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 .
The present Office Action is in response to the Request for Continued Examination dated 28 May 2026.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 28 May 2026 has been entered.
DETAILED ACTION
In the RCE filed 28 May 2026:
Claims 21-23 are new
Claims 6-7,14-15 are cancelled
Claims 1,4-5,9,12, 17 are amended
Claims 1-5, 8-13, 16-23 are pending
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 8-13, 16-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 9, 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1, 9, 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a system, computer program product and method, which are within a statutory category.
Step 2A1
[The limitations of:
Claims 1, 9, 17 (Claim 1 being representative)
receive healthcare data from a data source based on a device associated with the data source performing a patient procedure, wherein the data source comprises one of:
perform a feature extraction technique on the healthcare data to provide a plurality of features for an input to a model, wherein the plurality of features comprises:
at least one feature associated with anatomical aspects of a body;
at least one feature associated with a protocol;
at least one feature associated with a characteristic of natural language processing;
and at least one feature associated with a manual configuration of a device;
determine a classification of the healthcare data using a model, the model configured to provide an output based on the input, wherein the input comprises the plurality of features associated with the healthcare data and the output comprises a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications , and wherein one or more of the plurality of automated healthcare data analysis applications comprises an Al software application that performs an automated analysis of healthcare data to provide a prediction of a diagnosis regarding a medical condition or a recommendation regarding a treatment for a patient , wherein, when determining the classification of the healthcare data:
determines whether at least one feature of the plurality of features comprises:
a feature of a category associated with anatomical aspects of a body;
a feature of a category associated with protocol;
a feature of a category associated with a characteristic of natural language processing;
a feature of a category associated with a manual configuration;
or any combination thereof,
and the machine learning model determines the classification of the healthcare data based on determining whether the at least one feature includes a feature of a category associated with anatomical aspects of a body, a feature of a category associated with a protocol of a device, a feature of a category associated with a characteristic of natural language processing, or a feature of a category associated with a manual configuration;
and provide the healthcare data to the automated healthcare data analysis application based on the classification of the healthcare data,
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to classify healthcare data (see Spec. Para. 0005 describing analyzing data as a human activity) in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “receiving, performing, determining and providing” as indicated supra. Other than reciting generic computer components (discussed infra), i.e., a system implemented by a processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a processor and non-transitory computer readable medium that implements the identified abstract idea. The processor and non-transitory computer readable medium are not described by the applicant and is recited at a high-level of generality (i.e., a generic server performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional element of using a trained machine learning model to classify healthcare data. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to classify healthcare data merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
The claims further recite the additional elements of a device, an electronic medical record (EMR) system, a medical imaging system, a communication device associated with a medical device, a fluid injection system, a pathology information system, a laboratory information system, or a user device associated with a patient. The device, an electronic medical record (EMR) system, a medical imaging system, a communication device associated with a medical device, a fluid injection system, a pathology information system, a laboratory information system, or a user device associated with a patient merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processor and non-transitory computer readable medium to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using the device, an electronic medical record (EMR) system, a medical imaging system, a communication device associated with a medical device, a fluid injection system, a pathology information system, a laboratory information system, or a user device associated with a patient was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Claims 2-5, 8,10-13, 16,18-23 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 2, 10, 18 merely describe(s) outputting data, which further defines the abstract idea.
Claim(s) 2-3,10-11,18 also includes the additional element of “a destination system” which is analyzed the same as the “device” and does not provide a practical application or significantly more for the same reasons.
Claim(s) 3, 11 merely describe(s)…, which further defines the abstract idea.
Claim(s) 3, 11 also includes the additional element of “a picture archiving and communication system (PACS), an electronic medical record (EMR) system, a data reporting system, a communication device associated with a medical device, or a user device associated with a patient” which is analyzed the same as the “device” and does not provide a practical application or significantly more for the same reasons.
Claim(s) 4, 12, 19 merely describe(s) training the machine learning model, which further defines the abstract idea.
The Examiner notes that the training of a machine learning model is recited in the claim. The type of training utilized by the claimed invention is not described by the Applicant. As such the Examiner is required to analyze the training step given the broadest reasonable interpretation. The step(s) performed to train the model/algorithm is/are considered to be part of the abstract idea because it/they fall(s) under data manipulations that humans perform (i.e., fitting a model to data) and thus are interpreted to be part of the abstraction--the rules or instructions that fall under Certain Methods of Organizing Human Activity. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 12 (Fed. Cir. April 18, 2025) (finding that “[i]terative training using selected training material…are incident to the very nature of machine learning.”).
