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 .
This office Action is in response to Application 19022899 filed on 03/18/2025. Claim 1 was currently amended via the preliminary amendments. Claims 2-20 have been added. Claims 1, 11, and 19 are independent claims. Claims 1-20 have been examined and are pending in this application. This Office Action is made Non-Final.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/14/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12,229,314. Although the claims at issue are not identical, they are not patentably distinct from each other because:
The examiner notes that Claim 1-18 of U.S. patent No. 12,229,314 anticipates, more specifically:
Instant Application 19/022,899
US patent No. 12,229,314 B2
Claim 1. a system comprising:
at least one programmable processor; and a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising:
receiving one or more source data from one or more data sources including a plurality of original records, at least one record in the plurality of original records including identity-related information associated with at least one of an identifiable source, an identifiable entity, or an identifiable person;
searching for and removing missing values from the one or more source data;
generating one or more encoded source data from the one or more source data;
generating a synthetic data by decoding the one or more encoded source data;
selecting one or more variables in the synthetic data and associating one or more predetermined identifiability values and one or more predetermined anonymity values;
decoding the generated synthetic data including the selected variables using the associated one or more predetermined identifiability values and the one or more predetermined anonymity values; and
generating, from the decoded synthetic data, anonymous data having a plurality of records, wherein no record in the anonymous data is traceable to the plurality of original records, due to the identity-related information having been anonymized by decoding the synthetic data.
Claim 7. A system comprising:
at least one programmable processor; and a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising:
receiving one or more source data from one or more data sources;
generating one or more encoded source data from the one or more source data;
generating a synthetic data by decoding the one or more encoded source data;
selecting one or more variables in the synthetic data;
associating one or more predetermined identifiability values with the one or more variables in the synthetic data, a predetermined identifiability value determined by a latent space augmentation process as a first threshold that indicates a level of identifiability between the synthetic data and corresponding source data according to on one or more augmented vectors defined based on the one or more variables in the synthetic data and a distance between the one or more augmented vectors and one or more variables in the one or more source data;
associating one or more predetermined anonymity values with the one or more variables in the synthetic data, a predetermined anonymity value determined by the latent space augmentation process as a second threshold that indicates a level of anonymity between the synthetic data and the corresponding source data;
decoding the generated synthetic data including the selected variables using the associated one or more predetermined identifiability values and one or more predetermined anonymity values; and
outputting the decoded synthetic data.
Claim 11. a computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising:
receiving one or more source data from one or more data sources including a plurality of original records, at least one record in the plurality of original records including identity-related information associated with at least one of an identifiable source, an identifiable entity, or an identifiable person;
generating one or more encoded source data from the one or more source data and generating a synthetic data by decoding the one or more encoded source data;
selecting one or more variables in the synthetic data responsive to an association between one or more predetermined identifiability values and one or more predetermined anonymity values;
decoding the generated synthetic data including the selected variables using the associated one or more predetermined identifiability values and the one or more predetermined anonymity values; and
generating, from the decoded synthetic data, anonymous data having a plurality of records, wherein no record in the anonymous data is traceable to the original records, due to the identity-related information having been anonymized.
Claim 13. a computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising:
receiving one or more source data from one or more data sources;
generating one or more encoded source data from the one or more source data;
generating a synthetic data by decoding the one or more encoded source data;
selecting one or more variables in the synthetic data;
associating one or more predetermined identifiability values with the one or more variables in the synthetic data, a predetermined identifiability value determined by a latent space augmentation process as a first threshold that indicates a level of identifiability between the synthetic data and corresponding source data according to on one or more augmented vectors defined based on the one or more variables in the synthetic data and a distance between the one or more augmented vectors and one or more variables in the one or more source data;
associating one or more predetermined anonymity values with the one or more variables in the synthetic data, a predetermined anonymity value determined by the latent space augmentation process as a second threshold that indicates a level of anonymity between the synthetic data and the corresponding source data;
decoding the generated synthetic data including the selected variables using the associated one or more predetermined identifiability values and one or more predetermined anonymity values; and
outputting the decoded synthetic data.
