DETAILED ACTION
This non-final rejection is responsive to communication filed August 2, 2024. Claims 1-17 are pending in this application.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 2, 2024 and September 12, 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 1-17 are 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 limitations: "the data" in line 6 of the claim, “the absence of the presence” in line 7 of the claim, “the learning” in line 10 of the claim, “the parameters” in line 10 of the claim, “the observance” in line 10 of the claim, and “the optimization” in line 11 of the claim. There is insufficient antecedent basis for these limitations in the claim. Claims 2-17 are rejected as being dependent upon rejected claim 1.
Further regarding claim 1, there is insufficient antecedent basis for the term “the activation maps” in line 9 of the claim because the claim only previously mentions “an activation map”. Claims 2-17 are rejected as being dependent upon rejected claim 1.
Further regarding claim 1 the language “consisting at least” in line 9 and “consisting in” in line 11 is ambiguous. Claims 2-17 are rejected as being dependent upon rejected claim 1.
Claim 2 recites the limitation “the application” in line 2 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites the limitation “the simultaneous activation” in lines 5-6 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Further regarding claim 3, the language “consisting in” in line 5 is ambiguous.
Claim 4 recites the limitations “the maximum value” and “the sum” in line 3 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 5, the language “consisting in” in line 5 is ambiguous.
Claim 6 recites the limitations “the sum” in line 3 of the claim, “the maximum value” in line 4 of the claim, and “the filtering result” in line 5 of the claim. There is insufficient antecedent basis for these limitations in the claim.
Claim 7 recites the limitation “the SoftMax function” in line 3 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitations “the end” in line 2 of the claim, “the distribution” in line 3 of the claim, “the maximum values” in lines 3-4 of the claim, “the setting” in line 4 of the claim, and “the function” in line 5 of the claim. There is insufficient antecedent basis for these limitations in the claim.
Claim 11 recites the limitations “the end” in line 3 of the claim, “the distribution” in line 3 of the claim, “the maximum values” in lines 3-4 of the claim, “the setting” in line 4 of the claim, “the function” in line 5 of the claim, and “the determination” in lines 6-7 of the claim. There is insufficient antecedent basis for these limitations in the claim.
Claim 12 recites the limitation “the application” in line 2 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation “the images” in line 2 of the claim. There is insufficient antecedent basis for this limitation in the claim.
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 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the program is not stored in a manner so as to be executable (i.e. on a non-transitory computer-readable medium), and thus represents an arrangement of software, pe se.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the computer-readable storage medium is not limited to non-transitory embodiments. The broadest reasonable interpretation of a claim drawn to a computer readable medium typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable medium, particularly when the specification is silent. Because Applicants specification is silent, the broadest reasonable interpretation of the computer readable storage medium covers a signal per se, and therefore claim 17 is rejected as covering non-statutory subject matter.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 10, 16 and 17 recite: normalizing the activation maps, the learning of the parameters of each detector being limited to the observance of a locality criterion, by means of the optimization of a first cost function L1 (K) consisting in maximizing a region of the activation map. The broadest reasonable interpretation of this/these step(s) is that the step(s) fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. A user can mentally (or with the aid of pen and paper) normalize activation maps, observe a locality criterion, and optimize a first cost function L1 (K) consisting in maximizing a region of the activation map. Further, optimization of a first cost function L1 (K) consisting in maximizing a region of the activation map represents a mathematical concept.
This judicial exception is not integrated into a practical application. The additional elements of: a computer, unsupervised training of a model for detecting repeating patterns in a dataset of image, audio or command control type, the model being composed of a detection layer; an activation layer are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The limitations “a detector of repeating patterns configured to receive a set of features extracted from the data and supply as output an activation map composed of a set of activation scores, an activation score being characteristic of the absence or the presence of a pattern detected in the data by the detector” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of: a computer, unsupervised training of a model for detecting repeating patterns in a dataset of image, audio or command control type, the model being composed of a detection layer; an activation layer are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer. As discussed above, the recitations of “a detector of repeating patterns configured to receive a set of features extracted from the data and supply as output an activation map composed of a set of activation scores, an activation score being characteristic of the absence or the presence of a pattern detected in the data by the detector” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 2 recites “the application, to each activation map at the output of the activation layer, of a uniform filter of a dimension dependent on that of said region of the activation map.” This limitation represents a mathematical concept. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements in claim 2.
