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 preliminary amendment filed on 28 July 2025: claims 1-12 have been canceled; claims 13-21 are newly added.
Now claims 13-21 are pending.
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 14 November 2023, has been considered by the Examiner.
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 13-21 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 13 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite information processing device (i.e., system) and method for performing the limitations of:
Claim 13, which is representative of claim 18
a cardiac disease database; […], wherein the cardiac disease database is configured to [… organize …] a plurality of cases containing: one or more factors related to a cardiac disease; a variation amount of the one or more factors; an electrocardiogram and an echocardiographic parameter as elements, [… maintain …] the plurality of cases extracted from the cardiac disease database, […] create a plurality of groups by extracting a part or an entire of the plurality of cases […] and classifying or processing the plurality of cases in accordance with a predetermined standard, and […] compare the plurality of groups with each other and output an evaluation result.
as drafted, is a system, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with processing and storage units (claim 13) and a computer (claim 18) the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, but for processing and storage units (claim 13) and a computer (claim 18), the claim encompasses using a database to collect and organize data, to organize the data in the database to provide to a human user an output of the organized data. 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 “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of processing and storage units (claim 13) and a computer (claim 18), which implements the abstract idea. The processing and storage units (claim 13) and a computer (claim 18) are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification […]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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 recites the additional elements of “store… to store…”. The “store… to store…” is recited at a high-level of generality (i.e., as a general means of storing data) and amounts to the mere storage of data, which is a form of extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does 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 processing and storage units (claim 13) and a computer (claim 18), to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using generic hardware components cannot provide an inventive concept ("significantly more").
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “store… to store…” were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The “store… to store…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(iv) “Storing and retrieving information in memory” is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 14-17 and 19-21 are similarly rejected because either further define 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.
Claim 14 and 19 further describes the classes used to classify and comparison of data, however no additional elements are claimed, but does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claims 15 and 20 further describe extraction (i.e., organization) of data for further comparisons, but does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claims 16 and 21 recite the additional element of a “classifier”, however this “classifier” is recited at a high-level of generality (i.e., training an off-the-shelf machine learning algorithm in a generic manner) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does 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 a “classifier” was considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Narayan (2021/0236053): see below but at least paragraph [0022]; Haeusser (2020/0245885): see below but at least paragraph [0016]; training and use of a machine learning model is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claim 17 further describes the creation of groups and comparison of data, but does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 13-21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2021/0236053 (hereafter “Narayan”), in view of U.S. Patent Pub. No. 2020/0245885 (hereafter “Haeusser”).
Regarding (New) claim 13, Narayan teaches an information processing device (Narayan: Figures 1-4, paragraph [0003], “a system and method for defining digital phenotypes for a disease based on data collected for a specific person”), comprising:
a cardiac disease database (Narayan: Figures 1-4, 15, paragraph [0098]-[0099], “Population database 120 provides a mathematical reference for the personal data and may include population stored data, and optional data streams. The data in this database may take the form of time-varying streamed data, but may also include accumulated and stored data from a database”, paragraph [0022], “Exemplary implementations include cardiac applications in heart rhythm disorders, in coronary artery disease and in heart failure”, paragraph [0027], “a de-identified large digital prospective registry of data”, paragraph [0106], “Other sources of streamed or input data 205 obtained from clinical systems, hospital databases”, paragraph [0113], “the database 290 can store maps of potential AF source locations for other individuals of known de-identified personal digital phenotypes, which can be used to guide therapy in this individual”, paragraph [0128], “Data from population stored database 508 (database 449 in FIG. 4)”. The Examiner notes the all of the various database all read on a cardiac disease database under the broadest reasonable interpretation);