Claim 19. a computer-implemented method comprising:
receiving one or more source data from one or more data sources including a plurality of original records, at least one record in the plurality of original records including identity-related information;
generating one or more encoded source data from the one or more source data and generating a synthetic data by decoding the one or more encoded source data;
determining one or more predetermined identifiability values associated with one or more variables in the synthetic data based on a latent space augmentation process that indicates a level of identifiability between the synthetic data and corresponding source data;
selecting one or more variables in the synthetic data responsive to an association between the one or more predetermined identifiability values and one or more predetermined anonymity values;
decoding the generated synthetic data including the selected variables using the associated one or more predetermined identifiability values and the one or more predetermined anonymity values; and
generating, from the decoded synthetic data, anonymous data having a plurality of records, wherein no record in the anonymous data is traceable to at least one of the identifiable source, the identifiable entity, or the identifiable person.
1. A computer implemented method, comprising:
receiving, using at least one processor, one or more source data from one or more data sources;
generating, using the at least one processor, one or more encoded source data from the one or more source data;
generating a synthetic data by decoding the one or more encoded source data;
selecting one or more variables in the synthetic data;
associating one or more predetermined identifiability values with the one or more variables in the synthetic data, a predetermined identifiability value determined by a latent space augmentation process as a first threshold that indicates a level of identifiability between the synthetic data and corresponding source data according to on one or more augmented vectors defined based on the one or more variables in the synthetic data and a distance between the one or more augmented vectors and one or more variables in the one or more source data;
associating one or more predetermined anonymity values with the one or more variables in the synthetic data, a predetermined anonymity value determined by the latent space augmentation process as a second threshold that indicates a level of anonymity between the synthetic data and the corresponding source data;
decoding the generated synthetic data including the selected variables using the associated one or more predetermined identifiability values and one or more predetermined anonymity values; and
outputting the decoded synthetic data.
The examiner notes that the features emphasized above anticipate what is claimed in the limitations of Claims 1-20 of the Instant Application.
Therefore, the claims are rejected under nonstatutory double patenting.
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 1-20 are rejected under 35 U. S. C. 101 as being directed to non-statutory subject matter as being directed to an abstract idea without being integrated into a practical application or significantly more.
Regarding claims 1, 11, and 19 the claim is directed to an abstract idea as reciting the limitations “generating one or more encoded source data …;” “generating a synthetic data…;” “determining one or more predetermined identifiability values…;” “selecting one or more variables…;” “ decoding the generated synthetic data …;” ““generating [] anonymous data….” Said steps are “mental process” as broadly interpreted said steps could be performed in the human mind or using pencil/paper. Therefore, the claims recite an abstract idea.
Said abstract idea and/or judicial exception is not integrated into a practical application as the claim does not recite any other active steps that utilize determination result into a practical application. It’s noted that the claims recite the limitation “receiving one or more source data….” “searching for and removing missing values…. “ However, said steps are not sufficiently to consider that the abstract idea is being integrated into a practical application as the steps cited at high level of generality of data gathering/processing which is a form of insignificant extra-solution activity (See MPEP 2106.05 for details).
It's also noted that claim 1 recites additional elements (i.e., programmable processor, computer system, etc.,). However, said additional elements are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function etc.,) such that it amounts no more than mere instructions to apply the exception or abstract idea using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As mentioned above, although the claims recite additional elements, said elements taken individually or as a combination, do not result in the claim amounting to significantly more than the abstract idea because as the additional elements perform generic computer content distributing functions routinely used in information technology field. See US Applications 20210241075, US Application 20210335029, and US Application 20230305824. As discussed above, the additional elements recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using a generic computer component. Therefore, the claim is directed to non-statutory subject matter.