Claim(s) 5, 13, 20 merely describe(s) receiving data, which further defines the abstract idea.
Claim(s) 8, 16 merely describe(s) the automated healthcare data analysis application, which further defines the abstract idea.
Claim(s) 8, 16 also includes the additional element of “an AI based healthcare data analysis application” which is analyzed the same as the “device” and does not provide a practical application or significantly more for the same reasons.
Claim(s) 21-23 merely describe(s) providing data to the automated healthcare data analysis application, which further defines the abstract idea.
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.
The Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 1-5, 8-13, 16-20 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Saripalli et al (US Publication No. 20200337648) in view of SUNDARARAMAN et al (US Publication No. 20200050949) in view of Spurlock et al (US Publication No. 20190108912).
Regarding Claim 1
Saripalli teaches a system for managing automated healthcare data analysis applications using artificial intelligence (AI), comprising:
at least one processor programmed or configured to [Saripalli at Para. 0186 teaches the processor platform 1400 of the illustrated example includes a processor 1412. The processor 1412 of the illustrated example is hardware. For example, the processor 1412 can be implemented by one or more integrated circuits, logic circuits, microprocessors, GPUs, DSPs, or controllers from any desired family or manufacturer]:
receive healthcare data from a data source based on a device associated with the data source performing a patient procedure [Saripalli at Para. 0030 teaches medical data can be obtained from imaging devices, sensors, laboratory tests, and/or other data sources], wherein the data source comprises one of:
an electronic medical record (EMR) system;
a medical imaging system;
a communication device associated with a medical device;
a fluid injection system;
a pathology information system;
a laboratory information system;
or a user device associated with a patient [Saripalli at Para. 0136 teaches thus, the example hybrid RL framework 900 includes data, models, and evaluations. The data can be pre-processed after acquisition from a data source (e.g., patient monitor, medical device, medical record system, etc.) (interpreted as an electronic medical record system)];
at least one feature associated with anatomical aspects of a body [Saripalli at Para. 0031 teaches for example, imaging devices (e.g., gamma camera, positron emission tomography (PET) scanner, computed tomography (CT) scanner, X-Ray machine, magnetic resonance (MR) imaging machine, ultrasound scanner, etc.) generate two-dimensional (2D) and/or three-dimensional (3D) medical images (e.g., native Digital Imaging and Communications in Medicine (DICOM) images) representative of the parts of the body (e.g., organs, tissues, etc.) to diagnose and/or treat diseases (interpreted as anatomical aspects of the body)];
at least one feature associated with a protocol of a device [Saripalli at Para. 0057 teaches as shown in an example system 100 of FIG. 1, one or more medical devices 110 (e.g., ventilator, anesthesia machine, intravenous (IV) infusion drip, etc.) administer to a patient 120, while one or more monitoring devices 130 (e.g., electrocardiogram (ECG) sensor, blood pressure sensor, respiratory monitor, etc.) gather data regarding patient vitals, patient activity, medical device operation, etc. Such data can be used to train an AI model, can be processed by a trained AI model, etc (medical device operation interpreted as protocol of a device)];
and at least one feature associated with a manual configuration of a device [Saripalli at Para. 0033 teaches acquisition, processing, analysis, and storage of time-series data (e.g., one-dimensional waveform data, etc.) obtained from one or more medical machines and/or devices play an important role in diagnosis and treatment of patients in a healthcare environment. Devices involved in the workflow can be configured, monitored, and updated throughout operation of the medical workflow. Machine learning can be used to help configure, monitor, and update the medical workflow and devices.];
determine a classification of the healthcare data using a machine learning model, wherein the machine learning model is configured to provide an output based on the input [Saripalli at Para. 0042 teaches deep learning is a class of machine learning techniques employing representation learning methods that allows a machine to be given raw data and determine the representations needed for data classification,
[ … ] … wherein, when determining the classification of the healthcare data using the machine learning model:
the machine learning model determines whether at least one feature of the plurality of features comprises:
a feature of a category associated with anatomical aspects of a body;
a feature of a category associated with protocol of a device;
a feature of a category associated with a characteristic of natural language processing;
a feature of a category associated with a manual configuration of a device;
or any combination thereof [Saripalli at Para. 0045 teaches deep learning operates on the understanding that many datasets include high level features which include low level features. While examining an image, for example, rather than looking for an object, it is more efficient to look for edges which form motifs which form parts, which form the object being sought (a feature of a category associated with anatomical aspects of a body)],
and the machine learning model determines the classification of the healthcare data based on determining whether the at least one feature includes a feature of a category associated with anatomical aspects of a body, a feature of a category associated with a protocol of a device, a feature of a category associated with a characteristic of natural language processing, or a feature of a category associated with a manual configuration of a device [Saripalli at Para. 0045];
Saripalli does not teach perform a feature extraction technique on the healthcare data to provide a plurality of features for an input to a machine learning model, wherein the plurality of features comprises:
at least one feature associated with a characteristic of natural language processing;
wherein the input comprises the plurality of features associated with the healthcare data and the output comprises a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications, and wherein one or more of the plurality of automated healthcare data analysis applications comprises an Al software application that performs an automated analysis of healthcare data to provide a prediction of a diagnosis regarding a medical condition or a recommendation regarding a treatment for a patient, … [ … ]
and provide the healthcare data to the automated healthcare data analysis application based on the classification of the healthcare data.