a storage unit; and a processing unit (Narayan: Figures 1-4, 15, paragraph [0119], “RAM 358… Data and control signals are sent to another Central processing unit (CPU) 375 which may comprise fast processors, parallel processors including graphical processing units (GPU) 378 that can be used to perform more demanding computations”, paragraph [0201], “The computer system may include a main memory 2304 and a static memory 2306 that can communicate with each other via a bus 2326”), wherein
the cardiac disease database is configured to store a plurality of cases containing: one or more factors related to a cardiac disease (Narayan: paragraph [0018], “the presence or absence of specific features of electrical data”, paragraphs [0041]-[0042], “The patient data elements may include one or more indexes of electrical signals, hemodynamic data, cardiac structure from imaging, clinical factors associated with heart or lung conductions, nerve signals, genetic profile, biomarkers of metabolic status, and patient movement… using a machine learning algorithm trained on one or more reference signals associated with different heart rhythms”, paragraph [0070], ““Biological signal” means a signal is produced by the body, and can reflect one or more bodily systems. For instance, the heart rate reflects cardiac function, autonomic tone and other factors”, paragraph [0129], “featurize the data in step 514”, paragraph [0157], “Steps 812-824 identify different features of AF that may be identified by mapping”);
a variation […] of the one or more factors (Narayan: paragraph [0008], “variability in disease expression or response to therapy”, paragraph [0016], “different patient subtypes”, paragraphs [0030]-[0033], “Data types used within the inventive methods may be selected and sorted to represent different facets of a biological organ or process… Time-invariant and time-varying data streams are important inputs that may represent normal variations or disease states”, paragraph [0100], “distributed representations to create a personal digital phenotype”, paragraph [0157], “Steps 812-824 identify different features of AF that may be identified by mapping”);
an electrocardiogram and an echocardiographic parameter as elements (Narayan: paragraph [0104], “sensors 250 detect cardiac activation via the patient's surface (e.g., electrocardiogram—ECG). Other sensors (not shown) may detect cardiac activation remotely without contact with the patient (e.g., magnetocardiogram). As another example, some sensors may also derive cardiac activation information from cardiac motion of a non-electrical sensing device (e.g., echocardiogram, Doppler signals of blood flow, red cell tagged scans).”),
the storage unit is configured to store the plurality of cases extracted from the cardiac disease database (Narayan: paragraph [0044], “a computing device configured to: collect the at least one data stream; collect patient data elements comprising one or more of demographic, clinical, laboratory, pathology, chemical, image, historical, genetic, and activity data for the patient; process the at least one data stream and the patient data elements in a processing module configured to execute a partitioning algorithm to generate a personalized digital phenotype (PDP)”, paragraph [0098], “store data”, paragraph [0120], “Computations by the device may comprise reading, compressing or storing voltage output”, paragraph [0128], “Data from population stored database 508 (database 449 in FIG. 4) are incorporated using statistical associations”, paragraph [0133], “using mathematical models to integrate data streams and stored data”),
the processing unit is configured to create a plurality of groups by extracting a part or an entire of the plurality of cases from the storage unit and classifying or processing the plurality of cases in accordance with a predetermined standard (Narayan: paragraph [0027], “creates a quantitative personal digital phenotype (PDP)… Mathematical, statistical, and machine learning techniques are used to classify or partition these datasets to define quantitative forms of a disease process”, paragraph [0041], “comparing the PDP to a digital taxonomy constructed from prior data to classify the patient into one or more quantitative disease classifications; and personalizing treatment for the patient based on the one or more quantitative disease classifications”, paragraph [0129], “featurization steps can be used, using widely available libraries such as TSFresh (Time Series FeatuRe Extraction)”, paragraph [0140], “processed prior to entering the final network, using feature extraction, cluster analysis and pre-processing networks” paragraph [0154], “Inputs to decision trees will be extracted features from the images and time series signals”), and
the processing unit is configured to compare the plurality of groups with each other and output an evaluation result (Narayan: paragraph [0027], “creates a quantitative personal digital phenotype (PDP)… Mathematical, statistical, and machine learning techniques are used to classify or partition these datasets to define quantitative forms of a disease process”, paragraph [0041], “comparing the PDP to a digital taxonomy constructed from prior data to classify the patient into one or more quantitative disease classifications; and personalizing treatment for the patient based on the one or more quantitative disease classifications”, paragraph [0101], “The status information is communicated in step 150 to a unit for display, e.g., a smartphone app, a dedicated device, or existing medical device. The health/disease information can be used to guide therapy via a therapy unit 155.”).
