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 .
Claims 1-6 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on September 3, 2024, and April 3, 2025, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
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.
The disclosure is objected to because of the following informalities:
[0025]: "exist" should read "exists"
[0036]: "non-liner" should read "non-linear"
[0059]: "As illustrated FIG. 1" should read "As illustrated in FIG. 1"
Appropriate correction is required.
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 1-6 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.
Claims 1, 3, and 5 are rejected for containing the following indefinite language:
There is insufficient antecedent basis in the claims for the limitation “the second attributes”. The claims previously recite the singular “a second attribute”; however, there is insufficient antecedent basis for the plural form “second attributes”.
There is insufficient antecedent basis in the claims for the limitation “the single or the plurality of attributes”. The claims previously recite “a single or a plurality of the second attributes” as well as “a plurality of attributes”; thus, it is unclear which attributes “generating training data” is supposed to be based on.
Claims 2, 4, and 6 are rejected for containing the following indefinite language:
The claims recite “the second attributes of two groups that include a group of values of the first attribute and a group of values of the second attribute”. It is unclear what “the second attributes of two groups” is referring to given that the two groups are “a group of values of the first attribute” and “a group of values of the second attribute”.
The claims recite “a first constraint that approximates a variance of the second attributes of two groups… to the selected second attribute”. It is unclear what approximating a variance of attributes to another attribute means.
The claims recite “a second constraint that approximates an average of the second attributes of the two groups to the selected second attribute”. It is unclear what approximating an average of attributes to another attribute means.
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 1-6 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Claim 1
Step 1: The claim recites a non-transitory computer-readable recording medium and thus is directed to the statutory category of articles of manufacture.
Step 2A Prong One: The claim recites, inter alia:
“calculating an occurrence probability of a value of a second attribute other than a first attribute of a plurality of attributes, for each of a first value and a second value of the first attribute of the plurality of attributes included in data”: This limitation recites a mathematical calculation of calculating occurrence probabilities.
“selecting a single or a plurality of the second attributes from among the plurality of attributes, based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value”: This limitation encompasses mentally selecting a single or a plurality of the second attributes from among the plurality of attributes based on a loss function.
“generating training data, based on the single or the plurality of attributes”: This limitation encompasses mentally generating training data based on the single or the plurality of attributes.
Step 2A Prong Two: This judicial exception is not integrated into a practical application. The claim further recites “A non-transitory computer-readable recording medium storing a training data generation program for causing a computer to execute processing comprising: [the method]”; however, this limitation amounts to mere instructions to apply the judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong Two above. As an ordered whole, the claim is directed to an abstract idea of calculating an occurrence probability of a value of a second attribute other than a first attribute of a plurality of attributes, for each of a first value and a second value of the first attribute of the plurality of attributes included in data; selecting a single or a plurality of the second attributes from among the plurality of attributes, based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value; and generating training data, based on the single or the plurality of attributes. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: An article of manufacture, as above.
Step 2A Prong One: The claim recites, inter alia:
“approximating a minimization problem of the difference between the occurrence probabilities respectively for the first value and the second value based on a first constraint that approximates a variance of the second attributes of two groups that include a group of values of the first attribute and a group of values of the second attribute to the selected second attribute and a second constraint that approximates an average of the second attributes of the two groups to the selected second attribute”: This limitation recites a mathematical concept of approximating a minimization problem of the difference between the occurrence probabilities based on a first and second constraint.
Step 2A Prong Two: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 1.
Step 2B: The claim does not contain significantly more than the judicial exception. No further additional elements are recited, see analysis of claim 1.
Claim 3
Step 1: The claim recites a method and thus is directed to the statutory category of processes.
Step 2A Prong One: The claim recites, inter alia:
“calculating an occurrence probability of a value of a second attribute other than a first attribute of a plurality of attributes, for each of a first value and a second value of the first attribute of the plurality of attributes included in data”: This limitation recites a mathematical calculation of calculating occurrence probabilities.
“selecting a single or a plurality of the second attributes from among the plurality of attributes, based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value”: This limitation encompasses mentally selecting a single or a plurality of the second attributes from among the plurality of attributes based on a loss function.
“generating training data, based on the single or the plurality of attributes”: This limitation encompasses mentally generating training data based on the single or the plurality of attributes.
