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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-4, 6-8, 10 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6, 9-10 of copending Application No. 18/281,828 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in this application are anticipated by the claims in the ‘828 reference application. The correspondence is shown in the table below:
Instant Application Claim(s): 18/014,676
Reference Application Claim(s): 18/281,828
An information processing apparatus comprising:
a memory configured to store instructions; and
a processor configured to execute the instructions to:
acquire a plurality of data each classified into one of a plurality of classes; and
calculate a projection matrix used for
dimensionality reduction of the plurality of data based on an objective function including a statistic of the plurality of data,
wherein the objective function includes a first function including a first term indicating interclass dispersion of the plurality of data between a first class and a second class included in the plurality of classes and a second function including a second term indicating intraclass dispersion of the plurality of data in at least one of the first class and the second class.
An information processing apparatus comprising:
a memory configured to store instructions; and
a processor configured to execute the instructions to:
acquire a plurality of data each classified into one of a plurality of classes; and
calculate a projection matrix used for
dimensionality reduction of the plurality of data based on an objective function including a statistic of the plurality of data,
wherein the objective function includes a first function including a first term indicating interclass dispersion of the plurality of data between a first class and a second class included in the plurality of classes and a second function including a second term indicating intraclass dispersion of the plurality of data in at least one of the first class and the second class, and wherein the projection matrix is calculated by optimizing the objective function under a constraint in which the first class and the second class are selected so that a combination of the first class and the second class includes a specific class that is one of the plurality of classes.
The information processing apparatus according to claim 1, wherein the objective function includes a minimum value or a maximum value of a ratio of the first function to the second function over the plurality of classes.
The information processing apparatus according to claim 1, wherein the objective function includes a minimum value or a maximum value of a ratio of the first function to the second function over the plurality of classes.
The information processing apparatus according to , claim 1, wherein the second function includes a weighted average of intraclass dispersion of the plurality of data in the first class and intraclass dispersion of the plurality of data in the second class.
3. The information processing apparatus according to , claim 1, wherein the second function includes a weighted average of intraclass dispersion of the plurality of data in the first class and intraclass dispersion of the plurality of data in the second class.
The information processing apparatus according to claim 1, wherein the first function further includes a third term indicating an average of interclass dispersion of the plurality of data over the plurality of classes, and wherein the second function further includes a fourth term indicating an average of intraclass dispersion of the plurality of data over the plurality of classes.
The information processing apparatus according to claim 1, wherein the first function further includes a third term indicating an average of interclass dispersion of the plurality of data over the plurality of classes, and wherein the second function further includes a fourth term indicating an average of intraclass dispersion of the plurality of data over the plurality of classes.
The information processing apparatus according to claim 1, wherein the projection matrix is determined by performing optimization to maximize or minimize the objective function under a predetermined constraint.
1. An information processing apparatus comprising:
a memory configured to store instructions; and
a processor configured to execute the instructions to:
acquire a plurality of data each classified into one of a plurality of classes; and
calculate a projection matrix used for
dimensionality reduction of the plurality of data based on an objective function including a statistic of the plurality of data,
wherein the objective function includes a first function including a first term indicating interclass dispersion of the plurality of data between a first class and a second class included in the plurality of classes and a second function including a second term indicating intraclass dispersion of the plurality of data in at least one of the first class and the second class, and wherein the projection matrix is calculated by optimizing the objective function under a constraint in which the first class and the second class are selected so that a combination of the first class and the second class includes a specific class that is one of the plurality of classes.
5. The information processing apparatus according to claim 1, wherein the optimization is a process of maximizing or minimizing the objective function.
7. The information processing apparatus according to claim 1, wherein the data are feature amount data extracted from biometric information.
6. The information processing apparatus according to claim 1,wherein the data are feature amount data extracted from biometric information.