SUNDARARAMAN teaches perform a feature extraction technique on the healthcare data to provide a plurality of features for an input to a machine learning model, wherein the plurality of features comprises [SUNDARARAMAN at Para. 0035 teaches initially, in some implementations, the healthcare data platform may extract the data elements from the data files and associate the extracted data elements with file identifiers. In this case, the file identifiers may identify a data file from which the data elements are extracted; SUNDARARAMAN at Para. 0038 teaches for example, the machine learning model may use, as input, historical data based on knowledge of the data element notations being implemented by specific data sources to determine assignments for newly received data elements in data files received from the same data sources]:
at least one feature associated with a characteristic of natural language processing [SUNDARARAMAN at Para. 0023 teaches the digital assistant platform may extract the keywords from the query using a natural language processing model, and identify the intent classification and/or the entity using a machine learning model];
wherein the input comprises the plurality of features associated with the healthcare data [SUNDARARAMAN at Para. 0035] and the output comprises a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications [SUNDARARAMAN at Para. 0040 teaches accordingly, in some implementations, the healthcare data platform may examine a data file, of the plurality of data files, to identify a pattern or combination of data elements present in the data file. The healthcare data platform may determine, using a machine learning model, a score (e.g., a map score) for a data element in the data file based on the combination of data elements present in the data file. The score may predict a type of healthcare data (e.g., the member's age, in the example above) represented by the data element based on the combination of data elements present in the data file], … [ … ]
and provide the healthcare data to the automated healthcare data analysis application based on the classification of the healthcare data [SUNDARARAMAN at Para. 0040 teaches accordingly, in some implementations, the healthcare data platform may examine a data file, of the plurality of data files, to identify a pattern or combination of data elements present in the data file. The healthcare data platform may determine, using a machine learning model, a score (e.g., a map score) for a data element in the data file based on the combination of data elements present in the data file. The score may predict a type of healthcare data (e.g., the member's age, in the example above) represented by the data element based on the combination of data elements present in the data file].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine classification of Saripalli with the analysis of SUNDARARAMAN with the motivation to improve the overall process of performing data transformations and analyses [SUNDARARAMAN at Para. 0019].
Saripalli/SUNDARARAMAN do not teach [ … ] … and wherein one or more of the plurality of automated healthcare data analysis applications comprises an Al software application that performs an automated analysis of healthcare data to provide a prediction of a diagnosis regarding a medical condition or a recommendation regarding a treatment for a patient, … [ … ]
Spurlock teaches [ … ] … and wherein one or more of the plurality of automated healthcare data analysis applications comprises an Al software application that performs an automated analysis of healthcare data to provide a prediction of a diagnosis regarding a medical condition or a recommendation regarding a treatment for a patient [Spurlock at Para. 0045 teaches the method 101 and system 401 may be provided with patient data from an individual. That is, the machine learning system 201 has learned, from the plurality of data sources 207, patterns or associations that are predictive of disease. The system 201 may then be applied to an individual to predicting a health state for the individual when the patient data presents one or more of the discovered associations. The predicted health state may be in any suitable format. For example, the predicted health state is presented as a form of future diagnosis. The machine learning system alerts a health professional that the individual patient is presenting results that are most consistent with a diagnosis, within a certain time frame in the future, of a specific disease], … [ … ]
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Saripalli/SUNDARARAMAN with the diagnosis of Spurlock with the motivation to improve quality of life for patients.
