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 . 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 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.
Claims 1-20 are pending.
Information Disclosure Statement PTO-1449
The Information Disclosure Statement submitted by applicant on 08-02-2024 has been considered. Please see attached PTO-1449.
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.
Claims 1, 2, 11, 19 and 20, are rejected under 35 U.S.C. 103 as being unpatentable over song et al. (US Publication No. 2022/0044117 ), hereinafter Song in view of Ou et al. (US Publication No.2022/0398343), hereinafter Ou, further in view of Karame et al (US patent No. 11,616,804), hereinafter Karame.
AS per claim 1, 19 and 20, Song discloses 1 a computer implemented method for edge device level security analytics (paragraph [0004], “collecting model exemplar information from edge devices…”), the computer implemented method comprising:
receiving, at a central model processing system, a first trained model from a first user device (paragraph [0032], “At block 302, the edger devices 104 transmit their trained edge device models to the server 106”), wherein the first trained model is trained using first local digital data obtained at the first user device (paragraph [0018], “The local maintenance system 104 can for example, identify abnormal behavior by monitoring the multivariate time series that are generate by the sensor”), the first local digital data is screened at the first user device through an edge sensor operable to identify data anomalies (paragraph [0017], “One or more sensors within each respective monitored system 10 record information about the state of the monitored system 102”);
receiving, at a central model processing system, a second trained model from a second user device (paragraph [0032], “At block 302, the edger devices 104 transmit their trained edge device models to the server 106”, it is noted that in system discloses by Song there are multiple (i.e., first, second, etc.) edge devices which transmit their trained model device (i.e., first, second, etc.) model to the server 106), wherein the second trained model is trained using second local digital data obtained at the second user device (paragraph [0018], “The local maintenance system 104 can for example, identify abnormal behavior by monitoring the multivariate time series that are generate by the sensor”), the second local digital data is screened at the second user device through an edge sensor operable to identify data anomalies (paragraph [0017], “One or more sensors within each respective monitored system 10 record information about the state of the monitored system 102”);
combining, at the central model processing system, the first and second trained models (paragraph [0026], [0032], the server 106 gathers and aggregates the locally trained models to construct a global model);
updating, at the central model processing system, the central model associated with the combined first and second trained models (paragraph [0025], “This aggregation is used to update the server’s model”); and
deploying the updated central model to a plurality of user devices (paragraph [0028], “The server 106 sends the global model to different edge devices 104 to update their local models).
Although Song discloses protect privacy of at least one of the first local digital data and the first trained model, and protect privacy of at least one of the second local digital data and the second trained model (paragraph [009], “training edge device models using without transmitting potentially sensitive local sensor information to a central server”, paragraph [0021], “Rather than passing raw data to the model training server 106, the maintenance systems 104 may provide their respective locally trained parts…In this manner, information that is collected at the respective local maintenance systems 104 may be used to improve the anomaly detection performed of other such maintenance systems 104, without risking potentially sensitive local data”), Song does not explicitly disclose applying differential privacy techniques; applying, at a central model processing system, a central model security review to the first and second trained models, and central model based on audit metric.
However, in an analogous art, Ou discloses applying differential privacy techniques at the first user device and second user device to protect privacy of at least one of the first local digital data and the first trained model, and to protect privacy of at least one of the second local digital data and the second trained model ( paragraph [0005], “differential privacy may be applied to the input data to each ML model and to ML modelling data through, for example, the introduction of noise, anonymization, and encryption. Different, and preferably distinct, hierarchies of loss requirements may be dynamically applied to different stages and/or forms of data in a federal learning framework. For example, a first privacy loss requirement may be applied to input data (e.g., "raw" data, anonymized data, encrypted data, etc.), a second privacy loss requirement may be applied to ML modelling data generated by individual data owner(s)”); and updating the central model based on audit metrics (paragraph [0041], “aggregate model data 172 may include encrypted model weights or gradients (e.g., corresponding to parameters of model data 126, 226, and 236), and a data owner (e.g., corresponding to data owner 102) may update its ML model according to the aggregated weights or gradients. Additionally, coordinator 170 may store aggregate model data 172 in database(s) 278. In some embodiments, modelling engine 280 of coordinator 170 may be configured to generate or update a global ML model based on model data 126, 226, and 236).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to combine Song and Ou. This would have been obvious because one of ordinary skill in the art would have been motivated to ensure that data sets of the first and the second data owner are not divulged to each other.
Song in view of Ou does not explicitly disclose, but in an analogous art, Karame discloses applying, at a central model processing system, a central model security review to the first and second trained models (column 6, lines 45-55, clients C1, C2 send their respective local model to server, and the server executes an aggregation protocol in a privacy-preserving manner).
