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
This is Non-Final Office Action, in responses to Patent Application filed 01/25/2024; claims foreign priority to 2023-013284, filed 01/31/2023. Claim(s) 1-14 are pending. Claim(s) 1 and 13-14 is/are independent.
In addition, 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.
Information Disclosure Statement
A signed and dated copy of applicant’s IDS, which was filed 01/25/2024 and 02/26/2024 is/are attached to this Office Action.
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.
Claim(s) 1-14 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because: The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more.
Step 1: YES (Claim(s) is/are process, machine, manufacture or composition of the matter). ... An information processing apparatus .. perform, for each of a plurality of recognition models that perform different recognition tasks, learning using corresponding learning data; and reconstruct, during the learning, the plurality of recognition models by replacing weight parameters respectively acquired by shared parts of the recognition models with an integrated parameter obtained by integrating the weight parameters; and set, during the learning, an integration cycle for performing the integration... and therefore, fall into one of the four categories of patent eligible subject matter (process, machine, manufacture or composition of the matter).
Step 2A : Prong One: ( whether a claim recites a judicial exception ?) the claim(s) recite ... plurality of recognition models that perform different recognition tasks, learning using corresponding learning data; reconstruct, during the learning, the plurality of recognition models by replacing weight parameters respectively acquired by shared parts of the recognition models with an integrated parameter obtained by integrating the weight parameters; and set, during the learning, an integration cycle for performing the integration …These limitation(s) recite mental processes and mathematical calculation...since... perform recognition models that perform different recognition task … by replacing weight parameters respectively acquired by shared parts of the recognition models with an integrated parameter obtained by integrating the weight parameters; and set, during the learning, an integration cycle for performing the integration … [is a high level mathematical calculation(s) (see the current specifications USPGPUB 2024025689735, Para(s) 35 and 42-50 for this interpretations...] ... then [APPLY IT] “during the learning, an integration cycle for performing the integration”....Thus these limitation(s) recite mental processes and mathematical calculation(s).
--------------Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as “Apparatus/medium” and “reconstructing process” …during the learning, of the recognition models by replacing weight parameters respectively acquired by shared parts of the recognition models with an integrated parameter obtained by integrating the weight parameters; and set, during the learning, an integration cycle for performing the integration ...it is noted, the improvement in the abstract idea itself ... but do not integrate the judicial exception into a practical application, i.e., recognition models with an integrated parameter obtained by integrating the weight parameters, that that perform different recognition tasks, learning using corresponding learning data... for performing the integration…Also, these limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Step 2B: (Whether a Claim Amounts to Significantly More) ? The claim(s) recite additional limitation(s) such as ... Apparatus/medium” and “reconstructing process” .. during the learning, of the recognition models by replacing weight parameters respectively acquired by shared parts of the recognition models with an integrated parameter obtained by integrating the weight parameters; and set, during the learning, an integration cycle for performing the integration ......These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not amount to significantly more than the abstract idea itself (MPEP 2106.05, 2106.04(d) and 2106.05(f)).
As to the dependent claim(s) 2-12 further recite, addition limitation(s) such as, (difference in recognition results, recognition accuracies, integration cycle to be longer than before setting, in a case where the difference in the evaluation values is greater than a first threshold, losses of the plurality of recognition models, losses is greater than a second threshold, difference in weight vectors, weight vectors is less than a third threshold, timing…, learning before the integration is performed and weighted average., etc.,) These limitation(s) only amounts to mere instructions to implement the abstract idea ...and do not include elements that amount to significantly more than the abstract idea and are also rejected under the same rational.
Accordingly, claims 1-14 fail to recite statutory subject matter, as defined in 35 U.S.C. 101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 4, 6 and 8 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. As drafted, Claim(s) 4, 6 and 8 recite the limitation(s) said “the evaluation value is greater than the first/second/third threshold”. There is insufficient antecedent basis for this limitation in the claim.
Claims Rejection – 35 U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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) 1-14 rejected under 35 U.S.C. 103 as being unpatentable over Okuno et al., (“US-20170344881-A1” filed 05/23/2017 [hereinafter “Okuno”], in view of Ukrainczyk et al., (“US-20060143175-A1” filed 02/21/2006 [hereinafter “Ukrainczyk”].
