DETAILED ACTION
This application is in response to claims filed 03 July 2024 for application 18726466 filed 03 July 2024. Currently claims 1-10 are pending.
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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 2, 5, 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pittaluga et al. (Learning Privacy Preserving Encodings through Adversarial Training) in view of Abedin et al. (Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables)
Regarding claims 1, 9 and 10, Pittaluga discloses:
A machine learning device comprising:
a memory storing instructions (§4 models are computed using digital files necessitating a computer having memory and processor); and
a processor connected to the memory and configured to execute the instructions to:
acquire a training data set including first sensor data, second sensor data, and correct answer data (“For each identification task, we train and evaluate classifiers on a balanced dataset where half the images belong to that category—therefore, the “prior” for each task is chance. These sets are constructed from two groups of ten categories each, from Places-3651. The negative examples for each identification task are uniformly sampled from the other nine categories in the same group.” §4.1 ¶1);
encode the first sensor data into a first code using a first encoding model and (“We consider the following setting: when training the encoder, we have a training set labeled with values of the private attribute. Once the encoder has been trained, we seek to limit the ability of an adversary, with knowledge of this encoding function, to train an estimator for the private attribute. This means that after the encoder is fixed, we assume the adversary is able to train an estimator on an encoded labeled training set (by applying the encoding function on a regular training set), and we seek to restrict the performance of this estimator on encoded validation and test sets.” §3.1 ¶1);
input the first code and … to an estimation model and output an estimation result output from the estimation model (“We consider the following setting: when training the encoder, we have a training set labeled with values of the private attribute. Once the encoder has been trained, we seek to limit the ability of an adversary, with knowledge of this encoding function, to train an estimator for the private attribute. This means that after the encoder is fixed, we assume the adversary is able to train an estimator on an encoded labeled training set (by applying the encoding function on a regular training set), and we seek to restrict the performance of this estimator on encoded validation and test sets.” §3.1 ¶1);
an adversarial estimation means configured to input the first code to a first adversarial estimation model that outputs an estimated value of the second code in response to input of the first code and estimate the estimated value of the second code (“We consider the following setting: when training the encoder, we have a training set labeled with values of the private attribute. Once the encoder has been trained, we seek to limit the ability of an adversary, with knowledge of this encoding function, to train an estimator for the private attribute. This means that after the encoder is fixed, we assume the adversary is able to train an estimator on an encoded labeled training set (by applying the encoding function on a regular training set), and we seek to restrict the performance of this estimator on encoded validation and test sets.” §3.1 ¶1); and
train the first encoding model, the second encoding model, the estimation model, and the first adversarial estimation model by machine learning (“To address this issue, we consider a formulation to learn an encoding function, through adversarial training against a classifier that is simultaneously training to succeed at re covering the private attribute from encoded data.” §1 ¶4);
train the first encoding model, … and the estimation model in such a way that an estimation result of the estimation model matches the correct answer data (“We consider the following setting: when training the encoder, we have a training set labeled with values of the private attribute. Once the encoder has been trained, we seek to limit the ability of an adversary, with knowledge of this encoding function, to train an estimator for the private attribute. This means that after the encoder is fixed, we assume the adversary is able to train an estimator on an encoded labeled training set (by applying the encoding function on a regular training set), and we seek to restrict the performance of this estimator on encoded validation and test sets. Note that we do not seek to prevent the estimator from performing well on the training set itself, e.g., through memorization. Our goal is to limit generalization accuracy.” §3.1 ¶1);
train the first encoding model in such a way that the estimated value of the second code by the first adversarial estimation model does not match the second code output from the second encoding model §3.1 ¶1.
Pittaluga does not explicitly disclose: measured by a first measuring device, measured by a second measuring device, encode the second sensor data into a second code using a second encoding model, the second code, the second encoding model; and train the first adversarial estimation model in such a way that the estimated value of the second code by the first adversarial estimation model matches the second code output from the second encoding mode.
Abedin teaches: measured by a first measuring device, measured by a second measuring device, encode the second sensor data into a second code using a second encoding model, the second code, the second encoding model (Fig 1 x and z encoding);
train the first adversarial estimation model in such a way that the estimated value of the second code by the first adversarial estimation model matches the second code output from the second encoding model (§IV training of network modules).
