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 .
DETAILED ACTION
2. This action is in response to the amendment filed March 19, 2026.
3. Claims 21, 28, and 35 have been amended.
4. Claims 21-40 have been examined and are pending with this action.
Response to Arguments
5. Applicant's arguments filed March 19, 2026 with respect to the rejection of claims 21-40, previously rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Bradshaw et al. (US 2021/0056405 A1), have been fully considered but they are not persuasive.
The applicant seems to be requesting a mapping of primarily every word that has been claimed, and further asserting that the anticipation rejection is erroneous. The examiner disagrees.
The citations in the rejections set forth below clearly and explicitly disclose, teach, and in the very least suggests each and every element of the recited claims. The applicant is reminded that each element does not mean each word. The elements are the associated functionalities recited in the claims in the context of the relevant art. Therefore, to argue that Bradshaw et al. (US 2021/0056405 A1), herein referenced Bradshaw, does not teach from the list of words/phrases, starting from a neural network (see Remarks, page 9) is also erroneous. The applicant is suggested to be specific if there is a specific functionality of a neural network that is not taught by Bradshaw, or any other word or phrase for that matter. Bradshaw explicitly teaches a neural network starting from the Abstract, (“artificial neural network (ANN)”) and continuing throughout.
In response to the argument that the instant application is directed to obfuscation of internal computations of neural network rather than obfuscation of data that is used for training of neural network (see Remarks, page 10), the examiner disagrees. It is noted during patent examination, the pending claims must be given their broadest reasonable interpretation consistent with the specification. See In re Hyatt, 211 F.3d 1367, 1372, 54 USPQ2d 1664, 1667 (Fed. Cir. 2000). Furthermore, while the claims of issued patents are interpreted in light of the specification, prosecution history, prior art and other claims, this is not the mode of claim interpretation to be applied during examination. During examination, the claims must be interpreted as broadly as their terms reasonably allow. See In re American Academy of Science Tech Center, F.3d 2004 WL 1067528 (Fed. Cir. May 13, 2004).
The claim states contrary to the above argument because the claim states, “wherein the neural network operations comprise obfuscation computations causing the one or more output values to be independent of the inconsequential input values”. Clearly the instant application claims an “obfuscation computations causing the one or more output values to be…”, which clearly suggests the instant application is also directed to obfuscation of data and not obfuscation of internal computations as argued.
If such is the case, the application is suggested to “particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention” as recited in 35 USC § 112.
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.
6. Claims 21-40 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Bradshaw et al. (US 2021/0056405 A1).
INDEPENDENT:
As per claim 21, Bradshaw teaches a method comprising:
processing input data to obtain output data using a neural network (NN) comprising an NN portion
(i) receiving, from at least one upstream portion of the NN, one or more input values comprising one or more inconsequential input values (see Bradshaw, Abstract: “In the system, inputs for the ANN can be obfuscated for centralized training of a master version of the ANN at a first computing device. A second computing device in the system includes memory that stores a local version of the ANN and user data for inputting into the local version. The second computing device includes a processor that extracts features from the user data and obfuscates the extracted features to generate obfuscated user data. The second device includes a transceiver that transmits the obfuscated user data. The first computing device includes a memory that stores the master version of the ANN, a transceiver that receives obfuscated user data transmitted from the second computing device, and a processor that trains the master version based on the received obfuscated user data using machine learning.”, and
(ii) applying neural operations to the input values to obtain one or more output values (see Bradshaw, [0003]: “Usually, an ANN is implemented by a signal at a connection (or edge) between artificial neurons being a real number, and the output of each artificial neuron being computed by a non-linear function of the sum of its inputs.”; [0009]: “Also, at least some aspects of the present disclosure are directed to a computer network that can be configured to implement obfuscating inputs for centralized training of a master version of an artificial neural network (ANN)”; [0015]: “there are many other ways of obfuscation that can be used, such as substitution, shuffling, numerical variance methods, scrambling, masking out characters, some other types of encryption not using hashing, and deletion of some values in particular fields.”; and [0038]: “The obfuscating of the extracted plurality of features can include combining different sets of inputs in the extracted plurality of features using one or more arithmetic operations to combine the different sets (e.g., see obfuscations 124a and 124b as well as step 312 illustrated in FIG. 3). The extraction of features (e.g., see extracted features 122a and 122b) can include selecting the different sets of inputs randomly for the combining of the different sets. Or, the extraction of features can include selecting the different sets of inputs deliberately for the combining of the different sets.”),
