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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/30/2025 have been considered by the examiner and been placed of record in the file.
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.
Claim 20 are rejected under 35 U.S.C. 101 as not falling within one of the four statutory categories of invention. The element ” A computer program product” disclosed in claim 20. These elements are disclosed in the specification (The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure [0170]), and when treated as a whole, claim 20 is more toward a non-statutory embodiment and not necessarily a hardware embodiment.
Also, claim 22 is cited as depending on method of claim 20. However, claim 20 is a computer product.
Claims 1-24 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to Independent claims 1, 17, 20, 21 and 23:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
For claim 1, Yes, the claim is a process.
For claim 17, Yes, the claim is a process.
For claim 20, No, as explained above.
For claim 21, Yes, the claim is a process.
For claim 23, Yes, the claim is a process.
Step 2A Prong One Analysis: Do the claims recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “reading a plurality of synthetic images and associated latent representations; presenting each of the plurality of synthetic images to one or more users via a client computing platform; reading a plurality of inputs to the client computing platform, the plurality of inputs characterizing a plurality of values for associated attributes of each of the plurality of synthetic images;” recited in independent claims 1, 20, and 21 is the abstract idea of a Mental process. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitations “based on the values of the associated attributes and the latent representations, training a regression model to predict the values of the attributes from the latent representations.” recited in claims 1, 20, and 21 are an additional elements that amounts to train a generic regression model to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitations “based on the values of the associated attributes and the latent representations, training a regression model to predict the values of the attributes from the latent representations.” recited in claims 1, 20, and 21 are an additional elements that amounts to train a generic regression model to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
As to claims 17 and 23:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
For claim 17, Yes, the claim is a process.
For claim 23, Yes, the claim is a process.
Step 2A Prong One Analysis: Do the claims recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “reading a plurality of synthetic images and associated latent representations; presenting each of the plurality of synthetic images to a user via a client computing platform; reading a plurality of inputs to the client computing platform, the plurality of inputs characterizing a plurality of values for associated attributes of each of the plurality of synthetic images” recited in independent claims 1, 20, and 21 is the abstract idea of a Mental process. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Do the claims recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitations “determining a plurality of summary latent representations, each corresponding to a unique value of the plurality of associated attributes..” recited in claims 17 and 23 are an additional elements that amounts to presenting summary of data. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B Analysis: Do the claims recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitations “determining a plurality of summary latent representations, each corresponding to a unique value of the plurality of associated attributes..” recited in claims 17 and 23 are an additional elements that amounts to presenting summary of data. See MPEP §§ 2106.04(d), 2106.05(f)(2).
As to Independent claims 1-16:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
For claims 1-16, Yes, the claims are processes.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitations included in claims 1-16 merely cite elements of a generative model and displayable content” limitation identified as an abstract idea in the parent claims.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-24 are rejected under 35 U.S.C. 103 as being unpatentable over Todorov et al. (US 2021/0089759 A1).
Claim 1. Todorov et al. disclose A method of constructing a customized generative model (read as a method for photorealistic social face trait encoding, prediction, and manipulation [0016]), comprising:
reading a plurality of synthetic images (read as the reconstructions, samples, and/or transformations 431, which includes one or more images 432 that have been modified or transformed based on the image of a face 402. [0054]. FIG. 4, item 432) and associated latent representations (read as faces (e.g., latent representational features in the GAN's space), or traits (e.g., latent representational features from a semantic model 470) [0060]);
presenting each of the plurality of synthetic images to one or more users via a client computing platform (read as the images 432 that are output from the decoder 430 can then be passed to human participants or users, who can annotate or rate those images for a given subjective trait. Those annotated or rated images can be provided to a trait model 460 [0054]);
reading a plurality of inputs to the client computing platform, the plurality of inputs characterizing a plurality of values for associated attributes of each of the plurality of synthetic images (read as the images 432 that are output from the decoder 430 can then be passed to human participants or users, who can annotate or rate those images for a given subjective trait. Those annotated or rated images can be provided to a trait model 460 [0054]); and
based on the values of the associated attributes and the latent representations, training a regression model (read as The trait model 460 may be a linear or nonlinear, and preferably linear, function or model that maps image features to average ratings for each image in a given dataset, which also yields a single vector for each trait, the visual trait representation 465 (i.e., a learned function) [0055]) to predict the values of the attributes from the latent representations (read as the predictive power of the system can be enhanced by “side information”—available information that is uniquely associated with participants (e.g., demographic data of the participants, such as their age or gender identity), faces (e.g., latent representational features in the GAN's space), or traits (e.g., latent representational features from a semantic model 470) [0060]).
