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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) 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 www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1–14 and 16–20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1–25 of U.S. Patent No. 12,182,911 B2 (Application No. 17/447,081). Although the claims at issue are not identical, they are not patentably distinct from each other because
Claim 1 of the instant application is determined to be unpatentable over claim 1 in view of claim 10 of U.S. Patent No. 12,182,911 B2, based on the reasons below for having substantially similar limitations.
Instant Application (18/952,232)
Reference Patent US 12,182,911 B2 (17/447,081)
1. A data generation apparatus comprising:
1. An image generation apparatus comprising:
at least one memory; and
at least one memory; and
at least one processor,
at least one processor configured to:
wherein the at least one processor is configured to generate data by using latent information and a generative model, and
acquire first latent information of a first image and second latent information of a second image [...] generate fusion latent information by using the first latent information and the second latent information, and generate a fusion image by inputting the fusion latent information into a trained generative model [...]
wherein the latent information is represented in a blockchain compliant code.
[Claim 10] wherein the first latent information and the second latent information are represented in blockchain compliant codes.
Although the claims at issue are not identical, they are not patentably distinct from each other. Instant claim 1 recites a data generation apparatus that generates data using latent information and a generative model, wherein the latent information is represented in a blockchain compliant code. Reference patent claim 1, when read in view of dependent claim 10, recites the same core combination: latent information represented in blockchain compliant codes is used as input to a trained generative model to produce an output. The differences—namely, that the reference patent requires acquiring two separate latent inputs from two images to produce a fusion output for a third character, whereas the instant claim does not—do not render the instant claim patentably distinct. Generalizing the reference patent's image-fusion framework to a broader "data generation" structure using latent information and a generative model would have been obvious to one of ordinary skill in the art.
Claim 2 of the instant application is determined to be unpatentable over claims 1 and 10 of U.S. Patent No. 12,182,911 B2.
Instant Application (18/952,232)
Reference Patent US 12,182,911 B2 (17/447,081)
2. The data generation apparatus as claimed in claim 1, wherein the at least one processor is configured to generate the data by inputting the latent information into the generative model.
[Claim 1] generate a fusion image by inputting the fusion latent information into a trained generative model [...][Claim 10] wherein the first latent information and the second latent information are represented in blockchain compliant codes.
Instant claim 2 recites the same fundamental operation as reference patent claim 1: inputting latent information into a generative model to produce an output. The instant claim merely broadens the output from a fusion image to generic "data." This broadening does not constitute a patentably distinct variation.
Claims 3–6 of the instant application are determined to be unpatentable over claims 1 and 10 of U.S. Patent No. 12,182,911 B2.
Instant Application (18/952,232)
Reference Patent US 12,182,911 B2 (17/447,081)
3. [...] configured to convert the latent information that is represented in the blockchain compliant code to latent information that is not represented in the blockchain compliant code, and generate the data by inputting the converted latent information into the generative model.
[Claim 1] generate fusion latent information by using the first latent information and the second latent information, and generate a fusion image by inputting the fusion latent information into a trained generative model.[Claim 10] wherein the first latent information and the second latent information are represented in blockchain compliant codes.
4. [...] convert the latent information [...] based on predetermined correspondence.
[Claim 10] — Implementing a correspondence-based decode/conversion of blockchain-coded latent information into model-usable form is an obvious design choice given the reference patent's blockchain embodiment.
5. [...] wherein the predetermined correspondence is between an element of the latent information that is represented in the blockchain compliant code and an element of the latent information that is not represented in the blockchain compliant code.
[Claim 10] — Element-by-element correspondence between coded and decoded latent information is an obvious implementation of the decode step implied by claim 10.
6. [...] wherein the latent information that is represented in the blockchain compliant code has a smaller data amount than the latent information that is not represented in the blockchain compliant code.
[Claim 10] — Using a compact blockchain-coded representation that expands upon decode is an obvious and expected property of a coded/encoded data format, consistent with the reference patent's blockchain embodiment.
Reference patent claims 1 and 10 together disclose using blockchain-compliant-code-represented latent information as input to a trained generative model. It would have been obvious to one of ordinary skill in the art to decode or convert the blockchain-represented latent information into a model-usable format via predetermined element-to-element correspondence, and that the coded representation would be more compact. Instant claims 3–6 do not define a patentably distinct invention.