Pittaluga and Abedin are in the same field of endeavor of adversarial models and are analogous. Pittaluga discloses a method of training an encoder and an adversarial model with a hidden private attribute. Abedin discloses adversarial learning on data from multiple wearable devices and encoding models. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the adversarial model structure as taught by Pittaluga to take the wearables data and second model as taught by Abedin to yield predictable results of activity recognition of human tasks.
Regarding claim 2, Pittaluga discloses:
The machine learning device according to claim 1, wherein
the processor is configured to execute the instructions to
train the first encoding model, the second encoding model, and the estimation model in such a way that an error between the estimation result of the estimation model and the correct answer data decreases (“We train the encoder to minimize I(E;u) by applying alternating gradient updates to the encoder and classifier(s). These gradients are computed on mini-batches of training data for the classification tasks, with different batches used to update the encoder and classifiers. The classifiers take gradient steps to minimize their losses with respect to the true labels of the encoded data. When optimizing with the desirable attribute classifier, the encoder’s update is also based on minimizing that classifier’s loss.” §3.2 ¶1),
train the first adversarial estimation model in such a way that an error between the estimated value of the second code by the first adversarial estimation model and the second code output from the second encoding model decreases §3.1 ¶1, and
train the first encoding model in such a way that an error between the estimated value of the second code by the first adversarial estimation model and the second code output from the second encoding model increases §3.1 ¶1.
Regarding claim 5, Pittaluga discloses:
An estimation system in which a first encoding model, a second encoding model, and an estimation model constructed by the machine learning device according to claim 1 is implemented, the estimation system comprising:
a first measuring device including
a memory storing instructions, and a processor connected to the memory and configured to execute the instructions to (§4 models are computed using digital files necessitating a computer having memory and processor)
a memory storing instructions, and a processor connected to the memory and configured to execute the instructions to (§4 models are computed using digital files necessitating a computer having memory and processor)
an estimation device including the estimation model, the estimation device is configured to (“We consider the following setting: when training the encoder, we have a training set labeled with values of the private attribute. Once the encoder has been trained, we seek to limit the ability of an adversary, with knowledge of this encoding function, to train an estimator for the private attribute. This means that after the encoder is fixed, we assume the adversary is able to train an estimator on an encoded labeled training set (by applying the encoding function on a regular training set), and we seek to restrict the performance of this estimator on encoded validation and test sets.” §3.1 ¶1)
a memory storing instructions, and a processor connected to the memory and configured to execute the instructions to (§4 models are computed using digital files necessitating a computer having memory and processor)
input the received first code and second code to the estimation model, and output an estimation result output from the estimation model in response to input of the first code and the second code (“We consider the following setting: when training the encoder, we have a training set labeled with values of the private attribute. Once the encoder has been trained, we seek to limit the ability of an adversary, with knowledge of this encoding function, to train an estimator for the private attribute. This means that after the encoder is fixed, we assume the adversary is able to train an estimator on an encoded labeled training set (by applying the encoding function on a regular training set), and we seek to restrict the performance of this estimator on encoded validation and test sets.” §3.1 ¶1).
Pittaluga does not explicitly disclose, however, Abedin teaches:
at least one first sensor (§1 wearable device),
input first sensor data measured by the first sensor to the first encoding model, and transmit a first code output from the first encoding model in response to input of the first sensor data (Fig 1 x and z encoding);
at least one second sensor (§1 wearable device),
input second sensor data measured by the second sensor to the second encoding model (Fig 1 x and z encoding), and
transmit a second code output from the second encoding model in response to input of the second sensor data (Fig 1 x and z encoding); and
receive the first code transmitted from the first measuring device and the second code transmitted from the second measuring device (Fig 1 x and z encoding).
Claim(s) 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Pittaluga in view of Abedin and further in view of Purusothaman et al. (US 20210343384)(herein after “Puru”)
Regarding claim 6, Pittaluga does not explicitly disclose: The estimation system according to claim 5, wherein
the first measuring device and the second measuring device are configured to be worn on different body parts of a user who is an estimation target of a body condition.