Step 2A Prong Two: This judicial exception is not integrated into a practical application. The claim further recites that the method is “implemented by a computer”; however, this limitation amounts to mere instructions to apply the judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong Two above. As an ordered whole, the claim is directed to an abstract idea of calculating an occurrence probability of a value of a second attribute other than a first attribute of a plurality of attributes, for each of a first value and a second value of the first attribute of the plurality of attributes included in data; selecting a single or a plurality of the second attributes from among the plurality of attributes, based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value; and generating training data, based on the single or the plurality of attributes. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 4
Step 1: A process, as above.
Step 2A Prong One: The claim recites, inter alia:
“approximating a minimization problem of the difference between the occurrence probabilities respectively for the first value and the second value based on a first constraint that approximates a variance of the second attributes of two groups that include a group of values of the first attribute and a group of values of the second attribute to the selected second attribute and a second constraint that approximates an average of the second attributes of the two groups to the selected second attribute”: This limitation recites a mathematical concept of approximating a minimization problem of the difference between the occurrence probabilities based on a first and second constraint.
Step 2A Prong Two: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 3.
Step 2B: The claim does not contain significantly more than the judicial exception. No further additional elements are recited, see analysis of claim 3.
Claim 5
Step 1: The claim recites an information processing device and thus is directed to the statutory category of machines.
Step 2A Prong One: The claim recites, inter alia:
“calculating an occurrence probability of a value of a second attribute other than a first attribute of a plurality of attributes, for each of a first value and a second value of the first attribute of the plurality of attributes included in data”: This limitation recites a mathematical calculation of calculating occurrence probabilities.
“selecting a single or a plurality of the second attributes from among the plurality of attributes, based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value”: This limitation encompasses mentally selecting a single or a plurality of the second attributes from among the plurality of attributes based on a loss function.
“generating training data, based on the single or the plurality of attributes”: This limitation encompasses mentally generating training data based on the single or the plurality of attributes.
Step 2A Prong Two: This judicial exception is not integrated into a practical application. The claim further recites “An information processing device comprising: a memory; and a processor coupled to the memory, the processor being configured to perform processing comprising: [the method]”; however, this limitation amounts to mere instructions to apply the judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong Two above. As an ordered whole, the claim is directed to an abstract idea of calculating an occurrence probability of a value of a second attribute other than a first attribute of a plurality of attributes, for each of a first value and a second value of the first attribute of the plurality of attributes included in data; selecting a single or a plurality of the second attributes from among the plurality of attributes, based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value; and generating training data, based on the single or the plurality of attributes. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 6
Step 1: A machine, as above.
Step 2A Prong One: The claim recites, inter alia:
“approximating a minimization problem of the difference between the occurrence probabilities respectively for the first value and the second value based on a first constraint that approximates a variance of the second attributes of two groups that include a group of values of the first attribute and a group of values of the second attribute to the selected second attribute and a second constraint that approximates an average of the second attributes of the two groups to the selected second attribute”: This limitation recites a mathematical concept of approximating a minimization problem of the difference between the occurrence probabilities based on a first and second constraint.
Step 2A Prong Two: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claim 5.
Step 2B: The claim does not contain significantly more than the judicial exception. No further additional elements are recited, see analysis of claim 5.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Rajabi et al. (TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks) (hereinafter “Rajabi”) in view of Jia et al. (US20220101069) (hereinafter “Jia”).
Regarding claim 1, Rajabi discloses
“…calculating an occurrence probability of a value of a second attribute other than a first attribute of a plurality of attributes, for each of a first value and a second value of the first attribute of the plurality of attributes included in data (Rajabi, II. Discrimination Score: “In a binary case, let D = {X,S,Y} be a labelled dataset [i.e. plurality of attributes], where X ∈
R
n
is the unprotected attributes, S is the protected attribute [i.e. first attribute], and Y is the decision [i.e. second attribute other than a first attribute]. Demographic fairness for the labeled dataset is defined as follows [7]: P(y = 1|s = 1) = P(y =1|s = 0) In this context, demographic parity is defined by the difference between the conditional probability and its marginal. We define the discrimination with respect to the protected attribute S by discrimination score (DS) and calculate it by: DS = P(y = 1|s = 1) −P(y = 1|s = 0)”; Examiner notes that y=1 corresponds to “a value of a second attribute”, s=1 corresponds to “a first value of the first attribute”, and s=0 corresponds to “a second value of the first attribute”);
… [generating training data] based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value” (Rajabi, III. C. 2) Phase II: Training for Fairness and Accuracy: “The new loss function for the generator in Phase II of training is as follows: [see (12)] With the above loss function for the generator, the model aims to generate a fair dataset{ˆX, ˆY, ˆS}∼Pg which achieves the demographic fairness with respect to the protected attribute ˆS in the generated samples, by minimizing discrimination score in the generated data P(ˆY|ˆS=1)−P(ˆY|ˆS=0)”).