Claims 7–9 of the instant application are determined to be unpatentable over claims 1, 7, 8, and 9 of U.S. Patent No. 12,182,911 B2.
Instant Application (18/952,232)
Reference Patent US 12,182,911 B2 (17/447,081)
7. [...] wherein the latent information includes information for characterizing an object.
[Claim 1] wherein the first latent information includes information of at least one of a hair style, a hair color, an eye color, a skin color, an expression, or an attachment of the first character [...]
8. [...] wherein the latent information includes information on at least one of a hair style, a hair color, an eye color, a skin color, an expression, or an attachment of the object.
[Claim 1] wherein the first latent information includes information of at least one of a hair style, a hair color, an eye color, a skin color, an expression, or an attachment of the first character [verbatim attribute enumeration]
9. [...] wherein the latent information includes a characteristic and a variable characteristic of the object.
[Claims 7–9] wherein the first latent information includes main and sub information [...] select one of the main and the sub information of the first latent information [...]
Instant claim 8 uses attribute language verbatim to reference patent claim 1 (hair style, hair color, eye color, skin color, expression, attachment). Instant claim 9's "characteristic and variable characteristic" maps directly to the reference patent's "main and sub information" framework recited in parent claims 7–9. These claims are not patentably distinct from the reference patent claims.
Claims 10–14 and 16 of the instant application are determined to be unpatentable over claims 1, 2, 3, 6, and 10 of U.S. Patent No. 12,182,911 B2.
Instant Application (18/952,232)
Reference Patent US 12,182,911 B2 (17/447,081)
10. [...] configured to generate the latent information based on a first latent information and a second latent information.
[Claim 1] acquire first latent information [...] and second latent information [...] generate fusion latent information by using the first latent information and the second latent information.
11. [...] configured to generate the latent information by performing a genetic operation using the first latent information and the second latent information.
[Claim 2] wherein the at least one processor is configured to perform a genetic operation on the first latent information and the second latent information to generate the fusion latent information.
12. [...] wherein the genetic operation includes at least one of crossover, mutation, or selection.
[Claim 3] wherein the genetic operation includes at least one of crossover, mutation, or selection. [verbatim]
13. [...] configured to generate the latent information by performing at least one of an arithmetic operation or a logical operation using the first latent information and the second latent information.
[Claim 6] wherein the at least one processor is configured to perform at least one of an arithmetic operation or a logical operation of the first latent information and the second latent information to generate the fusion latent information.
14. [...] wherein the first latent information is represented in the blockchain compliant code.
[Claim 10] wherein the first latent information and the second latent information are represented in blockchain compliant codes.
16. [...] wherein the first latent information includes information for characterizing a first object, and wherein the second latent information includes information for characterizing a second object.
[Claim 1] the first image including data related to a visual representation of a first object [...] the second image including data related to a visual representation of a second object [...] [first character / second character framework]
Instant claims 10–14 and 16, taken together, reconstruct the entirety of reference patent claim 1 in combination with dependent claims 2, 3, 6, and 10. Notably, instant claim 12 is verbatim to reference patent claim 3, and instant claim 13 is substantively identical to reference patent claim 6. The only material variation is that instant claim 10 frames the latent information generation step as a standalone operation rather than as part of a fusion-image workflow — a distinction that would have been obvious to one of ordinary skill in the art. These claims are not patentably distinct.
Claim 17 of the instant application is determined to be unpatentable over claim 11 of U.S. Patent No. 12,182,911 B2.
Instant Application (18/952,232)
Reference Patent US 12,182,911 B2 (17/447,081)
17. [...] wherein the generative model is a neural network.
[Claim 11] wherein the trained generative model is a generator trained in accordance with a generative adversarial network.
A generative adversarial network (GAN) is a well-known type of neural network. Reciting "a neural network" is a broader genus that necessarily encompasses the GAN species disclosed and claimed in the reference patent. Instant claim 17 is not patentably distinct from reference patent claim 11.