The rejection is based the combined teaching of ideas and embodiments, from FIG. 1-4.
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to use the teaching of Todorov et al. in order to realize all limitations of the claimed invention. Namely, the idea of training a model to predict the picture traits. The motivation is related to generating, or modifying, an image in a realistic manner such that a specific degree of a given social trait is encoded in the created image (Todorov et al. [0002]).
Claim 2. The method of claim 1, Todorov et al. disclose,
further comprising:
receiving target values for the plurality of attributes;
using the regression model, determining a target latent representation corresponding to the target values; and
providing the target latent representation to a generative model and receiving therefrom an image embodying the target values for the plurality of attributes (read as A generative adversarial network (GAN) has two components: (15) a generator network, which learns to map from random vectors—lists of random numbers that are typically constrained to be close to zero on average—to images (i.e., faces) that “look” as realistic as possible, and (2) a discriminator network which learns to discriminate between real images and those synthesized by the generator. [0047]).
Claim 3. The method of claim 1, Todorov et al. disclose,
wherein each of the plurality of synthetic images was generated by a generative model based on its associated latent representation representations (read as the predictive power of the system can be enhanced by “side information”—available information that is uniquely associated with participants (e.g., demographic data of the participants, such as their age or gender identity), faces (e.g., latent representational features in the GAN's space), or traits (e.g., latent representational features from a semantic model 470) [0060]).
Claim 4. The method of claim 1, Todorov et al. disclose,
further comprising:
generating the plurality of synthetic images by a generative model (read as which outputs the reconstructions, samples, and/or transformations 431, which includes one or more images 432 that have been modified or transformed based on the image of a face 402. [0054]. FIG. 4 item 432).
Claim 5. The method of claim 3, Todorov et al. disclose,
wherein generating the plurality of synthetic images comprises selecting randomly from a latent space of the generative model (read as the image set should use randomly generated images from a GAN generator instead of real images [0038]).
Claim 6. The method of claim 3, Todorov et al. disclose,
further comprising:
pretraining the generative model on a single object type (read as a generative adversarial network A generative adversarial network (GAN) [0047]).
Claim 7. The method of claim 3, Todorov et al. disclose,
further comprising:
training the generative model using a training dataset comprising neutral-appearing images (read as least about 1,000; at least about 2,500; at least about 5,000; at least about 10,000; at least about 50,000; or at least about 100,000 images. In some embodiments, the plurality of images is less than 200,000; less than 100,000; less than 50,000; less than 10,000; less than 5,000; or less than 2,500 images. In some embodiments, the number of subjects that each image is rated by is at least 25; at least 50; at least 100; or at least 200 subjects. In some embodiments, the number of subjects that each image is rated by is less than 500; less than 250; less than 100; or less than 50 subjects. [0035].).
Claim 8. The method of claim 1, Todorov et al. disclose,
further comprising:
generating the associated latent representation of each synthetic image by providing the synthetic image to an encoder (FIG. 4).
Claim 9. The method of claim 7, Todorov et al. disclose,
wherein the encoder comprises an artificial neural network (read as a neural network trained to generate realistic synthetic [0046]).
Claim 10. The method of claim 1, Todorov et al. disclose,
wherein the plurality of synthetic images is presented to exactly one user, thereby customizing the regression model to the exactly one user (read as One can use any known generator of this type, including, e.g., the generator from a state-of-the-art GAN developed by NVIDIA called StyleGAN, and then discard the discriminator [0047]).
Claim 11. The method of claim 1, Todorov et al. disclose,
wherein the plurality of synthetic images is presented to a plurality of users, thereby customizing to a group comprising the plurality of users (read as then be passed to human participants or users, who can annotate or rate those images for a given subjective trait [0054]).
