarks, page 10, that amendments have been made to claim 23, thereby previously made interpretation of claims under 112(f) shall be withdrawn.
In reply, examiner asserts that applicant has still failed to address the limitations of claim 23 pertaining to different modules, therefore, the interpretation of claim 23 under 112(f) has been again repeated below.
Applicant argues, see remarks, page 10-13, that previously made 101 rejection shall be withdrawn as the amended claims recite a specific technological apparatus/process which cannot be practically performed in human mind and therefore claims provide a practical application along with specific improvement of technology/apparatus by providing a technological solution to a technological problem and moreover claims provide a particular architecture and particular way of achieving result, which is significantly more, thus overcomes the 101 abstract idea rejection.
In reply, examiner strongly asserts that claims as amended still recite an abstract idea in terms of a mental process, which is fully executable by a human mental activity because they cover concepts which can be performed in the human mind while utilizing generic neural networks which is part of generic computer computation functions and provide nothing more than mere instructions to implement an abstract idea/mental process on a generic computer while utilizing generic neural networks (emphasis added).
Claims as amended, just recite generic neural network to obtain generic loss function values and determining importance based on tasks corresponding to loss function values pertaining to respective objects and thereby performing loss function weight adjustments. There is no explicit recitation regarding usage of specific technological apparatus which provides a specific improvement by delivering a technological solution to a technological problem with use of a particular architecture as seemed to be argued/asserted by the applicant. It is not clear what that specific technological apparatus is? And what specific improvement or technical solution is being provided via use of a particular architecture? other than using a generic neural network as recited in the amended claims.
For instance, first steps of claim 1, do nothing more that gathering the result and loss function data which can either be categorized as extra-solutional data gathering in step 2A prong 2 and 2B or alternatively as a mental process, e.g., data collection using generic computer functionality and thereafter performing conventional neural networks processing’s by determining which data is important among the gathered data by executing plurality of tasks since a person can determine/analyze the provided images and draw a conclusion related to its importance and further based on executed plurality of tasks adjusting and updating the data based on its importance belonging to a respective task by adjusting weight of different pieces/objects within the image(s) containing data using pen and paper.
Furthermore with emphasis added that these neural network operations would constitute a mathematical algorithm under the BRI since these NN’s are not specified with any degree of specificity in claims or specification and in general GANs and generic neural network are known for use in image processing.
Additionally, claims still do provide any sort of concrete evidence proving that it is a practical application with significantly more or improvements per se as seemed to be argued by the applicant.
Please note that particular set of elements using a particular machine which are solving a particular problem by describing exactly how the process is carried out with improvements in order to provide a practical application are generally required to overcome the 101 rejection(s).
Applicant argues, see remarks, page 15, that cited references do not teach all the newly amended limitations of claim 1 such as “neural network outputs processing results of at least two tasks for detecting respective object; determining importance of the at least two tasks based on loss function values of at least two tasks for detecting respective object and adjusting a weight of the loss function value, based on the determined importance”.
In reply, Examiner disagrees because the new 103 combination made in view of just Tang and Li with removal of Stent reference, thereby teaching all the newly amended limitations of claim 1.
For instance, Tang teaches results for each type (both types) of target task is outputted, for example, when the target task is classification task, it classifies the input image by using the sub-network, and outputs a classification result, that is, output data is the classification result of the image, paragraphs 34-35, else when the target task a regression task, it outputs different result, paragraph 114, for detecting/predicting the target task to obtain a detection result which is presented on a display interface, not that, target detection task is usually for detecting a target object in an image. In this case, the input data is usually an input image. The execution device prunes the trained target convolutional neural network based on the input image to obtain a pruned sub-network, and performs target detection on the input image by using the sub-network to obtain a detection result, that is, output data is the detection result, paragraphs 33, 105 and training device further obtains the second sub-loss function based on the channel importance function and the dynamic weight which is determined based on the first sub-loss function belonging to the type of associated target tasks (i.e., at least two tasks) such as classification and regression task as explained above to identify similarity between different training samples corresponding to different target tasks which are input to different sub-networks, paragraphs 118, 152.
And, Li teaches importance of the first training sample to the verification loss is evaluated based on the impact function value. In other words, the importance of the first training sample is determined by measuring impact of data disturbance on a verification loss of the first recommendation model on the second training sample, paragraph 458, wherein, the weight of the first training sample consisting of respective object(s) is adjusted based on determined importance of second training sample, in other words, the importance of the first training sample is determined by measuring impact of data disturbance on a verification loss of the first recommendation model on a second training sample consisting of said respective object(s) in second sample image, so that the recommendation model can more accurately fit data distribution without a bias resulting in retraining based on the first training sample with a weight that can better fit data distribution of the second training sample, to improve accuracy of predicting the second training sample by the recommendation model, paragraphs 208, 458.