Claim 18 of the instant application is determined to be unpatentable over claim 1 of U.S. Patent No. 12,182,911 B2.
Instant Application (18/952,232)
Reference Patent US 12,182,911 B2 (17/447,081)
18. [...] wherein the data includes at least one of an image, a video, or a sound.
[Claim 1] generate a fusion image by inputting the fusion latent information into a trained generative model, the fusion image including data related to a visual representation of a third object [...]
Reference patent claim 1 expressly generates an image as its output. Instant claim 18 recites that the generated data may be an image — one of the positively recited alternatives. The claim is therefore not patentably distinct from the reference patent with respect to the image alternative, and extending the output to video or sound is an obvious variation of the same latent-information/generative-model framework.
Claim 19 of the instant application is determined to be unpatentable over claims 17 and 10 of U.S. Patent No. 12,182,911 B2.
Instant Application (18/952,232)
Reference Patent US 12,182,911 B2 (17/447,081)
19. A data generation method comprising generating, by at least one processor, data by using latent information and a generative model, wherein the latent information is represented in a blockchain compliant code.
[Claim 17] An image generation method comprising: acquiring, by at least one processor, first latent information of a first image and second latent information of a second image [...] generating, by the at least one processor, fusion latent information by using the first latent information and the second latent information, and generating, by the at least one processor, a fusion image by inputting the fusion latent information into a trained generative model [...][Claim 10] wherein the first latent information and the second latent information are represented in blockchain compliant codes.
For the same reasons discussed with respect to apparatus claim 1, instant method claim 19 is not patentably distinct from reference patent method claim 17 as read in view of dependent claim 10. Changing the statutory class from apparatus to method does not impart patentable distinctness where the underlying steps are the same or obvious variations thereof.
Claim 20 of the instant application is determined to be unpatentable over claims 17 and 10 of U.S. Patent No. 12,182,911 B2.
Instant Application (18/952,232)
Reference Patent US 12,182,911 B2 (17/447,081)
20. A non-transitory computer-readable recording medium having stored therein a program for causing at least one computer to perform a process comprising generating data by using latent information and a generative model, wherein the latent information is represented in a blockchain compliant code.
[Claim 17] An image generation method comprising: [...] generating, by the at least one processor, a fusion image by inputting the fusion latent information into a trained generative model [...][Claim 10] wherein the first latent information and the second latent information are represented in blockchain compliant codes.
Recasting the method steps of reference patent claim 17 (as read with claim 10) into a computer-readable medium claim is merely a change in statutory form and does not render the instant claim patentably distinct from the reference patent.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-7, 13-17, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Truong et al. (US 10460235 B1, hereinafter Truong), in view of Raman et al. (US 11032063 B2, hereinafter Raman).
Regarding Claim 1, Truong teaches a data generation apparatus comprising: at least one memory; and at least one processor (Truong, Column 7, Line 14-18, "The system for generating data models can include at least one secure system processor and at least one secure system non-transitory memory"), wherein the at least one processor is configured to generate data by using latent information and a generative model (Truong, Column 7, Line 33-35, "generating synthetic data using the data model in response to the data generation request"; Column 10, Line 13-15, "the data model can be configured to map from a random or pseudorandom vector <read on latent information> to elements in the training data space"; Column 11, Line 48-50, "neural network, recurrent neural network, generative adversarial network <read on generative model>").
But Truong does not explicitly disclose wherein the latent information is represented in a blockchain compliant code.
However, Raman teaches wherein the latent information is represented in a blockchain compliant code (Raman, Column 10, Line 1-10, "A state of the system evolves over iterations of the simulation as X t+1 =f(X t,θt) Xt ∈ d, system state vector θt ∈ d′ is a shared source of (possibly random) <read on latent> information ... report the state Xt at checkpoints t ∈{T1, T2, . . . }. ... report compressed update t. ... the validated frame of data may be stored on the blockchain 160").