It would have been obvious to one of ordinary skill in the art before effective filing date of the invention to combine Song and Ou with Karame. This would have been obvious because one of ordinary skill in the art would have been motivated to prevent model-poisoning attempts in federated learning system.
As per claim 2, Ou furthermore discloses, wherein the digital data is obtained through an API (paragraph [0016], “user one or more application Programming Interface (API) to interact). The motivation is similar to the motivation used in claim 1.
As per claim 11, Song furthermore discloses, training the updated central model using locally obtained digital data (paragraph [0025], aggregation is used to update the server’s model).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over song, Ou and Karame, further in view of Mathews et al. (US Publication No. 2024/0195826), hereafter Mathew.
As per claim 3, Song as modified does not explicitly disclose, but in an analogous art, Mathew discloses, wherein the screening comprises generating a set of edge thresholds; monitoring data on the user device using the thresholds, and filtering data anomalies associated with the thresholds. (paragraph [0044], “responsive to determining the difference for a computing device exceeds the threshold, the anomaly detector 130 may detect a potential anomaly in the computing device”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified Song with Mathews. This would have been obvious because one of ordinary skill in the art would have been motivated to do so in order to achieve the predictable result of prevent attacks on the system.
Claims 4-10, 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over song, Ou and Karame, further in view of Cella et al. (US Publication No. 2021/0133650), hereafter Cella.
As per claim 4, Song as modified does not explicitly disclose converting the first digital data set into a standardized format. However, converting the first digital data set into a standardized format is well known as illustrated by Cella (paragraph [1147], “a digital twin data model may refer to an abstract model that organizes elements of enterprise-related data and standardizes the manner by which those elements relate to one another and to the properties of digital twin entities”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified Song to include the well known feature of standardized format in order to relate elements of data to one another and to the properties of digital twin entities.
As per claim 5, Song as modified does not explicitly disclose, but in an analogous art Cella discloses, wherein the audit metrics comprises at least one of failure mode and effects analysis (FMEA), security testing, and non-failure mode and effects analysis (paragraph [1245], “the types of data that may populate a CTO digital twin may include, but are not limited to, technology performance and specification data, interoperability and compatibility data, cybersecurity data, competitor data, failure mode effects analysis (FMEA) data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified Song with Cella. This would have been obvious because one of ordinary skill in the art would have been motivated to do so in order to identify and mitigate potential failures.
As per claim 6, Song as modified furthermore discloses , wherein updating the first central model based comprises electronic design automation, feature engineering, training the model, evaluation of the model (Ou, paragraph [0033], “aggregate model data 172 may be used to calculate an updated set of parameters for an "updated" model based on a greater amount of parameters to replace the model parameters used by modeling device 110 and corresponding to model data 126. Additionally or alternatively, aggregate model data 172 may be used to retrain ML models”, corresponding to training the model). The motivation is similar to the motivation provided in claim 1.
Song as modified does not explicitly disclose but in an analogous art Cella discloses auditing and FMEA is conducted on the model during each of the steps of updating the central model (paragraph [1245], “In embodiments, the types of data that may populate a CTO digital twin may include, but are not limited to, technology performance and specification data, interoperability and compatibility data, cybersecurity data, competitor data, failure mode effects analysis (FMEA) data, technology/engineering roadmap data, information technology systems data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified Song with Cella. This would have been obvious because one of ordinary skill in the art would have been motivated to do so in order to identify and mitigate potential failures
As per claim 7, Song as modified does not explicitly disclose, but in an analogous art, Cella discloses registering an audited and finalized model; and receiving user input to grant the permission to update the central model (paragraph [1096], “The parameters for configuration of a role specific digital twin may include permissions (such as for data access), communication settings, availability of features (such as role-specific views of data and analytics, simulation features, control features, and many other features described throughout this disclosure), and the like. In embodiments the artificial intelligence services system may incorporate any of the techniques described throughout this disclosure or the documents incorporated by reference, such as a machine learning, deep learning, convolutional neural networks, robotic process automation, or the like”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified Song with Cella. This would have been obvious because one of ordinary skill in the art would have been motivated to do so in order to prevent unauthorized updates.
As per claim 8, Song as modified does not explicitly disclose, but in an analogous art, Cella discloses, wherein updating the central model comprises failure modes and effects analysis (FMEA), smoke testing, checking the obtained data, and unit testing ((Paragraph [1245], [0354], " a cyber security system (such as for virus detection and remediation, intrusion detection and remediation, spam detection and remediation, phishing detection and remediation, social engineering detection and remediation, cyber-attack detection and remediation, packet inspection, traffic inspection, DNS attack remediation and detection, and others) or other security application); Paragraph [0775], “The platform 604 may deploy digital twins 1700 of value chain network entities 652 for automatically managing a set of incidents relating to a set of value chain network entities and activities. The incidents may include any events causing disruption to the value chain network like accidents, fires,").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified Song with Cella. This would have been obvious because one of ordinary skill in the art would have been motivated to do so in order to automatically manage incident relating to network entities and activities.