Applicant’s rest of the arguments related to other cited references, other independent and/or dependent claims have been rendered moot because they are based on same assertions related to limitations of claim 1 which have been successfully taught by cited references as explained above.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “an attention weight is assigned to the task loss function ... based on the importance of the sample task ... an attention value corresponding to each task is calculated according to the importance, and the attention value is assigned as a weight to the loss function corresponding to each task" and "Because different loss functions have different weights, the influence of each loss function is different, and the influence is larger as the weight of the loss function is higher” and “adjusting weights to the loss function so as to preferentially improve learning for difficult tasks”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “storage module”, “receiving module” and “processing module” in claim 23.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification, shows that the following appears to be the corresponding structure described in the specification, according to PG-Pub, for the 35 U.S.C. 112(f) or pre- AIA 35 U.S.C. 112, sixth paragraph limitation:
storage module - CPU executes various application programs stored in the ROM 130 or the hard disk 140, such as a memory, paragraph 35
receiving module - input device 150 receives a neural network, paragraph 43
processing module - CPU 110 as a processor, and may perform various functions to be described hereinafter by executing various application programs stored, paragraph 35
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-10, 21 and 23-24 are directed towards a method, apparatus and computer readable medium which falls within one of the four statutory categories of invention but do not meet the three-prong test for patentability.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites: “obtaining, for at least two tasks, a processing result and loss function value thereof after performing processing in a neural network on a sample image; wherein the neural network outputs processing results of at least two tasks for detecting respective object; determining importance of the at least two tasks based on loss function values of at least two tasks for detecting respective object; adjusting a weight of a loss function for obtaining the loss function value of respective task, based on the determined importance; and updating the neural network based on the loss function the weight of which is adjusted”.
Due to the expansively broad nature of steps involved in the method of claim 1, these steps encompass “mental process” with using a generic algorithm/computation functions of a computer with utilizing mathematical concepts which provide nothing more than mere instructions to implement an abstract idea on a generic computer.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
For instance, first steps of claim 1, do nothing more that gathering the result and loss function data which can either be categorized as extra-solutional data gathering in step 2A prong 2 and 2B or alternatively as a mental process, e.g., data collection using generic computer functionality and thereafter performing conventional neural networks processing’s by determining which data is important among the gathered data by executing plurality of tasks since a person can determine/analyze the provided images and draw a conclusion related to its importance and further based on executed plurality of tasks adjusting and updating the data based on its importance belonging to a respective task by adjusting weight of different pieces/objects within the image(s) containing data using pen and paper.
Furthermore, the neural network operations would constitute a mathematical algorithm under the BRI since these NN’s are not specified with any degree of specificity in claims or specification and in general GANs and generic neural network are known for use in image processing.
Additionally, note that because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract").
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 2 recites wherein, in the determining, regarding different tasks within same object in the sample image, determining importance of the processing result of each task is determined.
Due to the expansively broad nature of steps involved in the method of claim 2, these steps encompass “mental process” with using a generic algorithm/computation functions of a computer which provide nothing more than mere instructions to implement an abstract idea on a generic computer.
For instance, said steps do nothing more that gathering data which can either be categorized as extra-solutional data gathering in step 2A prong 2 and 2B or alternatively as a mental process, e.g., data determination using generic computer functionality and thereafter determining which can also be processed mentally since a person can determine/analyze the provided images and draw a conclusion related to its importance.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 3 recites wherein, in the determining, regarding same tasks across different objects in the sample image, determining importance of the processing result of each task is determined.
Due to the expansively broad nature of steps involved in the method of claim 3, these steps encompass “mental process”.
For instance, said steps do nothing more that gathering data which can either be categorized as extra-solutional data gathering in step 2A prong 2 and 2B or alternatively as a mental process, e.g., data determination using generic computer functionality and thereafter determining which can also be processed mentally since a person can determine/analyze the provided images and draw a conclusion related to its importance.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 4 recites wherein, in the determining, the greater the loss function value of the processing result is, the higher the importance of the processing result is.
Due to the expansively broad nature of steps involved in the method of claim 4, these steps encompass “mental process” with using a generic algorithm/computation functions of a computer which provide nothing more than mere instructions to implement an abstract idea on a generic computer.
For instance, said steps do nothing more that gathering data which can either be categorized as extra-solutional data gathering in step 2A prong 2 and 2B or alternatively as a mental process, e.g., data determination using generic computer functionality and thereafter determining which can also be processed mentally since a person can determine/analyze the provided images and draw a conclusion related to its importance.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 5 recites wherein, in the determining, the greater the loss function value of the processing result is, the lower the importance of the processing result is.
Due to the expansively broad nature of steps involved in the method of claim 5, these steps encompass “mental process” with using a generic algorithm/computation functions of a computer which provide nothing more than mere instructions to implement an abstract idea on a generic computer.