Raman and Truong are analogous since both deal with the management of complex data models and the generation of synthetic data or simulation states in distributed computing environments. Truong provided a way of generating high-quality synthetic data models using "code space" (latent) representations, while Raman provided a way of securely storing and sharing evolving state vectors and random source information (latent information) on a distributed blockchain ledger. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the blockchain-based storage of state and random source vectors taught by Raman into the data generation system of Truong such that the latent information driving the generative model is represented and stored as a blockchain-compliant code. The motivation is to provide a "shared, immutable, append-only record" as discussed by Raman in Paragraph to ensure the "trustworthy and transferable" nature of the generation parameters among disparate nodes.
Regarding Claim 2, the combination of Truong and Raman teaches the invention in Claim 1.
The combination further teaches wherein the at least one processor is configured to generate the data by inputting the latent information into the generative model (Truong, Column 27, Line 65-66, "a decoder maps from a code space <read on latent information> to a sample space. ... generating a sample using the decoder").
Regarding Claim 3, the combination of Truong and Raman teaches the invention in Claim 1.
The combination further teaches [[ wherein the at least one processor is configured to convert the latent information that is represented in the blockchain compliant code to latent information that is not represented in the blockchain compliant code, ]] generate the data by inputting the converted latent information into the generative model (Truong, Column 2, Line 63-67, "The generative network can include a decoder network configured to generate decoder output data in a sample space having a first dimensionality from decoder input data in a code space having a second dimensionality less than the first dimensionality").
Truong does not explicitly disclose but Raman teaches wherein the at least one processor is configured to convert the latent information that is represented in the blockchain compliant code to latent information that is not represented in the blockchain compliant code (Raman, Column 18, Line 17-19, "the data frame may be decompressed to reveal the state of the simulation before the compression"; it is noted the "state" represents the underlying variables needed to continue iterative computation).
Raman and Truong are analogous since both of them are dealing with the management and generation of simulation states and synthetic data models using distributed computing and blockchain ledgers. Truong provided a way of generating synthetic datasets by mapping from a "code space" (latent information) to a "sample space" using a generative decoder. Raman provided a way of storing simulation state vectors on a blockchain and then decompressing them to reveal the original state (converting from blockchain code to non-compliant information) for further processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the decompression and state-reconstruction mechanisms taught by Raman into the modified invention of Truong such that blockchain-stored latent variables can be converted and input into the generative model. The motivation is to ensure the results inferred by individual agents are trustworthy and transferable.
Regarding Claim 4, the combination of Truong and Raman teaches the invention in Claim 3.
The combination further teaches [[ wherein the at least one processor is configured to convert the latent information that is represented in the blockchain compliant code to the latent information that is not represented in the blockchain compliant code ]], based on predetermined correspondence (Truong, Column 1, Line 64-66, "tokenization can result in tokenized data, sensitive data values, and a mapping <read on predetermined correspondence> between the tokens and the values").
Truong does not explicitly disclose but Raman teaches wherein the at least one processor is configured to convert the latent information that is represented in the blockchain compliant code to the latent information that is not represented in the blockchain compliant code (Raman, Column 18, Line 16-19, "the data frame <read on blockchain compliant code> may be decompressed to reveal the state of the simulation <read on non-compliant latent information> before the compression"), based on predetermined correspondence (Raman, Column 8, Line 61-62, "constructs frames of states of the simulation and performs delta encoding and lattice vector quantization <read on predetermined correspondence> to compress the frames").
Raman and Truong are analogous since both of them are dealing with the management and transformation of high-dimensional state vectors (latent information) for storage and generation within distributed or cloud-computing environments. Truong provided a way of protecting data through tokenization and maintaining a mapping between the code (tokens) and actual sensitive values. Raman provided a way of storing and retrieving simulation states on a blockchain by using a mathematical schema (quantization and delta encoding) to translate between raw state data and compressed, blockchain-compliant frames. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the blockchain storage and decompression mechanisms taught by Raman into the data generation system of Truong such that the latent information driving synthetic data generation is stored in an efficient blockchain-compliant format and then converted back to usable latent variables using the predetermined mathematical correspondence. The motivation is to ensure the integrity, security, and storage efficiency of the generation parameters.
Regarding Claim 5, the combination of Truong and Raman teaches the invention in Claim 1.