8. An information processing method performed by a computer, comprising:
acquiring a plurality of data each classified into one of a plurality of classes; and
calculating a projection matrix used for dimensionality reduction of the plurality of data based on an objective function including a statistic of the plurality of data,
wherein the objective function includes a first function including a first term indicating interclass dispersion of the plurality of data between a first class and a second class included in the plurality of classes and a second function including a second term indicating intraclass dispersion of the plurality of data in at least one of the first class and the second class.
9. An information processing method performed by a computer, comprising:
acquiring a plurality of data each classified into one of a plurality of classes; and
calculating a projection matrix used for dimensionality reduction of the plurality of data based on an objective function including a statistic of the plurality of data,
wherein the objective function includes a first function including a first term indicating interclass dispersion of the plurality of data between a first class and a second class included in the plurality of classes and a second function including a second term indicating intraclass dispersion of the plurality of data in at least one of the first class and the second class, and wherein the calculating the projection matrix includes performing calculation of the projection matrix by optimizing the objective function under a constraint in which the first class and the second class are selected so that a combination of the first class and the second class includes a specific class that is one of the plurality of classes.
10. A non-transitory storage medium storing a program that causes a computer to perform an information processing method, the information processing method comprising:
acquiring a plurality of data each classified into one of a plurality of classes; and
calculating a projection matrix used for dimensionality reduction of the plurality of data based on an objective function including a statistic of the plurality of data,
wherein the objective function includes a first function including a first term indicating interclass dispersion of the plurality of data between a first class and a second class included in the plurality of classes and a second function including a second term indicating intraclass dispersion of the plurality of data in at least one of the first class and the second class.
10. A non-transitory storage medium storing a program that causes a computer to perform an information processing method, the information processing method comprising:
acquiring a plurality of data each classified into one of a plurality of classes; and
calculating a projection matrix used for dimensionality reduction of the plurality of data based on an objective function including a statistic of the plurality of data,
wherein the objective function includes a first function including a first term indicating interclass dispersion of the plurality of data between a first class and a second class included in the plurality of classes and a second function including a second term indicating intraclass dispersion of the plurality of data in at least one of the first class and the second class, and wherein the calculating the projection matrix includes performing calculation of the projection matrix by optimizing the objective function under a constraint in which the first class and the second class are selected so that a combination of the first class and the second class includes a specific class that is one of the plurality of classes.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: the recitation of “non-transitory” in claim 10 lacks antecedent basis from the specification. Examiner suggests amending the specification to include “non-transitory” in the description of the storage medium. No new matter would be considered as being entered.
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 1-4, 6-8, 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, under the Alice Framework Step 1 Analysis, the claim falls within the four statutory categories of patentable subject matter: an apparatus.
Under the Alice Framework Step 2A Prong 1 Analysis, claim 1 recites Mathematical Concepts and/or Mental Processes. The claim recites Mathematical Calculations, which is specifically identified as an exemplar in the Mathematical Concepts grouping of abstract ideas, and/or recites Evaluations, which is specifically identified as an exemplar in the Mental Processes grouping of abstract ideas:
“a plurality of data each classified into one of a plurality of classes; and
calculate a projection matrix used for dimensionality reduction of the plurality of data based on an objective function including a statistic of the plurality of data,
wherein the objective function includes a first function including a first term indicating interclass dispersion of the plurality of data between a first class and a second class included in the plurality of classes and a second function including a second term indicating intraclass dispersion of the plurality of data in at least one of the first class and the second class.”
See specification ([0026], [0032]) describing classified plurality of data. See specification ([0035-0039]) describing calculating a projection matrix. See specification ([0066-0072]) describing the objective function. The limitations of “classified plurality of data”, “calculating”, and “the objective function” under the broadest reasonable interpretation cover performance in the mind. That is, nothing in the claim element precludes the limitations from practicality being performed in the human mind. For example, the claim encompasses using classified data elements to calculate a matrix based on the objective function, using at least a pen and paper as shown in the equations disclosed in the above cited paragraphs. For these reasons, the claim recites Mathematical Concepts and/or Mental Processes.
