Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
In the amendments filed 18 December 2025:
Claims 1,5-6,9,13-14,17,20 are amended
Claim(s) 1-20 are pending
Claim Objections
Claim 5 is objected to because the wherein clause at the end of the claim does not state what is further limited. Per mirrored claims, 13 and 20, the wherein clause should be directed to “the plurality of features”. The claim has been evaluated in accordance with Claims 13 and 20. Appropriate correction is required.
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 1-20 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. The claim recites a system, computer program product and method, which are within a statutory category. The limitations of:
Claim 1
receive healthcare data from a data source;
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;
determine a classification of the healthcare data, wherein the model is 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,
an AI software application 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 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.
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 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 element of using the trained machine learning model to classify healthcare data 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 2-8, 10-16, and 18-20 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)…, 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) 6, 14 merely describe(s) the machine learning model, which further defines the abstract idea.
Claim(s) 7, 15 merely describe(s) what the data source comprises, which further defines the abstract idea.
Claim(s) 7, 15 also includes the additional element 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” which is analyzed the same as the “device” and does not provide a practical application or significantly more for the same reasons.
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 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 1-20 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 [Saripalli at Para. 0030 teaches medical data can be obtained from imaging devices, sensors, laboratory tests, and/or other data sources];
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], … [ … ]
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 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 [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. 0003 teaches analyzing, by the device and based on the intent classification, the plurality of services, to identify a target service to enable (intent classification interpreted as healthcare data classification)].
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 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 [Saripalli at Para. 0168 teaches if detecting, then, at block 1316, one or more AI models are trained on aggregated time series data. For example, an RL model, a hybrid RL model, a deep learning model, a combination of hybrid RL+DL, etc., are trained on a set of aggregated time series data such as patient physiological data, machine data, etc. Patient physiological data includes one or more of vitals, heart rate, heart rhythm, blood pressure (systolic and diastolic), etc. Machine data includes one or more of tidal volume, patient-controlled anesthesia (PCA) (e.g., an injection, etc.), PCA lockout (minimum), PCA medication, PCA total dose, etc].
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 [SUNDARARAMAN at Para. 0035 (see Claim 1 for explanation)], wherein the data record comprises:
Regarding Claim 6
Saripalli/SUNDARARAMAN/Spurlock teach the system of claim 5,
Saripalli/SUNDARARAMAN/Spurlock further teach wherein the machine learning model is configured to determine 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)].
Regarding Claim 7
Saripalli/SUNDARARAMAN/Spurlock teach the system of claim 1,
Saripalli/SUNDARARAMAN/Spurlock further teach wherein the data source comprises: 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)].
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 AI 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
Saripalli teaches a computer program product for managing automated healthcare data analysis applications using artificial intelligence (AI), 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)];
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)], … [ … ]
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 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;
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 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;
Spurlock teaches 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 [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 14
Claim(s) 14 is/are analogous to Claim(s) 6, thus Claim(s) 14 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 6.
Regarding Claim 15
Claim(s) 15 is/are analogous to Claim(s) 7, thus Claim(s) 15 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
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 (AI), comprising:
receiving healthcare data from a data source [Saripalli at Para. 0030 (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)];
determining a classification of the healthcare data using a machine learning model, wherein the machine learning model is configured to provide an output the input [Saripalli at Para. 0042 (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 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;
and providing the healthcare data to the automated healthcare data analysis application based on the classification of the healthcare data.
SUNDARARAMAN teaches 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 [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 providing 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 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 [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.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-20, 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:
As seen above, 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: 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 use of a machine learning model that outputs a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications.
In addition, under the second prong of the Alice decision, Applicants submit 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 (a), the Examiner respectfully disagrees. There is no technical problem caused by the computer. The manually programmed processes in Para. 0005 of the Specification is a manual problem, not a technical problem caused by the computer. Therefore, a practical application is not found.
Regarding (b), the Examiner respectfully disagrees. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to classify healthcare data. The Examiner notes that Applicant’s Specification describes processing (see Spec. Para. 0005) as a human activity. Furthermore, the Examiner submits that healthcare itself is inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to classify healthcare data, the claimed invention is directed to an abstract idea.
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).” As such, whether the limitation of the claim that are part of the abstraction are unconventional is irrelevant. 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 no technical problem can be found with the routing of input data accuracy. The technological environment to which the claims are confined (a general-purpose computer performing generic computer functions [see Spec. Para. 0007]) 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.).
As stated by the applicant, it is the additional elements of the claim that are evaluated as to whether they are unconventional. There is nothing in the use of a computer to process data that is unconventional. The Applicant has pointed to nothing that indicates otherwise.
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.
Rejection under 35 U.S.C. § 112
Regarding the indefiniteness rejection of Claims 1, 9, 17, the Applicant has amended the Claims to overcome the basis of the rejection.
Rejection under 35 U.S.C. § 102/103
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment and/or afforded by the present RCE.
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:
Vdovjak et al (US Publication No. 20220076078) discloses a method and system for training and applying a classifier.
Qadir et al (US Publication No. 20190347571) discloses methods and systems for training a classifier.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683