For instance, said steps do nothing more that gathering data which can either be categorized as extra-solutional data gathering in step 2A prong 2 and 2B or alternatively as a mental process, e.g., data determination using generic computer functionality and thereafter determining which can also be processed mentally since a person can determine/analyze the provided images and draw a conclusion related to its importance.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 6 recites wherein, in the determining, the processing results are sorted according to the loss function values, and the importance of the processing result is determined based on a sorted order thereof.
Due to the expansively broad nature of steps involved in the method of claim 6, these steps encompass “mental process”.
For instance, said steps do nothing more that gathering data which can either be categorized as extra-solutional data gathering in step 2A prong 2 and 2B or alternatively as a mental process, e.g., data determination using generic computer functionality and thereafter determining which can also be processed mentally since a person can determine/analyze/sort the provided images and draw a conclusion related to its importance.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 7 recites wherein, in the determining, in a case where the loss function is a regression loss function or an intersection-over-union loss function, the importance of the processing result is determined based on a likelihood value of the loss function value; wherein, the greater the likelihood value is, the lower the importance of the processing result is.
Due to the expansively broad nature of steps involved in the method of claim 7, these steps encompass generic computer components recited at a high level of generality, such as using a generic algorithm/computation functions of a computer with utilizing mathematical concepts which provide nothing more than mere instructions to implement an abstract idea on a generic computer.
Furthermore, last steps do nothing more that gathering data which is a mental process, e.g., data determination using generic computer functionality and thereafter determining which can also be processed mentally since a person can determine/analyze the provided images and draw a conclusion related to its importance.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 8 recites wherein, in the determining, in a case where the loss function is a regression loss function or an intersection-over-union loss function, the importance of the processing result is determined based on a likelihood value of the loss function value; wherein, the greater the likelihood value is, the higher the importance of the processing result is.
Due to the expansively broad nature of steps involved in the method of claim 8, these steps encompass generic computer components recited at a high level of generality, such as using a generic algorithm/computation functions of a computer with utilizing mathematical concepts which provide nothing more than mere instructions to implement an abstract idea on a generic computer.
Furthermore, last steps do nothing more that gathering data which is a mental process, e.g., data determination using generic computer functionality and thereafter determining which can also be processed mentally since a person can determine/analyze the provided images and draw a conclusion related to its importance.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 9 recites wherein, the at least one network structure in the neural network can process one or more tasks.
Due to the expansively broad nature of steps involved in the method of claim 9, these steps encompass generic computer components recited at a high level of generality, such as using a generic algorithm/computation functions of a computer with utilizing mathematical concepts which provide nothing more than mere instructions to implement an abstract idea on a generic computer.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 10 recites wherein, in a case where the neural network is a network where tasks are cascaded, the processing result of a latter task is adjusted and obtained based on the processing result of a previous task.
Due to the expansively broad nature of steps involved in the method of claim 10, these steps encompass generic computer components recited at a high level of generality, such as using a generic algorithm/computation functions of a computer with utilizing mathematical concepts which provide nothing more than mere instructions to implement an abstract idea on a generic computer.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 21 recites: one or more memories; and one or more processors to execute instruction stored in the memories to function as: an obtaining unit configured to, for at least
Due to the expansively broad nature of steps involved in the apparatus of claim 21, these steps encompass “mental process” with using a generic algorithm/computation functions of a computer with utilizing mathematical concepts which provide nothing more than mere instructions to implement an abstract idea on a generic computer.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
For instance, said steps do nothing more that gathering the result and loss function data which can either be categorized as extra-solutional data gathering in step 2A prong 2 and 2B or alternatively as a mental process, e.g., data collection using generic computer functionality and thereafter performing conventional neural networks processing by determining which data is important among the gathered data and further adjusting and updating the data based on its importance by adjusting/updating its weight as pieces of images containing data using pen and paper.
Furthermore, the neural network operations would constitute a mathematical algorithm under the BRI since these NN’s are not specified with any degree of specificity in claims or specification and in general GANs and generic neural network are known for use in image processing.
Additionally, note that because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract").
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 23 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 23 recites: a storage module configured to store a neural network trained based on a training method; a receiving module configured to receive a data set corresponding to requirements of a task that the neural network can perform; a processing module configured to process the data set in each layer from top to bottom in the neural network, and output a result of the process, wherein the training method, comprising: obtaining, for at least and updating the neural network based on the loss function the weight of which is adjusted.
Due to the expansively broad nature of steps involved in the apparatus of claim 23, these steps encompass “mental process” with using a generic algorithm/computation functions of a computer with utilizing mathematical concepts which provide nothing more than mere instructions to implement an abstract idea on a generic computer.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
For instance, said steps do nothing more that gathering the result and loss function data which can either be categorized as extra-solutional data gathering in step 2A prong 2 and 2B or alternatively as a mental process, e.g., data collection using generic computer functionality and thereafter performing conventional neural networks processing by determining which data is important among the gathered data and further adjusting and updating the data based on its importance by adjusting/updating its weight as pieces of images containing data using pen and paper.