Regarding dependent claims 2-10, 12-18, and 20; claims 2-10, 12-18, and 20 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without being integrated into a practical application or significantly more for the same reason discussed above. It’s noted that claims 6 and 14 recite the limitation “generating one or more encoded source data …;” Claims 8 and 16 recite the limitation “generated latent space data ....” … etc., Said limitations/steps are in a form of insignificant extra-solution activity or also mental processes. Therefore, said limitations are not sufficiently to be considered as applying the abstract idea into a practical application. As result claims 2-10, 12-18, and 20 are also rejected under 35 U.S.C. 101 as being directed to an abstract idea without being integrated into a practical application or significantly more.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1-2, 4, 6, 9, 11, 14, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over MAYER et al. (“MAYER,” US 20210241075, published on 08/05/2021) in view of KOWALSKI et al. (“KOWALSKI,” US 20210335029, published on 10/28/2021), and further in view of ALLAMANIS et al. (“ALLAMANIS,” US 20230305824, filed on 03/24/2022).
Regarding Claim 1;
MAYER discloses a system comprising:
at least one programmable processor; and a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising (par 0018; the processing unit can be a computer or other data processing device. In particular, the neural network encoder and/or the at least one neural network decoder are executed by the processing unit):
receiving one or more source data from one or more data sources including a plurality of original records, at least one record in the plurality of original records including identity-related information associated with at least one of an identifiable source, an identifiable entity, or an identifiable person (par 0072; fig. 2a; receive the digital dataset and to generate a compressed representation of the digital dataset; par 0079; the processing unit can be configured to generate the decoder input data by superimposing random or pseudorandom data with information based on the detected);
searching for and removing missing values from the one or more source data (par 0099; the input dataset can be compared to the reconstructed dataset, and a loss function is determined, the loss function representing an loss between the data and the corresponding reconstructed data. In particular, the loss function represents a difference between the original dataset and the reconstructed dataset);
generating one or more encoded source data from the one or more source data (par 0010; autoencoders (VAE) learn to distribute input data and to generate new data from the input data; par 0072; receive the digital dataset and to generate a compressed representation of the digital dataset);
generating a synthetic data by decoding the one or more encoded source data (par 0073; fig. 2b; the processing unit is configured to generate decoder input data based on the detected correlation, and a trained neural network decoder which is configured to receive the decoder input data and to generate synthetic digital data representing signal characteristics of the at least one measured signal based on the decoder input data); (par 0073; fig. 2b; the processing unit is configured to generate decoder input data based on the detected correlation, and a trained neural network decoder which is configured to receive the decoder input data and to generate synthetic digital data representing signal characteristics of the at least one measured signal based on the decoder input data).
MAYER discloses generated synthetic data as recited above, but do not explicitly disclose selecting one or more variables in the synthetic data and associating one or more predetermined identifiability values; decoding the generated synthetic data including the selected variables using the associated one or more predetermined identifiability values; and generating, from the decoded synthetic data, data having a plurality of records.
However, in an analogous art, KOWALSKI discloses image generation system/method that includes:
selecting one or more variables in the synthetic data and associating one or more predetermined identifiability values (KOWALSKI: fig. 3; par 0118; the attributes are selected from one or more of: beard style, eyebrow style; different individual attributes have been controlled by setting parameter values. An end user is able to set the parameter values [] image was generated with a parameter for a smile selected. Image was generated with a parameter for eyes closed selected; par 0132; select parameter values for input to the synthetic data encoder);
decoding the generated synthetic data including the selected variables using the associated one or more predetermined identifiability values (KOWALSKI: par 0132; fig. 3; select parameter values for input to the synthetic data encoder; par 0032; It takes parameter values as input and it computes a mapping from the parameter values to an embedding which is typically expressed as a vector specifying a location; par 0033; the embedding is then input to the decoder which generates an output image depicting the object and with the illumination as specified by the parameter values); and
generating, from the decoded synthetic data, data having a plurality of records (KOWALSKI: par 0132; fig. 3; select parameter values for input to the synthetic data encoder; par 0032; It takes parameter values as input and it computes a mapping from the parameter values to an embedding which is typically expressed as a vector specifying a location; par 0033; the embedding is then input to the decoder which generates an output image depicting the object and with the illumination as specified by the parameter values).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of KOWALSKI with the method/system of MAYER to include s selecting one or more variables in the synthetic data and associating one or more predetermined identifiability values; decoding the generated synthetic data including the selected variables using the associated one or more predetermined identifiability values; and generating, from the decoded synthetic data, data having a plurality of records. One would have been motivated to receive a value of at least one parameter of a synthetic image rendering apparatus for rendering synthetic images of objects of the specified type (KOWALSKI: abstract).
