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 .
This Office Action is in response to application 18/951,860 filed on 11/19/2024.
Claims 1-20 have been examined and are pending in this application.
The examiner notes the IDS filed on 1/15/2025 has been considered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a processing system configured to: receive...; confirm....; detect...; and filter...” in claim 1, “the processing system is further configured to use an augmented mode...” in claim 10, “a processing system configured to execute [code to: receive...; produce...; perform...; detect...; filter....]” in claim 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 1-2, 6-8, 10-12 and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Palani et al. (US 2021/0344695 A1) in view of Mota et al. (US 2017/0279685 A1).
Regarding Claim 1;
Palani discloses a machine-learning (ML) engine including a data store networked to a hardware instrument (FIG. 1 – anomaly detection system w/ ensemble of deep learning models and FIG. 4 – memory/storage), the hardware instrument including at least one sensor used to collect data from a data source (FIG. 1 and [0024]-[0025] - The time series data 104 can be data generated by, for example, routers, hubs, modems, storage volumes, servers, databases, applications, and the like. The time series data 104 can be numerical data, textual data, or a combination of both, such as, but not limited to, logfile data. The time series data 104 can be received from (or be consistent with) data that would be utilized by, for example, any Security Information and Event Management (SIEM) system), wherein the data in the data store is operable for prospectively retraining a trained data model ([0003] - Aspects of the present disclosure are directed toward a computer-implemented method comprising training an ensemble of deep learning models using clustered time series training data from numerous components in an Information Technology (IT) infrastructure), the ML engine further including a processing system (FIG. 1 and FIG. 4 - cpu) configured to:
receive the data from the data store (FIG. 4 and [0025] - The time series data 104 can be data generated by, for example, routers, hubs, modems, storage volumes, servers, databases, applications, and the like.... The time series data 104 can be received from (or be consistent with) data that would be utilized by, for example, any Security Information and Event Management (SIEM) system.... The time series data 104 can be provided to the anomaly detection system 102 in real-time (e.g., continuously), approximately real-time, and/or in batches (e.g., intermittently), according to various embodiments);
confirm, using three models, whether anomalies are in the data, each respective one of the three models ... sequentially ... detecting prospective anomalies within a configuration of the data relevant... ([0020] - Aspects of the present disclosure are directed toward an automated anomaly detection system exhibiting improved accuracy, improved efficiency, improved usability, and other benefits relative to existing technologies. Regarding improved accuracy, aspects of the present disclosure can detect anomalies using a voting method amongst an ensemble of deep learning models. Thus, although specific types of models may incorrectly label a non-anomalous event as anomalous (e.g., a false positive) or incorrectly label an anomalous event as non-anomalous (e.g., a false negative), aspects of the present disclosure label events according to a majority of the ensemble of deep learning models and [0048] - In various embodiments, operations 304-308 can occur in parallel or sequentially according to the configuration of the anomaly detection system 102 and [0049] - two out of three),
detect, for each respective anomaly type of the three anomaly types, the prospective anomalies using at least two-out-of-three (2oo3) ... models as a threshold for determining whether the data includes anomalous data corresponding to the respective anomaly type ([0020] - ...aspects of the present disclosure label events according to a majority of the ensemble of deep learning models and [0049] - Operation 310 includes assigning a majority classification to corresponding outputs of the ensemble of deep learning models 110. For example, two of the three models may classify a respective portion of the aggregated time series data 108 as anomalous, whereas the third model may classify the respective portion of the aggregated time series data 108 as non-anomalous. In this example, operation 310 classifies the respective portion of the aggregated time series data 108 as anomalous because a majority (e.g., two out of three) of models in the ensemble of deep learning models 110 classified the respective portion of the aggregated time series data 108 as anomalous. In some embodiments, operation 310 is referred to as a voting method for determining classifications of respective portions of the aggregated time series data 108. In some embodiments, operation 310 can further assign a confidence to the majority classification, where the confidence can be based, at least in part, on the number of models that shared the same majority classification);
filter the anomalous data from the data ... from at least two out of the three models identify the anomalous data for a relevant anomaly type ([0032] - In some embodiments, outliers (e.g., anomalous data) are removed from the training data using statistical methods and [0049] - Operation 310 includes assigning a majority classification to corresponding outputs of the ensemble of deep learning models 110. For example, two of the three models may classify a respective portion of the aggregated time series data 108 as anomalous, whereas the third model may classify the respective portion of the aggregated time series data 108 as non-anomalous. In this example, operation 310 classifies the respective portion of the aggregated time series data 108 as anomalous because a majority (e.g., two out of three) of models in the ensemble of deep learning models 110 classified the respective portion of the aggregated time series data 108 as anomalous)l and
use the data without the filtered anomalous data in retraining the trained data model ([0032] - In some embodiments, outliers (e.g., anomalous data) are removed from the training data using statistical methods and [0037] - Operation 204 includes training an ensemble of deep learning models 110 for anomaly detection using the clustered time series training data 118).
