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: “input unit configured to” in claims 1 and 3 and “processing unit… configured to” in claims 1-5.
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.
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 limitations “1-5” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of any language to provide structure to the limitation’s configured units and merely discloses their functionality and nothing more. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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.
Claims 3-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Regarding claim 3, in Step 1 of the 101 analysis set forth in MPEP 2106, the claim recites an information processing device that uses sensor data to process information. A device is one of the four statutory categories of invention.
In Step 2a Pong 1 of the 101 analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
And a processing unit, comprising one or more processors, that uses configured to use the plurality of pieces of the time-series data to separately generate a plurality of primary capsules each including a feature vector of each piece of the time-series data and a plurality of primary capsules each including a feature vector at each predetermined time interval. (a person can generate a set of data based on given data as a process of simply evaluating data and making a judgement based on the observed overlap. (MPEP 2106))
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it
falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
An information processing device comprising: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
an input unit configured to input a plurality of pieces of time-series data measured by a respective plurality of sensors at different positions; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g)).
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, the additional element (ii) recites generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 4, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further, claim 4 recites The information processing device according to The information processing device according to wherein the processing unit is configured to: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). not only generate the two pluralities of primary capsules, but also generate a feature vector by extracting an entire feature included in the plurality of pieces of the time-series data for each partial region and generate primary capsules each including the feature vector at the each predetermined time interval by using the plurality of pieces of the time-series data. (In step 2A, prong 1, this recites a mental process but for recitation of generic computer components which is not indicative of integration into a practical application). Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 5, it is dependent upon claim 4, and thereby incorporates the limitations of, and corresponding analysis applied to claim 4. Further, claim 5 recites The information processing device according to The information processing device according to wherein the processing unit is configured to: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). perform attention routing on each of the three pluralities of primary capsules to generate three digital capsules and infer a task on the basis of a size of feature vectors included in the three digital capsules. (In step 2A, prong 1, this recites a mental process but for recitation of generic computer components which is not indicative of integration into a practical application). Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 3-5 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being unpatentable over NARWARIYA et al. (U.S. Pub. No. US 20210406603 A1)
Regarding claim 3, NARWARIYA teaches the invention substantially as claimed, including:
An information processing device comprising: an input unit configured to input a plurality of pieces of time-series data measured by a respective plurality of sensors at different positions; and a processing unit, comprising one or more processors, configured to use the plurality of pieces of the time-series data to separately generate a plurality of primary capsules each including a feature vector of each piece of the time-series data and a plurality of primary capsules each including a feature vector at each predetermined time interval. ([0045] Say there are total 4 dimensions and 10 time series instances. In the 1st time series instance, the 2nd dimension values are missing, while it is present in all other time series.
[0046] Conditioning Module: The sensor embedding vector of size where ‘d.sub.i’, is the time series instances that is constructed for each 4 dimensions. A graph neural network is then constructed using active dimension say 1st, 3.sup.rd and 4th in 1st time series instance in which node represents sensor embedding and edges represents the connection between nodes. The max pool is then on the output of graph neural network to obtain conditioning vector of ‘d.sub.i’ dimension.
[0047] Time series: To compute fixed-dimension time series, the 2nd dimension value for 1st time series instance is computed from mean value of 2nd dimension present in rest of time series instances. Now, the output of conditioning module which is conditioning vector and fixed-dimension time series are input to core dynamics module, which is gated recurrent network that learns features according with the combination of available sensors/active sensors. All the learning is in an end-to-end fashion.)
NARWARIYA further teaches:
The information processing device according to The information processing device according to wherein the processing unit is configured to: not only generate the two pluralities of primary capsules, but also generate a feature vector by extracting an entire feature included in the plurality of pieces of the time-series data for each partial region ([0047] Time series: To compute fixed-dimension time series, the 2nd dimension value for 1st time series instance is computed from mean value of 2nd dimension present in rest of time series instances. Now, the output of conditioning module which is conditioning vector and fixed-dimension time series are input to core dynamics module, which is gated recurrent network that learns features according with the combination of available sensors/active sensors. All the learning is in an end-to-end fashion.) and generate primary capsules each including the feature vector at the each predetermined time interval by using the plurality of pieces of the time-series data. ([0048] Each sensor is associated with a vector or embedding, and the vectors for any given combination of available sensors/active sensors in a time series are used to obtain the combination-specific conditioning vector. This is equivalent to mapping a set of (sensor) vectors to another (conditioning) vector. Though the core module based on RNNs can only ingest fixed-dimensional time series input, the conditioning vector can be obtained by summarizing a variable number of sensor vectors via a GNN. This conditioning vector serves as an additional input that allows the core module to adjust its processing according to the variable number of available sensors/active sensors within each time series. A key advantage of using a GNN for processing the combination of sensors is that once the GNN is learned, it can process any previously unseen combination of sensors apart from those seen during training, thus depicting combinatorial generalization.)
