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
Claims 1-20 are presented for examination in this application, 18/107,040, filed 2023-02-08 having an effective filing date via PCT application, PCT/CN2022/118116, of 2022-02-28.
The Examiner cites particular sections in the references as applied to the claims
below for the convenience of the applicant(s). Although the specified citations are
representative of the teachings in the art and are applied to the specific limitations within
the individual claim, other passages and figures may apply as well. It is respectfully
requested that, in preparing responses, the applicant(s) fully consider the references in
their entirety as potentially teaching all or part of the claimed invention, as well as the
context of the passage as taught by the prior art or disclosed by the Examiner.
Response to Arguments
Applicant’s arguments and remarks filed 2025-12-03 have been fully considered. The arguments and remarks regarding the 35 U.S.C 101 rejections were found to be persuasive. The arguments and remarks regarding the 35 U.S.C 112 rejections were found to be persuasive however the amendments have introduced new grounds for rejection. The 35 U.S.C 112 rejections have been maintained via new ground of rejection.
35 U.S.C 112
Applicant’s response:
Applicant asserts “Examiner alleges that it is unclear how the hidden layer comprises a hidden layer and a visible layer.
As understood by Examiner, according to common knowledge, the restricted Boltzmann machine is a special topological structure of the Boltzmann machine (BM), which is a symmetric coupled random feedback type binary unit neural network consisting of visible layers and hidden layers.
As recited in claim 1, the DBN model includes an input layer, hidden layers 1-n, and an output layer, and the hidden layers 1-n are restricted Boltzmann machines. According to mentioned above, that is, the DBN model of claim 1 refers to a model including an input layer, multiple restricted Boltzmann machines (which include multiple pairs of a visible layer and a hidden layer), and an output layer.
Therefore, Applicant agrees with opinions of Examiner.”
Examiner’s response:
Examiner notes that while the Applicant has agreed to the opinions of the Examiner, the amendments made do not reflect any changes to reflect that agreement. The claim remains rejected as it is still unclear how a hidden layer can comprise a visible layer as that is not customary in the art in accordance with the structure of a Restricted Boltzmann Machine. Restricted Boltzmann Machines comprise hidden and visual layer(s) but they are not customary to be labeled as the same layer, as currently claimed.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3 are rejected under 35 U.S.C 112(b) for being indefinite by failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention.
Claim 1 reads: “…wherein each hidden layer comprises 1 visible layer and 1 hidden layer”. The closest portions of the specification that aid in understanding the above limitation can be found at para [11]: “step 5: training the DBN model: pre-training initial parameters of the hidden layer 1-hidden layer n layer by layer, and then adjusting the initial parameters of each hidden layer finely through an error back propagation method, 1121 where each hidden layer includes 1 visible layer and 1 hidden layer,” and para [69]: “ Each hidden layer includes 1 visible layer and I hidden layer…”. These portions of the specification do not make clear how the hidden layer comprises a hidden layer and a visible layer. The examiner notes that this might come from the use of restricted Boltzmann machines that have both a hidden and visible layer, of which the claimed invention uses, however further clarification is needed.
Additionally, step 3 of claim 1 recites “preprocessing input data relevant to atmospheric visibility: obtaining the input data relevant to atmospheric visibility, and normalizing the input data relevant to atmospheric visibility in advance, and dividing the input data relevant to atmospheric visibility into a training set and a prediction set”. The closest portion of the specification that aids in understanding the above limitation can be found at para [03]: “As an important meteorological parameter, atmospheric visibility can significantly reflect a degree of air pollution.”. This portion of the specification does not make it clear on what exactly the input data consists of. The Examiner notes that the term “relevant” has a large relative indefinite scope and renders the claim as indefinite, as the specification does not aid in clarifying the scope.
As claim 2 and 3 depend from claim 1, they too contain the same deficiencies that render the 35 U.S.C 112(b) rejection to be made and are thus rejected for the same reasons.
Allowable Subject Matter
Claim 1 would be allowable provided the 112b rejections are overcome.
The closest prior art, Guo et al, (“Establishment of Air Quality of Forecast Model Based on Deep Learning”), discloses a deep belief network, in the context of atmospheric predictions, with restricted Boltzmann machines that preferably selects the number of hidden layers and nodes within the hidden layers with a visibility prediction accuracy as a target according to a step size and a preset number of nodes (see generally Guo section IV page 4).
However, the examiner has found that the distinct features of the applicant’s claimed invention over the prior art is the explicit claiming of the aforementioned limitations as specified in claim 1. When viewed individually or as a combination with other prior art of record, the limitations specified in claim 1 are distinct.
Claim 2 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims provided the 112b and 101 rejections are overcome.
The closest prior art to this limitation, Pandey et al., (“Comparative Analysis of KNN Algorithm using Various Normalization Techniques”), teaches the min-max normalization method and eq. 18. However, because of dependency to claim 1, claim 2 would be allowable provided the 112b and 101 rejections are overcome.
Claim 3 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims provided the 112b and 101 rejections are overcome.
The closest prior art to this limitation, Pandey et al., (“Comparative Analysis of KNN Algorithm using Various Normalization Techniques”), teaches the Z-score normalization method and eq. 19. However, because of dependency to claim 1, claim 2 would be allowable provided the 112b and 101 rejections are overcome.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
“Establishment of Air Quality Forecast Model Based on Deep Learning” — Guo et al. — discloses a deep belief network in the context of atmospheric predictions
“Understanding the scaling of L^2 regularization in the context of neural networks” — Affek — discloses equations related to neural networks
“Restricted Boltzmann Machine” — Machine Learning for Science Team — discloses equations related to neural networks
“Loss Functions”; “ml-cheatsheet” — github user: ad889a82 — discloses equations related to neural networks
“Chapter 2: How the backpropagation algorithm works” — Nielsen — discloses equations related to neural networks
“Comparative Analysis of KNN Algorithm using Various Normalization Techniques” — Pandey et al — discloses equations related to neural networks
“Medical Multimedia Big Data Analysis Modeling Based on DBN Algorithm” — Yang — discloses equations related to neural networks
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew A Bracero whose telephone number is (571)270-0592. The examiner can normally be reached Monday - Thursday 7:30a.m. - 5:00 p.m. ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at 571-270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW BRACERO/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126