The combination further teaches wherein the predetermined correspondence is between an element of the latent information that is represented in the blockchain compliant code and an element of the latent information that is not represented in the blockchain compliant code (Truong, Column 1, Line 64-67, "tokenization can result in tokenized data, sensitive data values, and a mapping <read on correspondence> between the tokens <read on code> and the values <read on non-code information>").
Regarding Claim 6, the combination of Truong and Raman teaches the invention in Claim 1.
The combination further teaches wherein the latent information that is represented in the blockchain compliant code has a smaller data amount than the latent information that is not represented in the blockchain compliant code (Raman, Column 20, Line 20-23, " compressing the simulation content within the data frame based on previous simulation content stored in another data frame to generate a compressed data frame").
Raman and Truong are analogous since both of them are dealing with the efficient storage and transmission of high-dimensional vector data (latent codes and simulation states) in a distributed network. Truong provided a way of defining a reduced-dimensionality "code space" to represent complex data features for a generative model . Raman provided a way of reducing network traffic demand and hardware resources by performing delta encoding and lattice vector quantization to generate compressed data frames for blockchain storage. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the data compression and quantization schema taught by Raman into the modified invention of Truong such that the latent information represented in the blockchain code occupies a smaller data amount than its raw form. The motivation is to reduce network traffic demand and reduce hardware resources.
Regarding Claim 7, the combination of Truong and Raman teaches the invention in Claim 1.
The combination further teaches wherein the latent information includes information for characterizing an object (Truong, Column 17, Line 5-6, "classes could include employee identification numbers, employee names, employee addresses <read on characterizing an employee object>").
Regarding Claim 13, the combination of Truong and Raman teaches the invention in Claim 1.
The combination further teaches [[ wherein the at least one processor is configured to generate the latent information by performing at least one of an arithmetic operation or a logical operation ]], using the first latent information and the second latent information (Truong, Column 3, Line 6-10, "Generating the synthetic dataset ... can further include determining a first representative point ... and a second representative point ... and determining a vector connecting the first representative point and the second representative point.").
But Truong does not explicitly disclose wherein the at least one processor is configured to generate the latent information by performing at least one of an arithmetic operation or a logical operation.
However, Raman teaches wherein the at least one processor is configured to generate the latent information by performing at least one of an arithmetic operation or a logical operation (Raman, Column 8, Line 60-65, "performs delta encoding <read on arithmetic operation> and lattice vector quantization to compress the frames"), using the first latent information and the second latent information (Raman, Column 21, Line 25-27, "iterative simulation content may be compressed based on differences in state with a previous iteration <read on first and second latent information>").
Raman and Truong are analogous since both of them are dealing with the mathematical manipulation of high-dimensional vectors representing model states or latent information. Truong provided a way of finding a difference vector between two representative points in a code space to enable the translation of other points. Raman provided a way of generating compressed state updates by performing delta encoding, which involves calculating differences (arithmetic operations) between a current state and a previous state. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the arithmetic delta encoding techniques taught by Raman into the latent information generation process of Truong such that new latent codes are derived from the arithmetic combination of existing latent points. The motivation is to provide a mathematically efficient way to represent and evolve the underlying parameters of a generative model.
Regarding Claim 14, the combination of Truong and Raman teaches the invention in Claim 1.
The combination further teaches wherein the first latent information is represented in the blockchain compliant code (Raman, Column 9, Line 49-50, "Once validated by a subset, the validated frame of data may be stored on the blockchain 160 ").
Raman and Truong are analogous since both of them are dealing with the secure storage and validation of machine-learned parameters and state vectors. Truong provided a way of determining representative points (latent information) in a code space to enable the generation or transformation of synthetic data. Raman provided a way of committing ordered frame data (evolving states) to a distributed ledger shared among multiple nodes. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the blockchain-based ledger storage taught by Raman into the modified invention of Truong such that the primary latent information used to drive the data generation is represented in a blockchain compliant code. The motivation is to ensure that entities in the system can trust the validity of the conclusions obtained by individual agents
Regarding Claim 15, the combination of Truong and Raman teaches the invention in Claim 1.
The combination further teaches wherein the first latent information is purchased from a first user by a second user (Truong, Column 16, Line 60-65, "The actual data may have been purchased in whole or in part by an entity associated with system 100").