wherein the output values are used as an input into at least one downstream portion of the NN (see Bradshaw, [0011]: “he second computing device in the system can include memory that is configured to store a local version of the ANN and user data for inputting into the local version of the ANN. The second computing device can also include a processor that is configured to extract features from the user data and obfuscate the extracted features to generate obfuscated user data. The second device also can include a transceiver that is configured to transmit the obfuscated user data such as to the first computing device. The first computing device can include a memory that is configured to store the master version of the ANN, a transceiver that is configured to receive obfuscated user data transmitted from the second computing device or another device of the system hosting a local version of the ANN, and a processor that is configured to train the master version based on the received obfuscated user data using machine learning.”), and
wherein the neural operations comprise obfuscation computations causing the one or more output values to be independent of the inconsequential input values (see Bradshaw, [0009]: “Also, at least some aspects of the present disclosure are directed to a computer network that can be configured to implement obfuscating inputs for centralized training of a master version of an artificial neural network (ANN)”; [0011]: “inputs for centralized training of the master version of the ANN can be obfuscated. The obfuscation can occur at any one of the multiple computing devices that host different versions of the ANN such as devices hosting local versions of the ANN.”; [0015]: “there are many other ways of obfuscation that can be used, such as substitution, shuffling, numerical variance methods, scrambling, masking out characters, some other types of encryption not using hashing, and deletion of some values in particular fields.”; [0038]: “The obfuscating of the extracted plurality of features can include combining different sets of inputs in the extracted plurality of features using one or more arithmetic operations to combine the different sets (e.g., see obfuscations 124a and 124b as well as step 312 illustrated in FIG. 3). The extraction of features (e.g., see extracted features 122a and 122b) can include selecting the different sets of inputs randomly for the combining of the different sets. Or, the extraction of features can include selecting the different sets of inputs deliberately for the combining of the different sets.”; and [0058]: “when the input is Xf=f(X1, X2) (e.g., see extracted features 122a and 122b), the expected output may not necessarily be Yf=f(Y1, Y2). Thus, Yf is an estimate. From Xf, the computer hosting the master version of the ANN 110 (e.g., see first computing device 108) cannot guess or calculate X1, and/or X2 which is hosted by one of the other computing devices hosting a local version of the ANN (e.g., see computing devices 104a and 104b).”).
As per claim 28, Bradshaw teaches a method to obfuscate operations of a neural network (NN), the method comprising:
modifying at least a portion of the NN, wherein the modified NN portion is configured to:
receive, from at least one upstream portion of the NN, one or more input values comprising one or more inconsequential input values (see Claim 21 rejection above), and
apply neural operations to the input values to obtain one or more output values (see Claim 21 rejection above),
wherein the output values are used as an input into at least one downstream portion of the NN (see Claim 21 rejection above), and
wherein the neural operations comprise obfuscation computations causing the one or more output values to be independent of the inconsequential input values (see Claim 21 rejection above).
As per claim 35, Bradshaw teaches a system comprising:
a memory device (see Bradshaw, [0083]: “With respect to the method 300, method 400, or any other method, process, or operation described herein, in some embodiments, a non-transitory computer-readable storage medium stores instructions that, when executed by at least one processing device (such as processor 506 shown in FIG. 5), cause the at least one processing device to perform the method 300, method 400, or any other method, process, or operation described herein, and/or any combination thereof.”); and
a processing device communicatively coupled to the memory device (see Bradshaw, [0083]: “With respect to the method 300, method 400, or any other method, process, or operation described herein, in some embodiments, a non-transitory computer-readable storage medium stores instructions that, when executed by at least one processing device (such as processor 506 shown in FIG. 5), cause the at least one processing device to perform the method 300, method 400, or any other method, process, or operation described herein, and/or any combination thereof.”), the processing device to:
process input data to obtain output data using a neural network (NN) comprising an NN portion
(i) receiving, from at least one upstream portion of the NN, one or more input values comprising one or more inconsequential input values (see Claim 21 rejection above), and
(ii) applying neural operations to the input values to obtain one or more output values (see Claim 21 rejection above),
wherein the output values are used as an input into at least one downstream portion of the NN (see Claim 21 rejection above), and
wherein the neural operations comprise obfuscation computations causing the one or more output values to be independent of the inconsequential input values (see Claim 21 rejection above).
DEPENDENT:
As per claims 22, 29, and 37, which respectively depend on claims 21, 28, and 35, Bradshaw further teaches wherein the NN portion comprises: a neural node having an activation function that generates a null value independent of the one or more inconsequential input values into the neural node (see Bradshaw, [0044]: “For example, the obfuscation can include nulling out or deleting at least part of data in the extracted plurality of features randomly. Or, the nulling out or deleting at least part of data in the extracted plurality of features can be done deliberately.”; and [0047]: “Further, the second device can obfuscate the extracted plurality of features using nulling out or deleting at least part of the extracted plurality of features (e.g., either randomly or deliberately) or using a masking out or character scrambling method.”).