NARWARIYA further teaches:
The information processing device according to The information processing device according to wherein the processing unit is configured to: perform attention routing on each of the three pluralities of primary capsules to generate three digital capsules and infer a task on the basis of a size of feature vectors included in the three digital capsules. ([0056] Referring to steps of FIG. 3, in an embodiment of the present disclosure, at step 210, one or more internal computations and one or more activations are re-configured in the neural network corresponding to the one or more imputed multivariate time series data based on the one or more generated conditional vectors to obtain a re-configured neural network. The above step 210 is better understood by way of the following description illustrated by an example and such example shall not be construed as limiting the scope of the present disclosure. [0057] Any time series x.sub.iϵ[AltContent: rect] is first converted to the d-dimensional time series {tilde over (x)}.sub.i with mean-imputation for the unavailable sensors. This time series along with its conditioning vector v.sub.S.sub.i are processed by the core dynamics model as follows:
z.sub.i.sup.t=GRU([{tilde over (x)}.sub.i.sup.t,v.sub.S.sub.i],z.sub.i.sup.t-1;θ.sub.GRU)t: 1, . . . ,T.sub.i (4)
ŷ.sub.i=f.sub.o(z.sub.i.sup.T.sup.i,θ.sub.o) (5)
where GRU is a (multi-layered) GRU-based RNN as known in the art having θ.sub.GRU learnable parameters that gives feature vector z.sup.T.sup.i at the last time step T.sub.i. At last, the estimate ŷ.sub.i for y.sub.i is obtained via f.sub.o consisting of ReLU layer(s) followed by softmax or sigmoid layer depending upon whether the task is classification or regression, respectively, such that ŷ.sub.iϵ[0,1].sup.K in case of classification, and ŷ.sub.iϵ[AltContent: rect] in case of regression. For the RUL estimation regression task, system and method use min-max normalized target variables (also referred as ‘target’) such that they lie in [0, 1]. In other words, a target variable corresponding to the one or more imputed multivariate time series data is estimated via the re-configured neural network.)
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.
Claim(s) 1 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over NARWARIYA et al. (U.S. Pub. No. US 20210406603 A1) in view of SAID et al. (U.S. Pub. No. US 20170359584 A1).
Regarding claim 1, NARWARIYA teaches the invention substantially as claimed, including:
An information processing device comprising: an input unit configured to input a plurality of pieces of time-series data measured by a respective plurality of sensors at different positions; and a processing unit, comprising one or more processors, configured to divide the plurality of pieces of the time-series data into a plurality of pieces of partial time-series data at predetermined time intervals, generate a plurality of primary capsules each including a feature vector of each of the plurality of pieces of the partial time-series data regarding the plurality of pieces of the time-series data, ([0045] Say there are total 4 dimensions and 10 time series instances. In the 1st time series instance, the 2nd dimension values are missing, while it is present in all other time series.
[0046] Conditioning Module: The sensor embedding vector of size where ‘d.sub.i’, is the time series instances that is constructed for each 4 dimensions. A graph neural network is then constructed using active dimension say 1st, 3.sup.rd and 4th in 1st time series instance in which node represents sensor embedding and edges represents the connection between nodes. The max pool is then on the output of graph neural network to obtain conditioning vector of ‘d.sub.i’ dimension.
[0047] Time series: To compute fixed-dimension time series, the 2nd dimension value for 1st time series instance is computed from mean value of 2nd dimension present in rest of time series instances. Now, the output of conditioning module which is conditioning vector and fixed-dimension time series are input to core dynamics module, which is gated recurrent network that learns features according with the combination of available sensors/active sensors. All the learning is in an end-to-end fashion.)
While NARIWARIYA does teach getting time series input from sensors and dividing that data into features and capsules, it does not explicitly teach:
perform graph modeling to generate a weighting matrix in which a connection relationship between the plurality of primary capsules is indicated by a weight corresponding to a distance between the plurality of sensors and the predetermined time interval, and perform graph Fourier transform on the feature vector of each of the plurality of primary capsules based on the weighting matrix.
However, in analogous art that similarly handles signal data, SAID teaches:
perform graph modeling to generate a weighting matrix in which a connection relationship between the plurality of primary capsules is indicated by a weight corresponding to a distance between the plurality of sensors and the predetermined time interval, ([0080] A graph edge may mean a line connecting graph vertexes. The graph edge is used to represent some form of statistical dependence in the signal, with a positive weight representing its strength. For example, each vertex can be connected to every other vertex, and a zero weight is assigned for edges connecting vertexes that are unrelated or weakly related. However, to simplify representation, edges having the zero weight can be completely removed.
[0081] In another embodiment of the present invention, the edges connecting the graph vertexes may be preset according to signal characteristics. For instance, vertexes can be arranged on 1-D arrays for audio signals, 2-D arrays for images, and 3-D arrays for video frames. In this case, a time axis can be used as third dimension. For example, in the graph of FIG. 3(a), the graph edges can be defined to connect each vertex to four of its nearest neighbors. However, the graph edges at block boundaries can be defined differently. Furthermore, in the graph of FIG. 3(b), they are connected to the eight nearest neighbors.
[0082] Meanwhile, the present invention may be applied to any graph configurations.
[0083] The Laplacian matrix of a graph signal G is represented as Equation 1 below.