Furthermore, the neural network operations would constitute a mathematical algorithm under the BRI since these NN’s are not specified with any degree of specificity in claims or specification and in general GANs and generic neural network are known for use in image processing.
Additionally, note that because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract").
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
Claim 24 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 24 recites: non-transitory computer readable storage medium storing instructions for causing a computer to perform a method of training a neural network, the method comprising: obtaining, for at least
Due to the expansively broad nature of steps involved in the computer readable medium of claim 24, these steps encompass “mental process” with using a generic algorithm/computation functions of a computer with utilizing mathematical concepts which provide nothing more than mere instructions to implement an abstract idea on a generic computer.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As discussed above, the broadest reasonable interpretation of said steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Accordingly, the claim recites an abstract idea.
For instance, said steps do nothing more that gathering the result and loss function data which can either be categorized as extra-solutional data gathering in step 2A prong 2 and 2B or alternatively as a mental process, e.g., data collection using generic computer functionality and thereafter performing conventional neural networks processing by determining which data is important among the gathered data and further adjusting and updating the data based on its importance by adjusting/updating its weight as pieces of images containing data using pen and paper.
Furthermore, the neural network operations would constitute a mathematical algorithm under the BRI since these NN’s are not specified with any degree of specificity in claims or specification and in general GANs and generic neural network are known for use in image processing.
Additionally, note that because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract").
This judicial exception is not integrated into a practical application. In particular, there are no limitations regarding how the information received is used by the computer or system. There are no additional elements that integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, claim limitations amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept since claim elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea The claim is not patent eligible.
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 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.
Claims 1, 6, 9, 21 and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al., US 2023/0401446 in view of Li et al., US 2023/0153857.
Regarding claim 1, Tang discloses a method of training a neural network (training device may first select, from a plurality of existing types of convolutional neural networks, a convolutional neural network that is suitable for processing the target task, paragraph 113), comprising:
obtaining, for at least two tasks, a processing result and loss function value thereof after performing processing in a neural network on a sample image (trained target convolutional neural network is obtained through training based on the objective loss function constructed and the objective loss function may be specifically obtained based on a first sub-loss function and a second sub-loss function, wherein, the first sub-loss function is determined based on target task, and the second sub-loss function is determined based on a channel importance function and a dynamic weight. The first sub-loss function represents a difference between a training sample input to the target convolutional neural network and an output prediction result, paragraph 180,
and note that target task could be based on plurality of task such as i.e., at least two tasks, for example, target task can be a classification task making the first sub-loss function a cross-entropy loss function. Wherein, the target task can be another specific task (for example, a regression task), where the first sub-loss function is be a common loss function such as a perceptual loss function or a hinge loss function. Selection of the first sub-loss function is specifically related to a type of the target task, and processing’s related to plurality of tasks could be obtained, paragraph 114);
wherein the neural network outputs processing results of at least two tasks for detecting respective object (results for each type (both types) of target task is outputted, for example, when the target task is classification task, it classifies the input image by using the sub-network, and outputs a classification result, that is, output data is the classification result of the image, paragraphs 34-35, else when the target task a regression task, it outputs different result, paragraph 114, for detecting/predicting the target task to obtain a detection result which is presented on a display interface, not that, target detection task is usually for detecting a target object in an image. In this case, the input data is usually an input image. The execution device prunes the trained target convolutional neural network based on the input image to obtain a pruned sub-network, and performs target detection on the input image by using the sub-network to obtain a detection result, that is, output data is the detection result, paragraphs 33, 105);
determining importance of the at least two tasks based on loss function values of at least two tasks for detecting respective object (training device further obtains the second sub-loss function based on the channel importance function and the dynamic weight which is determined based on the first sub-loss function belonging to the type of associated target tasks (i.e., at least two tasks) such as classification and regression task as explained above to identify similarity between different training samples corresponding to different target tasks which are input to different sub-networks, paragraphs 118, 152).
Tang fails to explicitly disclose adjusting a weight of a loss function for obtaining the lost function value of respective task, based on determined importance; and updating neural network based on the loss function the weight of which is adjusted.
However, Li teaches determining importance of task based on loss function values for detecting respective task (importance of the first training sample to the verification loss is evaluated based on the impact function value. In other words, the importance of the first training sample is determined by measuring impact of data disturbance on a verification loss of the first recommendation model on the second training sample, paragraph 458);
adjusting a weight of a loss function for obtaining the lost function value of respective task, based on determined importance (the weight of the first training sample consisting of respective object(s) is adjusted based on determined importance of second training sample, in other words, the importance of the first training sample is determined by measuring impact of data disturbance on a verification loss of the first recommendation model on a second training sample consisting of said respective object(s) in second sample image, so that the recommendation model can more accurately fit data distribution without a bias resulting in retraining based on the first training sample with a weight that can better fit data distribution of the second training sample, to improve accuracy of predicting the second training sample by the recommendation model, paragraphs 208, 458); and updating neural network based on the loss function the weight of which is adjusted (recommendation model based on neural network is trained and continuously updated based on reverse gradient of the value of the loss function whose weight is adjusted, to improve a prediction effect of the recommendation model, paragraphs 114, 457).