Palani fails to explicitly disclose
[a] model comprising three tests for detecting three anomaly types... including applying each respect test of the three tests for detecting ... anomalies... relevant to the respective test / two out of three (2oo3) tests / when corresponding tests...
However, in an analogous art, Mota teaches [a] model comprising three tests for detecting three anomaly types... including applying each respect test of the three tests for detecting ... anomalies... relevant to the respective test/ two out of three (2oo3) tests / when corresponding tests... ([0042]-[0043] - Anomalies may also take a number of forms in a computer network: 1.) point anomalies (e.g., a specific data point is abnormal compared to other data points), 2.) contextual anomalies (e.g., a data point is abnormal in a specific context but not when taken individually), or 3.) collective anomalies (e.g., a collection of data points is abnormal with regards to an entire set of data points)... Machine learning processes may detect these types of anomalies using advanced approaches capable of modeling subtle changes or correlation between changes (e.g., unexpected behavior) in a highly dimensional space).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Mota to three models ... of Palani to include [a] model comprising three tests for detecting three anomaly types... including applying each respect test of the three tests for detecting ... anomalies... relevant to the respective test/ two out of three (2oo3) tests / when corresponding tests...
One would have been motivated to combine the teachings of Mota to Palani to do so as it provides / allows adjusting anomaly detection operations based on network resources (Mota, [0002]).
Regarding Claim 2;
Palani in view of Mota disclose the system of claim 1.
Palani further discloses wherein the three models include... for confirming a presence of the anomalous data ([0020] - ...aspects of the present disclosure label events according to a majority of the ensemble of deep learning models and [0049]).
Mota further teaches wherein ... models include respective tests for point, collective, and contextual anomaly types, respectively, for confirming a presence of the anomalous data ([0042]-[0043] - Anomalies may also take a number of forms in a computer network: 1.) point anomalies (e.g., a specific data point is abnormal compared to other data points), 2.) contextual anomalies (e.g., a data point is abnormal in a specific context but not when taken individually), or 3.) collective anomalies (e.g., a collection of data points is abnormal with regards to an entire set of data points)... Machine learning processes may detect these types of anomalies using advanced approaches capable of modeling subtle changes or correlation between changes (e.g., unexpected behavior) in a highly dimensional space).
Similar rationale and motivation is noted for the combination of Mota to Palani in view of Mota, as per claim 1, above.
Regarding Claim 6;
Palani in view of Mota disclose the system of claim 1.
Palani further discloses wherein ... use an ensemble of techniques or measurements for confirming potential anomalies for the respective anomaly type ([0020] - ...aspects of the present disclosure label events according to a majority of the ensemble of deep learning models and [0049]).
Mota further teaches wherein one or more of the three tests use .... techniques or measurements for confirming potential anomalies for the respective anomaly type ([0042]-[0043] - Anomalies may also take a number of forms in a computer network: 1.) point anomalies (e.g., a specific data point is abnormal compared to other data points), 2.) contextual anomalies (e.g., a data point is abnormal in a specific context but not when taken individually), or 3.) collective anomalies (e.g., a collection of data points is abnormal with regards to an entire set of data points)... Machine learning processes may detect these types of anomalies using advanced approaches capable of modeling subtle changes or correlation between changes (e.g., unexpected behavior) in a highly dimensional space).