The combination of MAYER and KOWALSK disclose one or more predetermined identifiability values; generating, from the decoded synthetic data, data having a plurality of records as recited above, but do not explicitly disclose one or more predetermined anonymity values; anonymous data having a plurality of records, wherein no record in the anonymous data is traceable to the plurality of original records, due to the identity-related information having been anonymized by decoding the synthetic data.
However, in an analogous art, ALLAMANISI discloses deep learning system/method that includes:
one or more predetermined anonymity values (ALLAMANIS: par 0052; the training dataset includes input sequences, where each input sequence represents an extended context with anonymized values and the anonymized value mapping; par 0101; anonymizing each of the undefined variable names in each extended context with a select one of a plurality of anonymized values);
anonymous data having a plurality of records, wherein no record in the anonymous data is traceable to the plurality of original records, due to the identity-related information having been anonymized by decoding the synthetic data (ALLAMANIS: par 0025; the variable name anonymizer uses the data flow analysis to identify the variable names from the pasted source code snippet. The variable names used in the pasted source code snippet are replaced with a masked or anonymized value; par 0101; anonymizing each of the undefined variable names in each extended context with a select one of a plurality of anonymized values).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of ALLAMANIS with the method/system of MAYER and KOWALSKI to include one or more predetermined anonymity values; anonymous data having a plurality of records, wherein no record in the anonymous data is traceable to the plurality of original records, due to the identity-related information having been anonymized by decoding the synthetic data. One would have been motivated to train on numerous variable usage patterns from various source code programs to learn to predict the most likely mapping of an undefined variable name from the pasted source code snippet to a variable name in the pre-existing partial source code program thereby generating a syntactically and semantically correct program (ALLAMANIS: abstract).
Regarding Claim 2;
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose the system of claim 1,
KOWALSKI discloses wherein one or more predetermined identifiability values are associated with the one or more variables in the synthetic data (KOWALSKI: fig. 3; par 0118; the attributes are selected from one or more of: beard style, eyebrow style; different individual attributes have been controlled by setting parameter values. An end user is able to set the parameter values [] image was generated with a parameter for a smile selected. Image was generated with a parameter for eyes closed selected; par 0132; select parameter values for input to the synthetic data).
The motivation is the same that of claim 1 above.
Regarding Claim 4;
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose the system of claim 1,
ALLAMANIS discloses wherein the one or more predetermined anonymity values are associated with the selected one or more variables in the data (ALLAMANIS: each decoder unit is associated with a select anonymized value, wherein each of the decoder units outputs a probability of the select anonymized value being a select defined variable name from the pre-existing partial source code program).
The motivation is the same that of claim 1 above.
KOWALSKI further discloses the synthetic data (KOWALSKI: par 0118; the attributes are selected [] generated with a parameter for a smile selected. Image was generated with a parameter for eyes closed selected; par 0132; select parameter values for input to the synthetic data).
The motivation is the same that of claim 1 above.
Regarding Claim 6;
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose the system of claim 1,
MAYER discloses wherein the generating of the one or more encoded source data and the generating of the synthetic data is performed using a neural network-based generative model to retain relevant statistical properties of data included in the original records (MAYER: par 0037; fig. 2a and 2b; feeding said digital dataset to the at least one neural network encoder, wherein the neural network encoder is configured to generate a compressed representation of the digital dataset; the method further comprising: analyzing the compressed representation to detect a correlation between the digital dataset and the compressed representation, generating decoder input data based on the detected correlation, feeding the decoder input data to a trained neural network decoder, wherein the trained neural network decoder is configured to generate synthetic digital data representing signal characteristics of the at least one measured signal based on the decoder input data. This achieves the advantage, that synthetic data resembling data attained by one or more real world measurements can be generated efficiently).
