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 .
DETAILED ACTION
The action is in response to claims dated 6/26/2025
Claims pending in the case: 1, 3-8, 10-15, 17-20
Claims cancelled: 2, 9, 16
Claim Rejections - 35 USC § 101
Claim(s) 1, 3-8, 10-15, 17-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step1: determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If YES, proceed to Step 2A, broken into two prongs.
Step 2A, Prong 1: determine whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If YES, the analysis proceeds to the second prong
Step 2A, Prong 2: determine whether or not the claims integrate the judicial exception into a practical application. If NOT, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B).
Step 2B: If any element or combination of elements in the claim is sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Step 1 Analysis
According to the first part of the analysis, the instant case all claims are directed to one of the statutory categories of invention.
Step 2A Prong 1, Step 2A Prong 2, and Step 2B Analysis
Independent Claim 1 includes the following recitation of an abstract idea:
post-processing the plurality of original results of testing to output the plurality of results of testing; wherein the post-processing the plurality of original results of testing to output the plurality of results of testing comprises: inputting each of the plurality of original results of testing into a post-processing function to output the results of testing in a test form or a graphic form (This is data processing using a function to generate another form of the data and practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.).
Claim 1 recites the following additional elements, which, considered individually and as an ordered combination do not integrate the abstract idea into a practical application:
obtaining a weighting which is generated when a neural network model is being trained with a plurality of multiple modes of training samples, the neural network model comprising an input layer, a neural network backbone coupled to the input layer, and a plurality of different output layers coupled to the neural network backbone (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
inputting the weighting into the neural network model to output a plurality of results of testing by the neural network model testing a multiple modes of test sample; each of the multiple modes corresponds to one of the plurality of different output layers (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
wherein the inputting the weighting into the neural network model to output a plurality of results of testing by the neural network model testing a multiple modes of test sample comprises:
inputting the weighting into the neural network model to output a plurality of original results of testing by the neural network model testing the multiple modes of test sample (Inputting and outputting data using a neural network is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
These claimed limitations therefore do not integrate the abstract idea into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons given above with respect to integration of the abstract idea into a practical application.
Therefore the claim is not patent eligible.
Independent Claims 8 and 15, are similar in scope as claim 1 and therefore rejected under the same rationale. The additional elements of “a storage device; at least one processor” in claim 8 and “non-transitory storage medium storing a set of commands” also do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
The dependent claims recite at least the abstract idea identified above in the claim upon which it depends and recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Dependent claim 3-5 pertain to a high level description of a neural network (This appears to be directed to the description of data and a neural network model. This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
Dependent claim 6-7 pertain to a high level training of a neural network (This high level recitation of the machine training of the model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
The dependent claims therefore, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea
Dependent Claims 10-14 and 17-20, are similar in scope as claim 3-7 and therefore rejected under the same rationale.
Hence these claims are rejected as being abstract.
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.
Claim(s) 1, 3-8, 10-15, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kojima (US 20200151497) in view of Wang (CN-110689025-A – please refer to the attached English translation) and Rocco (US 20200250603).
Rocco not used in the prior office action.
Regarding claim 1, Kojima teaches a method for processing multiple modes of data comprising:
obtaining a weighting which is generated when a neural network model is being trained with a plurality of multiple modes of training samples (Kojima: [113]: training model by updating weights; [21, 56]: data sets of views of 3D environment from images taken to cover the scene ( photos from different directions and devices - modes)), the neural network model comprising an input layer, a neural network backbone coupled to the input layer, and a plurality of different output layers coupled to the neural network backbone (Kojima: Fig. 5A, [78]: neural network with input, output and backbone layer; Fig. 1 illustrates plurality of output layers 114a-n);
inputting the weighting into the neural network model to output a plurality of results of testing by the neural network model testing a multiple modes of test sample (Kojima: [20]: training and testing model; [113]: obtain classification result);
wherein the inputting the weighting into the neural network model to output a plurality of results of testing by the neural network model testing a multiple modes of test sample comprises:
inputting the weighting into the neural network model to output a plurality of original results of testing by the neural network model testing the multiple modes of test sample (Kojima: [95-96]: obtain results by the model);
post-processing the plurality of original results of testing to output the plurality of results of testing (Kojima: [95-97]: computation of scores to form a score map);
wherein the post-processing the plurality of original results of testing to output the plurality of results of testing comprises:
inputting each of the plurality of original results of testing into a post-processing function to output the results of testing (Kojima: [95-97]: computation of scores to form a score map) ….;
Kojima does not specifically teach,
each of the multiple modes corresponds to one of the plurality of different output layers;
a post-processing function to output the results of testing in a test form or a graphic form.