Regarding Claim 2
Saripalli/SUNDARARAMAN/Spurlock teach the system of claim 1,
Saripalli/SUNDARARAMAN/Spurlock further teach the system of claim 1, wherein the at least one processor is further programmed or configured to: transmit an output of the automated healthcare data analysis application to a destination system [Saripalli at Para. 0173 teaches at block 1324, the labeled event is output. For example, the labeled event can be written to an electronic medical record, used to trigger an appointment in a clinical scheduling system, used to trigger a lab request, used to trigger an exam request, used to prioritize image and/or other exam data in a radiology reading, displayed for user view and interaction, etc.].
Regarding Claim 3
Saripalli/SUNDARARAMAN/Spurlock teach the system of claim 2,
Saripalli/SUNDARARAMAN/Spurlock further teach wherein the destination system comprises: a picture archiving and communication system (PACS); an electronic medical record (EMR) system; a data reporting system; a communication device associated with a medical device; or a user device associated with a patient [Saripalli at Para. 0173 (interpreted as electronic medical record system)].
Regarding Claim 4
Saripalli/SUNDARARAMAN/Spurlock teach the system of claim 1,
Saripalli/SUNDARARAMAN/Spurlock further teach wherein the at least one processor is further programmed or configured to: train the machine learning model based on historic healthcare data from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system; a medical imaging system; a communication device associated with a medical device; a fluid injection system; a pathology information system; a laboratory information system; or a user device associated with a patient [Saripalli at Para. 0084 teaches Such artifacts are filtered from the stream using one or more statistics (e.g., median, beyond six sigma range, etc.) that can be obtained from the patient (e.g., current) and/or from prior records of patients who have undergone a similar procedure and may have involved one or more normalization techniques with respect to age, gender, weight, body type, etc. (patient records interpreted as electronic medical record system)].
Regarding Claim 5
Saripalli/SUNDARARAMAN/Spurlock teach the system of claim 1,
Saripalli/SUNDARARAMAN/Spurlock further teach wherein the machine learning model is configured to receive a data record as the input, wherein the data record comprises the plurality of features [SUNDARARAMAN at Para. 0035 (see Claim 1 for explanation)].
Regarding Claim 8
Saripalli/SUNDARARAMAN/Spurlock teach the system of claim 1,
Saripalli/SUNDARARAMAN/Spurlock further teach wherein the automated healthcare data analysis application comprises an Al based healthcare data analysis application [Saripalli at Para. 0095 teaches as such, trained, deployed AI models can be applied to 1D patient data to convert the patient time series data into a visual indication of a comparative value of the data. For example, processing the 1D time series patient data using an AI model, such as one or more models disclosed above, quantifies, qualifies, and/or otherwise compares the data to a normal value or values, a threshold, a trend, other criterion(-ia) to generate a color-coded, patterned, and/or shaded representation of the underlying time series (e.g., waveform, etc.) data (interpreted as an AI based healthcare data analysis application)].
Regarding Claim 9
A computer program product for managing automated healthcare data analysis applications using artificial intelligence (Al), the computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to:
receive healthcare data from a data source [Saripalli at Para. 0030 (see Claim 1 for explanation)] based on a device associated with the data source performing a patient procedure, wherein the data source comprises one of:
an electronic medical record (EMR) system;
a medical imaging system;
a communication device associated with a medical device;
a fluid injection system;
a pathology information system;
a laboratory information system;
or a user device associated with a patient [Saripalli at Para. 0136 (see Claim 1 for explanation)];
at least one feature associated with anatomical aspects of a body [Saripalli at Para. 0031 (see Claim 1 for explanation)];
at least one feature associated with a protocol of a device [Saripalli at Para. 0057 (see Claim 1 for explanation)];
and at least one feature associated with a manual configuration of a device [Saripalli at Para. 0033 (see Claim 1 for explanation)];
determine a classification of the healthcare data using a machine learning model, wherein the machine learning model is configured to provide an output based on the input [Saripalli at Para. 0042 (see Claim 1 for explanation)], … [ … ] … wherein, when determining the classification of the healthcare data using the machine learning model:
the machine learning model determines whether at least one feature of the plurality of features comprises:
a feature of a category associated with anatomical aspects of a body;
a feature of a category associated with protocol of a device;
a feature of a category associated with a characteristic of natural language processing;
a feature of a category associated with a manual configuration of a device;
or any combination thereof [Saripalli at Para. 045 (see Claim 1 for explanation)], and the machine learning model determines the classification of the healthcare data based on determining whether the at least one feature includes a feature of a category associated with anatomical aspects of a body, a feature of a category associated with a protocol of a device, a feature of a category associated with a characteristic of natural language processing, or a feature of a category associated with a manual configuration of a device [Saripalli at Para. 045 (see Claim 1 for explanation)];
Saripalli does not teach perform a feature extraction technique on the healthcare data to provide a plurality of features for an input to a machine learning model, wherein the plurality of features comprises:
at least one feature associated with a characteristic of natural language processing;
[ … ] …wherein the input comprises the plurality of features associated with the healthcare data and the output comprises a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications, and wherein one or more of the plurality of automated healthcare data analysis applications comprises an Al software application that performs an automated analysis of healthcare data to provide a prediction of a diagnosis regarding a medical condition or a recommendation regarding a treatment for a patient, … [ … ]
and provide the healthcare data to the automated healthcare data analysis application based on the classification of the healthcare data.