Independent Claim 1, Okumo teaches: An information processing apparatus comprising one or more memories storing instructions and one or more processors that execute the instructions to: perform, for each of a plurality of recognition models that perform different recognition tasks, learning using corresponding learning data; (in Okuno the Abstract and Para(s) 8 and 14, i.e., perform, for each of a plurality of recognition models that perform different recognition tasks, learning using corresponding learning...Also, Okumo further teaches: and set, during the learning, an integration cycle for performing the integration; (in Okuno Para 75, i.e., he learning apparatus 10 repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks...)
It is noted, Okuno discloses learning unit configured to learn a plurality of multi-layer neural networks configured to carry out a plurality of tasks, a generation unit configured to generate a shared layer candidate at a predetermined layer between or among the plurality of multi-layer neural networks ...(as describes herein) However, Okuno does not expressly teach .However, the combination of Okuno and Ukrainczyk teach the limitation said reconstruct, during the learning, the plurality of recognition models by replacing weight parameters respectively acquired by shared parts of the recognition models with an integrated parameter obtained by integrating the weight parameters. (in Ukrainczyk Para(s) 31, 69 and 76, i.e., "learned" derived via statistical means during training model preparation phase (re, classification categories, document features (vocabulary) and evidence edits are identified and inputted by the user into term list 20 and REE table 50, respectively, wherein weight parameters respectively acquired by shared parts of the recognition models with an integrated parameter obtained by integrating the weight parameters, wherein the model preparation phase is an iterative process in which manual user inputs are integrated with automatically generated inputs to support model preparation…)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Okuno’s learning unit configured to learn a plurality of multi-layer neural networks configured to carry out a plurality of tasks, a generation unit configured to generate a shared layer candidate at a predetermined layer between or among the plurality of multi-layer neural networks, to include a means said, reconstruct, during the learning, the plurality of recognition models by replacing weight parameters respectively acquired by shared parts of the recognition models with an integrated parameter obtained by integrating the weight parameters as taught by Ukrainczyk, provides an effective method for classifying text in which user knowledge may be utilized very early in the construction of the statistical model...(in Ukrainczyk Para(s) 9 and 11). It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 2, Okuno and Ukrainczyk further teach: wherein the one or more processors execute the instructions to set the integration cycle, based on a difference in recognition results between the recognition models OR a difference in the shared parts between the recognition models; (in Okuno Para 75, i.e., learning apparatus 10 repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks...)
Claim 3, Okuno and Ukrainczyk further teach: wherein the one or more processors execute the instructions to: evaluate, during the learning, evaluation values of recognition accuracies of the plurality of recognition models with respect to the different recognition tasks, and set the integration cycle, based on a difference in the evaluation values as the difference in the recognition results; (in Okuno Para(s) 73- 75, i.e., The learning apparatus 10 individually calculates the accuracy of each of the recognition tasks in the DCNN having the initial structure learned in step S1 with use of the accuracy evaluation unit 21 in the first memory 14, and stores the calculated accuracy into the second memory 15 as the initial accuracy 34. The accuracy evaluation unit 21 can achieve this calculation only by calculating the accuracy of the learned DCNN 33 with use of the verification data 31 in the second memory 15. As described above, the definition of the accuracy calculation is defined for each of the recognition tasks learning apparatus 10 repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks...Also, in Okuno the Abstract and Para(s) 8 and 14, i.e., perform, for each of a plurality of recognition models that perform different recognition tasks, learning using corresponding learning..)
Claim 4, Okuno and Ukrainczyk further teach: wherein the one or more processors execute the instructions to set the integration cycle to be longer than before setting, in a case where the difference in the evaluation values is greater than a first threshold; (in Okuno Para(s) 73- 75, i.e., the accuracy calculation is defined for each of the recognition tasks learning apparatus that repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks...Also, in Okuno Para(s) 132-135 , i.e., in a case where the difference in the evaluation values is greater than a first threshold…as illustrates FIG. 1, that is allowed the degradation degree (32) (losses) which is set with the use of the real value of 0.0 or larger and 1.0 or smaller for each of the recognition tasks, and is stored in the second memory (15) in advance… Also, in Okuno the Abstract and Para(s) 8 and 14, i.e., perform, for each of a plurality of recognition models that perform different recognition tasks, learning using corresponding learning..)