Claim 12. The method of claim 1, Todorov et al. disclose,
wherein each value is selected from a positive, neutral, and negative value (read as then be passed to human participants or users, who can annotate or rate those images for a given subjective trait [0054]. The annotation can be done in any desired way like numbers or words.).
Claim 13. The method of claim 1, Todorov et al. disclose,
wherein the value is a scalar intensity value (read as then be passed to human participants or users, who can annotate or rate those images for a given subjective trait [0054]. The annotation can be done in any desired way like numbers or words.).
Claim 14. The method of claim 1, Todorov et al. disclose,
wherein the regression model comprises a linear regression (read as learned function is learned via a mapping (such as a linear mapping)… These functions are typically learned using a “least squares” procedure [0033]).
Claim 15. The method of claim 1, Todorov et al. disclose,
wherein the generative model is a generative adversarial network (GAN) (read as a generative adversarial network [0047]).
Claim 16. The method of claim 1, Todorov et al. disclose,
wherein the latent representation is a tensor (read as represented of as a three-dimensional tensor [0060]).
Claim 17. Todorov et al. disclose A method of constructing a customized generative model (FIG. 1-4), comprising:
reading a plurality of synthetic images (read as the reconstructions, samples, and/or transformations 431, which includes one or more images 432 that have been modified or transformed based on the image of a face 402. [0054]. FIG. 4, item 432) and associated latent representations (read as faces (e.g., latent representational features in the GAN's space), or traits (e.g., latent representational features from a semantic model 470) [0060]);
presenting each of the plurality of synthetic images to a user via a client computing platform (read as the images 432 that are output from the decoder 430 can then be passed to human participants or users, who can annotate or rate those images for a given subjective trait. Those annotated or rated images can be provided to a trait model 460 [0054]);
reading a plurality of inputs to the client computing platform, the plurality of inputs characterizing a plurality of values for associated attributes of each of the plurality of synthetic images (read as the images 432 that are output from the decoder 430 can then be passed to human participants or users, who can annotate or rate those images for a given subjective trait. Those annotated or rated images can be provided to a trait model 460 [0054]); and
determining a plurality of summary latent representations (read as faces (e.g., latent representational features in the GAN's space) [0060]), each corresponding to a unique value of the plurality of associated attributes (read as to map any image of any face to a multi-dimensional vector of learned image feature [0026]).
The rejection is based the combined teaching of ideas and embodiments, from FIG. 1-4.
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to use the teaching of Todorov et al. in order to realize all limitations of the claimed invention. Namely, the idea of training a model to predict the picture traits. The motivation is related to generating, or modifying, an image in a realistic manner such that a specific degree of a given social trait is encoded in the created image (Todorov et al. [0002]).
Claim 18. The method of claim 16, Todorov et al. disclose,
further comprising:
receiving target values for the plurality of attributes (read as the system may be configured to establish the learned function defining the relationship between the subjective social trait and the one or more learned image features the adjustments to the multi-dimensional vector are based upon. The learned function will be established based on a dataset of a plurality of images [0009]. The learned function must include the target values for the plurality of attributes);
selecting summary latent representations from the plurality of summary latent representations corresponding to the received target values (read as To provide confidence estimates of our predictions and transformations of the psychological traits of individual faces, one can fit Bayesian variants of the linear models above [0042]);
providing the selected summary latent representations to a generative model and receiving therefrom an image embodying the target values for the plurality of attributes (read as one can fit Bayesian variants of the linear models above. In this case, regularization strategies are interpreted as priors that constrain the final posterior weight distributions (which in turn yield distributions over predictions, which may be high or low variance). Single, image-wise predictions with low variance are quantitatively justified in this way [0042]).
Claim 19. The method of claim 16, Todorov et al. disclose,
wherein determining each summary latent representation comprises averaging the latent representations of each synthetic image having the unique value (read as yi is the average trait judgment for image i [0039]).