Regarding Claim 16, the combination of Truong and Raman teaches the invention in Claim 1.
The combination further teaches wherein the first latent information includes information for characterizing a first object, and wherein the second latent information includes information for characterizing a second object (Truong, Column 28, Line 29-31, "identify one or more first accounts belonging to users in their 20s and one or more second accounts belonging to users in their 40s").
Regarding Claim 17, the combination of Truong and Raman teaches the invention in Claim 1.
The combination further teaches wherein the generative model is a neural network (Truong, Column 2, Line 14-16, "a dataset generator can generate a synthetic dataset for training the data model using a generative network of a generative adversarial network ").
Regarding Claim 19, it recites limitations similar in scope to the limitations of Claim 1 but as a method and the combination of Truong and Raman teaches all the limitations as of Claim 1. Therefore is rejected under the same rationale.
Regarding Claim 20, it recites limitations similar in scope to the limitations of claim 1 and the combination Truong and Raman teaches all the limitations as of Claim 1. And Truong discloses these features can be implemented on a computer-readable storage medium (Truong, Column 38, Line 6-10, “Furthermore, although aspects of the disclosed embodiments are described as being associated with data stored in memory and other tangible computer-readable storage mediums”; Column 4, Line 11-15, “The cloud computing system can include at least one processor and at least one non-transitory memory storing instructions that, when executed by the at least one processor cause the cloud computing system to perform the following operations”).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Truong et al. (US 10460235 B1, hereinafter Truong), in view of Raman et al. (US 11032063 B2, hereinafter Raman) as applied to Claim 1 above and further in view of Liu et al. (US 20180247201 A1, hereinafter Liu).
Regarding Claim 8, the combination of Truong and Raman teaches the invention in Claim 1.
The combination does not explicitly disclose but Liu teaches wherein the latent information includes information on at least one of a hair style, a hair color, an eye color, a skin color, an expression, or an attachment of the object (Liu, Paragraph [0061], "Examples of face attributes, include hair color, expression, facial hair, and eyeglasses").
Liu and Truong are analogous since both of them are dealing with the classification and generation of objects using machine-learned feature vectors and attributes. Truong provided a way of identifying sensitive data classes to generate specific synthetic portions for objects like employees or financial accounts. Liu provided a way of translating and generating specific face attributes including hair color, expression, and eyeglasses within a latent space. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the specific visual attribute types taught by Liu into the modified invention of Truong such that the latent information includes specific descriptive characteristics of an object like hair style or expression. The motivation is to accurately differentiate between target classes and capture biological variance.
Claim(s) 9, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Truong et al. (US 10460235 B1, hereinafter Truong), in view of Raman et al. (US 11032063 B2, hereinafter Raman) as applied to Claim 1 above and further in view of Srinivasan (US 20190266442 A1).
Regarding Claim 9, the combination of Truong and Raman teaches the invention in Claim 1.
The combination does not explicitly disclose but Srinivasan teaches wherein the latent information includes a characteristic and a variable characteristic of the object (Srinivasan, Paragraph [0045], "first image and a second input, which is indicative of a desire for more or less of the at least one user-defined attribute").
Srinivasan and Truong are analogous since both of them are dealing with the refinement of generative model outputs based on specific user-defined parameters. Truong provided a way of generating synthetic data using class-specific and subclass-specific models to match the statistical distributions of real data. Srinivasan provided a way of generating an image with a visual attribute (characteristic) and then tuning that attribute with a second input indicative of "more or less" of it (variable characteristic). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the tunable attribute stacking taught by Srinivasan into the modified invention of Truong such that the latent information used for generation includes both a base characteristic and a relative variable characteristic. The motivation is to enable the end-user to vary one or more visual attributes of an item in an image in a relative manner.
Regarding Claim 10, the combination of Truong and Raman teaches the invention in Claim 1.
The combination does not explicitly disclose but Srinivasan teaches wherein the at least one processor is configured to generate the latent information based on a first latent information and a second latent information (Srinivasan, Paragraph [0013], "a tunable adversarial network includes a stacked GAN network, wherein a first GAN... generate an image of an item with a visual attribute and a second GAN... generate an image of the item with more or less of the visual attribute").