Under the Alice Framework Step 2A Prong 2 Analysis, the claim recites the combination of the following additional elements: a memory configured to store instructions; a processor configured to execute the instructions to; acquiring a plurality of data. The memory and processor are recited at a high level of generality, and are examples of generic computing elements, and/or merely generally linked to a particular technological environment (see MPEP 2106.05(h)(vi): Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment). Further, it is recited at a high-level of generality such that it amounts to no more than mere instructions using a generic computer component or merely as tools to implement the abstract idea or merely reciting the words “apply it” (or an equivalent) with the judicial exception. (See MPEP2106.05(f): Mere Instructions to Apply an Exception). The “to store instructions”, “to execute the instructions to”, and “acquiring” a plurality of data limitations are examples of insignificant extra-solution activity, mere data gathering (see MPEP 2106.05(g): Insignificant Extra-Solution Activity). Taken alone or in combination, they fail to integrate the judicial exception into a practical application.
Under the Alice Framework Step 2B Analysis, the additional elements recited above, taken alone or in combination, do not amount to significantly more than the judicial exception. As discussed in the Step 2A Prong 2 Analysis, the claim recites limitations described above as recited at a high level of generality merely results in “apply it” on a computer (or an equivalent) with the judicial exception. The limitations described above as an insignificant extra-solution activity are also well-understood, routine, or conventional (for storing: see MPEP 2106.05(d)(II)(iv): Storing and retrieving information in memory; for executing: see MPEP 2106.05(d)(II)(ii): Performing repetitive calculations; for acquiring: see MPEP 2106.05(d)(II)(i): Receiving or transmitting data over a network). Since the claim does not include additional elements that, alone or in combination, amount to significantly more than the judicial exception, claim 1 is ineligible.
Claims 2-4, 6-7 merely further limit the abstract idea, the Mathematical Concepts and/or Mental Processes. Claims 2-4, 6-7 do no recite any new additional elements.
Claim 8 is directed to a method that would be performed by the apparatus of claim 1. The claim 1 analysis similarly applies, and claim 8 is similarly rejected.
Claim 10 is directed to a computer program product that would be executed by the apparatus of claim 1. The claim 1 analysis similarly applies, and claim 10 is similarly rejected.
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 1-4, 6-8, 10 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170243113 A1 Fukuda et al. (hereinafter “Fukuda”) in view of Su, Bing et al. “Heteroscedastic Max-Min Distance Analysis for Dimensionality Reduction”, IEEE Transactions on Image Processing. August 2018, vol. 27, no. 8, pp. 4052-4065. (hereinafter “Su”, as cited on the IDS filed 01/05/2023).
Regarding claim 1, Fukuda discloses an information processing apparatus comprising:
a memory (Fig. 8 “28” [0136]) configured to store instructions ([0005], [0058], [0060], [0135]); and
a processor (Fig. 8 “16” [0136]) configured to execute the instructions ([0005], [0058], [0060], [0135]) to:
acquire a plurality of data (Fig. 4 INPUT FEATURE VECTOR [0062]; Fig. 3 “S101” [0061, 0063]) each classified into one of a plurality of classes (Fig. 4 CLASS LABEL [0062]; Fig. 3 “S101” [0061, 0063]); and
calculate a projection matrix (Fig. 3 “S104” [0066]) used for dimensionality reduction of the plurality of data ([0062]) based on an objective function ([0066]
J
θ
) including a statistic of the plurality of data ([0063-0065], [0067] within-class and between-class covariance matrix possess class means
m
i
in calculation),
wherein the objective function ([0066]
J
θ
) includes a first function ([0066]
J
θ
numerator) including a first term indicating interclass dispersion ([0065] between-class covariance matrix [0066]
∑
B
θ
) of the plurality of data between a first class and a second class included in the plurality of classes ([0065]
∑
B
calculated by
∑
i
=
1
C
[0062] per class
i
(
i
=
1
,
.
.
.