Claim 20. Todorov et al. disclose A computer program product for constructing a customized generative model, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor (read as the system or method 400 generally utilize one or more processors that are configured with instructions (that may be stored on non-transitory computer readable media) that, when executed, cause the processors to perform certain actions. [0053]) to:
read a plurality of synthetic images (read as the reconstructions, samples, and/or transformations 431, which includes one or more images 432 that have been modified or transformed based on the image of a face 402. [0054]. FIG. 4, item 432) and associated latent representations (read as faces (e.g., latent representational features in the GAN's space), or traits (e.g., latent representational features from a semantic model 470) [0060]);
present each of the plurality of synthetic images to one or more users via a client computing platform (read as the images 432 that are output from the decoder 430 can then be passed to human participants or users, who can annotate or rate those images for a given subjective trait. Those annotated or rated images can be provided to a trait model 460 [0054]);
reading a plurality of inputs to the client computing platform, the plurality of inputs characterizing a plurality of values for a plurality of associated attributes of each of the plurality of synthetic images (read as the images 432 that are output from the decoder 430 can then be passed to human participants or users, who can annotate or rate those images for a given subjective trait. Those annotated or rated images can be provided to a trait model 460 [0054]); and
based on the values of the associated attributes and the latent representations, train a regression model (read as The trait model 460 may be a linear or nonlinear, and preferably linear, function or model that maps image features to average ratings for each image in a given dataset, which also yields a single vector for each trait, the visual trait representation 465 (i.e., a learned function) [0055]) to predict the values of the attributes from the latent representations (read as the predictive power of the system can be enhanced by “side information”—available information that is uniquely associated with participants (e.g., demographic data of the participants, such as their age or gender identity), faces (e.g., latent representational features in the GAN's space), or traits (e.g., latent representational features from a semantic model 470) [0060]).
The rejection is based the combined teaching of ideas and embodiments, from FIG. 1-4.
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to use the teaching of Todorov et al. in order to realize all limitations of the claimed invention. Namely, the idea of training a model to predict the picture traits. The motivation is related to generating, or modifying, an image in a realistic manner such that a specific degree of a given social trait is encoded in the created image (Todorov et al. [0002]).
Claim 21. Todorov et al. disclose A method of generating a synthetic image, the method (FIG. 1-4) comprising:
reading an input image (FIG. 4, items 401-402);
encoding the input image into a latent representation in a latent space (FIG. 4, item 410);
reading target values for one or more image attributes (FIG. 4 item 440);
modifying the latent representation to conform with the target values using a regression model (read as Based on input that is received from a user, the system will determine which visual trait representation 465 will be used to modify the image [0055]), the regression model relating locations in the latent space to values of the one or more image attributes (read as learned function is learned via a mapping (such as a linear mapping) which typically consists of a set of coefficients, one for each input image feature, where higher coefficients reflect the importance of the feature in predicting the trait [0033]), thereby generating a modified latent representation in the latent space conforming with the target values (read as modifying the multi-dimensional vector to adjust an objective appearance-based dimension, such as color of skin, appearance of freckles, etc. [0032]);
providing the modified latent representation to an image generator (read as Once learned, images can be transformed along each learned latent factor w in the encoding space by adding or subtracting a scalar multiple of w to the image encoding [0040]); and
reading a synthetic image generated by the image generator, the synthetic image embodying the target values (read as outputs the reconstructions, samples, and/or transformations 431, which includes one or more images 432 that have been modified or transformed based on the image of a face 402. [0054]).
The rejection is based the combined teaching of ideas and embodiments, from FIG. 1-4.
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to use the teaching of Todorov et al. in order to realize all limitations of the claimed invention. Namely, the idea of training a model to predict the picture traits. The motivation is related to generating, or modifying, an image in a realistic manner such that a specific degree of a given social trait is encoded in the created image (Todorov et al. [0002]).