L=D−A [Equation 1]
[0084] Herein, D represents a degree matrix. For example, the degree matrix may mean a diagonal matrix including the information of a degree of each vertex. A represents an adjacency matrix that represents the interconnection (edge) with an adjacent pixel by a weighting value.
) and perform graph Fourier transform on the feature vector of each of the plurality of primary capsules based on the weighting matrix. ([0087] The columns of graph-based transform matrix U may include eigenvectors of graph Laplacian matrix L. The eigenvalues of graph Laplacian matrix L that corresponds to a diagonal matrix may be represented by Equation 3 below.
A=diag(λ) [Equation 3]
[0088] Generally, eigenvectors are not defined as a specific shape. However, according to an object of the present invention, since graph Laplacian matrix L is symmetric, all eigenvectors are real values, and at least one of decomposition may be existed. In graph signal G, the graph-based Fourier transform of signal vector g may be defined as Equation 4 below.)
It would have been obvious to a person skilled in the art before the effective filing date of the invention to have combined with SAID’s teaching of performing a GFT and forming a graph with weights and, with NARWARIYA teaching of receiving and splitting a time series data set, to realize, with a reasonable expectation of success, a method that creates a weighted graph to perform a GFT, as in SAID, using the processed time series data capsules, as in NARWARIYA. A person of ordinary skill would have been motivated to make this combination to perform a better prediction using the graph (SAID [0007]).
Regarding claim 6, it comprises of limitations similar to those of claim 1 and is therefore rejected for similar rationale.
Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over NARWARIYA et al. (U.S. Pub. No. US 20210406603 A1), SAID et al. (U.S. Pub. No. US 20170359584 A1) in further view of KUMAR (U.S. Pub. No. US 20200073909 A1) in further view of BORTNYK (U.S. Pub. No. US 20030040880 A1).
While NARWARIYA, as modified by SAID, does teach claim 1, which claim 2 is dependent upon, it does not explicitly teach:
The information processing device according to The information processing device according to wherein the processing unit is configured to: generate a plurality of capsules in a spectral domain by classifying a plurality of signal values included in a respective plurality of bases obtained by the graph Fourier transform according to signal values at the same position in the bases and calculate attention of a classification class on the basis of a magnitude of the signal values included in the plurality of capsules.
However, in analogous art that similarly uses a GFT, KUMAR teaches:
The information processing device according to The information processing device according to wherein the processing unit is configured to: generate a plurality of capsules in a spectral domain by classifying a plurality of signal values included in a respective plurality of bases obtained by the graph Fourier transform according to signal values at the same position in the bases ( [0006] (iii) obtaining, a Graph Fourier Transform (GFT) matrix corresponding to the constructed graph by the graph signal processing technique; and (iv) estimating, from the GFT matrix and the first set of azimuth values, the second set of azimuth values comprising unambiguous azimuth values corresponding to a second region of the object by the graph signal processing technique; classify, based upon a comparison of estimated the second set of azimuth values and the phase angle, a polarized specular reflection dominant region and a polarized diffuse reflection dominant region of the object;)
It would have been obvious to a person skilled in the art before the effective filing date of the invention to have combined with KUMAR’s teaching of classifying the data relating to the GFT and, with NARWARIYA, as modified by SAID, teaching of receiving and splitting a time series data set and using that data to generate a graph and perform a GFT, to realize, with a reasonable expectation of success, a method that classifies the result of a GFT, as in KUMAR, after using the processed time series data capsules in the GFT, as in NARWARIYA, as modified by SAID. A person of ordinary skill would have been motivated to make this combination to better handle low feature data (KUMAR [0004]).
While KUMAR does teach classifying the result of a GFT, it does not explicitly teach:
and calculate attention of a classification class on the basis of a magnitude of the signal values included in the plurality of capsules.
However, in analogous art that similarly handles signals form sensors, BORTNYK teaches:
and calculate attention of a classification class on the basis of a magnitude of the signal values included in the plurality of capsules. ([0040] Weight calculators 60a-d calculate weights W.sub.i from signal magnitude estimates M.sub.i. According to classical theory, maximal-ratio combining requires that signal images x.sub.i(t) be weighted in accordance with S/N.sup.2 and that {E[.vertline.x.sub.i(t).vertline.]}=C is maintained by the automatic gain control.)
It would have been obvious to a person skilled in the art before the effective filing date of the invention to have combined with BORTNYK’s teaching of calculating the classes weight based on the signal value and, with NARWARIYA, as modified by SAID and KUMAR, teaching of receiving and splitting a time series data set and using that data to generate a graph and perform a GFT, to realize, with a reasonable expectation of success, a method that ways the class given to the data, as in BORTNYK, after using the processed time series data capsules in the GFT and the result is classified, as in NARWARIYA, as modified by SAID and KUMAR. A person of ordinary skill would have been motivated to make this combination to signal quality (BORTNYK [0005]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SKIELER A KOWALIK whose telephone number is (571)272-1850. The examiner can normally be reached 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela D Reyes can be reached at (571)270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SKIELER ALEXANDER KOWALIK/Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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