Puru teaches: the first measuring device and the second measuring device are configured to be worn on different body parts of a user who is an estimation target of a body condition (“Referring now to FIG. 13, a process flow diagram of a routine 1300 of an auto-immune disease management system is shown. In accordance with various aspects of the present disclosure, routine 1300 is configured to collect data associated with an autoimmune condition from a patient user via a mobile application and one or more wearable electronic devices, analyze that data according to one or more analytical framework comprising at least one supervised learning model, and generate a recommendation for the diagnosis, management, and/or treatment of a current or future state of the autoimmune condition. In accordance with certain embodiments, routine 1300 is initiated by collecting patient data 1302 via a mobile device, mobile application, and/or wearable electronic device associated with a patient-user account. Routine 1300 proceeds by analyzing the patient data according to one or more data model(s) 1304. In accordance with certain embodiment, routine 1300 may comprise a different data model for each module within the auto-immune disease management system. The one or more data model(s) may be configured to determine the presence of one or more trigger condition 1306 associated with a predicted current or future state of the autoimmune condition.” [0144]).
Pittaluga, Abedin and Puru are in the same field of endeavor of data analysis and are analogous. Pittaluga discloses a method of training an encoder and an adversarial model with a hidden private attribute. Abedin discloses adversarial learning on data from multiple wearable devices and encoding models. Puru discloses the use of multiple wearables together for analysis of a user’s medical condition. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the adversarial model structure with wearables data models as taught by Pittaluga and Abedin with the recommendation based on wearables on the user to yield predictable results.
Regarding claim 7, Pittaluga does not explicitly disclose, however, Puru teaches:
The estimation system according to claim 5, wherein
the first measuring device and the second measuring device are configured to be worn on a pair of body parts of a user who is an estimation target of a body condition (“Referring now to FIG. 13, a process flow diagram of a routine 1300 of an auto-immune disease management system is shown. In accordance with various aspects of the present disclosure, routine 1300 is configured to collect data associated with an autoimmune condition from a patient user via a mobile application and one or more wearable electronic devices, analyze that data according to one or more analytical framework comprising at least one supervised learning model, and generate a recommendation for the diagnosis, management, and/or treatment of a current or future state of the autoimmune condition. In accordance with certain embodiments, routine 1300 is initiated by collecting patient data 1302 via a mobile device, mobile application, and/or wearable electronic device associated with a patient-user account. Routine 1300 proceeds by analyzing the patient data according to one or more data model(s) 1304. In accordance with certain embodiment, routine 1300 may comprise a different data model for each module within the auto-immune disease management system. The one or more data model(s) may be configured to determine the presence of one or more trigger condition 1306 associated with a predicted current or future state of the autoimmune condition.” [0144]).
Regarding claim 8, Pittaluga does not explicitly disclose, however, Puru teaches:
The estimation system according to claim 6, wherein the processor included in the estimation device is configured to execute the instructions to
transmit recommendation information regarding the estimation result to a terminal device having a screen visually recognizable by the user ([0014] graphical user interface), and wherein
the recommendation information is information that supports the user for making decision about taking an action for the body condition of the user (“Referring now to FIG. 13, a process flow diagram of a routine 1300 of an auto-immune disease management system is shown. In accordance with various aspects of the present disclosure, routine 1300 is configured to collect data associated with an autoimmune condition from a patient user via a mobile application and one or more wearable electronic devices, analyze that data according to one or more analytical framework comprising at least one supervised learning model, and generate a recommendation for the diagnosis, management, and/or treatment of a current or future state of the autoimmune condition. In accordance with certain embodiments, routine 1300 is initiated by collecting patient data 1302 via a mobile device, mobile application, and/or wearable electronic device associated with a patient-user account. Routine 1300 proceeds by analyzing the patient data according to one or more data model(s) 1304. In accordance with certain embodiment, routine 1300 may comprise a different data model for each module within the auto-immune disease management system. The one or more data model(s) may be configured to determine the presence of one or more trigger condition 1306 associated with a predicted current or future state of the autoimmune condition.” [0144]).
Allowable Subject Matter
Claims 3-4 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151
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