Similar rationale and motivation is noted for the combination of Mota to Palani in view of Mota, as per claim 1, above.
Regarding Claim 7;
Palani in view of Mota disclose the system of claim 1.
Palani further discloses wherein the ML engine further comprises a data repository for storing the data without the anomalous data prior to the data being used for retraining the trained data model (FIG. 4 – memory/storage and [0032] and [0036] Operation 202 includes generating clustered time series training data 118 by removing anomalies from historical data using a statistical method and [0037] - Operation 204 includes training an ensemble of deep learning models 110 for anomaly detection using the clustered time series training data 118).
Regarding Claim 8;
Palani in view of Mota disclose the system of claim 1.
Palani further discloses wherein the ML engine is implemented in a secure cloud (FIG. 1 and [0061] - It is to be understood that although this disclosure includes a detailed description on cloud computing).
Regarding Claim 10;
Palani in view of Mota disclose the system of claim 1.
Palani further discloses wherein the processing system is further configured to use an augmented model, the augmented model being configured to combine two or more datasets within the data for detecting anomalies hidden in the combined datasets ([0026] - In some embodiments, there is a respective ensemble of deep learning models 110 corresponding to respective clusters of aggregated time series data 108 (e.g., where the aggregated time series data 108 can be clustered into similar clusters as clustered time series training data 118)).
Regarding Claim(s) 11, 12 and 15-17; claim(s) 11, 12 and 15-17 is/are directed to a/an method associated with the system claimed in claim(s) 1, 2 and 6-8. Claim(s) 11, 12 and 15-17 is/are similar in scope to claim(s) 1, 2 and 6-8, and is/are therefore rejected under similar rationale.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Palani et al. (US 20210344695 A1) in view of Mota et al. (US 2017/0279685 A1) and further in view of Ramirez et al. (US 2022/0309407 A1).
Regarding Claim 3;
Palani in view of Mota discloses the system of claim 1.
Palani further discloses the three models include... detection... when the anomaly types of the models are sequentially applied to the data ([0020] - ...aspects of the present disclosure label events according to a majority of the ensemble of deep learning models and [0049]).
Mota further teaches wherein the three tests... include detection... when the anomaly types... are... applied to the data ([0042]-[0043] - Anomalies may also take a number of forms in a computer network: 1.) point anomalies (e.g., a specific data point is abnormal compared to other data points), 2.) contextual anomalies (e.g., a data point is abnormal in a specific context but not when taken individually), or 3.) collective anomalies (e.g., a collection of data points is abnormal with regards to an entire set of data points)... Machine learning processes may detect these types of anomalies using advanced approaches capable of modeling subtle changes or correlation between changes (e.g., unexpected behavior) in a highly dimensional space).
Similar rationale and motivation is noted for the combination of Mota to Palani in view of Mota, as per claim 1, above.
Palani in view of Mota fail to explicitly disclose [a] model includes unique detection signature configured to mintage artificial intelligence (AS) bias.
However, in an analogous art, Ramirez teaches [a] model includes unique detection signature configured to mitigate artificial intelligence (AS) bias (Abstract - Systems and methods provide a HybridOps model for the identification, capture, isolation, feature engineering and adjudication of source signal data signatures for inclusion in calibration quality standard reference signal data signature libraries that improve machine learning and validation, reduces model bias and reduces model drift and [0004] and [0076] - In some embodiments and, optionally, in combination of any embodiment described above or below, the exemplary trained neural network model may also be specified to include other parameters, including but not limited to, bias values/functions and/or aggregation functions).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Ramirez to the three tests in the three models of Palani in view of Mota to include [a] model includes unique detection signature configured to mintage artificial intelligence (AS) bias.
