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 .
Notice to Applicant
This office action is in response to application filed on 11/20/2024.
Limitations appearing inside of {} are intended to indicate the limitations not taught by said prior art(s)/combinations.
Claims 1-20 are pending in the application.
Information Disclosure Statement
Information Disclosure Statement(s) filed on 06/30/2026 have been considered. NPL not filed/entered listed in IDS were not considered.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1 and 2, are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. US 12175740 B2 to parent application 17/614,929. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim of reference anticipate and/or renders obvious independent claims of the instant application.
Regarding claim 1, the instant application ‘894 discloses:
A computer-implemented method comprising (claim 1, line 1):
receiving, by one or more computing devices, data representing one or more machine learning (ML) models configured, at least in part, to encode images comprising objects of a particular type (claim 1, lines 2-5);
receiving, by the one or more computing devices, data representing an image comprising one or more objects of the particular type (claim 1, lines 5-8) ;
generating, by the one or more computing devices (claim 1, line 9), data representing
an encoded version of the image that alters at least a portion of the image comprising the one or more objects such that when the encoded version of the image is decoded (claim 1, lines 12-15),
the one or more objects are unrecognizable as being of the particular type by one or more object-recognition ML models (claim 1, lines 15-17) ; and
communicating, by the one or more computing devices and to a remotely located computing system, the data representing the encoded version of the image,
wherein the remotely located computing system is configured to (claim 1, lines 20-24) :
receive the data representing the encoded version of the image (claim 1, lines 25-26) ; and
generate, based at least in part on the data representing the encoded version of the image (claim 1, lines 27-28), data representing a decoded version of the image in which the one or more objects are unrecognizable as being of the particular type by the one or more object-recognition ML models (claim 1, lines 31-34) .
Claim 2 (claim 2).
Claims 2-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. US 12175740 B2 in view of Edwards et al., US Patent Application Publication No. US 20190188830 A1, hereinafter Edwards. Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations of the instant application are obvious over the limitations of the instant application ‘894 in view of Edwards as mapped below.
Claims directed to different statutory categories are rejected under Obviousness type Double Patenting since they are otherwise congruent in scope.
Claims 8 and 15 are similarly rejected as analogous claim 1.
Claim 9 is similarly rejected as analogous claim 2.
Claim 3 of the instant application ‘894 discloses
The computer-implemented method of claim 1, further comprising:
{receiving, by the one or computing devices, the data representing the decoded version of the image; }
determining, by the one or more computing devices, a difference between data representing the one or more objects of the particular type identified in the image and the data representing the decoded version of the image (claim 9, lines 19-22); and
modifying, by the one or more computing devices, at least one of the one or more ML models based on the difference (claim 9, lines 23-25) .
The instant application ‘894 does not explicitly disclose receiving, by the one or computing devices, the data representing the decoded version of the image.
However, Edwards, a similar field of endeavor of image obfuscation, teaches receiving, by the one or computing devices, the data representing the decoded version of the image (Edwards teaches that the computing device receives the decoded image to perform an operation at step 340: ¶[0070] The image recognition service operates on (i.e., receives) the obfuscated image data (i.e. decoded data)to generate entity/action identification and classification results by performing image recognition to identify and classify any entities and or indications of motions or actions for which the image recognition service is employed (step 340). An error in the discrimination result is then determined (step 350).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include receiving the decoded data as taught by Edwards to the instant application ‘894. The motivation to do so would be to perform an action based on the decoded image data such as calculating loss.
Regarding claim 4, the combination of the instant application ‘894 and Edwards discloses the computer-implemented method of claim 3. The instant application ‘894 further discloses wherein modifying the at least one of the one more ML models based on the difference comprises evaluating a loss function for the at least one of the one or more ML models based on the difference (claim 9, lines 23-28: the difference is interpreted as the loss: “determining a difference between data representing the one or more objects identified in the image and the data representing the one or more objects identified in the decoded version of the image; and modifying the one or more ML models configured to encode the images such that the modifying reduces the difference”).