Claim 5, Okuno and Ukrainczyk further teach: wherein the one or more processors execute the instructions to: acquire, during the learning, losses of the plurality of recognition models with respect to the different recognition tasks, and set the integration cycle, based on a difference in the losses as the difference in the recognition results; (in Okuno Para(s) 73- 75, i.e., the accuracy calculation is defined for each of the recognition tasks learning apparatus that repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks...Also, in Okuno Para(s) 132-135 , i.e., in a case where the difference in the evaluation values is greater than a first threshold…as illustrates FIG. 1, that is allowed the degradation degree (32) (losses) which is set with the use of the real value of 0.0 or larger and 1.0 or smaller for each of the recognition tasks, and is stored in the second memory (15) in advance… Also, in Okuno the Abstract and Para(s) 8 and 14, i.e., perform, for each of a plurality of recognition models that perform different recognition tasks, learning using corresponding learning … Also, in Okuno the Abstract and Para(s) 8 and 14, i.e., perform, for each of a plurality of recognition models that perform different recognition tasks, learning using corresponding learning..)
Claim 6, Okuno and Ukrainczyk further teach: wherein the one or more processors execute the instructions to set the integration cycle to be longer than before setting, in a case where the difference in the losses is greater than a second threshold. (in Okuno Para(s) 73- 75, i.e., the accuracy calculation is defined for each of the recognition tasks learning apparatus that repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks...Also, in Okuno Para(s) 132-135 , i.e., in a case where the difference in the evaluation values is greater than a first threshold…as illustrates in FIG. 1, that is allowed the degradation degree (32) (losses) which is set with the use of the real value of 0.0 or larger and 1.0 or smaller for each of the recognition tasks, and is stored in the second memory (15) in advance… Also, in Okuno the Abstract and Para(s) 8 and 14, i.e., perform, for each of a plurality of recognition models that perform different recognition tasks, learning using corresponding learning..)
Claim 7, Okuno and Ukrainczyk further teach: wherein the one or more processors execute the instructions to set the integration cycle, based on a difference in vector… of the shared parts as the difference in the shared part. (in Okuno Para(s) 73- 75, i.e., the accuracy calculation is defined for each of the recognition tasks learning apparatus that repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks...Also, in Okuno Para(s) 132-135 , i.e., in a case where the difference in the evaluation values is greater than a first threshold…as illustrates in FIG. 1, that is allowed the degradation degree (32) (losses) which is set with the use of the real value of 0.0 or larger and 1.0 or smaller for each of the recognition tasks, and is stored in the second memory (15) in advance… Also, in Okuno Para(s) 157-164, further mentions convolutional filter utilizing the “Speeding up Convolutional Neural Networks with Low Rank Expansions” written by M. Jaderberg, A. Vedaldi, and A. Zisserman (2014) on arXiv preprint, arXiv: 1405.3866. In this literature, N convolutional filters having approximated by being decomposed into K (K<N) vectors of d*1*1 and N vectors of d*1*K… of the shared parts as the difference in the shared part…)
In addition, Ukrainczyk further teaches, set the integration cycle, based on a difference in weigh vector…(In Ukrainczyk Para(s) 29-31, i.e., set the integration cycle, based on a difference in weigh vector …)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Okuno’s learning unit configured to learn a plurality of multi-layer neural networks configured to carry out a plurality of tasks, a generation unit configured to generate a shared layer candidate at a predetermined layer between or among the plurality of multi-layer neural networks, to include a means said, set the integration cycle, based on a difference in weigh vector… as taught by Ukrainczyk, provides an effective method for classifying text in which user knowledge may be utilized very early in the construction of the statistical model...(in Ukrainczyk Para(s) 9 and 11). It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 8, Okuno and Ukrainczyk further teach: wherein the one or more processors execute the instructions to set the integration cycle to be longer than before setting, in a case where the difference in the… vectors is less than a third threshold. (in Okuno Para(s) 73- 75, i.e., the accuracy calculation is defined for each of the recognition tasks learning apparatus that repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks...Also, in Okuno Para(s) 132-135 , i.e., in a case where the difference in the evaluation values is greater than a first threshold…as illustrates in FIG. 1, that is allowed the degradation degree (32) (losses) which is set with the use of the real value of 0.0 or larger and 1.0 or smaller for each of the recognition tasks, and is stored in the second memory (15) in advance… Also, in Okuno Para(s) 157-164, further mentions convolutional filter utilizing the “Speeding up Convolutional Neural Networks with Low Rank Expansions” written by M. Jaderberg, A. Vedaldi, and A. Zisserman (2014) on arXiv preprint, arXiv: 1405.3866. In this literature, N convolutional filters having approximated by being decomposed into K (K<N) vectors of d*1*1 and N vectors of d*1*K… of the shared parts as the difference in the shared part…)