C
) where a first class corresponds to i=1 and a second class corresponds to i=2 or C) and a second function ([0066]
J
θ
denominator) including a second term indicating intraclass dispersion ([0064] within-class covariance matrix [0066]
∑
W
θ
) of the plurality of data in at least one of the first class and the second class ([0064]
∑
W
calculated by
∑
i
=
1
C
[0062] per class
i
(
i
=
1
,
.
.
.
C
) where a first class corresponds to i=1 and a second class corresponds to i=2 or C).
Fukuda appears to be silent to explicitly disclosing the projection matrix as being used for dimensionality reduction.
Su discloses used for dimensionality reduction (p. 4052 “Abstract”; p. 4052, co. 2, ⁋2; p. 4054, co. 2, ⁋2).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Fukuda’s projection matrix with it being used for dimensionality reduction as disclosed by Su’s features because they are in the claimed invention’s same field of endeavor of Linear Discriminant Analysis (LDA) approaches (p. 4054, co. 1, sec. II, ⁋1). Modifying with Su’s dimensionality reduction feature would have been obvious to one of ordinary skill in the art as this application for the projection matrix is utilized to optimize performance by applying LDA (p. 4054, co. 2, ⁋1-2 transformation matrix W) to maximize the ratio of between-class scatter over within-class scatter under the homoscedastic Gaussian assumption (p. 4052, co. 2, ⁋2), thereby yielding dimensionality reduction (p. 4052, co. 1, sec. 1, ⁋1). Using Su’s features to provide a predictable level of performance improvements in Fukuda’s projection matrix before the effective filing date would have been obvious since one of ordinary skill in the art would recognize that Fukuda’s matrix was ready for improvement to incorporate the dimensionality reduction features for simplifying calculations, as doing so would be beneficial.
Regarding claim 2, the teachings addressed in the claim 1 analysis and rejection are incorporated, and Fukuda in view of Su disclose the apparatus wherein Fukuda discloses:
the objective function ([0066]
J
θ
) includes a minimum value or a maximum value (Fig. 3 “S104” [0066], [0068] maximizes; Note: the logical “or” of the claim is interpreted as either one of a minimum value or a maximum value to satisfy the element, not both) of a ratio of the first function to the second function ([0066] numerator:
d
e
t
(
θ
T
∑
B
θ
)
, denominator:
d
e
t
(
θ
T
∑
W
θ
)
) over the plurality of classes ([0064-0065]
∑
i
=
1
C
).
Regarding claim 3, the teachings addressed in the claim 1 analysis and rejection are incorporated, and Fukuda in view of Su disclose the apparatus wherein Fukuda discloses:
the second function ([0066]
J
θ
denominator) includes a weighted average of intraclass dispersion of the plurality of data in the first class ([0063-0064] when
i
=
1
) and intraclass dispersion of the plurality of data in the second class ([0063-0064] when
i
=
2
or C).
Fukuda appears to be silent to explicitly disclosing a weighted average.
Su discloses a weighted average (p. 4055, co. 1, ⁋1
∑
i
j
^
).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Fukuda’s second function to include a weighted average as disclosed by Su’s features because they are in the claimed invention’s same field of endeavor of Linear Discriminant Analysis (LDA) approaches (p. 4054, co. 1, sec. II, ⁋1). Modifying with Su’s weighted average feature would have been obvious to one of ordinary skill in the art as the second function of the objective function is utilized to optimize performance by applying LDA (p. 4054, co. 2, ⁋1-2) to maximize the ratio of between-class scatter over within-class scatter under the homoscedastic Gaussian assumption (p. 4052, co. 2, ⁋2), thereby yielding dimensionality reduction (p. 4052, co. 1, sec. 1, ⁋1). Further, incorporating the weighted average features gives the mathematical formulation the capability to better account for covariance differences and better characterize the entanglement of classes (p. 4054, co. 2, ⁋4; p. 4064, ⁋1). Using Su’s features to provide a predictable level of performance improvements in Fukuda’s second function in the objective function before the effective filing date would have been obvious since one of ordinary skill in the art would recognize that Fukuda’s second function was ready for improvement to incorporate the dimensionality reduction features for simplifying calculations and for better characterization of classes’ entanglement, as doing so would be beneficial.
