DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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: feature identification unit, feature classification unit, feature classification evaluator, an undertrained network, image classification unit in claim 1.
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.
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 § 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 and 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the image classification unit" in lines 11 and 15. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation "the neural network" in lines 8, 10 and 11. There is insufficient antecedent basis for this limitation in the claim.
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.
Claims 1 and 2 as best understood are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al (US Pub. 2016/0189048).
With respect to claim 2 (exemplary claim), Chang discloses method for image classification (see figure 1), the method comprising:
receiving an image; identifying a plurality of features of the image, and generating a plurality of identified features; classifying the identified features, and generating a plurality of feature classifications and a plurality of feature percentages, (see paragraph 0018, wherein …an input image is received by the feature-extraction unit 120. At step S203, the extraction unit 120 extracts a plurality of eigenvalues of the input images according to the type of the machine learning algorithm, and classifies the eigenvalues into a plurality of groups according to the weightings…);
[checking if sum of the feature percentages associated with one of the feature classifications exceeds a prespecified value];
checking if the neural network was switched for prespecified times; switching to an undertrained network if the sum of the feature percentages does not exceed the prespecified value, and the neural network was not switched for prespecified times; notifying the image is unclassifiable if the neural network was switched for prespecified times, (see paragraph 0020, wherein …when the probability is less than the predetermined value, the method goes to step S207, the processing unit 130 determines whether all of the groups are selected or not. When there is an unselected group, the method goes back to step S204, the processing unit 130 selects the group with the largest weighting and the group with the second largest weighting as the eigenvalues of the machine learning algorithm. So on, when the probability is less than the predetermined value over and over again, the processing unit 130 continuously adds the unselected groups according to the weighting as the eigenvalues of the machine learning algorithm [machine learning algorithm is read as the neural network]. When there is no unselected-group and the probability is less than the predetermined value, the method goes to step S208, and the processing unit 130 determines that the input image is an unrecognized image);
passing the feature classifications to a plurality of image classification layers if the sum of the feature percentages exceeds the prespecified value; and classifying the image, and generating an image classification, (see figure numerical S205 and S206, the image is classified if the probability is greater than predetermined value, also paragraph 0018 wherein …the machine learning algorithm… [is read as neural network, and a neural network has various layers i.e. image classification layers]), as claimed.
However, Chang fails to explicitly disclose checking if sum of the feature percentages associated with one of the feature classifications exceeds a prespecified value, as claimed.
But, Chang in paragraph 0018 states “…the extraction unit 120 extracts a plurality of eigenvalues of the input images …, and classifies the eigenvalues into a plurality of groups according to the weightings. For example, the size of an input image are 100*100 pixels, each pixel can be considered an eigenvalue. In other words, the input image has 10000 eigenvalues. The extraction unit 120 classifies the eigenvalues into 40 groups…” this obviates a summation that is calculations or additions in order to get the weighting for the groups.
Therefore, it would have been obvious to one ordinary skilled in the art at the effective date of invention to simply utilize the teaching of grouping of the features as calculating the sum of the percentages in order to attain the prediction using the machine learning algorithm to classify the images as claimed.
Claim 1 is rejected for the same reasons as set forth in the rejections of claim 2, because claim 1 is claiming subject matter of similar scope as claimed in claim 2. Furthermore, Chang discloses system in figure 1.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663