As per claim 9, Song as modified does not explicitly disclose, but in an analogous art, Cella discloses, implementing a security system within the model training and data monitoring of the pipeline for adversarial attacks (paragraph [0304], “(Paragraph [0340]-"In example embodiments, the value chain control tower 360 may be connected to, in communication with, or otherwise operatively coupled with adaptive data pipelines 302 and processing facilities that may be further connected to, in communication with, or otherwise operationally coupled with external data sources 320 and a data handling stack 330 (e.g., value chain network technology) that may include intelligent, user-adaptive interfaces, adaptive intelligence and control 332, and/or adaptive data monitoring and storage 334, as described herein." ,Paragraph [0354]," a cyber security system (such as for virus detection and remediation, intrusion detection and remediation, spam detection and remediation, phishing detection and remediation, social engineering detection and remediation, cyber-attack detection and remediation, packet inspection, traffic inspection, DNS attack remediation and detection, and others) or other security application)").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified Song with Cella. This would have been obvious because one of ordinary skill in the art would have been motivated to do so in order to provide protection against attacks on the system.
As pe claim 10, Song as modified does not explicitly disclose, but in an analogous art, Cella discloses wherein the central model receives differentiated data if a user grants a sharing permission (paragraph [1095], "In some embodiments, an organizational digital twin may further incorporate data access rules for different divisions and/or roles within the organization, including permissions. access rights, and restrictions.").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified Song with Cella. This would have been obvious because one of ordinary skill in the art would have been motivated to do so in order to prevent unauthorized access.
As per claim 12, Song as modified does not explicitly disclose, but in an analogous art Cella discloses, wherein the local digital data comprises user health data comprised of at least one of dietary information, exercise and activity (Paragraph [0365], "data types…(including identity data, role data, task data, workflow data, health data, attention data, mood data, stress data, physiological data, performance data, quality data and many other types)" Paragraph [1055], "user interfaces, and control systems for value chain entities); physical process observation systems 1510 (such as for tracking physical activities of operators, workers, customers, or the like, physical activities of individuals").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified Song with Cella. This would have been obvious because one of ordinary skill in the art would have been motivated to do so in order to provide analysis and insight into physical health, movements and activities.
As per claim 13, Cella furthermore discloses, wherein the health data may comprise at least one of pulse, respiration rate, blood pressure, electrocardiogram, caloric expenditure, fetal kick counts, mental health, pain, bleeding, and contractions gathered over time at a first sampling frequency (Paragraph [0365], “(including identity data, role data, task data, workflow data, health data, attention data, mood data, stress data, physiological data, performance data, quality data and many other types)"). The motivation is similar to the motivation provided in claim 12.
Claims 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over song, Ou and Karame, further in view of Tuytten et al. (US Publication No. 2022/0005605), hereinafter Tuytten.
As per claim 14, Song as modified does not explicitly disclose, but in an analogous art, Tuytten discloses, wherein at least one of the models is operable to provide a predictive inference associated with pregnancy outcomes (paragraph [0141], "As used herein, the term "outcome" refers to the result of an event where two or more results are possible. For example, in medical prognostics, the outcome may be the subject developing or not developing a specific health condition. The methods of the invention involve generating models using a population of subjects that may be applied on test subject to predict the probability of the subject developing the health condition.").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified Song with Tuytten. This would have been obvious because one of ordinary skill in the art would have been motivated to do so in order to optimize medical diagnoses.
As per claim 15, Song as modified does not explicitly disclose, but in an analogous art, Tuytten discloses, obtaining a pregnancy outcome metric by applying the first trained model to the first local digital data (Paragraph [0037], "The method of the invention can be applied to generate models for predicting different types of outcome, in a number of different fields including medicine, radiology, biometrics, forecasting of natural hazards, meteorology, machine learning, and data mining research. One particular area of application is the area of medical diagnostics and prognostic, especially predicting the risk of a subject having or developing a particular health condition, especially syndromic conditions such as preeciampsia"; Paragraph [0178], “Therefore, when a diagnostic/prognostic test is assessed/applied in its clinically relevant context, metrics like positive and negative predictive value (PPV and NPV), which take the disease prevalence (or incidence) into account. are far more appropriate. Here, PPV corresponds the fraction of patients that will actually have/develop the condition (TP, True Positives) within the group of all patients that have a positive test result (True Positives+False Positives (FP)). NPV corresponds to the fraction of patients that will not have/develop the conditions (TN, True Negatives) within the group of all patients that have a negative test result (True Negatives+False Negatives (FN)).").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified Song with Tuytten. This would have been obvious because one of ordinary skill in the art would have been motivated to do so in order to optimize medical diagnoses.