As per claims 23, 30, and 36, which respectively depend on claims 21, 28, and 35, Bradshaw further teaches wherein the NN portion comprises:
a first neural node receiving the one or more inconsequential input values and generating an intermediate value (see Bradshaw, Abstract: “The second computing device includes a processor that extracts features from the user data and obfuscates the extracted features to generate obfuscated user data. The second device includes a transceiver that transmits the obfuscated user data.”; [0011]: “In the system, inputs for centralized training of the master version of the ANN can be obfuscated. The obfuscation can occur at any one of the multiple computing devices that host different versions of the ANN such as devices hosting local versions of the ANN. For example, the second computing device in the system can include memory that is configured to store a local version of the ANN and user data for inputting into the local version of the ANN. The second computing device can also include a processor that is configured to extract features from the user data and obfuscate the extracted features to generate obfuscated user data.”; and [0029]: “The computer network 100 also includes the first computing device 108 (which can be part of a cloud or another type of distributed computing network). The first computing device 108 includes the master version of the ANN 110”); and
a second neural node performing, using the intermediate value, one or more computations that cause an output value generated by the second neural node to be independent of the intermediate value (see Bradshaw, Abstract: “The first computing device includes a memory that stores the master version of the ANN, a transceiver that receives obfuscated user data transmitted from the second computing device, and a processor that trains the master version based on the received obfuscated user data using machine learning.”; [0011]: “The first computing device can include a memory that is configured to store the master version of the ANN, a transceiver that is configured to receive obfuscated user data transmitted from the second computing device or another device of the system hosting a local version of the ANN, and a processor that is configured to train the master version based on the received obfuscated user data using machine learning.”; and [0027]: “Each computing device of the set of computing devices 102 can host and execute a local version of an ANN (e.g., see second computing device 104a and Nth computing device 104b having respective local versions of an ANN 106a and 106b).”).
As per claims 24 and 31, which respectively depend on claims 21 and 28, Bradshaw further teaches wherein the NN portion:
computes a plurality of intermediate values, each of the plurality of intermediate values computed by applying a respective non-linear function of a plurality of non-linear functions to the one or more inconsequential input values (see Bradshaw, [0003]: “Usually, an ANN is implemented by a signal at a connection (or edge) between artificial neurons being a real number, and the output of each artificial neuron being computed by a non-linear function of the sum of its inputs.”; and Claim 23 rejection above); and
aggregates the plurality of intermediate values to obtain the one or more output values that are independent of the inconsequential input values (see Bradshaw, [0003]: “An artificial neuron can also have a threshold in which a signal is only sent from the artificial neuron if the aggregate signal exceeds the threshold.”).
As per claims 25 and 32, which respectively depend on claims 21 and 28, Bradshaw further teaches wherein the one or more input values further comprises one or more consequential input values, and wherein the NN portion:
computes a plurality of intermediate values, each of the plurality of intermediate values computed by combining the one or more consequential input values and the one or more inconsequential input values (see Bradshaw, [0003]: “An artificial neuron can also have a threshold in which a signal is only sent from the artificial neuron if the aggregate signal exceeds the threshold.”; [0016]: “… the user device can obfuscate the inputs by combining the sets and requesting a computer hosting the master version of the ANN to train the master version…”; and [0038]: “The obfuscating of the extracted plurality of features can include combining different sets of inputs in the extracted plurality of features using one or more arithmetic operations to combine the different sets (e.g., see obfuscations 124a and 124b as well as step 312 illustrated in FIG. 3). The extraction of features (e.g., see extracted features 122a and 122b) can include selecting the different sets of inputs randomly for the combining of the different sets. Or, the extraction of features can include selecting the different sets of inputs deliberately for the combining of the different sets.”); and
aggregates the plurality of intermediate values to obtain the one or more output values that are independent of the inconsequential input values (see Bradshaw, [0003]: “An artificial neuron can also have a threshold in which a signal is only sent from the artificial neuron if the aggregate signal exceeds the threshold.”).