One would have been motivated to combine the teachings of Ramirez to Palani in view of Mota to do so as it provides / allows development, calibration, adjudication, training, validation, verification, testing, version control, deployment, post-market testing, prospective maintenance and product development of artificial intelligence (AI)/machine learning (ML) based software, such as AI/ML based software for medical device (SaMD) systems and other signal data signature based AI/ML systems (Ramirez, [0002]).
Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Palani et al. (US 20210344695 A1) in view of Mota et al. (US 2017/0279685 A1) and further in view of Bhatt et al. (US 20230181026 A1).
Regarding Claim 9;
Palani in view of Mota disclose the system of claim 1.
Palani in view of Mota fail to explicitly disclose wherein the ML engine is operable to use one or more of the following quantities for detecting anomalies: White-To-White (WTW); K-Readings; Anterior Chamber Depth (ACD); Axial Length (AL); Not-A-Number (NAN); overflow or underflow values; Pre-op sphere, cylinder or spherical equivalent; or IOL power.
However, in an analogous art, Bhatt teaches wherein the ML engine is operable to use one or more of the following quantities for detecting anomalies: White-To-White (WTW); K-Readings; Anterior Chamber Depth (ACD); Axial Length (AL); Not-A-Number (NAN); overflow or underflow values; Pre-op sphere, cylinder or spherical equivalent; or IOL power ( [0105] - The horizontal distance between two echoes can be used to measure the distance between two structures, such as in a measurement of ocular axial length using an A-scan ultrasound probe during ocular biometry before cataract surgery. B-scan ultrasound can provide a 2D display showing the size and echotexture of a lesion and [0133]-[0134] - The system of any of Aspects 1 through 14, wherein the processor circuitry includes or is coupled to a machine learning (ML) model trained for image-processing of the images to identify a feature or anomaly in the image of the eye of the patient for use in providing a diagnostic indicator of the health condition of the patient).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Bhatt to the ML engine of Palani in view of Mota to include wherein the ML engine is operable to use one or more of the following quantities for detecting anomalies: White-To-White (WTW); K-Readings; Anterior Chamber Depth (ACD); Axial Length (AL); Not-A-Number (NAN); overflow or underflow values; Pre-op sphere, cylinder or spherical equivalent; or IOL power.
One would have been motivated to combine the teachings of Bhatt to Palani in view of Mota to do so as it provides / allows to create or support a health management system for any individual that can aid in early detection of various diseases, such as can help allow expanding the overall reach of health care at a low cost (Bhatt, [0083]).
Regarding Claim(s) 18; claim(s) 18 is/are directed to a/an method associated with the system claimed in claim(s) 9. Claim(s) 18 is/are similar in scope to claim(s) 9, and is/are therefore rejected under similar rationale.
Claim(s) 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Palani et al. (US 20210344695 A1) in view of Bhatt et al. (US 20230181026 A1) and Mota et al. (US 2017/0279685 A1) and Ramirez et al. (US 2022/0309407 A1).