Tang and Li are combinable because they both are in the same field of endeavor dealing with neural networks involving loss functions.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Tang to incorporate the teachings of Li to provide adjusting of a weight of a loss function based on determined importance for the benefit of improving accuracy of results of the recommendation model, which provides improved user experience as taught by Li at paragraph 9.
Regarding claim 6, Tang further discloses wherein, in the determining, the processing results are sorted according to the loss function values, and the importance of the processing result is determined based on a sorted order thereof (average importance of the convolution kernels (that is, the channels) in the trained target convolutional neural network for different input data are sorted and only a channel whose value of the channel importance function is larger than the threshold needs to be retained with a channel whose value of the channel importance function is smaller than the threshold is pruned, paragraph 184).
Regarding claim 9, Tang further discloses wherein, the at least one network structure in the neural network can process one or more tasks (constructing a network structure of a convolutional neural network that meets the target task, paragraph 113).
Regarding claim 21, Tang discloses a device of training a neural network (training device may first select, from a plurality of existing types of convolutional neural networks, a convolutional neural network that is suitable for processing the target task, paragraph 113), comprising:
one or more memories; and one or more processors to execute instruction stored in the memories to function (as described in paragraphs 217, 235) as:
an obtaining unit (CPU, paragraph 209) configured to, for at least two tasks, obtain a processing result and loss function value thereof after performing processing in a neural network on a sample image (trained target convolutional neural network is obtained through training based on the objective loss function constructed and the objective loss function may be specifically obtained based on a first sub-loss function and a second sub-loss function, wherein, the first sub-loss function is determined based on target task, and the second sub-loss function is determined based on a channel importance function and a dynamic weight. The first sub-loss function represents a difference between a training sample input to the target convolutional neural network and an output prediction result, paragraph 180,
and note that target task could be based on plurality of task such as i.e., at least two tasks, for example, target task can be a classification task making the first sub-loss function a cross-entropy loss function. Wherein, the target task can be another specific task (for example, a regression task), where the first sub-loss function is be a common loss function such as a perceptual loss function or a hinge loss function. Selection of the first sub-loss function is specifically related to a type of the target task, and processing’s related to plurality of tasks could be obtained, paragraph 114);
wherein the neural network outputs processing results of at least two tasks for detecting respective object (results for each type (both types) of target task is outputted, for example, when the target task is classification task, it classifies the input image by using the sub-network, and outputs a classification result, that is, output data is the classification result of the image, paragraphs 34-35, else when the target task a regression task, it outputs different result, paragraph 114, for detecting/predicting the target task to obtain a detection result which is presented on a display interface, not that, target detection task is usually for detecting a target object in an image. In this case, the input data is usually an input image. The execution device prunes the trained target convolutional neural network based on the input image to obtain a pruned sub-network, and performs target detection on the input image by using the sub-network to obtain a detection result, that is, output data is the detection result, paragraphs 33, 105);
determining importance of the at least two tasks based on loss function values of at least two tasks for detecting respective object (training device further obtains the second sub-loss function based on the channel importance function and the dynamic weight which is determined based on the first sub-loss function belonging to the type of associated target tasks (i.e., at least two tasks) such as classification and regression task as explained above to identify similarity between different training samples corresponding to different target tasks which are input to different sub-networks, paragraphs 118, 152).
Tang fails to explicitly disclose a determining unit configured to, determine importance of task; an adjustment unit configured to, adjust a weight of a loss function for obtaining the lost function value of respective task, based on determined importance; and an updating unit configured to, update the neural network based on the loss function the weight of which is adjusted.
However, Li teaches a determining unit (processing unit 3020, paragraph 610) configured to, determining importance task based on loss function values for detecting respective task (importance of the first training sample to the verification loss is evaluated based on the impact function value. In other words, the importance of the first training sample is determined by measuring impact of data disturbance on a verification loss of the first recommendation model on the second training sample, paragraph 458); an adjustment unit (processing unit 3020, paragraph 610) configured to, adjust a weight of a loss function for obtaining the lost function value of respective task, based on determined importance (the weight of the first training sample consisting of respective object(s) is adjusted based on determined importance of second training sample, in other words, the importance of the first training sample is determined by measuring impact of data disturbance on a verification loss of the first recommendation model on a second training sample consisting of said respective object(s) in second sample image, so that the recommendation model can more accurately fit data distribution without a bias resulting in retraining based on the first training sample with a weight that can better fit data distribution of the second training sample, to improve accuracy of predicting the second training sample by the recommendation model, paragraphs 208, 458); and an updating unit (processing unit 3020, paragraph 610) configured to, update the neural network based on the loss function the weight of which is adjusted recommendation model based on neural network is trained and continuously updated based on reverse gradient of the value of the loss function whose weight is adjusted, to improve a prediction effect of the recommendation model, paragraphs 114, 457).