Narayan may not explicitly teach (underlined below for clarity):
a variation amount of the one or more factors;
Haeusser teaches a variation amount of the one or more factors (Haeusser: paragraph [0130]-[0132], “The second step of method 200 takes the spatial distributions of all electrodes and their normalized voltage values at discrete times (e.g., the data represented by the box plots corresponding to selected discrete times within the selected time window over which electrogram signals were acquired and measured), and estimates or generates from such data or box plots corresponding to given discrete times respective continuous voltage surfaces (or action potential waveform estimates) in space… changes in the spatial shape or expression of the action potential wavefront from one sample to the next are typically relatively small (e.g., about 1 mm) compared to the electrode distances… wave shapes differ only by a small delta between individual samples, and minimum and maximum values are normalized, shift vectors can be calculated”);
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a variation amount as taught by Haeusser within the use of variations of various factors as taught by Narayan with the motivation of “improved means and methods of acquiring and processing intracardiac electrogram signals that reliably and accurately yield the precise locations and sources of cardiac rhythm disorders in a patient's heart” (Haeusser: paragraph [0012]).
Regarding (New) claim 14, Narayan and Haeusser teach the limitations of claim 13, and further teach the processing unit is configured to extract the entire of the plurality of cases from the storage unit and classify the plurality of cases into two groups of positive and negative or into a plurality of positive groups depending on a degree of progress of the cardiac disease by sequentially inputting the electrocardiogram and the echocardiographic parameter contained in the plurality of cases into a classifier which performs a diagnosis of the cardiac disease (Narayan: paragraph [0041], “classify the patient into one or more quantitative disease classifications”, paragraph [0106], “comparing metrics in an individual to normal and abnormal values”, paragraph [0148], “Mathematical and network analysis 606 are used to identify abnormalities, compared first to stored personal phenotypes in database 609… disease worsening (e.g., progression”, paragraph [0152], “predicts negative outcome… predicting positive outcome”, paragraph [0155], “inputs are deranged singly or in batches and the network 712 is rerun to identify which abnormal input combination causes the network to most closely recapitulate the “disease state.””, paragraph [0200], “executing a set of instructions (sequentially”), and
the processing unit is configured to compare the classified groups with each other and identify the one or more factors as a physiological biomarker associated with the cardiac disease when a statistically significant difference is recognized in the variation amount of the one or more factors (Narayan: paragraph [0096], “Deviations from normal can be quantified in the index individual compared to his/her own data, beyond pre-specified “tolerance limits”, and compared to different populations. In a preferred embodiment, this is accomplished by sensing data streams 100 or acquiring data on a repeated basis… a “population” range for “normal” and “disease” is applied”, paragraph [0150], “compared to population disease taxonomy 627 to determine if the abnormality for the individual falls into “out-of-healthful range” for the population”, paragraph [0157], “identify markers”).
The motivation to combine is the same as in claim 13, incorporated herein.
Regarding (New) claim 15, Narayan and Haeusser teach the limitations of claim 14, and further teach wherein the processing unit is configured to extract a case most approximate to an actual electrocardiogram and an actual echocardiographic parameter of an individual to be diagnosed or the actual electrocardiogram and the actual echocardiographic parameter of the individual corrected according to a predetermined standard, the case being extracted from the storage unit or the plurality of groups in which the electrocardiogram stored in the storage unit is corrected according to a physique of the individual to be diagnosed (Haeusser: Figure 5, paragraphs [0014]-[0016], “receive the body surface electrogram signals from the plurality of body surface electrodes located on the patient's body, where amplitudes of the body surface electrogram signals received by the at least one computing device have been at least one of conditioned, amplified, normalized, filtered, and adjusted by the data acquisition device before being provided to the computing device… classification of patients as at least one of types A, B and C”, paragraph [0123], “in step 210 the amplitudes of the various traces or electrograms can be normalized or adjusted in the time domain according to a selected standard deviation”, paragraph [0265], “where the data from the patients relate to one or more of atrial volume, atrial dimensions, patient age, patient weight, patient height, and patient body mass index”), and
the processing unit is configured to compare the variation amount of the one or more factors of the extracted case with the variation amount of the one or more factors identified as the physiological biomarker (Narayan: paragraph [0027], “creates a quantitative personal digital phenotype (PDP)… Mathematical, statistical, and machine learning techniques are used to classify or partition these datasets to define quantitative forms of a disease process”, paragraph [0041], “comparing the PDP to a digital taxonomy constructed from prior data to classify the patient into one or more quantitative disease classifications; and personalizing treatment for the patient based on the one or more quantitative disease classifications”; Haeusser: paragraph [0215], “a comparison of A/B-type patients to C-type patients”).