In addition, Ukrainczyk further teaches, set the integration cycle, based on a difference in weigh vector…(In Ukrainczyk Para(s) 29-31, i.e., set the integration cycle, based on a difference in weigh vector …)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Okuno’s learning unit configured to learn a plurality of multi-layer neural networks configured to carry out a plurality of tasks, a generation unit configured to generate a shared layer candidate at a predetermined layer between or among the plurality of multi-layer neural networks, to include a means said, set the integration cycle, based on a difference in weigh vector… as taught by Ukrainczyk, provides an effective method for classifying text in which user knowledge may be utilized very early in the construction of the statistical model...(in Ukrainczyk Para(s) 9 and 11). It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 9, Okuno and Ukrainczyk further teach: wherein the one or more processors execute the instructions to set the integration cycle, based on the difference in the recognition results between the recognition models at a timing of generating the integrated parameter OR the difference in the shared parts between the recognition models. (in Okuno Para(s) 73- 75, i.e., the accuracy calculation is defined for each of the recognition tasks learning apparatus that repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks… wherein the learning apparatus 10 individually calculates the accuracy of each of the recognition tasks in the DCNN having the initial structure learned in step S1 with use of the accuracy evaluation unit 21 in the first memory 14, and stores the calculated accuracy into the second memory 15 as the initial accuracy 34. The accuracy evaluation unit 21 can achieve this calculation only by calculating the accuracy of the learned DCNN 33 with use of the verification data 31 in the second memory 15. As described above, the definition of the accuracy calculation is defined for each of the recognition tasks learning apparatus 10 repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks....)
Claim 10, Okuno and Ukrainczyk further teach: wherein the one or more processors execute the instructions to set the integration cycle, based on the difference in the recognition results between the recognition models evaluated at a timing closest to a timing of generating the integrated parameter OR the difference in the shared parts between the recognition models. (in Okuno Para(s) 73- 75, i.e., the accuracy calculation is defined for each of the recognition tasks learning apparatus that repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks… wherein the learning apparatus 10 individually calculates the accuracy of each of the recognition tasks in the DCNN having the initial structure learned in step S1 with use of the accuracy evaluation unit 21 in the first memory 14, and stores the calculated accuracy into the second memory 15 as the initial accuracy 34. The accuracy evaluation unit 21 can achieve this calculation only by calculating the accuracy of the learned DCNN 33 with use of the verification data 31 in the second memory 15. As described above, the definition of the accuracy calculation is defined for each of the recognition tasks learning apparatus 10 repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks....)
Claim 11, Okuno and Ukrainczyk further teach: wherein the integration cycle is the number of iterations of the learning performed before the integration is performed. (in Okuno Para(s) 73- 75, i.e., the accuracy calculation is defined for each of the recognition tasks learning apparatus that repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks… wherein the learning apparatus 10 individually calculates the accuracy of each of the recognition tasks in the DCNN having the initial structure learned in step S1 with use of the accuracy evaluation unit 21 in the first memory 14, and stores the calculated accuracy into the second memory 15 as the initial accuracy 34. The accuracy evaluation unit 21 can achieve this calculation only by calculating the accuracy of the learned DCNN 33 with use of the verification data 31 in the second memory 15. As described above, the definition of the accuracy calculation is defined for each of the recognition tasks learning apparatus 10 repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks....)