Regarding Claim 9;
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose the system of claim 1,
KOWALSKI discloses wherein the one or more predetermined identifiability values are determined based on one or more augmented vectors defined based on one or more variables in the generated synthetic data and a distance between the one or more augmented vectors and one or more variables in the one or more source data (KOWALSKI: par 0049; each factor is a part of an embedding vector identified by the location of entries in the vector. The domain discriminator is trained with a domain adversarial loss between embeddings produced by the two encoders. It forces the distributions generated by the two encoders to be similar; par 0118; the attributes are selected from one or more [] image was generated with a parameter for a smile selected. Image was generated with a parameter for eyes closed selected; par 0132; select parameter values for input to the synthetic data).
The motivation is the same that of claim 1 above.
Regarding Claim 11;
This Claim recites a computer program product that perform the same steps as system of Claim 1, and has limitations that are similar to Claim 1, thus are rejected with the same rationale applied against claim 1.
Regarding Claim 14;
This Claim recites a computer program product that perform the same steps as system of Claim 6, and has limitations that are similar to Claim 6, thus are rejected with the same rationale applied against claim 6.
Regarding Claim 17;
This Claim recites a computer program product that perform the same steps as system t of Claim 9, and has limitations that are similar to Claim 9, thus are rejected with the same rationale applied against claim 9.
Regarding Claim 19;
This Claim recites a method that perform the same steps as system of Claim 1, and has limitations that are similar to Claim 1, thus are rejected with the same rationale applied against claim 1.
Claims 3, 5, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over MAYER et al. (US 20210241075) in view of KOWALSKI et al. (US 20210335029), and further in view of ALLAMANIS et al. (US 20230305824) and Soulhi et al. (“Soulhi,” US 10,616,257, published on 04/07/2020).
Regarding Claim 3;
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose the system of claim 2,
KOWALSKI discloses wherein at least one predetermined identifiability value from among the one or more predetermined identifiability values is determined by a latent space augmentation process (KOWALSKI: par 0032; the multi-dimensional space is referred to as a latent space since it is learnt by the neural renderer; par 0072; the neural renderer checks whether a parameter vector is available where the parameter vector specifies values of parameters used to generate the image using the synthetic rendering apparatus; par 0125; images generated by the autoencoder are closer in identity to the corresponding object depicted I the real image. The one-shot learning is found to give good results despite the fact that it changes the latent space and the decoder).
The motivation is the same that of claim 1 above.
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose herein at least one predetermined identifiability value from among the one or more predetermined identifiability values is determined by a latent space augmentation process as recited above, but do not explicitly disclose determined by a latent space augmentation process as a first threshold that indicates a level of identifiability between the synthetic data and corresponding source data.
However, in an analogous art, Soulhi discloses anomaly detection system/method that includes:
determined by a latent space augmentation process as a first threshold that indicates a level of identifiability between the synthetic data and corresponding source data (Soulhi: Col 14, lines 1-9; select the latent space data that provides the optimal dimensions of the network performance data. By way of further example, network management device compare various parametric values of the latent space data to one or multiple threshold values. For example, a threshold value pertaining to a reconstruction error of a generative model (e.g., autoencoder) result determine the number of potential generated anomalies to be clustered by a clustering algorithm).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Soulhi with the method/system of MAYER and KOWALSKI and ALLAMANIS to include determined by a latent space augmentation process as a first threshold that indicates a level of identifiability between the synthetic data and corresponding source data. One would have been motivated to assess latent space data representative of network performance data, which is generated by a generative model, based on quantitative values pertaining to quantitative criteria (Soulhi: abstract).
Regarding Claim 5;
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose the system of claim 4,
KOWALSKI discloses wherein a predetermined anonymity value is determined by the latent space augmentation process (KOWALSKI: par 0032; the multi-dimensional space is referred to as a latent space since it is learnt by the neural renderer; par 0072; the neural renderer checks whether a parameter vector is available where the parameter vector specifies values of parameters used to generate the image using the synthetic rendering apparatus; par 0125; images generated by the autoencoder are closer in identity to the corresponding object depicted I the real image. The one-shot learning is found to give good results despite the fact that it changes the latent space and the decoder).