Rajabi does not appear to explicitly disclose the further limitations of the claim.
However, Jia discloses
“A non-transitory computer-readable recording medium storing a training data generation program for causing a computer to execute processing (Jia, [0066]: “These methods, functions, and other processes may be embodied as machine-readable instructions stored on a computer-readable medium, which may be non-transitory (e.g., the non-transitory computer-readable medium or 1302 non-transitory processor-readable storage medium of FIG. 13), such as hardware storage devices (e.g., RAM (random access memory), ROM (read-only memory), EPROM (erasable, programmable ROM), EEPROM (electrically erasable, programmable ROM), hard drives, and flash memory)”) comprising…
selecting a single or a plurality of… second attributes from among… [a] plurality of attributes, based on… [maximum entropy estimation] (Jia, [0032]: “The candidate set of features 174 [i.e. plurality of attributes] is further processed by the selected feature set generator 204 which implements another automatic feature selection procedure based on maximum entropy estimation so that features that provide maximum possible information from different dimensions towards detecting anomalies are selected to form the selected set of features 176 [i.e. plurality of second attributes], S”); and
generating training data, based on the single or the plurality of attributes” (Jia, [0060]: “At block 1104, the method includes generating a training data set (e.g., the transformed set of features 178) by transforming the selected set of features 176, where the transformation further reduces feature dependency between the selected set of features 176”).
Jia and the instant application both relate to training data generation for machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Rajabi with the teachings of Jia such that the method is performed on a non-transitory computer-readable recording medium and comprises “selecting a single or a plurality of the second attributes from among the plurality of attributes, based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value; and generating training data, based on the single or the plurality of attributes” and one would have been motivated to do so. Doing so would simplify the analysis involved in exploring the dataset without significant loss of accuracy (see Jia, [0029]).
Regarding claim 3, Rajabi discloses
“A training data generation method implemented by a computer, the training data generation method comprising:
calculating an occurrence probability of a value of a second attribute other than a first attribute of a plurality of attributes, for each of a first value and a second value of the first attribute of the plurality of attributes included in data (Rajabi, II. Discrimination Score: “In a binary case, let D = {X,S,Y} be a labelled dataset [i.e. plurality of attributes], where X ∈
R
n
is the unprotected attributes, S is the protected attribute [i.e. first attribute], and Y is the decision [i.e. second attribute other than a first attribute]. Demographic fairness for the labeled dataset is defined as follows [7]: P(y = 1|s = 1) = P(y =1|s = 0) In this context, demographic parity is defined by the difference between the conditional probability and its marginal. We define the discrimination with respect to the protected attribute S by discrimination score (DS) and calculate it by: DS = P(y = 1|s = 1) −P(y = 1|s = 0)”; Examiner notes that y=1 corresponds to “a value of a second attribute”, s=1 corresponds to “a first value of the first attribute”, and s=0 corresponds to “a second value of the first attribute”);
… [generating training data] based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value” (Rajabi, III. C. 2) Phase II: Training for Fairness and Accuracy: “The new loss function for the generator in Phase II of training is as follows: [see (12)] With the above loss function for the generator, the model aims to generate a fair dataset{ˆX, ˆY, ˆS}∼Pg which achieves the demographic fairness with respect to the protected attribute ˆS in the generated samples, by minimizing discrimination score in the generated data P(ˆY|ˆS=1)−P(ˆY|ˆS=0)”).
Rajabi does not appear to explicitly disclose the further limitations of the claim.
However, Jia discloses
“selecting a single or a plurality of… second attributes from among… [a] plurality of attributes, based on… [maximum entropy estimation] (Jia, [0032]: “The candidate set of features 174 [i.e. a plurality of attributes] is further processed by the selected feature set generator 204 which implements another automatic feature selection procedure based on maximum entropy estimation so that features that provide maximum possible information from different dimensions towards detecting anomalies are selected to form the selected set of features 176 [i.e. a plurality of second attributes], S”); and
generating training data, based on the single or the plurality of attributes” (Jia, [0060]: “At block 1104, the method includes generating a training data set (e.g., the transformed set of features 178) by transforming the selected set of features 176, where the transformation further reduces feature dependency between the selected set of features 176”).