SUNDARARAMAN teaches perform a feature extraction technique on the healthcare data to provide a plurality of features for an input to a machine learning model, wherein the plurality of features comprises [SUNDARARAMAN at Para. 0035, 0038 (see Claim 1 for explanation)]:
at least one feature associated with a characteristic of natural language processing [SUNDARARAMAN at Para. 0023 (see Claim 1 for explanation)];
[ … ] …wherein the input comprises the plurality of features associated with the healthcare data [SUNDARARAMAN at Para. 0035 (see Claim 1 for explanation)] and the output comprises a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications [SUNDARARAMAN at Para. 0040 (see Claim 1 for explanation)], … [ … ]
and provide the healthcare data to the automated healthcare data analysis application based on the classification of the healthcare data [SUNDARARAMAN at Para. 0035, 0003 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine classification of Saripalli with the analysis of SUNDARARAMAN with the motivation to improve the overall process of performing data transformations and analyses [SUNDARARAMAN at Para. 0019].
Saripalli/SUNDARARAMAN do not teach [ … ] … and wherein one or more of the plurality of automated healthcare data analysis applications comprises an Al software application that performs an automated analysis of healthcare data to provide a prediction of a diagnosis regarding a medical condition or a recommendation regarding a treatment for a patient, … [ … ]
Spurlock teaches [ … ] … and wherein one or more of the plurality of automated healthcare data analysis applications comprises an Al software application that performs an automated analysis of healthcare data to provide a prediction of a diagnosis regarding a medical condition or a recommendation regarding a treatment for a patient [Spurlock at Para. 0045 (see Claim 1 for explanation)], … [ … ]
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Saripalli/SUNDARARAMAN with the diagnosis of Spurlock with the motivation to improve quality of life for patients.
Regarding Claim 10
Claim(s) 10 is/are analogous to Claim(s) 2, thus Claim(s) 10 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
Regarding Claim 11
Claim(s) 11 is/are analogous to Claim(s) 3, thus Claim(s) 11 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Regarding Claim 12
Claim(s) 12 is/are analogous to Claim(s) 4, thus Claim(s) 12 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4.
Regarding Claim 13
Claim(s) 13 is/are analogous to Claim(s) 5, thus Claim(s) 13 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 5.
Regarding Claim 16
Claim(s) 16 is/are analogous to Claim(s) 8, thus Claim(s) 16 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 8.