The motivation is the same that of claim 1 above.
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose wherein a predetermined anonymity value is determined by the latent space augmentation process as recited above, but do not explicitly disclose determined by the latent space augmentation process as a second threshold that indicates a level of anonymity between the synthetic data and the corresponding source data.
However, in an analogous art, Soulhi discloses anomaly detection system/method that includes:
determined by the latent space augmentation process as a second threshold that indicates a level of anonymity between the synthetic data and the corresponding source data (Soulhi: Col 14, lines 1-9; select the latent space data that provides the optimal dimensions of the network performance data. By way of further example, network management device compare various parametric values of the latent space data to one or multiple threshold values. For example, a threshold value pertaining to a reconstruction error of a generative model (e.g., autoencoder) result determine the number of potential generated anomalies to be clustered by a clustering algorithm).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Soulhi with the method/system of MAYER and KOWALSKI and ALLAMANIS to include determined by the latent space augmentation process as a second threshold that indicates a level of anonymity between the synthetic data and the corresponding source data. One would have been motivated to assess latent space data representative of network performance data, which is generated by a generative model, based on quantitative values pertaining to quantitative criteria (Soulhi: abstract).
Regarding Claim 12;
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose the computer program product of claim 11,
KOWALSKI discloses wherein one or more predetermined identifiability values are associated with the one or more variables in the synthetic data and at least one predetermined identifiability value from among the one or more predetermined identifiability values and the one or more predetermined anonymity values are associated with the selected one or more variables in the synthetic data (KOWALSKI: par 0118; the attributes are selected from one or more of: beard style, eyebrow style; different individual attributes have been controlled by setting parameter values. An end user is able to set the parameter values [] image was generated with a parameter for a smile selected. Image was generated with a parameter for eyes closed selected; par 0132; select parameter values for input to the synthetic data).
The motivation is the same that of claim 11 above.
KOWALSKI further discloses the one or more predetermined anonymity values are associated with the selected one or more variables in the synthetic data (ALLAMANIS: each decoder unit is associated with a select anonymized value, wherein each of the decoder units outputs a probability of the select anonymized value being a select defined variable name from the pre-existing partial source code program).
The motivation is the same that of claim 11 above.
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose predetermined identifiability value; predetermined anonymity values as recited above, but do not explicitly disclose determined by a latent space augmentation process as a first threshold that indicates a level of identifiability between the synthetic data and corresponding source data
However, in an analogous art, Soulhi discloses anomaly detection system/method that includes:
determined by a latent space augmentation process as a first threshold that indicates a level of identifiability between the synthetic data and corresponding source data (Soulhi: Col 14, lines 1-9; select the latent space data that provides the optimal dimensions of the network performance data. By way of further example, network management device compare various parametric values of the latent space data to one or multiple threshold values. For example, a threshold value pertaining to a reconstruction error of a generative model (e.g., autoencoder) result determine the number of potential generated anomalies to be clustered by a clustering algorithm).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Soulhi with the method/system of MAYER and KOWALSKI and ALLAMANIS to include determined by a latent space augmentation process as a first threshold that indicates a level of identifiability between the synthetic data and corresponding source data. One would have been motivated to assess latent space data representative of network performance data, which is generated by a generative model, based on quantitative values pertaining to quantitative criteria (Soulhi: abstract).
Regarding Claim 13;
This Claim recites a computer program product that perform the same steps as system of Claim 5, and has limitations that are similar to Claim 5, thus are rejected with the same rationale applied against claim 5.
Claims 7-8 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over MAYER et al. (US 20210241075) in view of KOWALSKI et al. (US 20210335029), and further in view of ALLAMANIS et al. (US 20230305824) and Metzger et al. (“Metzger,” US 20210372994, published on 12/02/2021).