Jia and the instant application both relate to training data generation for machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Rajabi with the teachings of Jia such that the method comprises “selecting a single or a plurality of the second attributes from among the plurality of attributes, based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value; and generating training data, based on the single or the plurality of attributes” and one would have been motivated to do so. Doing so would simplify the analysis involved in exploring the dataset without significant loss of accuracy (see Jia, [0029]).
Regarding claim 5, Rajabi discloses
“…calculating an occurrence probability of a value of a second attribute other than a first attribute of a plurality of attributes, for each of a first value and a second value of the first attribute of the plurality of attributes included in data (Rajabi, II. Discrimination Score: “In a binary case, let D = {X,S,Y} be a labelled dataset [i.e. plurality of attributes], where X ∈
R
n
is the unprotected attributes, S is the protected attribute [i.e. first attribute], and Y is the decision [i.e. second attribute other than a first attribute]. Demographic fairness for the labeled dataset is defined as follows [7]: P(y = 1|s = 1) = P(y =1|s = 0) In this context, demographic parity is defined by the difference between the conditional probability and its marginal. We define the discrimination with respect to the protected attribute S by discrimination score (DS) and calculate it by: DS = P(y = 1|s = 1) −P(y = 1|s = 0)”; Examiner notes that y=1 corresponds to “a value of a second attribute”, s=1 corresponds to “a first value of the first attribute”, and s=0 corresponds to “a second value of the first attribute”);
… [generating training data] based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value” (Rajabi, III. C. 2) Phase II: Training for Fairness and Accuracy: “The new loss function for the generator in Phase II of training is as follows: [see (12)] With the above loss function for the generator, the model aims to generate a fair dataset{ˆX, ˆY, ˆS}∼Pg which achieves the demographic fairness with respect to the protected attribute ˆS in the generated samples, by minimizing discrimination score in the generated data P(ˆY|ˆS=1)−P(ˆY|ˆS=0)”).
Rajabi does not appear to explicitly disclose the further limitations of the claim.
However, Jia discloses
“An information processing device comprising: a memory; and a processor coupled to the memory, the processor being configured to perform processing (Jia, [0064]: “In addition to showing the block diagram 1200, FIG. 12 shows the hardware of the system 100 that may execute the instructions of the block diagram 1200. The hardware may include a processor 1202, and a memory 1204 storing machine-readable instructions that when executed by the processor 1202 cause the processor to perform the instructions of block diagram 1200”) comprising…
selecting a single or a plurality of… second attributes from among… [a] plurality of attributes, based on… [maximum entropy estimation] (Jia, [0032]: “The candidate set of features 174 [i.e. plurality of attributes] is further processed by the selected feature set generator 204 which implements another automatic feature selection procedure based on maximum entropy estimation so that features that provide maximum possible information from different dimensions towards detecting anomalies are selected to form the selected set of features 176 [i.e. plurality of second attributes], S”); and
generating training data, based on the single or the plurality of attributes” (Jia, [0060]: “At block 1104, the method includes generating a training data set (e.g., the transformed set of features 178) by transforming the selected set of features 176, where the transformation further reduces feature dependency between the selected set of features 176).
Jia and the instant application both relate to training data generation for machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Rajabi with the teachings of Jia such that the method is performed on an information processing device and comprises “selecting a single or a plurality of the second attributes from among the plurality of attributes, based on a loss function that includes a parameter that indicates a difference between the occurrence probabilities respectively for the first value and the second value; and generating training data, based on the single or the plurality of attributes” and one would have been motivated to do so. Doing so would simplify the analysis involved in exploring the dataset without significant loss of accuracy (see Jia, [0029]).
Conclusion
The claims have been searched insofar as they can be understood. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Convex Formulations for Fair Principal Component Analysis (Olfat et al.): Olfat et al. discloses a method for performing fair principal component analysis utilizing a mean constraint and covariance constraints (see e.g. section 5 Designing formulations for fair PCA).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GWYNEVERE A DETERDING whose telephone number is (571)272-7657. The examiner can normally be reached Mon-Fri. 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/G.A.D./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
Prosecution Insights