Tang and Li are combinable because they both are in the same field of endeavor dealing with neural networks involving loss functions.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Tang to incorporate the teachings of Li to provide adjusting of a weight of a loss function based on determined importance for the benefit of improving accuracy of results of the recommendation model, which provides improved user experience as taught by Li at paragraph 9.
Regarding claim 23, Tang discloses a neural network application device comprising: a storage module configured to store a neural network trained based on a training method (store the training set into the database 230, paragraph 103); a receiving module configured to receive a data set corresponding to requirements of a task that the neural network can perform (training device may first select, from a plurality of existing types of convolutional neural networks, a convolutional neural network that is suitable for processing the target task, paragraph 113); a processing module (CPU, paragraph 209) configured to process the data set in each layer from top to bottom in the neural network, and output a result of the process (paragraphs 74, 154-161, all the layers from first to last of the neural network are processed and result as a representation form of a similarity between feature maps is outputted by the last layer of the target convolutional neural network for different training samples), wherein the training method, comprising: obtaining, for at least two tasks, a processing result and loss function value thereof after performing processing in a neural network on a sample image (trained target convolutional neural network is obtained through training based on the objective loss function constructed and the objective loss function may be specifically obtained based on a first sub-loss function and a second sub-loss function, wherein, the first sub-loss function is determined based on target task, and the second sub-loss function is determined based on a channel importance function and a dynamic weight. The first sub-loss function represents a difference between a training sample input to the target convolutional neural network and an output prediction result, paragraph 180,
and note that target task could be based on plurality of task such as i.e., at least two tasks, for example, target task can be a classification task making the first sub-loss function a cross-entropy loss function. Wherein, the target task can be another specific task (for example, a regression task), where the first sub-loss function is be a common loss function such as a perceptual loss function or a hinge loss function. Selection of the first sub-loss function is specifically related to a type of the target task, and processing’s related to plurality of tasks could be obtained, paragraph 114);
wherein the neural network outputs processing results of at least two tasks for detecting respective object (results for each type (both types) of target task is outputted, for example, when the target task is classification task, it classifies the input image by using the sub-network, and outputs a classification result, that is, output data is the classification result of the image, paragraphs 34-35, else when the target task a regression task, it outputs different result, paragraph 114, for detecting/predicting the target task to obtain a detection result which is presented on a display interface, not that, target detection task is usually for detecting a target object in an image. In this case, the input data is usually an input image. The execution device prunes the trained target convolutional neural network based on the input image to obtain a pruned sub-network, and performs target detection on the input image by using the sub-network to obtain a detection result, that is, output data is the detection result, paragraphs 33, 105);
determining importance of the at least two tasks based on loss function values of at least two tasks for detecting respective object (training device further obtains the second sub-loss function based on the channel importance function and the dynamic weight which is determined based on the first sub-loss function belonging to the type of associated target tasks (i.e., at least two tasks) such as classification and regression task as explained above to identify similarity between different training samples corresponding to different target tasks which are input to different sub-networks, paragraphs 118, 152).
Tang fails to explicitly disclose adjusting a weight of a loss function for obtaining the lost function value of respective task, based on determined importance; and updating neural network based on the loss function the weight of which is adjusted.
However, Li teaches determining importance task based on loss function values for detecting respective task (importance of the first training sample to the verification loss is evaluated based on the impact function value. In other words, the importance of the first training sample is determined by measuring impact of data disturbance on a verification loss of the first recommendation model on the second training sample, paragraph 458); adjusting a weight of a loss function for obtaining the lost function value of respective task, based on determined importance (the weight of the first training sample consisting of respective object(s) is adjusted based on determined importance of second training sample, in other words, the importance of the first training sample is determined by measuring impact of data disturbance on a verification loss of the first recommendation model on a second training sample consisting of said respective object(s) in second sample image, so that the recommendation model can more accurately fit data distribution without a bias resulting in retraining based on the first training sample with a weight that can better fit data distribution of the second training sample, to improve accuracy of predicting the second training sample by the recommendation model, paragraphs 208, 458); and updating neural network based on the loss function the weight of which is adjusted (recommendation model based on neural network is trained and continuously updated based on reverse gradient of the value of the loss function whose weight is adjusted, to improve a prediction effect of the recommendation model, paragraphs 114, 457).
Tang and Li are combinable because they both are in the same field of endeavor dealing with neural networks involving loss functions.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Tang to incorporate the teachings of Li to provide adjusting of a weight of a loss function based on determined importance for the benefit of improving accuracy of results of the recommendation model, which provides improved user experience as taught by Li at paragraph 9.