Regarding Claim 7;
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose the system of claim 6,
KOWALSKI discloses wherein the neural network-based generative model includes an autoencoder having an unsupervised model with an input layer, an output layer, and a hidden layer that connects the input layer and the output layer, the input layer including an encoder to reduce dimensionality of input data to generate latent space data (KOWALSKI: par 0067; a latent generative adversarial network (GAN) is used. The latent GAN is trained to map between its input .˜N (0,1) and the latent space z. This approach allows for sampling the latent space without the constraints on z imposed by variational auto encoders that lead to reduced quality. The latent GAN is trained with the GAN losses described with reference to FIG. 9 below. Both the discriminator and generator G.sub.lat are 3-layer multi-layer perceptron).
The motivation is the same that of claim 1 above.
KOWALSKI discloses wherein the neural network-based generative model includes an autoencoder having an unsupervised model with an input layer, an output layer, and a hidden layer that connects the input layer and the output layer, the input layer including an encoder to reduce dimensionality of input data to generate latent space data as recited above, but do not explicitly disclose autoencoder having an unsupervised model
However, in an analogous art, Metzger discloses machine learning system/method that includes:
autoencoder having an unsupervised model (Metzger: par 0027; fig. 1; the encoder and decoder neural networks are trained such that the reconstructed data matches the input data as closely as possible, resulting in a low-dimensional representation of the data in the latent space; par 0090; unsupervised learning includes the use of autoencoders. See FIG. 1. The encoder and decoder neural networks are trained such that the reconstructed data matches the input data as closely as possible, resulting in a low-dimensional representation of the data in the latent space. An autoencoder is a type of artificial neural network used to learn efficient data coding in an unsupervised manner; par 0091; a typical autoencoder contains an input layer, a hidden layer, and an output layer).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Metzger with the method/system of MAYER and KOWALSKI and ALLAMANIS to include d autoencoder having an unsupervised model. One would have been motivated to assigning the first organoid a probability score ranging between 0% and 100%; wherein the test molecule is biologically active against the disease if the probability score of the first organoid is greater than a cutoff probability score of non-disease or lower than a cutoff probability score of disease (Metzger: abstract).
Regarding Claim 8;
The combination of MAYER, KOWALSKI, ALLAMANIS, and Metzger disclose the system of claim 7,
Metzger discloses wherein the generated latent space data by the encoder is fed to the output layer including a decoder to generate first synthetic data that closely matches statistical properties of data included in the original records, and feeding the first synthetic data to decoder to generate second synthetic data that is unidentifiable (Metzger: par 0027; fig. 1; the encoder and decoder neural networks are trained such that the reconstructed data matches the input data as closely as possible, resulting in a low-dimensional representation of the data in the latent space; par 0090; unsupervised learning includes the use of autoencoders. See FIG. 1. The encoder and decoder neural networks are trained such that the reconstructed data matches the input data as closely as possible, resulting in a low-dimensional representation of the data in the latent space. An autoencoder is a type of artificial neural network used to learn efficient data coding in an unsupervised manner; par 0091; a typical autoencoder contains an input layer, a hidden layer, and an output layer).
The motivation is the same that of claim 1 above.
MAYER further discloses feeding the first synthetic data to decoder to generate second synthetic data that is unidentifiable (par 0102; the training routine shown in FIG. 2a can be repeated with a variety of datasets).
Regarding Claim 15;
This Claim recites a computer program product that perform the same steps as system of Claim 7, and has limitations that are similar to Claim 7, thus are rejected with the same rationale applied against claim 7.
Regarding Claim 16;
This Claim recites a computer program product that perform the same steps as system of Claim 8, and has limitations that are similar to Claim 8, thus are rejected with the same rationale applied against claim 8.
Claims 10, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over MAYER et al. (US 20210241075) in view of KOWALSKI et al. (US 20210335029), and further in view of ALLAMANIS et al. (US 20230305824) and Das et al. (“Das,” US 20200410379, published on 12/31/2020).
Regarding Claim 10;
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose the system of claim 4,
ALLAMANIS discloses wherein the one or more predetermined anonymity values are determined based on a comparison of the one or more variables in the one or more source data and one or more variables in the decoded synthetic data by determining one or more matches between one or more values associated with the one or more variables in the one or more source data and one or more variables in the decoded synthetic data (ALLAMANIS: par 0052; the training dataset includes input sequences, where each input sequence represents an extended context with anonymized values and the anonymized value mapping; par 0101; anonymizing each of the undefined variable names in each extended context with a select one of a plurality of anonymized values).
