DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Application filed on January 30, 2024. Claims 1-20 are pending in the case. Claims 1, 11, and 18 are the independent claims.
This action is non-final.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental steps) without significantly more. This judicial exception is not integrated into a practical application because any additional elements amount to implementing the abstract idea on a generic computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding independent claims 1, 11, and 18, and relying on the evaluation flowchart in MPEP 2106:
Step 1 (Is the claim to a process, machine, manufacture, or composition of matter?): Yes. Claim 1 is a method (process). Claim 11 is a computer system (machine). Claim 18 is a storage medium (article of manufacture).
Step 2a Prong One (Does the claim recite an abstract idea?): Yes. Claims 1, 11, and 18 recite:
determining…one or more signal classifications for the one or more data subsets…aggregating…the one or more signal classifications to determine a result classification (a mental process of observation and determination; Examiner notes that paragraph 0003 of the specification of the instant application indicates that conventionally, a veterinarian (i.e. a human) is capable of analyzing ECG signals to determine whether to classify them as normal or abnormal).
Under the broadest reasonable interpretation, these steps may be performed mentally, using mental observation and mental determination, including by a human using a physical aid such as pen and paper, including a human mentally performing observations and mentally performing mathematical calculations, and therefore correspond to the Mental Processes grouping.
Step 2a Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?): No. Claims 1, 11, and 18 additionally recite:
receiving, by one or more processors, electrocardiogram data of a canine, the electrocardiogram data including at least one electrocardiogram signal (insignificant extra-solution activity as discussed in MPEP 2106.05(g), with respect to the receiving limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by one or more processors; a field of use and technological environment as discussed in MPEP 2106.05(h) with respect to the received data being electrocardiogram data of a canine and including an electrocardiogram signal);
segmenting, by the one or more processors, the electrocardiogram data into one or more data subsets (insignificant extra-solution activity as discussed in MPEP 2106.05(g), with respect to the segmenting limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors);
pre-processing, by the one or more processors, the one or more data subsets, the pre-processing including excluding the one or more data subsets that include a poor electrocardiogram signal (insignificant extra-solution activity as discussed in MPEP 2106.05(g), with respect to the pre-processing and excluding limitations, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors);
augmenting, by the one or more processors, the one or more data subsets (insignificant extra-solution activity as discussed in MPEP 2106.05(g), with respect to the augmenting limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors);
determining, by the one or more processors and using a trained machine-learning model… (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
aggregating, by the one or more processors, the one or more signal classifications (insignificant extra-solution activity as discussed in MPEP 2106.05(g), with respect to the aggregating limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors);
outputting, by the one or more processors, the result classification to an electronic storage device and/or a display (insignificant extra-solution activity as discussed in MPEP 2106.05(g), with respect to the outputting limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors).
Claim 1 additionally recites that the method is computer-implemented (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Claim 11 additionally recites a computer system for classifying electrocardiogram signals of a canine, the computer system comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform operations comprising the steps discussed above (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Claim 18 additionally recites non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for classifying electrocardiogram signals of a canine, the operations comprising the limitations discussed above (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components.
Step 2b (Does the claim recite additional elements that amount to siqnificantly more than the judicial exception): No. Relying on the same analysis as Step 2a Prong Two (see MPEP 2106.05.I.A: Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:…Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP 2106.05(f));…Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception...; Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g);…)), claims 1 and 11 do not recite any additional elements that amount to significantly more than the abstract idea. As discussed above, Claims 1, 11, and 18 additionally recite:
receiving, by one or more processors, electrocardiogram data of a canine, the electrocardiogram data including at least one electrocardiogram signal (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting with respect to the receiving limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by one or more processors; a field of use and technological environment as discussed in MPEP 2106.05(h) with respect to the received data being electrocardiogram data of a canine and including an electrocardiogram signal);
segmenting, by the one or more processors, the electrocardiogram data into one or more data subsets (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting with respect to the segmenting limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors);
pre-processing, by the one or more processors, the one or more data subsets, the pre-processing including excluding the one or more data subsets that include a poor electrocardiogram signal (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting with respect to the pre-processing and excluding limitations, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors);
augmenting, by the one or more processors, the one or more data subsets (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting with respect to the augmenting limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors);
determining, by the one or more processors and using a trained machine-learning model… (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
aggregating, by the one or more processors, the one or more signal classifications (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting with respect to the aggregating limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors);
outputting, by the one or more processors, the result classification to an electronic storage device and/or a display (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting with respect to the outputting limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors).