Regarding Claim 19;
Palani discloses a system comprising:
a machine-learning (ML) engine comprising a data store coupled to a processing system (FIG. 1 – anomaly detection system w/ ensemble of deep learning models and FIG. 4 – memory/storage), the processing system configured to execute code to
receive the data from the data store, the dataset comprising [data] (FIG. 4 and [0025] - The time series data 104 can be data generated by, for example, routers, hubs, modems, storage volumes, servers, databases, applications, and the like.... The time series data 104 can be received from (or be consistent with) data that would be utilized by, for example, any Security Information and Event Management (SIEM) system.... The time series data 104 can be provided to the anomaly detection system 102 in real-time (e.g., continuously), approximately real-time, and/or in batches (e.g., intermittently), according to various embodiments);
produce three models... seeking to detect anomalous data as one of three anomaly types corresponding to each of the three models, wherein each of the three models ... for identifying the anomalous data in the dataset ([0020] - Aspects of the present disclosure are directed toward an automated anomaly detection system exhibiting improved accuracy, improved efficiency, improved usability, and other benefits relative to existing technologies. Regarding improved accuracy, aspects of the present disclosure can detect anomalies using a voting method amongst an ensemble of deep learning models. Thus, although specific types of models may incorrectly label a non-anomalous event as anomalous (e.g., a false positive) or incorrectly label an anomalous event as non-anomalous (e.g., a false negative), aspects of the present disclosure label events according to a majority of the ensemble of deep learning models and [0048] - In various embodiments, operations 304-308 can occur in parallel or sequentially according to the configuration of the anomaly detection system 102 and [0049] - two out of three),
perform at least two of the three ... relating to each of the three anomaly types, ... the three models ... are sequentially applied to the received dataset ([0020] - ...aspects of the present disclosure label events according to a majority of the ensemble of deep learning models and [0048] - In various embodiments, operations 304-308 can occur in parallel or sequentially according to the configuration of the anomaly detection system 102 and [0049] - Operation 310 includes assigning a majority classification to corresponding outputs of the ensemble of deep learning models 110. For example, two of the three models may classify a respective portion of the aggregated time series data 108 as anomalous, whereas the third model may classify the respective portion of the aggregated time series data 108 as non-anomalous. In this example, operation 310 classifies the respective portion of the aggregated time series data 108 as anomalous because a majority (e.g., two out of three) of models in the ensemble of deep learning models 110 classified the respective portion of the aggregated time series data 108 as anomalous. In some embodiments, operation 310 is referred to as a voting method for determining classifications of respective portions of the aggregated time series data 108. In some embodiments, operation 310 can further assign a confidence to the majority classification, where the confidence can be based, at least in part, on the number of models that shared the same majority classification);
separately for each of the anomaly types, detect an anomaly when two-out-of-three (2oo3) ... conclude that the anomaly is present in the dataset ([0049] - Operation 310 includes assigning a majority classification to corresponding outputs of the ensemble of deep learning models 110. For example, two of the three models may classify a respective portion of the aggregated time series data 108 as anomalous, whereas the third model may classify the respective portion of the aggregated time series data 108 as non-anomalous. In this example, operation 310 classifies the respective portion of the aggregated time series data 108 as anomalous because a majority (e.g., two out of three) of models in the ensemble of deep learning models 110 classified the respective portion of the aggregated time series data 108 as anomalous. In some embodiments, operation 310 is referred to as a voting method for determining classifications of respective portions of the aggregated time series data 108. In some embodiments, operation 310 can further assign a confidence to the majority classification, where the confidence can be based, at least in part, on the number of models that shared the same majority classification);
filter the anomaly from the dataset ([0032] - In some embodiments, outliers (e.g., anomalous data) are removed from the training data using statistical methods and [0049]); and
use data from the dataset to retrain an existing trained ML model. ([0032] - In some embodiments, outliers (e.g., anomalous data) are removed from the training data using statistical methods and [0037] - Operation 204 includes training an ensemble of deep learning models 110 for anomaly detection using the clustered time series training data 118).
Palani fails to explicitly disclose
...the dataset comprising an image.
...each of the models comprising three tests each of the tests ...include respective tests for point, collective, and contextual anomaly types for identifying the anomalous data in the dataset / two of the three tests...the three tests.../two-out of the three (2oo3) tests;
including unique detection signatures configured to mitigate artificial intelligence (AI) bias when the anomaly types of the models
However, in an analogous art, Bhatt teaches ...the dataset comprises an image and [a machine learning (ML) model trained for image-processing of the images to identify a feature or anomaly] ([0105] - The horizontal distance between two echoes can be used to measure the distance between two structures, such as in a measurement of ocular axial length using an A-scan ultrasound probe during ocular biometry before cataract surgery. B-scan ultrasound can provide a 2D display showing the size and echotexture of a lesion and [0133]-[0134] - The system of any of Aspects 1 through 14, wherein the processor circuitry includes or is coupled to a machine learning (ML) model trained for image-processing of the images to identify a feature or anomaly in the image of the eye of the patient for use in providing a diagnostic indicator of the health condition of the patient).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Bhatt to the ML engine /dataset of Palani to include ...the dataset comprises an image.