ALLAMANIS discloses wherein the one or more predetermined anonymity values are determined as recited above, but do not explicitly disclose determined based on a comparison of the one or more variables in the one or more source data and one or more variables in the decoded synthetic data by determining one or more matches between one or more values associated with the one or more variables in the one or more source data and one or more variables in the decoded synthetic data.
However, in an analogous art, Das discloses control function system/method that includes:
determined based on a comparison of the one or more variables in the one or more source data and one or more variables in the decoded synthetic data by determining one or more matches between one or more values associated with the one or more variables in the one or more source data and one or more variables in the decoded synthetic data (Das: par 0108; determine whether synthetic data comprises real or fake data samples (e.g., by comparing synthetic data). In some embodiments, discriminator can predict whether synthetic data comprises a certain creativity attribute (e.g., by comparing synthetic data). For example, discriminator can predict whether synthetic data comprises a certain creativity attribute including, but not limited to, a novelty attribute, a surprise attribute, a value attribute (e.g., in terms of risk versus reward), a risk attribute, a reward attribute, and/or another creativity attribute).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Das with the method/system of MAYER and KOWALSKI and ALLAMANIS to include a autoencoder having an unsupervised model. One would have been motivated to model to determined based on a comparison of the one or more variables in the one or more source data and one or more variables in the decoded synthetic data by determining one or more matches between one or more values associated with the one or more variables in the one or more source data and one or more variables in the decoded synthetic data. The computer executable components can further comprise a generator component that employs the model to generate a creative data sample based on the creativity control function (Das: abstract).
Regarding Claim 18;
This Claim recites a computer program product that perform the same steps as system of Claim 10, and has limitations that are similar to Claim 10, thus are rejected with the same rationale applied against claim 10.
Regarding Claim 20;
The combination of MAYER, KOWALSKI, and ALLAMANIS disclose the method of claim 19,
KOWALSKI discloses wherein the latent space augmentation process (KOWALSKI: par 0067; a latent generative adversarial network (GAN) is used. The latent GAN is trained to map between its input .˜N (0,1) and the latent space z. This approach allows for sampling the latent space without the constraints on z imposed by variational auto encoders that lead to reduced quality. The latent GAN is trained with the GAN losses described with reference to FIG. 9 below. Both the discriminator and generator G.sub.lat are 3-layer multi-layer perceptron).
ALLAMANIS discloses the latent space augmentation process as recited above, but do not explicitly disclose based on a comparison of the one or more variables in the one or more source data and one or more variables in the decoded synthetic data by determining one or more matches between one or more values associated with the one or more variables in the one or more source data and one or more variables in the decoded synthetic data.
However, in an analogous art, Das discloses control function system/method that includes:
based on a comparison of the one or more variables in the one or more source data and one or more variables in the decoded synthetic data by determining one or more matches between one or more values associated with the one or more variables in the one or more source data and one or more variables in the decoded synthetic data (Das: par 0108; determine whether synthetic data comprises real or fake data samples (e.g., by comparing synthetic data). In some embodiments, discriminator can predict whether synthetic data comprises a certain creativity attribute (e.g., by comparing synthetic data). For example, discriminator can predict whether synthetic data comprises a certain creativity attribute including, but not limited to, a novelty attribute, a surprise attribute, a value attribute (e.g., in terms of risk versus reward), a risk attribute, a reward attribute, and/or another creativity attribute).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Das with the method/system of MAYER and KOWALSKI and ALLAMANIS to include based on a comparison of the one or more variables in the one or more source data and one or more variables in the decoded synthetic data by determining one or more matches between one or more values associated with the one or more variables in the one or more source data and one or more variables in the decoded synthetic data. One would have been motivated to model to define a creativity control function of the model. The computer executable components can further comprise a generator component that employs the model to generate a creative data sample based on the creativity control function (Das: abstract).
Conclusion
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/C.W./Examiner, Art Unit 2439
/LUU T PHAM/Supervisory Patent Examiner, Art Unit 2439
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