Regarding claim 5, the instant application ‘894 discloses the computer-implemented method of claim 1. The instant application does not explicitly disclose wherein generating the data representing the decoded version of the image comprises:
reconstructing, by the remotely-located computing system, one or more portions of the image comprising the objects of the particular type such that the objects are at least partially visually rendered but are unrecognizable by the one or more object-recognition ML models
However, Edwards discloses wherein generating the data representing the decoded version of the image comprises:
reconstructing, by the remotely-located computing system, one or more portions of the image comprising the objects of the particular type such that the objects are at least partially visually rendered but are unrecognizable by the one or more object-recognition ML models (Edwards, ¶[0075];, the illustrative embodiments provide mechanisms for determining a privacy protection layer or level, i.e. an amount of obfuscation, of input images that permits protecting the identity of entities present in images while still providing sufficient image content to allow image recognition services and corresponding cognitive operations to be performed. ¶[0076]; if it is determined that a particular obfuscation algorithm or technology does not provide sufficient obfuscation while allowing sufficient image content for image recognition service operations, then the obfuscation engine may select a different obfuscation algorithm or technology.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include reconstruction of the image while allowing sufficient image content as taught by Edwards to the instant application ‘894. The motivation to do so would be to attempt to achieve a desired privacy protection layer or level while allowing operation of the image recognition services with sufficient accuracy.
Regarding claim 6, the combination of the instant application ‘894 and Edwards discloses the computer-implemented method of claim 5. Edwards further discloses wherein the reconstructing is performed using at least one of an autoencoder network and a generative adversarial network (Edwards teaches a generative adversarial network, ¶[0023]; With the adversarial neural network based learning framework, a generator is provided that is a pre-processor that performs some degree of image blurring or obfuscation and provides the modified image to one or more discriminators. The discriminator of the adversarial neural network may be implemented either as an inverse processor (decoder) or an image recognition processor (discriminator), depending on the particular desired embodiment.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a GAN as taught by Edwards to the instant application ‘894. The motivation to do so would be because the GAN framework comprises a generator and a discriminator, where the generator acts as an adversary and tries to fool the discriminator by producing synthetic images based on a noise input, and the discriminator tries to differentiate synthetic images from true images. The adversarial network seeks to achieve the highest loss (lowest accuracy) while achieving high accuracy output of the image recognition service.
Regarding claim 7, The instant application discloses the computer-implemented method of claim 1. The instant application does not explicitly disclose further comprising: identifying, by the one or more computing devices, the at least a portion of the image comprising the one or more objects based at least in part on a user preference, a device setting, an application setting, a device location, a jurisdictional regulation, or a privacy policy
However, Edwards discloses further comprising:
identifying, by the one or more computing devices, the at least a portion of the image comprising the one or more objects based at least in part on a user preference, a device setting, an application setting, a device location, a jurisdictional regulation, or a privacy policy (Edwards, [0021] Mechanisms may be employed to obfuscate images by applying noise based on ad hoc rules or k-anonymity approaches that preserve the privacy of the entities in the images. ¶[0049]; the image capture device 120 may monitor a monitored environment, such as a business location, governmental location, home location, or the like, images of which are captured by the image capture device 120.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include identifying the level to obfuscate using an application setting or device location as taught by Edwards to the instant application ‘894. The motivation to do so would be to preserve the privacy as needed by the application.
Claim 9 is similarly analyzed as analogous claim 2.
Claim 10 and claim 16 are similarly analyzed as analogous claim 3.
Claim 11 and claim 17 are similarly analyzed as analogous claim 4.
Claim 12 and claim 18 are similarly analyzed as analogous claim 5.
Claim 13 and claim 19 are similarly analyzed as analogous claim 6.
Claim 14 and claim 20 are similarly analyzed as analogous claim 7.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Edwards et al., US Patent Application Publication No. US 20190188830 A1, hereinafter Edwards.