Claim 3-4 recite “wherein the detection layer comprises several detectors of repeating patterns and the learning of the parameters of each detector is further limited to the observance of a uniqueness criterion, by means of the optimization of a second cost function Lu (K) consisting in avoiding the simultaneous activation of several detectors at a same point of the activation map” and “wherein the uniqueness criterion is implemented by limiting, in the optimization of the second cost function Lu (K), the maximum value of the sum at each point of the activation maps over all of the detectors to a first predefined maximum threshold.” These limitations represent a mathematical concept. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements in claims 3 and 4.
Claims 5 and 6 recite “wherein the detection layer comprises several detectors of repeating patterns and the learning of the parameters of each detector is further limited to the observance of a grouping criterion, by means of the optimization of a third cost function Lp (K) consisting in favoring the activation of said detectors on zones of the activation map that are contiguous” and “wherein the grouping criterion is implemented by applying, in the optimization of the third cost function Lp (K), a convolutional filter to the sum of the activation maps at the output of the activation layer and by limiting the maximum value of the filtering result to a second predefined maximum threshold.” These limitations represent a mathematical concept. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements in claims 5 and 6.
Claim 7 recites “wherein the activation layer implements a normalization function, for example the SoftMax function”. This limitation represents a mathematical concept. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements in claim 7.
Claim 8 recites the additional element “wherein the set of features is obtained by means of a model pre-trained on a training dataset.” This judicial exception is not integrated into a practical application because it is mere data gathering recited at a high level of generality and thus represents insignificant extra-solution activity. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claim 9 recites “at the end of the training, for each detector, an estimation of the distribution of the maximum values of the outputs of the detector and the setting of a confidence threshold (205) dependent on the function of aggregate distribution of said distribution.” This limitation represents a mathematical concept. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements in claim 9.
Claim 11 recites “at the end of the training, for each detector, an estimation of the distribution of the maximum values of the outputs of the detector and the setting of a confidence threshold dependent on the function of aggregate distribution of said distribution, and for each detector of repeating patterns and for each new dataset received, the determination of a confidence score dependent on said aggregate distribution function learned in the training applied to said dataset.” These limitations represent a mathematical concept. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements in claim 11.
Claim 12 recites “for each detector of repeating patterns, the application of a filter to the output values of the detector which are below the confidence threshold determined in the training.” This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements in claim 12.
Claim 13 recites “a step of conversion of the activation map produced at the output of the activation layer of the detection model into a map of location of the repeating patterns.” The limitation represents a mental process. A user can mentally or manually convert an activation map into a map of location of repeating patterns. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements in claim 13.
Claim 14-15 recite “wherein the data are of image type and the features extracted from the data are organized in a third order tensor” and “wherein the repeating patterns to be detected are parts of objects connected in the images.” The limitation regarding the data being parts of objects in images merely describes the data. Organizing that data in a third order tensor represents a mental process and mathematical concept. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements in claims 14-15.
Claim Rejections - 35 USC § 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, 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 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. “Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition” (‘Zheng’) (from IDS) in view of Simon, Marcel et al. “Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks” (‘Simon’).
With respect to claims 1, 10, 16 and 17, Zheng teaches a method, implemented by computer, for unsupervised training of a model for detecting repeating patterns in a dataset of image, audio or command control type, the model being composed of a detection layer (Abstract, sections 1, 2.2, 4.1 and 4.5)comprising at least:
a detector of repeating patterns configured to receive a set of features extracted from the data and supply as output an activation map composed of a set of activation scores (i.e. attention maps with grouping and weighting), an activation score being characteristic of the absence or the presence of a pattern detected in the data by the detector (sigmoid function produces probabilities/attention maps identify localized parts) (Fig. 2; sections 1, 2.2, 3, and 3.1),
an activation layer consisting at least in normalizing the activation maps (sections 3-3.2),
the learning of the parameters of each detector being limited to the observance of a locality criterion (Fig. 2; sections 3, 3.1, 3.2) by means of a loss function (section 3.3).
Zheng does not explicitly teach by means of the optimization of a first cost function L1 (K) consisting in maximizing a region of the activation map.