Regarding Claim 17
Saripalli teaches a method for managing automated healthcare data analysis applications using artificial intelligence (Al), comprising:
receiving healthcare data from a data source [Saripalli at Para. 0030 (see Claim 1 for explanation)] based on a device associated with the data source performing a patient procedure, wherein the data source comprises one of:
an electronic medical record (EMR) system;
a medical imaging system;
a communication device associated with a medical device;
a fluid injection system;
a pathology information system;
a laboratory information system;
or a user device associated with a patient [Saripalli at Para. 0136 (see Claim 1 for explanation)];
at least one feature associated with anatomical aspects of a body [Saripalli at Para. 0031 (see Claim 1 for explanation)];
at least one feature associated with a protocol of a device [Saripalli at Para. 0057 (see Claim 1 for explanation)];
and at least one feature associated with a manual configuration of a device;
determining a classification of the healthcare data using a machine learning model, wherein the machine learning model is configured to provide an output based on the input [Saripalli at Para. 0042 (see Claim 1 for explanation)], … [ … ] … wherein determining the classification of the healthcare data using the machine learning model comprises:
the machine learning model determining whether at least one feature of the plurality of features comprises:
a feature of a category associated with anatomical aspects of a body;
a feature of a category associated with protocol of a device;
a feature of a category associated with a characteristic of natural language processing;
a feature of a category associated with a manual configuration of a device;
or any combination thereof [Saripalli at Para. 045 (see Claim 1 for explanation)];
the machine learning model determining the classification of the healthcare data based on determining whether the at least one feature includes a feature of a category associated with anatomical aspects of a body, a feature of a category associated with a protocol of a device, a feature of a category associated with a characteristic of natural language processing, or a feature of a category associated with a manual configuration of a device [Saripalli at Para. 045 (see Claim 1 for explanation)];
Saripalli does not teach performing a feature extraction technique on the healthcare data to provide a plurality of features for an input to a machine learning model, wherein the plurality of features comprises:
at least one feature associated with a characteristic of natural language processing;
[ … ] … wherein the input comprises the plurality of features associated with the healthcare data and the output comprises a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications, and wherein one or more of the plurality of automated healthcare data analysis applications comprises an Al software application that performs an automated analysis of healthcare data to provide a prediction of a diagnosis regarding a medical condition or a recommendation regarding a treatment for a patient, … [ … ]
and providing the healthcare data to the automated healthcare data analysis application based on the classification of the healthcare data.
SUNDARARAMAN teaches perform a feature extraction technique on the healthcare data to provide a plurality of features for an input to a machine learning model, wherein the plurality of features comprises [SUNDARARAMAN at Para. 0035, 0038 (see Claim 1 for explanation)]:
at least one feature associated with a characteristic of natural language processing [SUNDARARAMAN at Para. 0023 (see Claim 1 for explanation)];
[ … ] …wherein the input comprises the plurality of features associated with the healthcare data [SUNDARARAMAN at Para. 0035 (see Claim 1 for explanation)] and the output comprises a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications [SUNDARARAMAN at Para. 0040 (see Claim 1 for explanation)], … [ … ]
and provide the healthcare data to the automated healthcare data analysis application based on the classification of the healthcare data [SUNDARARAMAN at Para. 0035, 0003 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine classification of Saripalli with the analysis of SUNDARARAMAN with the motivation to improve the overall process of performing data transformations and analyses [SUNDARARAMAN at Para. 0019].
Saripalli/SUNDARARAMAN do not teach [ … ] … and wherein one or more of the plurality of automated healthcare data analysis applications comprises an Al software application that performs an automated analysis of healthcare data to provide a prediction of a diagnosis regarding a medical condition or a recommendation regarding a treatment for a patient, … [ … ]
Spurlock teaches [ … ] … and wherein one or more of the plurality of automated healthcare data analysis applications comprises an Al software application that performs an automated analysis of healthcare data to provide a prediction of a diagnosis regarding a medical condition or a recommendation regarding a treatment for a patient [Spurlock at Para. 0045 (see Claim 1 for explanation)], … [ … ]
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Saripalli/SUNDARARAMAN with the diagnosis of Spurlock with the motivation to improve quality of life for patients.
Regarding Claim 18
Claim(s) 18 is/are analogous to Claim(s) 2, thus Claim(s) 18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
Regarding Claim 19
Claim(s) 19 is/are analogous to Claim(s) 4, thus Claim(s) 19 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4.
Regarding Claim 20
Claim(s) 20 is/are analogous to Claim(s) 5, thus Claim(s) 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 5.
Claims 21-23 rejected under 35 U.S.C. 103(a) as being unpatentable over Saripalli, SUNDARARAMAN, Spurlock as applied to claims 1, 9, 17 above, and further in view of TWEEDIE et al (US Publication No. 20210158930).
Regarding Claim 21
Saripalli/SUNDARARAMAN/Spurlock teach the computer product of claim 1,
Saripalli/SUNDARARAMAN/Spurlock further teach wherein the one or more instructions further cause the at least one processor to:
and wherein, the one or more instructions that cause the at least one processor to provide the healthcare data to the automated healthcare data analysis application based on the classification of the healthcare data, cause the at least one processor to [Saripalli at Para. 0103 teaches metadata extracted from gathered data 710, 720, associated machine configuration information, etc., can be used to select one or more model(s) 752-758 as appropriate to analyze certain data 710, 720, predict certain events, classify certain conditions, etc. Selected model(s) 752-758 then map to selected data points 740 from the set of data 710, 720, for example]:
provide the healthcare data to the automated healthcare data analysis application based on the classification of the healthcare data based on determining that the imaging data included in the healthcare data is complete for analysis by the automated healthcare data analysis application [Saripalli at Para. 0103 (interpreted to combine with completed imaging data of TWEEDIE)].