1. Edwards teaches a computer-implemented method comprising:
receiving, by one or more computing devices (Edwards¶[0078]; server computing device, such as a server 104), data representing one or more machine learning (ML) models configured, at least in part, to encode images comprising objects of a particular type (Edwards, ¶[0062]; Based on the current settings of the weights of the nodes in the obfuscation engine 122 (e.g., an obfuscation engine configured as a convolutional neural network or the like) or the operational parameters of the obfuscation engine 122, a degree of obfuscation or blurring of the original input image X 200 is determined and applied to generate an obfuscated image output 220.);
receiving, by the one or more computing devices (Edwards¶[0078]; server computing device, such as a server 104), data representing an image comprising one or more objects of the particular type (Edwards, ¶[0062]; an input image X 200 is received);
generating, by the one or more computing devices, data representing an encoded version of the image that alters at least a portion of the image comprising the one or more objects such that when the encoded version of the image is decoded, the one or more objects are unrecognizable as being of the particular type by one or more object-recognition ML models (Edwards, ¶[0051]; the pre-processing is performed by an obfuscation engine 122 to generate a modified or obfuscated version of the original captured image data which is then transmitted, via the network 102, by the computing device 110 to the cognitive system 100 at the remote server 104A.); and
communicating, by the one or more computing devices and to a remotely located computing system, the data representing the encoded version of the image (Edwards, [0051]; a modified or obfuscated version of the original captured image data which is then transmitted, via the network 102, by the computing device 110 to the cognitive system 100 at the remote server 104A),
wherein the remotely located computing system is configured to:
receive the data representing the encoded version of the image; and
generate, based at least in part on the data representing the encoded version of the image, data representing a decoded version of the image in which the one or more objects are unrecognizable as being of the particular type by the one or more object-recognition ML models (Edwards,¶[0052] The cognitive system 100 provides an image recognition service 140 that operates on the obfuscated image data to perform a cognitive operation, e.g., entity/action identification and classification, alert generation, notification transmission, logging, or the like. It should be appreciated that the entity/action identification, or detection, performed by the cognitive system 100 is not personally identifying the entities in the obfuscated image data).
2. Edwards teaches the computer-implemented method of claim 1. Edwards further teaches wherein:
generating the data representing the encoded version of the image comprises generating data representing the encoded version of the image such that one or more objects of a different type from the particular type are recognizable in the decoded version of the image as being of the different type by at least one of the one or more object-recognition ML models; and
the remotely located computing system is configured to utilize the at least one of the one or more object-recognition ML models to identify the one or more objects of the different type in the decoded version of the image as being of the different type.
(Edwards, [0052] The cognitive system 100 provides an image recognition service 140 that operates on the obfuscated image data to perform a cognitive operation, e.g., entity/action identification and classification, alert generation, notification transmission, logging, or the like. It should be appreciated that the entity/action identification, or detection, performed by the cognitive system 100 is not personally identifying the entities in the obfuscated image data, but rather identifying the entities or actions in terms of a more general type or classification such that corresponding cognitive logic may be applied to determine how to respond to the identification and classification of such entities or actions. For example, in a security surveillance based cognitive system, the cognitive system 100 is enlisted by the computing device 110 to recognize portions of modified image data that represent particular types of entities or actions, e.g., a person (entity) stealing (action) a good/currency, a person (entity) concealing a weapon (action), a vehicle (entity) crossing a double white line on the roadway (action), or the like, but without personally identifying the persons involved, places involved, or the like, e.g., the image recognition service 140 determines that a person is present for purposes of performing a cognitive operation, but does not identify the identity of that person.)
3. Edwards teaches the computer-implemented method of claim 1. Edwards further teaches further comprising:
receiving, by the one or computing devices, the data representing the decoded version of the image (Edwards teaches that the computing device receives the decoded image to perform an operation at step 340: ¶[0070] The image recognition service operates on (i.e., receives) the obfuscated image data (i.e. decoded data)to generate entity/action identification and classification results by performing image recognition to identify and classify any entities and or indications of motions or actions for which the image recognition service is employed (step 340). An error in the discrimination result is then determined (step 350).);
determining, by the one or more computing devices, a difference between data representing the one or more objects of the particular type identified in the image and the data representing the decoded version of the image (Edwards, ¶[0070]; if the discriminator is implemented as an inverse processor or decoder, such as inverse processor (decoder) 124A in FIG. 2, then the discrimination error may be determined based on a comparison of a recreated image generated by the inverse processor from the obfuscated image generated by the obfuscation engine, to the original input image); and
modifying, by the one or more computing devices, at least one of the one or more ML models based on the difference (Edwards, ¶[0072]; Based on the discrimination error and the results of the comparison, the operational parameters or weights of nodes in the obfuscation engine are modified, and thus, the obfuscation engine (generator) is trained (step 370).).