Claim 22. The method of claim 20, Todorov et al. disclose,
wherein the regression model was constructed by:
reading a plurality of synthetic images (read as the reconstructions, samples, and/or transformations 431, which includes one or more images 432 that have been modified or transformed based on the image of a face 402. [0054]. FIG. 4, item 432) and associated latent representations (read as faces (e.g., latent representational features in the GAN's space), or traits (e.g., latent representational features from a semantic model 470) [0060]);
presenting each of the plurality of synthetic images to one or more users via a client computing platform (read as the images 432 that are output from the decoder 430 can then be passed to human participants or users, who can annotate or rate those images for a given subjective trait. Those annotated or rated images can be provided to a trait model 460 [0054]);
reading a plurality of inputs to the client computing platform, the plurality of inputs characterizing a plurality of values for a plurality of associated attributes of each of the plurality of synthetic images (read as the images 432 that are output from the decoder 430 can then be passed to human participants or users, who can annotate or rate those images for a given subjective trait. Those annotated or rated images can be provided to a trait model 460 [0054]); and
based on the values of the associated attributes and the latent representations, training a regression model (read as The trait model 460 may be a linear or nonlinear, and preferably linear, function or model that maps image features to average ratings for each image in a given dataset, which also yields a single vector for each trait, the visual trait representation 465 (i.e., a learned function) [0055]) to predict the values of the attributes from the latent representations (read as the predictive power of the system can be enhanced by “side information”—available information that is uniquely associated with participants (e.g., demographic data of the participants, such as their age or gender identity), faces (e.g., latent representational features in the GAN's space), or traits (e.g., latent representational features from a semantic model 470) [0060]).
Claim 23. Todorov et al. disclose A method of generating a synthetic image, the method (FIG. 1-4) comprising:
reading an input image (FIG. 4 items 401-402);
encoding the input image into a latent representation in a latent space (FIG. 4, item 410);
reading target values for one or more image attributes (FIG. 4 item 440);
modifying the latent representation to conform with the target values by adjusting the latent representation according to one or more summary latent representations corresponding to the target values (read as Based on input that is received from a user, the system will determine which visual trait representation 465 will be used to modify the image [0055]), thereby generating a modified latent representation in the latent space conforming with the target values (read as modifying the multi-dimensional vector to adjust an objective appearance-based dimension, such as color of skin, appearance of freckles, etc. [0032]);
providing the modified latent representation to an image generator (read as Once learned, images can be transformed along each learned latent factor w in the encoding space by adding or subtracting a scalar multiple of w to the image encoding [0040])r; and
reading a synthetic image generated by the image generator, the synthetic image embodying the target values (read as outputs the reconstructions, samples, and/or transformations 431, which includes one or more images 432 that have been modified or transformed based on the image of a face 402. [0054]).
The rejection is based the combined teaching of ideas and embodiments, from FIG. 1-4.
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to use the teaching of Todorov et al. in order to realize all limitations of the claimed invention. Namely, the idea of training a model to predict the picture traits. The motivation is related to generating, or modifying, an image in a realistic manner such that a specific degree of a given social trait is encoded in the created image (Todorov et al. [0002]).
Claim 24. The method of claim 23, Todorov et al. disclose,
wherein the summary latent representations were constructed by:
reading the target values for the one or more image attributes (read as the system may be configured to establish the learned function defining the relationship between the subjective social trait and the one or more learned image features the adjustments to the multi-dimensional vector are based upon. The learned function will be established based on a dataset of a plurality of images [0009]. The learned function must include the target values for the plurality of attributes);
selecting summary latent representations from the plurality of summary latent representations corresponding to the received target values (read as To provide confidence estimates of our predictions and transformations of the psychological traits of individual faces, one can fit Bayesian variants of the linear models above [0042]);
providing the selected summary latent representations to a generative model (read as a linear or nonlinear, and preferably linear, function or model that maps image features to average ratings for each image in a given dataset, which also yields a single vector for each trait, the visual trait representation [0055]); and
receiving therefrom an image embodying the target values for the plurality of attributes (read as Since humans exhibit some disagreement in their judgments (e.g., level of perceived trustworthiness on a scale from 0 to 10, or preferably 1 to 100), many individual judgments must be obtained for each image, and the average is taken as the final value [0038]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED RACHEDINE whose telephone number is (571)272-9249. The examiner can normally be reached Mon-Fri 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeanette J. Parker can be reached at (571)270-3647. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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MOHAMMED . RACHEDINE
Examiner
Art Unit 2649
/MOHAMMED RACHEDINE/Primary Examiner, Art Unit 2646
Prosecution Insights