Saripalli/SUNDARARAMAN/Spurlock do not teach determine whether imaging data included in the healthcare data is complete for analysis by the one or more of the plurality of automated healthcare data analysis applications;
TWEEDIE teaches determine whether imaging data included in the healthcare data is complete for analysis by the one or more of the plurality of automated healthcare data analysis applications [TWEEDIE at Para. 0274 teaches the method 860 may be for determining that a current study that is received at a platform has been completely collected at an image acquisition device 112 in FIG. 1A];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Saripalli/SUNDARARAMAN/Spurlock with the completed studies of TWEEDIE with the motivation to improve medical image processing.
Regarding Claim 22
Claim(s) 22 is/are analogous to Claim(s) 21, thus Claim(s) 22 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 21.
Regarding Claim 23
Claim(s) 23 is/are analogous to Claim(s) 21, thus Claim(s) 23 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 21.
Response to Arguments
Claim Objections
Regarding the objection(s) to Claims 5, the Applicant has amended the claims to overcome the basis/bases of objection.
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-5, 8-13, 16-23, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
The limitations of claim 1 demonstrate that claim 1 is directed to an unconventional system that improves determining how to route input data accurately to specific health informatics applications through the use of a machine learning model that is configured to output a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications.
Regarding (a), the Examiner respectfully disagrees. There is no technical problem caused by the computer, machine learning model or technological environment in regards to routing. If no technical problem can be found, no practical application can be found. In regards to the statement regarding an “unconventional system”, Examiner respectfully refers to the response to (c) ahead in this section of the response to argument. Further, the ‘unconventionality’ of ab abstract idea is immaterial to the subject matter eligibility analysis.
the method of claim 1 provides a practical application of a solution to the technical problem of routing input data accurately to specific health informatics applications. Furthermore, the limitations of claim 1 show that claim 1 is not a "method of organizing human activity" based on the performance of a feature extraction technique on the healthcare data to provide a plurality of features for an input to a machine learning model, where the plurality of features comprises: … [ … ]
Regarding (b), the Examiner respectfully disagrees. The problem of routing input data accurately is not a technical problem caused by the computer, machine learning model, or technological environment. As such, it is not a technical problem caused by the technological environment to which the abstraction is confined, a generic computer. Further, there is no claimed indication as to how the feature extraction occurs and thus given the broadest reasonable interpretation, this is interpreted for be part of the abstraction.
In addition, under the second prong of the Alice decision, Applicant submits that the claims recite "an invention that is not merely the routine or conventional use" of computers or the Internet. DDR Holdings, 773 F.3d at 1259. Taking the limitations of claim 1 individually or in combination, they provide meaningful limitations with regard to an alleged judicial exception of a mental process. See MPEP § 2106.05(e) ("When evaluating whether additional elements meaningfully limit the judicial exception, it is particularly critical that examiners consider the additional elements both individually and as a combination. When an additional element is considered individually by an examiner, the additional element may be enough to qualify as 'significantly more' if it meaningfully limits the judicial exception and may also add a meaningful limitation by integrating the judicial exception into a practical application. However, even in the situation where the individually-viewed elements do not add significantly more or integrate the exception, those additional elements when viewed in combination may render the claim eligible.").
Regarding (c), the Examiner respectfully disagrees. MPEP 2106.05(d) states: “Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry (emphasis added).” Further, MPEP 2106.05(I) states: “As made clear by the courts, the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter (internal quotations omitted, emphasis original).” As such, it is only the additional elements identified by the Examiner to not be part of the abstract idea that are analyzed to determine whether they represent well-understood, routine, conventional activities in the field of the invention.
In that regard, MPEP 2106.05(d)(I) indicates that in determining whether the additional elements represent are well-understood, routine, conventional activities, the Examiner should consider whether the additional elements (1) provide an improvement to the technological environment to which the claim is confined, (2) whether the additional elements are mere instructions to apply the judicial exception, or (3) whether the additional elements represent insignificant extra-solution activity. The additional elements of the claims do not provide significantly more based on this inquiry.