The motivation to combine is the same as in claim 13, incorporated herein.
Regarding (New) claim 16, Narayan and Haeusser teach the limitations of claim 13, and further teach wherein the processing unit is configured to classify the plurality of cases into two groups of effective and ineffective by extracting the entire of the plurality of cases from the storage unit and sequentially inputting the electrocardiogram and the echocardiographic parameter contained in the plurality of cases into a classifier which predicts a therapeutic effect of a medical equipment or a medical agent (Narayan: paragraph [0032], “predict optimal therapy based on specific characteristics of the individual”, paragraph [0041], “classify the patient into one or more quantitative disease classifications”, paragraph [0106], “comparing metrics in an individual to normal and abnormal values”, paragraph [0148], “Mathematical and network analysis 606 are used to identify abnormalities, compared first to stored personal phenotypes in database 609… disease worsening (e.g., progression”, paragraph [0152], “prediction of an AF outcome (e.g., success or failure of ablation)… predicts negative outcome… predicting positive outcome”, paragraph [0155], “inputs are deranged singly or in batches and the network 712 is rerun to identify which abnormal input combination causes the network to most closely recapitulate the “disease state.””, paragraph [0200], “executing a set of instructions (sequentially”), and
the processing unit is configured to compare the classified groups with each other and identify the one or more factors as a physiological biomarker associated with the therapeutic effect when a statistically significant difference is recognized in the variation amount of the one or more factors (Narayan: paragraph [0096], “Deviations from normal can be quantified in the index individual compared to his/her own data, beyond pre-specified “tolerance limits”, and compared to different populations. In a preferred embodiment, this is accomplished by sensing data streams 100 or acquiring data on a repeated basis… a “population” range for “normal” and “disease” is applied”, paragraph [0150], “compared to population disease taxonomy 627 to determine if the abnormality for the individual falls into “out-of-healthful range” for the population”, paragraph [0157], “identify markers”).
The motivation to combine is the same as in claim 13, incorporated herein.
Regarding (New) claim 17, Narayan and Haeusser teach the limitations of claim 13, and further teach wherein the processing unit is configured to create a first group formed by extracting the plurality of cases having a predetermined feature from the storage unit, the processing unit is configured to create a second group formed by adjusting the variation amount of the one or more factors with respect to the first group assuming a case where a medical agent is administered to the first group, the processing unit is configured to create a third group formed by extracting a group most approximate to a variation distribution of the one or more factors of the second group from the storage unit (Narayan: paragraph [0041], “classify the patient into one or more quantitative disease classifications”, paragraph [0152], “prediction of an AF outcome (e.g., success or failure of ablation)… predicts negative outcome… predicting positive outcome”; Haeusser: paragraph [0016], “classification of the patient as one of types A, B and C; and further wherein the computing device is configured to: (iv) process the conditioned electrogram data and positional data in the trained machine learning model to generate the one or more predictions or results; and (v) display the one or more predictions or results on the display or monitor to the user”), and
the processing unit is configured to compare a distribution of the electrocardiogram and the echocardiographic parameter of the plurality of cases included in the first group with the electrocardiogram and the echocardiographic parameter of the plurality of cases included in the third group (Narayan: paragraph [0100], “distributed representations to create a personal digital phenotype (PDP) in step 130 and compare the PDP to a digital taxonomy… compare these to quantitative traits in a relevant population… comparator population”; Haeusser: paragraph [0241], “calculated potential distributions”, paragraph [0248], “comparison to other data”).
The motivation to combine is the same as in claim 13, incorporated herein.
REGARDING CLAIM(S) 18-21
Claim(s) 18-21 is/are analogous to Claim(s) 13-16, thus Claim(s) 18-21 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 13-16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Pub. No. 20120215122 (hereafter “Marrouche”) teaches a dynamic display of cardio data for arrhythmia management.
U.S. Patent Pub. No. 20230050834 (hereafter “Tenbrink”) teaches atrial fibrillation determination by signal processing electrical signals of the heart.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew E Lee whose telephone number is (571)272-8323. The examiner can normally be reached M-Th 9-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached on 571-270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684