One would have been motivated to combine the teachings of Bhatt to Palani to do so as it provides / allows to create or support a health management system for any individual that can aid in early detection of various diseases, such as can help allow expanding the overall reach of health care at a low cost (Bhatt, [0083]).
Further, in an analogous art, Mota teaches ...each of the models comprising three tests each of the tests ...include respective tests for point, collective, and contextual anomaly types for identifying the anomalous data in the dataset / two of the three tests...the three tests.../two-out of the three (2oo3) tests ([0042]-[0043] - Anomalies may also take a number of forms in a computer network: 1.) point anomalies (e.g., a specific data point is abnormal compared to other data points), 2.) contextual anomalies (e.g., a data point is abnormal in a specific context but not when taken individually), or 3.) collective anomalies (e.g., a collection of data points is abnormal with regards to an entire set of data points)... Machine learning processes may detect these types of anomalies using advanced approaches capable of modeling subtle changes or correlation between changes (e.g., unexpected behavior) in a highly dimensional space).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Mota to three models ... of Palani and Bhatt to include ...each of the models comprising three tests each of the tests ...include respective tests for point, collective, and contextual anomaly types for identifying the anomalous data in the dataset / two of the three tests...the three tests.../two-out of the three (2oo3) tests.
One would have been motivated to combine the teachings of Mota to Palani and Bhatt to do so as it provides / allows adjusting anomaly detection operations based on network resources (Mota, [0002]).
Further, in an analogous art, Ramirez teaches including unique detection signatures configured to mitigate artificial intelligence (AI) bias when the anomaly types of the model[[s]](Abstract - Systems and methods provide a HybridOps model for the identification, capture, isolation, feature engineering and adjudication of source signal data signatures for inclusion in calibration quality standard reference signal data signature libraries that improve machine learning and validation, reduces model bias and reduces model drift and [0004] and [0076] - In some embodiments and, optionally, in combination of any embodiment described above or below, the exemplary trained neural network model may also be specified to include other parameters, including but not limited to, bias values/functions and/or aggregation functions).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Ramirez to the three models of Palani in view of Bhatt and Mota to include including unique detection signatures configured to mitigate artificial intelligence (AI) bias when the anomaly types of the model[[s]].
One would have been motivated to combine the teachings of Ramirez to Palani in view of Bhatt and Mota to do so as it provides / allows development, calibration, adjudication, training, validation, verification, testing, version control, deployment, post-market testing, prospective maintenance and product development of artificial intelligence (AI)/machine learning (ML) based software, such as AI/ML based software for medical device (SaMD) systems and other signal data signature based AI/ML systems (Ramirez, [0002]).
Regarding Claim 20;
Palani in view of Bhatt and Mota and Ramirez disclose the system of claim 19.
Palani further teaches ...the ML engine is implemented in a secure cloud (FIG. 1 and [0061] - It is to be understood that although this disclosure includes a detailed description on cloud computing).
Bhatt further teaches wherein the image comprises an optical coherence tomography (OCT) image and the ML engine is implemented... ( [0105] - The horizontal distance between two echoes can be used to measure the distance between two structures, such as in a measurement of ocular axial length using an A-scan ultrasound probe during ocular biometry before cataract surgery. B-scan ultrasound can provide a 2D display showing the size and echotexture of a lesion and [0133]-[0134] - The system of any of Aspects 1 through 14, wherein the processor circuitry includes or is coupled to a machine learning (ML) model trained for image-processing of the images to identify a feature or anomaly in the image of the eye of the patient for use in providing a diagnostic indicator of the health condition of the patient).
Similar rationale and motivation is noted for the combination of Bhatt to Palani in view of Bhatt and Mota and Ramirez, as per claim 19, above.
Allowable Subject Matter
Claims 4, 5, 13 and 14 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 attached.
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/KARI L SCHMIDT/ Primary Examiner, Art Unit 2439
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