As per claim16, Tuytten furthermore discloses, providing an alert to at least one of a user and a healthcare practitioner based on the pregnancy outcome metric; wherein the healthcare practitioner comprises at least one of emergency medical services, a physician or practice associated with providing care for the user (Paragraph [0247], "In medicine, prognosis commonly relates to the probability or risk of an individual developing a particular state of health (an outcome) over a specific time, based on his or her clinical and non-clinical profile, i.e., a set of variables."; Paragraph [0359]- "Preferentially, but not a condition sine qua non, is the availability of additional variables, like relevant (clinical) risk factors as collected at time of sampling or as available in (medical) records, or the results of relevant, well-established clinical tests (e.g., glucose measurements) or quantification data of other types of relevant putative biomarker molecules, as available for the same sample/originator individual. For the avoidance of doubt, the diagnosis of pre-eclampsia (or absence thereof), is at a time point in the pregnancy which is distinctively later (typically, but not restrictive, 20 or more weeks later) in the pregnancy compared to the timepoint when the biospecimen is taken which is used for establishing the future risk of pre-eclampsia occurring."). The motivation is similar to the motivation provided in claim 15.
As per claim 17, Tuytten furthermore discloses, wherein providing an alert comprises comparing the pregnancy outcome metric to a threshold, wherein the pregnancy outcome metric comprises an indication of a positive outcome or a negative outcome (Paragraph [0247], medicine, prognosis commonly relates to the probability or risk of an individual developing a particular state of health (an outcome) over a specific time, based on his or her clinical and non-clinical profile, i.e., a set of variables.", Paragraph [0359] "Preferentially, but not a condition sine qua non, is the availability of additional variables, like relevant (clinical) risk factors as collected at time of sampling or as available in (medical) records, or the results of relevant, well-established clinical tests (e.g., glucose measurements) or quantification data of other types of relevant putative biomarker molecules, as available for the same sample/originator individual. For the avoidance of doubt, the diagnosis of pre-eclampsia (or absence thereof), is at a time point in the pregnancy which is distinctively later (typically, but not restrictive, 20 or more weeks later) in the pregnancy compared to the timepoint when the biospecimen is taken which is used for establishing the future risk of pre-eclampsia occurring ).
The motivation is similar to the motivation provided in claim 15.
As per claim 18, Tuytten furthermore discloses, wherein updating the central model comprises re-training the central model to predict pregnancy outcomes based on the combined first and second trained models (Paragraph [0023], "The present invention overcomes the technical limitations of existing predictive models and provides a solution to achieve a superior predictive model delivering an accurate risk prediction result, and this in a less computationally Intensive way. By isolating and segmenting particular population subsets in a way as defined in claim 1 as much more robust and accurate way of detecting, or predict risk of, an outcome is achieved. A significant improvement in the accuracy of prognostic models is achieved when comparing the figures of merit for two prediction models established with and without the application of the Invention"; Para [0417], "Additionally, all the above statistics are also generated as well as for the complete sample sets. In this later case models were trained and evaluated on all controls and cases.").
.It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the modified Song with Tuytten. This would have been obvious because one of ordinary skill in the art would have been motivated to do so in order to optimize medical diagnoses.
References Cited, Not Used
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Beser et al. ( US Publication No.2019/0012592) discloses, a federated architecture of artificial neural networks includes a first federation comprising a first plurality of artificial neural networks; a second federation comprising a second plurality of artificial neural networks; and a central server in communication with the first plurality of artificial neural networks and with the second plurality of artificial neural networks; wherein at least one artificial neural network is in the first federation and in the second federation; wherein communication between the central server and the first plurality of artificial neural networks is based on the first federation; and wherein communication between the central server and the second plurality of artificial neural networks is based on the second federation.
Mimassi (US Patent No. 11,238,849) discloses, a system and method for federated context-sensitive language models comprising a federated language model server and a plurality of edge devices. The federated language model server may comprise one or more machine learning models trained and developed centrally on the server, and distribute these one or more machine learning models to edge devices wherein they may be operated locally on the edge devices. The edge devices may gather or generate context data that can be used by a speech recognition engine, and the local language models contained therein, to develop adaptive, context-sensitive, user-specific language models. Periodically, the federated language model server may select a subset of edge devices from which to receive uploaded local model parameters, that may be aggregated to perform central model updates wherein the updated model parameters may then be sent back to edge devices in order to update the local model parameters.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ali Abyaneh whose telephone number is (571) 272-7961. The examiner can normally be reached on Monday-Friday from (8:00-5:00). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexander Lagor can be reached on (571) 270-5143. The fax phone numbers for the organization where this application or proceeding is assigned as (571) 273-8300 Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/ALI S ABYANEH/Primary Examiner, Art Unit 2437