Regarding claim 24, which recites a non-transitory computer readable storage medium version of claim 1, see rationale as applied above. Note that non-transitory computer readable storage medium is taught by Tang in paragraph 235.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al., US 2023/0401446 in view of Li et al., US 2023/0153857 as applied in claim 1 above and further in view of Stent et al., US 2020/0074589.
Regarding claim 2, Tang further discloses wherein, in the determining, regarding tasks within same object in the sample image, determining importance (paragraph 209, determine a first sub-loss function based on a target task, where the first sub-loss function represents a difference between a training sample input to a convolutional neural network (that is, a target convolutional neural network) on which pruning processing is to be performed and an output prediction result. For example, when the target task is a classification task, the first sub-loss function may be a cross-entropy loss function. In addition to determining the first sub-loss function based on the target task, the processor may further obtain a second sub-loss function based on a channel importance function and a dynamic weight).
Combination of Tang with Li fails to explicitly teach regarding different tasks, determining importance of processing result of each task is determined.
However, Stent teaches regarding different tasks within same object in sample image, determining importance of processing result of each task is determined (sampling process may be considered in two stages. In the first stage, a CNN may be used to produce a saliency map from the original high-resolution image. This map may be task specific, as for each different task different regions of the original high-resolution image may be relevant. In the second stage, the original high-resolution image may be sampled in proportion to their importance, paragraph 41).
Tang and Li are combinable with Stent because they all are in the same field of endeavor dealing with neural networks involving tasks.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Tang and Li with the teachings of Stent for the benefit of providing improved systems and methods for sampling datasets to reduce the loss of relevant details as taught by Stent at paragraphs 3-4.
Regarding claim 3, Combination of Tang with Li and Stent further teaches wherein, in the determining, regarding same tasks across different objects in the sample image, determining importance of the processing result of each task is determined (Stent, “system may achieve this by analyzing the original high-resolution image 520 and then sampling areas of the original high-resolution image 520 in proportion to their perceived importance. That is, the CNN model may capture the benefit of increased resolution without significant and/or additional computational burdens or the risk of overfitting. For example and without limitation, the sampling process may be considered in two stages. In the first stage, a CNN may be used to produce a saliency map 530 from the original high-resolution image 520. This map may be task specific, as for each different task different regions of the original high-resolution image 520 may be relevant. In the second stage, the original high-resolution image 520 may be sampled in proportion to their importance as indicated by the saliency map 530. In some embodiments, a SoftMax operation may be applied to the saliency map to normalize the output map”, paragraph 41).
Tang and Li are combinable with Stent because they all are in the same field of endeavor dealing with neural networks involving tasks.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Tang and Li with the teachings of Stent for the benefit of providing improved systems and methods for sampling datasets to reduce the loss of relevant details as taught by Stent at paragraphs 3-4.
Claims 4-5 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al., US 2023/0401446 in view of Li et al., US 2023/0153857 as applied in claim 1 above and further in view of Turner et al., US 2022/0414469.
Regarding claim 4, Combination of Tang with Li fails to explicitly teach the greater the loss function value of the processing result is, the higher the importance of the processing result is.
However, Turner teaches the greater the loss function value of the processing result is, the higher the importance of the processing result is (Lagrangian multiplier λ can adjust the relative importance between enforcing the constraint and minimizing the loss function L(w). A higher value of λ would indicate enforcing the constraints has higher weight and the value of L(w) might not be optimized properly. A lower value of λ would indicate that optimizing the loss function is more important and the constraints might not be satisfied, paragraph 58).
Tang, Li are combinable with Turner because they all are in the same field of endeavor dealing with neural networks with loss function.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Tang, Li with the teachings of Turner for the benefit of providing machine learning using artificial neural networks for emulating intelligence that are trained for assessing risks or performing other operations and for providing explainable outcomes associated with these outputs as taught by Turner at paragraph 2.
Regarding claim 5, Combination of Tang, Li fails to explicitly teach the greater the loss function value of processing result is, the lower the importance of the processing result is.
However, Turner teaches the greater the loss function value of processing result is, the lower the importance of the processing result is (Lagrangian multiplier λ can adjust the relative importance between enforcing the constraint and minimizing the loss function L(w). A higher value of λ would indicate enforcing the constraints has higher weight and the value of L(w) might not be optimized properly. A lower value of λ would indicate that optimizing the loss function is more important and the constraints might not be satisfied, paragraph 58).
Tang, Li are combinable with Turner because they all are in the same field of endeavor dealing with neural networks with loss function.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Tang, Li with the teachings of Turner for the benefit of providing machine learning using artificial neural networks for emulating intelligence that are trained for assessing risks or performing other operations and for providing explainable outcomes associated with these outputs as taught by Turner at paragraph 2.