4. Edwards teaches the computer-implemented method of claim 3. Edwards further teaches wherein modifying the at least one of the one more ML models based on the difference comprises evaluating a loss function for the at least one of the one or more ML models based on the difference (Edwards, ¶[0060]; The image recognition neural network may be trained using one loss function, e.g., Loss 1 in this example, which may be a cross-entropy loss. From the second loss function or measure Loss 2, if the optimization goal is maximizing the measure, the domain specific parameter k would be negative; otherwise, k should be positive. This k value is a domain specific parameter whose value is set to control the trade-off between the performance (accuracy of the image recognition service 140) and the privacy (obfuscation performed by the obfuscation engine 122). Optimization methods, such as stochastic gradient descent, Adam, Adagrad, etc. may be applied on the combined loss function Loss=Loss 1+k*Loss 2.).
5. Edwards teaches the computer-implemented method of claim 1. Edwards further teaches wherein generating the data representing the decoded version of the image comprises:
reconstructing, by the remotely-located computing system, one or more portions of the image comprising the objects of the particular type such that the objects are at least partially visually rendered but are unrecognizable by the one or more object-recognition ML models (Edwards, ¶[0075]; the illustrative embodiments provide mechanisms for determining a privacy protection layer or level, i.e. an amount of obfuscation, of input images that permits protecting the identity of entities present in images while still providing sufficient image content to allow image recognition services and corresponding cognitive operations to be performed. ¶[0076]; if it is determined that a particular obfuscation algorithm or technology does not provide sufficient obfuscation while allowing sufficient image content for image recognition service operations, then the obfuscation engine may select a different obfuscation algorithm or technology).
6. Edwards teaches the computer-implemented method of claim 5. Edwards further teaches wherein the reconstructing is performed using at least one of an autoencoder network and a generative adversarial network (Edwards, ¶[0023]; With the adversarial neural network based learning framework, a generator is provided that is a pre-processor that performs some degree of image blurring or obfuscation and provides the modified image to one or more discriminators. The discriminator of the adversarial neural network may be implemented either as an inverse processor (decoder) or an image recognition processor (discriminator), depending on the particular desired embodiment.).
7. Edwards further teaches the computer-implemented method of claim 1. Edwards further teaches further comprising:
identifying, by the one or more computing devices, the at least a portion of the image comprising the one or more objects based at least in part on a user preference, a device setting, an application setting, a device location, a jurisdictional regulation, or a privacy policy (Edwards, [0021] Mechanisms may be employed to obfuscate images by applying noise based on ad hoc rules or k-anonymity approaches that preserve the privacy of the entities in the images. ¶[0049]; the image capture device 120 may monitor a monitored environment, such as a business location, governmental location, home location, or the like, images of which are captured by the image capture device 120.)
Claim 8 and claim 15 are similarly analyzed as analogous claim 1.
Claim 9 is similarly analyzed as analogous claim 2.
Claim 10 and claim 16 are similarly analyzed as analogous claim 3.
Claim 11 and claim 17 are similarly analyzed as analogous claim 4.
Claim 12 and claim 18 are similarly analyzed as analogous claim 5.
Claim 13 and claim 19 are similarly analyzed as analogous claim 6.
Claim 14 and claim 20 are similarly analyzed as analogous claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bloom (US 20180336463 A1) teaches systems and methods that implement domain-specific obfuscating of data when processing the data through machine learning (ML), which can secure and preserve privacy of information contained within the data. For instance, various embodiments provide domain-specific techniques for obscuring, and possibly compressing, data. Additionally, various embodiments provide for remote inference using domain-specific techniques for obscuring, and possibly compressing, data for transport.
Padilla-Lopez (2015) provides a survey of the existing privacy-aware intelligent monitoring systems including face de-identification.
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/CHANDHANA PEDAPATI/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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