Srinivasan and Truong are analogous since both of them are dealing with the derivation of new data representations by combining or evolving multiple input sources. Truong provided a way of determining a vector connecting a first representative point and a second representative point in code space to translate or generate new data. Srinivasan provided a way of using a stacked GAN configuration where a first GAN's output and a second user input (first and second latent information) are used together to generate a final result. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the stacked generation architecture taught by Srinivasan into the modified invention of Truong such that new latent information is generated based on a combination of first and second latent information inputs. The motivation is to improve the accuracy of images generated via a GAN.
Claim(s) 11, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Truong et al. (US 10460235 B1, hereinafter Truong), in view of Raman et al. (US 11032063 B2, hereinafter Raman), further in view of Srinivasan (US 20190266442 A1) as applied to Claim 10 above and further in view of Aliper et al. (US 20190034581 A1, hereinafter Aliper).
Regarding Claim 11, the combination of Truong, Raman, and Srinivasan teaches the invention in Claim 10.
The combination does not explicitly disclose but Aliper teaches wherein the at least one processor is configured to generate the latent information by performing a genetic operation using the first latent information and the second latent information (Aliper, Paragraph [0267], "a specific type of DNN called Deep Feature Selection (DFS) is trained on blood gene expression samples using standard backpropagation algorithm... the DFS model is applied to select a set of age-related genes using different DNN-based feature selection methods combined into one ensemble model via genetic algorithm"; Paragraph [0281], "On each iteration the GA performed 35 crossover operations between its populations and 15 mutation operations, during which random genes were injected in the training of GA").
Aliper and Truong are analogous since both of them are dealing with the generation and optimization of data representations using machine learning models in computational data processing environments. Truong provided a way of generating synthetic data using generative models that map from latent vectors to elements in a training data space. Aliper provided a way of using a genetic algorithm to combine and evolve multiple latent feature representations (first and second latent information from different feature selection methods) through crossover and mutation operations to produce optimized new latent information. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the genetic algorithm-based combination of latent feature representations taught by Aliper into the modified invention of Truong such that the latent information is generated by performing a genetic operation using the first latent information and the second latent information. The motivation is to obtain an optimized "final gene ranking list" that yields improved prediction accuracy, as discussed by Aliper in Paragraph [0281].
Regarding Claim 12, the combination of Truong, Raman, Srinivasan, and Aliper teaches the invention in Claim 11.
Th combination further teaches wherein the genetic operation includes at least one of crossover, mutation, or selection (Aliper, Paragraph [0281], " by applying the forementioned feature selection algorithms on both DNN and DFS models. On each iteration the GA performed 35 crossover operations between its populations and 15 mutation operations, during which random genes were injected in the training of GA").
As explained in rejection of claim 11, the obviousness for combining of genetic algorithm of Aliper into Truong is provided above.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Truong et al. (US 10460235 B1, hereinafter Truong), in view of Raman et al. (US 11032063 B2, hereinafter Raman) as applied to Claim 1 above and further in view of Kalchbrenner et al. (US 11468295 B2, hereinafter Kalchbrenner).
Regarding Claim 18, the combination of Truong and Raman teaches the invention in Claim 1.
The combination does not explicitly disclose but Kalchbrenner teaches wherein the data includes at least one of an image, a video, or a sound (Kalchbrenner, Column 3, Line 14-24, "output example 152 can be a sequence of audio data, e.g., a waveform. ... As another example, the output example 152 can be an image. ... As another example, the output example 152 can be a video frame").
Kalchbrenner and Truong are analogous since both of them are dealing with the generation of synthetic approximations of real-world data using neural network models. Truong provided a way of creating synthetic datasets that mimic the structure and statistics of original data types like JSON logs or financial records. Kalchbrenner provided a way of generating high-quality output examples including audio data waveforms, images, and video frames. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the multimedia output generation capabilities taught by Kalchbrenner into the modified invention of Truong such that the generated synthetic data includes at least one of an image, a video, or a sound. The motivation is to leverage the structure of output examples to achieve speed ups without degradation in quality.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/YuJang Tswei/Primary Examiner, Art Unit 2614
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