Regarding claim 7, Combination of Tang with Li further teaches wherein, in the determining, in a case where the loss function is a regression loss function or an intersection-over-union loss function (Li, first recommendation model is a logistic regression model, and a loss function of the first recommendation model is a loss function with a weight, paragraph 455), the importance of the processing result is determined based on a likelihood value of the loss function value (Li, importance of the first training sample to the verification loss is evaluated based on the impact function value. In other words, the importance of the first training sample is determined by measuring impact of data disturbance on a verification loss of the first recommendation model on the second training sample, paragraph 458).
Tang and Li are combinable because they both are in the same field of endeavor dealing with neural networks involving loss functions.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Tang with the teachings of Li for the benefit of improving accuracy of results of the recommendation model, which provides improved user experience as taught by Li at paragraph 9.
Combination of Tang with Li fails to explicitly teach wherein, the greater the likelihood value is, the lower the importance of the processing result is.
However, Turner teaches wherein, the greater the likelihood value is, the lower the importance of the processing result is (Lagrangian multiplier λ can adjust the relative importance between enforcing the constraint and minimizing the loss function L(w). A higher value of λ would indicate enforcing the constraints has higher weight and the value of L(w) might not be optimized properly. A lower value of λ would indicate that optimizing the loss function is more important and the constraints might not be satisfied, paragraph 58).
Tang, Li are combinable with Turner because they all are in the same field of endeavor dealing with neural networks with loss function.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Tang, Li with the teachings of Turner for the benefit of providing machine learning using artificial neural networks for emulating intelligence that are trained for assessing risks or performing other operations and for providing explainable outcomes associated with these outputs as taught by Turner at paragraph 2.
Regarding claim 8, Combination of Tang with Li further teaches wherein, in the determining, in a case where the loss function is a regression loss function or an intersection-over-union loss function (Li, first recommendation model is a logistic regression model, and a loss function of the first recommendation model is a loss function with a weight, paragraph 455), the importance of the processing result is determined based on a likelihood value of the loss function value (Li, importance of the first training sample to the verification loss is evaluated based on the impact function value. In other words, the importance of the first training sample is determined by measuring impact of data disturbance on a verification loss of the first recommendation model on the second training sample, paragraph 458).
Tang and Li are combinable because they both are in the same field of endeavor dealing with neural networks involving loss functions.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Tang with the teachings of Li for the benefit of improving accuracy of results of the recommendation model, which provides improved user experience as taught by Li at paragraph 9.
Combination of Tang with Li fails to explicitly teach wherein, the greater the likelihood value is, the higher the importance of the processing result is.
However, Turner teaches wherein, the greater the likelihood value is, the higher the importance of the processing result is (Lagrangian multiplier λ can adjust the relative importance between enforcing the constraint and minimizing the loss function L(w). A higher value of λ would indicate enforcing the constraints has higher weight and the value of L(w) might not be optimized properly. A lower value of λ would indicate that optimizing the loss function is more important and the constraints might not be satisfied, paragraph 58).
Tang, Li are combinable with Turner because they all are in the same field of endeavor dealing with neural networks with loss function.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Tang, Li with the teachings of Turner for the benefit of providing machine learning using artificial neural networks for emulating intelligence that are trained for assessing risks or performing other operations and for providing explainable outcomes associated with these outputs as taught by Turner at paragraph 2.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Tang et al., US 2023/0401446 in view of Li et al., US 2023/0153857 as applied in claim 1 above and further in view of Yin et al., US 2021/0056293.
Regarding claim 10, Combination of Tang with Li fails to explicitly teach wherein, in a case where the neural network is a network where tasks are cascaded, the processing result of a latter task is adjusted and obtained based on the processing result of a previous task.
However, Yin teaches wherein, in a case where the neural network is a network where tasks are cascaded (structure of a cascaded convolutional neural network, paragraph 22), the processing result of a latter task is adjusted and obtained based on the processing result of a previous task (“results of the cascaded network structure is also cascaded, which will cause previous wrong results to be irrecoverable at a later stage. In the present method, the angle classification tasks of the preceding two networks are completely identical, both performing classification and prediction in four direction ranges, but is also different in that the input samples of the second-level network contain more positive samples and therefore have more credible prediction results. The angle arbitration mechanism combines the preceding two angle prediction results by providing a predefined threshold value. Specifically, when the prediction result of the second-level network is higher than the threshold, or when the two prediction results having the highest confidence respectively in the two networks are identical, the prediction of the second-level network is taken as the final prediction result”, paragraph 36).
Tang with Li are combinable with Yin because they all are in the same field of endeavor dealing with neural networks.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Tang with Li with the teachings of Yin for the benefit of providing effective and efficient face detection method by applying regression and cascading features as taught by Yin at paragraphs 7 and 22.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Metzler Jr. et al., US 2021/0125108
Kim et al., US 2021/0125056
Zhang et al., US 2021/0027081
Sikka et al., US 20190325243
Alakuijala et al., US 2019/0251444
Tieg et al., US 11,531,879
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/PAWAN DHINGRA/Examiner, Art Unit 2683