Claim 12, Okuno and Ukrainczyk further teach: wherein the one or more processors execute the instructions to generate the integrated parameter by integrating weights corresponding to the shared parts of the recognition models by weighted. (in Okuno Para(s) 73- 75, i.e., the accuracy calculation is defined for each of the recognition tasks learning apparatus that repeats processing for generating the shared layer candidate and processing for determining whether to adopt this shared layer candidate for each of the recognition tasks...Also, in Okuno Para(s) 132-135 , i.e., in a case where the difference in the evaluation values is greater than a first threshold…as illustrates in FIG. 1, that is allowed the degradation degree (32) (losses) which is set with the use of the real value of 0.0 or larger and 1.0 or smaller for each of the recognition tasks, and is stored in the second memory (15) in advance …)
In addition, Ukrainczyk further teaches, the recognition models by weighted average…(In Ukrainczyk Para 12 , i.e., recognition models by the weight; which is indicative of a degree of association between the category feature and the category; associating a plurality of document features with each document, wherein each document feature represents one of a plurality of tokens found in the document; producing a feature vector for each document, wherein each feature vector includes the plurality of document features with a count corresponding to each document feature, wherein the count is indicative of the number of times the document feature appears in the document; multiplying the category vector by the document vector, in accordance with the mathematical convention of multiplication of a vector by a vector, to produce a plurality of category scores for each document; and for each perspective, classifying a document into a category provided the category score exceeds a predetermined threshold (in the BRI, i.e., as weight average).)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Okuno’s learning unit configured to learn a plurality of multi-layer neural networks configured to carry out a plurality of tasks, a generation unit configured to generate a shared layer candidate at a predetermined layer between or among the plurality of multi-layer neural networks, to include a means said, the recognition models by weighted average…… as taught by Ukrainczyk, provides an effective method for classifying text in which user knowledge may be utilized very early in the construction of the statistical model...(in Ukrainczyk Para(s) 9 and 11). It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Regarding Claim(s) 13 and 14 (respectively) is/are fully incorporated similar subject of claim(s) 1, and 1 (respectively) cited above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dey et al., (“ US 20220375199 A1” filed 10/12/2021, relates to a method and system for co-operative and cascaded inference on the edge device using an integrated Deep Learning (DL) model for object detection and localization, which comprises a strong classifier trained on largely available datasets and a weak localizer trained on scarcely available datasets, and work in coordination to first detect object (fire) in every input frame using the classifier, and then trigger a localizer only for the frames that are classified as fire frames. The classifier and the localizer of the integrated DL model are jointly trained using Multitask Learning approach. Works in literature hardly address the technical challenge of embedding such integrated DL model to be deployed on edge devices. The method provides an optimal hardware software partitioning approach for components or segments of the integrated DL model which achieves a tradeoff between latency and accuracy in object classification and localization... [the Abstract].
Bilen et al., NPL (“Integrated Perception with Recurrent Multi-Task Neural Networks” Published 2016 by 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, 9 pages, describing, Modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classification, object and part detection, boundary extraction, etc. However, a major advantage that natural intelligences still have is that they work well for all perceptual problems together, solving them efficiently and coherently in an integrated manner. In order to capture some of these advantages in machine perception, we ask two questions: whether deep neural networks can learn universal image representations, useful not only for a single task but for all of them, and how the solutions to the different tasks can be integrated in this framework. We answer by proposing a new architecture, which we call multinet, in which not only deep image features are shared between tasks, but where tasks can interact in a recurrent manner by encoding the results of their analysis in a common shared representation of the data. In this manner, we show that the performance of individual tasks in standard benchmarks can be improved first by sharing features between them and then, more significantly, by integrating their solutions in the common representation.. [The Abstract].
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/QUOC A TRAN/Primary Examiner, Art Unit 2145
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