As per claims 26, 33, and 39, which respectively depend on claims 21, 28, and 35, Bradshaw further teaches wherein the one or more input values further comprises one or more consequential input values, and wherein the NN portion:
applies a masking transformation to the one or more input values to obtain a plurality of masked input values, each of the plurality of masked input values being determined by the one or more consequential input values and the one or more inconsequential input values (see Bradshaw, [0015]: “Thus, hashing cannot be used for feature obfuscation in the system. But, there are many other ways of obfuscation that can be used, such as substitution, shuffling, numerical variance methods, scrambling, masking out characters, some other types of encryption not using hashing, and deletion of some values in particular fields.”; and [0045]: “The obfuscating of the extracted plurality of features (e.g., see obfuscations 124a and 124b) can include using a masking out or character scrambling method (e.g., see step 324 illustrated in FIG. 3). The masking out or character scrambling method can include masking out or character scrambling part of data in the extracted plurality of features.”);
applies an unmasking transformation to the plurality of masked input values to obtain one or more intermediate values that are independent of the inconsequential input values (see Bradshaw, [0021]: “The unmasked user data however can be used to locally train a local version of the ANN on the device of the user. For example, the user data can be only used to train a local version of the ANN on a user's mobile device (e.g., the user's smart phone, tablet, etc.). When it is shared in the system for training of other versions of the ANN it is always obfuscated by the system.”); and
computes the one or more output values using the one or more intermediate values (see Bradshaw, [0003]: “Usually, an ANN is implemented by a signal at a connection (or edge) between artificial neurons being a real number, and the output of each artificial neuron being computed by a non-linear function of the sum of its inputs”).
As per claims 27, 34, and 40, which respectively depend on claims 21, 28, and 35, Bradshaw further teaches wherein the one or more inconsequential input values comprise a plurality of inconsequential input values, wherein each of the plurality of inconsequential input values is obtained by applying a respective one of a plurality of non-linear obfuscation functions, at least one non-linear obfuscation function of the plurality of non-linear obfuscation functions being linearly-dependent on one or more other non-linear obfuscation functions of the plurality of non-linear obfuscation functions (see Bradshaw, [0003]: “Usually, an ANN is implemented by a signal at a connection (or edge) between artificial neurons being a real number, and the output of each artificial neuron being computed by a non-linear function of the sum of its inputs”), and wherein the NN portion:
computes, using a combination of the inconsequential input values, one or more intermediate values that are independent of the inconsequential input values (see Bradshaw, [0003]: “An artificial neuron can also have a threshold in which a signal is only sent from the artificial neuron if the aggregate signal exceeds the threshold.”; [0016]: “… the user device can obfuscate the inputs by combining the sets and requesting a computer hosting the master version of the ANN to train the master version…”; and [0038]: “The obfuscating of the extracted plurality of features can include combining different sets of inputs in the extracted plurality of features using one or more arithmetic operations to combine the different sets (e.g., see obfuscations 124a and 124b as well as step 312 illustrated in FIG. 3). The extraction of features (e.g., see extracted features 122a and 122b) can include selecting the different sets of inputs randomly for the combining of the different sets. Or, the extraction of features can include selecting the different sets of inputs deliberately for the combining of the different sets.”); and
computes the one or more output values using the one or more intermediate values (see Bradshaw, [0003]: “Usually, an ANN is implemented by a signal at a connection (or edge) between artificial neurons being a real number, and the output of each artificial neuron being computed by a non-linear function of the sum of its inputs”).
As per claim 38, which depends on claims 35, Bradshaw further teaches wherein the NN portion: computes a plurality of intermediate values, each of the plurality of intermediate values computed by performing at least one of:
applying a respective non-linear function of a plurality of non-linear functions to the one or more inconsequential input values (see Bradshaw, [0003]: “Usually, an ANN is implemented by a signal at a connection (or edge) between artificial neurons being a real number, and the output of each artificial neuron being computed by a non-linear function of the sum of its inputs.”; and Claim 23 rejection above); or
combining one or more consequential input values and the one or more inconsequential input values (see Bradshaw, [0003]: “An artificial neuron can also have a threshold in which a signal is only sent from the artificial neuron if the aggregate signal exceeds the threshold.”; [0016]: “… the user device can obfuscate the inputs by combining the sets and requesting a computer hosting the master version of the ANN to train the master version…”; and [0038]: “The obfuscating of the extracted plurality of features can include combining different sets of inputs in the extracted plurality of features using one or more arithmetic operations to combine the different sets (e.g., see obfuscations 124a and 124b as well as step 312 illustrated in FIG. 3). The extraction of features (e.g., see extracted features 122a and 122b) can include selecting the different sets of inputs randomly for the combining of the different sets. Or, the extraction of features can include selecting the different sets of inputs deliberately for the combining of the different sets.”); and
aggregates the plurality of intermediate values to obtain the one or more output values that are independent of the inconsequential input values (see Bradshaw, [0003]: “An artificial neuron can also have a threshold in which a signal is only sent from the artificial neuron if the aggregate signal exceeds the threshold.”).
Conclusion
7. For the reasons above, claims 21-40 have been rejected and remain pending.
8. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL Y WON whose telephone number is (571)272-3993. The examiner can normally be reached on Wk.1: M-F: 8-5 PST & Wk.2: M-Th: 8-7 PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas R Taylor can be reached on 571-272-3889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael Won/Primary Examiner, Art Unit 2443