Taking these in turn, whether the additional elements of the claim provide an improvement was analyzed/addressed in the 2A2 analysis no improvement was present because there is no technical problem caused by the computer or machine learning model. The technological environment to which the claims are confined (a general-purpose computer performing generic computer functions [see Spec. Para. 0006]) is recited at a high level of generality and has been found by the courts to be insufficient to provide a practical application (see MPEP 2106.05(d)(II); Alice Corp.).
Finally, none of the additional elements of the claim were found to represent extra-solution activity and thus no well-understood, routine, conventional analysis is required. MPEP 2106.07(a) states “At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B.” This was not asserted. As such, when viewed either individually or as an ordered combination, the additional elements do not provide significantly more to the abstract idea and the claims are not subject matter eligible.
Rejection under 35 U.S.C. § 102/103
Regarding the rejection of Claims 1-5, 8-13, 16-23, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Applicant argues:
The cited references do not teach or suggest, alone or in combination, all of the limitations of claim 1. For example, none of Saripalli, Sundararaman, or Spurlock, alone or in combination, teaches or suggests a processor to perform a feature extraction technique on the healthcare data to provide a plurality of features for an input to a machine learning model, where the plurality of features comprises: at least one feature associated with anatomical aspects of a body; at least one feature associated with a protocol of a device; at least one feature associated with a characteristic of natural language processing; and at least one feature associated with a manual configuration of a device.
Regarding (a), the Examiner respectfully disagrees. No evidence is provided to support statement. Given the broadest reasonable interpretation, the combination of references teaches the claimed features as provided in the basis of rejection.
Further, none of Saripalli, Sundararaman, or Spurlock, alone or in combination, teaches or suggests a processor to determine a classification of the healthcare data using a machine learning model, wherein the machine learning model is configured to provide an output based on the input, wherein the input comprises the plurality of features associated with the healthcare data: at least one feature associated with anatomical aspects of a body; at least one feature associated with a protocol of a device; at least one feature associated with a characteristic of natural language processing; and at least one feature associated with a manual configuration of a device, and the output comprises a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications, and wherein one or more of the plurality of automated healthcare data analysis applications comprises an AI software application that performs an automated analysis of healthcare data to provide a prediction of a diagnosis regarding a medical condition or a recommendation regarding a treatment for a patient.
Regarding (b), the Examiner respectfully disagrees. No evidence is provided to support statement. Given the broadest reasonable interpretation, the combination of references teaches the claimed features as provided in the basis of rejection.
Additionally, none of Saripalli, Sundararaman, or Spurlock, alone or in combination, teaches or suggests that when determining the classification of the healthcare data using a machine learning model, the machine learning model determines whether at least one feature of the plurality of features comprises: a feature of a category associated with anatomical aspects of a body; a feature of a category associated with protocol of a device; a feature of a category associated with a characteristic of natural language processing; a feature of a category associated with a manual configuration of a device; or any combination thereof, and the machine learning model determines the classification of the healthcare data based on determining whether the at least one feature includes a feature of a category associated with anatomical aspects of a body, a feature of a category associated with a protocol of a device, a feature of a category associated with a characteristic of natural language processing, or a feature of a category associated with a manual configuration of a device.
Regarding (c), the Examiner respectfully disagrees. No evidence is provided to support statement. Given the broadest reasonable interpretation, the combination of references teaches the claimed features as provided in the basis of rejection. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
As seen above, in paragraph [0035] Sundararaman addresses extracting data elements from data files and, in paragraph [0040], Sundararaman teaches that a healthcare data platform may use a machine learning model to determine a score for a data element in a data file based on a combination of data elements present in the data file. Nothing in paragraphs [0035] and [0040], or any other part of Sundararaman teaches or suggests that an input to a machine learning model comprises a plurality of features that include at least one feature associated with anatomical aspects of a body; at least one feature associated with a protocol of a device; at least one feature associated with a characteristic of natural language processing; and at least one feature associated with a manual configuration of a device, or that an output of a machine learning model comprises a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications.
Regarding (d), the Examiner respectfully disagrees. Applicant’s argument ignores the prior art of Saripalli used to cover the limitation. Sundararaman alone at Para. 0035 and 0040 do not teach the limitation, but the combination of Paras of Saripalli does. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Bhotika et al (US Publication No. 20160283657) discloses methods and apparatus for analyzing, mapping and structuring healthcare data.
CHEN et al (US Publication No. 20210035680) discloses systems and methods for automating clinical workflow decisions.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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