Regarding claim 4, the teachings addressed in the claim 1 analysis and rejection are incorporated, and Fukuda in view of Su disclose the apparatus wherein Fukuda discloses:
the first function further ([0066]
J
θ
numerator) includes a third term indicating an average of interclass dispersion of the plurality of data over the plurality of classes ([0065]
m
for each class
i
(
i
=
1
,
.
.
.
,
C
)
), and
wherein the second function ([0066]
J
θ
denominator) further includes a fourth term indicating an average of intraclass dispersion of the plurality of data over the plurality of classes ([0063]
m
i
for each class
i
(
i
=
1
,
.
.
.
,
C
)
).
Regarding claim 6, the teachings addressed in the claim 1 analysis and rejection are incorporated, and Fukuda in view of Su disclose the apparatus wherein Fukuda discloses:
the projection matrix (Fig. 3 “S104” [0066]) is determined by performing optimization to maximize or minimize (Fig. 3 “S104” [0066], [0068] maximizes; Note: the logical “or” of the claim is interpreted as either one of a minimum value or a maximum value to satisfy the element, not both) the objective function ([0066]
J
θ
) under a predetermined constraint.
Fukuda appears to be silent to explicitly disclosing under a predetermined constraint.
Su discloses under a predetermined constraint (p. 4054, co. 2, EQN. (2) s.t.
W
2
T
W
2
=
I
d
'
).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Fukuda’s objective function to be determined by a predetermined constraint as disclosed by Su’s features because they are in the claimed invention’s same field of endeavor of Linear Discriminant Analysis (LDA) approaches (p. 4054, co. 1, sec. II, ⁋1). Modifying with Su’s predetermined constraint feature would have been obvious to one of ordinary skill in the art as the objective function optimizes performance by applying LDA (p. 4054, co. 2, ⁋1-2) to maximize the ratio of between-class scatter over within-class scatter under the homoscedastic Gaussian assumption (p. 4052, co. 2, ⁋2), thereby yielding dimensionality reduction (p. 4052, co. 1, sec. 1, ⁋1). Further, incorporating the predetermined constraint feature gives the mathematical formulation the capability to better account under desired criteria (p. 4054, co. 2, EQN. (2) s.t.
W
2
T
W
2
=
I
d
'
; p. 4064, ⁋1). Using Su’s features to provide a predictable level of performance improvements in Fukuda’s objective function before the effective filing date would have been obvious since one of ordinary skill in the art would recognize that Fukuda’s objective function was ready for improvement to incorporate the dimensionality reduction features for simplifying calculations and configurability of the mathematical formulation based on the constraints, as doing so would be beneficial.
Regarding claim 7, the teachings addressed in the claim 1 analysis and rejection are incorporated, and Fukuda in view of Su disclose the apparatus wherein Fukuda discloses:
the data (Fig. 4 INPUT FEATURE VECTOR [0062]; Fig. 3 “S101” [0061, 0063]) are feature amount data extracted from biometric information ([0021], [0036-0037] speech, [0061]).
Claim 8 is directed to a method that would be performed by the apparatus of claim 1. The claim 1 analysis similarly applies, and claim 8 is similarly rejected.
Claim 10 is directed to a computer program product that would be executed by the apparatus of claim 1. The claim 1 analysis similarly applies, and claim 10 is similarly rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARKUS A VILLANUEVA whose telephone number is (703)756-1603. The examiner can normally be reached M - F 8:30 am - 5:30 pm.
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/MARKUS ANTHONY VILLANUEVA/Examiner, Art Unit 2151
/James Trujillo/Supervisory Patent Examiner, Art Unit 2151
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