Wang further teaches,
each of the multiple modes corresponds to one of the plurality of different output layers (Wang: Pg. 3 [1, 3]: each output layer to classify as per corresponding target characterizing information (modes));
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Kojima and Wang because the combination would enable using multiple output layers for multi classification. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would improve speed by processing multiple data classification at the same time and thus improve the accuracy and efficiency of recognition results (see Wang Pg. 2 [5]).
Rocco further teaches, a post-processing function to output the results of testing in a test form or a graphic form (Rocco: [24, 60]: represented graphically by using a tool on the model outputs);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Kojima, Wang and Rocco because the combination would enable representing output model in graphic formal. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would make the system more user friendly by providing option to review data in formats familiar to the user (see Rocco [5]).
Regarding claim 3, Kojima, Wang and Rocco teach the invention as claimed in claim 1 above and further, further comprising: establishing the neural network model, the input layer being configured to receive multiple modes of samples, the multiple modes of samples comprising the plurality of multiple modes of training samples and the multiple modes of test sample (Kojima: [20, 113]: input parameters to train model and testing trained model using scene images (multiple modes of samples));
the neural network backbone being configured to receive the input of the input layer and extract features of the input multiple modes of samples (Kojima: [79-80]: input layer to convolution layer for feature extraction. It is noted that this block may be considered as part of the backbone block after the input layer);
each of the plurality of different output layers being configured to combine the features, and each of the plurality of different output layers corresponding to one mode (Wang: Pg. 3 [1, 3]: each output layer to classify as per corresponding target characterizing information (modes)).
Regarding claim 4, Kojima, Wang and Rocco teach the invention as claimed in claim 3 above and further, wherein the neural network backbone comprises a residual block of a deep residual network, an inception module of an inception network, and an encoder and decoder of an autoencoder (Kojima: [78, 86, 91-92]: backbone layers including input, pooling, convolution… layers (inception)).
Regarding claim 5, Kojima, Wang and Rocco teach the invention as claimed in claim 3 above and further, wherein each of the plurality of different output layers comprises a convolutional layer or a fully connected layer (Wang: pg. 11 [4, 7]: convolution layers with output layer comprising a fully connected layer).
Regarding claim 6, Kojima, Wang and Rocco teach the invention as claimed in claim 3 above and further, wherein before the obtaining the weighting which is generated when the neural network model is being trained with the plurality of multiple modes of training samples, the method further comprises:
obtaining the plurality of multiple modes of training samples; performing training by inputting the plurality of multiple modes of training samples into the neural network model to generate the weighting of the neural network model (Kojima: [113]: training model by updating weights) (Wang: pg. 3 [5]: training model).
Regarding claim 7, Kojima, Wang and Rocco teach the invention as claimed in claim 6 above and further, wherein: the method further comprises:
establishing a group of loss functions, where the group of loss functions comprising a plurality of different loss functions, each of the loss functions being coupled to one output layer; each of the loss functions corresponding to one mode; the group of loss functions being coupled to the input layer and the neural network backbone (Wang: pg. 3 [3-5]: for each output layer mode, optimize corresponding loss function) (Kojima: [113]: training model by updating weights);
the performing training by inputting the plurality of multiple modes of training samples into the neural network model to generate the weighting of the neural network model comprises:
performing training by inputting the plurality of multiple modes of training samples into the neural network model to generate a result of training via each of the plurality of different output layers; employing the group of loss functions to adjust a training weighting of the neural network model by inputting each of the plurality of results of training into a corresponding loss function until the training of the neural network model is completed, to generate the weighting of the neural network model (Kojima: [113]: training model by updating parameters) (Wang: pg. 3 [3-5]: training using loss function for the specific output layer among the plurality of loss functions).
Regarding Claim(s) 8, 10-14, this/these claim(s) is/are similar in scope as claim(s) 1, 3-7 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Regarding Claim(s) 15, 17-20, this/these claim(s) is/are similar in scope as claim(s) 1, 3-4, 6-7 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Response to Arguments and Examiner notes
Applicants’ amendments to the independent claims have added limitations that fall under abstract idea introducing a 35 U.S.C. § 101 rejection. the applicant is requested to review the rejection above.
Applicants’ prior art arguments have been fully considered but since they pertain to the amended sections of the claim, they are considered moot in view of the new grounds of rejection presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in attached 892.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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/Mandrita Brahmachari/Primary Examiner, Art Unit 2144