Simon teaches by means of the optimization of a first cost function L1 (K) consisting in maximizing a region of the activation map (CNN channels can function as part detectors; calculate a neural activation map for each channel; and determine part proposal location by point of maximum activation)(Section 3; Equation 2).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Zheng to maximize a region of an activation map as taught by Simon to enable an approach that is able to learn part models in a completely unsupervised manner, without part annotations and even without given bounding boxes during learning (Simon, abstract, section 3).
With respect to claim 2, Zheng in view of Simon teaches further comprising the application, to each activation map at the output of the activation layer, of a uniform filter of a dimension dependent on that of said region of the activation map (Zheng, sections 3.2 and 4.1; Simon, sections 3 and 4.2).
With respect to claim 3, Zheng in view of Simon teaches wherein the detection layer comprises several detectors of repeating patterns and the learning of the parameters of each detector is further limited to the observance of a uniqueness criterion, by means of the optimization of a second cost function Lu (K) consisting in avoiding the simultaneous activation of several detectors at a same point of the activation map (Zheng, section 4.3; Simon, sections 3 and 4).
With respect to claim 4, Zheng in view of Simon teaches wherein the uniqueness criterion is implemented by limiting, in the optimization of the second cost function Lu (K), the maximum value of the sum at each point of the activation maps over all of the detectors to a first predefined maximum threshold (Zheng, sections 3, 3.1 and 4.3; Simon, sections 3 and 4).
With respect to claim 5, Zheng in view of Simon teaches wherein the detection layer comprises several detectors of repeating patterns and the learning of the parameters of each detector is further limited to the observance of a grouping criterion, by means of the optimization of a third cost function Lp (K) consisting in favoring the activation of said detectors on zones of the activation map that are contiguous (Zheng, sections 3, 3.1 and 4.3; Simon, sections 3 and 4).
With respect to claim 6, Zheng in view of Simon teaches wherein the grouping criterion is implemented by applying, in the optimization of the third cost function Lp (K), a convolutional filter to the sum of the activation maps at the output of the activation layer and by limiting the maximum value of the filtering result to a second predefined maximum threshold (Zheng, sections 3, 3.1 and 4.3; Simon, sections 3 and 4).
With respect to claim 7, Zheng in view of Simon teaches wherein the activation layer implements a normalization function, for example the SoftMax function (Fig. 2, section 3).
With respect to claim 8, Zheng in view of Simon teaches wherein the set of features is obtained by means of a model pre-trained on a training dataset (Zheng, sections 1 and 3.1)
With respect to claim 9, Zheng in view of Simon teaches further comprising, at the end of the training, for each detector, an estimation of the distribution of the maximum values of the outputs of the detector and the setting of a confidence threshold (205) dependent on the function of aggregate distribution of said distribution (Zheng, sections 3 and 3.2; Simon, sections 4.2 and 5.1).
With respect to claim 11, Zheng in view of Simon teaches further comprising, at the end of the training, for each detector, an estimation of the distribution of the maximum values of the outputs of the detector and the setting of a confidence threshold dependent on the function of aggregate distribution of said distribution, and for each detector of repeating patterns and for each new dataset received, the determination of a confidence score dependent on said aggregate distribution function learned in the training applied to said dataset (Zheng, sections 3 and 3.2; Simon, sections 4.2 and 5.1).
With respect to claim 12, Zheng in view of Simon teaches further comprising, for each detector of repeating patterns, the application of a filter to the output values of the detector which are below the confidence threshold determined in the training (Zheng, sections 2.2 and 4.1; Simon, sections 3, 4, and 5.1)
With respect to claim 13, Zheng in view of Simon teaches further comprising, a step of conversion of the activation map produced at the output of the activation layer of the detection model into a map of location of the repeating patterns (Zheng, sections 3.1 and 4.3; Simon, sections 3, 4, and 4.2).
With respect to claim 14, Zheng in view of Simon teaches wherein the data are of image type and the features extracted from the data are organized in a third order tensor (Zheng, sections 3.1 and 4.3).
With respect to claim 15, Zheng in view of Simon teaches wherein the repeating patterns to be detected are parts of objects connected in the images (Zheng, abstract, sections 3 and 3.1; Simon, section 3).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M WILLOUGHBY whose telephone number is (571)272-5599. The examiner can normally be reached 9-5:30, EST, M-F.
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/ALICIA M WILLOUGHBY/Primary Examiner, Art Unit 2156 September 11, 2026
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