Claim 1 additionally recites that the method is computer-implemented (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Claim 11 additionally recites a computer system for classifying electrocardiogram signals of a canine, the computer system comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform operations comprising the steps discussed above (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Claim 18 additionally recites non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for classifying electrocardiogram signals of a canine, the operations comprising the limitations discussed above (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
The additional elements as discussed above, in combination with the abstract idea, are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea.
Regarding dependent claims 2 and 12:
Step 2a Prong One: incorporates the rejection of claims 1 and 11. The claims additionally recite analyzing… the one or more signal classifications and the canine metadata to determine whether to update the one or more signal classifications (a mental process of observation and determination; Examiner notes that paragraph 0003 of the specification of the instant application indicates that conventionally, a veterinarian (i.e. a human) is capable of analyzing ECG signals to determine whether to classify them as normal or abnormal).
Step 2a Prong Two: the claims additionally recite
receiving, by the one or more processors, canine metadata associated with the electrocardiogram data (insignificant extra-solution activity as discussed in MPEP 2106.05(g), with respect to the receiving limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors); and
analyzing, by the one or more processors and using the trained machine-learning model, the one or more signal classifications and the canine metadata (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Step 2b: the claims additionally recite
receiving, by the one or more processors, canine metadata associated with the electrocardiogram data (insignificant extra-solution activity as discussed in MPEP 2106.05(g), with respect to the receiving limitation, and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to performance by processors); and
analyzing, by the one or more processors and using the trained machine-learning model, the one or more signal classifications and the canine metadata (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Regarding dependent claims 3 and 13:
Step 2a Prong One: incorporates the rejection of claims 2 and 12.
Step 2a Prong Two: the claims additionally recite wherein the canine metadata includes at least one of a breed, an age, a gender, and a weight (a field of use and technological environment as discussed in MPEP 2106.05(h)).
Step 2b: the claims additionally recite wherein the canine metadata includes at least one of a breed, an age, a gender, and a weight (a field of use and technological environment as discussed in MPEP 2106.05(h)).
Regarding dependent claims 4 and 14:
Step 2a Prong One: incorporates the rejection of claims 1 and 11. The claims additionally recite wherein the pre-processing the one or more data subsets includes at least one of…a heart rate computation (a mental process of determination, including a mental process involving a mathematical calculation with or without a physical aid, such as a heart rate computation).
Step 2a Prong Two: the claims additionally recite wherein the pre-processing the one or more data subsets includes at least one of a baseline wander removal, a signal normalization, a frequency removal, a heart rate computation, or an unsuitable signal removal (insignificant extra-solution activity as discussed in MPEP 2106.05(g)).
Step 2b: the claims additionally recite wherein the pre-processing the one or more data subsets includes at least one of a baseline wander removal, a signal normalization, a frequency removal, a heart rate computation, or an unsuitable signal removal (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as data gathering and output).
Regarding dependent claims 5 and 15:
Step 2a Prong One: incorporates the rejection of claims 1 and 11.
Step 2a Prong Two: the claims additionally recite the augmenting including: applying, by the one or more processors, at least one transformation to the one or more data subsets (insignificant extra-solution activity as discussed in MPEP 2106.05(g) with respect to applying at least one transformation and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to applying with processors).
Step 2b: the claims additionally recite the augmenting including: applying, by the one or more processors, at least one transformation to the one or more data subsets (insignificant extra-solution activity as discussed in MPEP 2106.05(g) such as data gathering and outputting with respect to applying at least one transformation and mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to applying with processors).
Regarding dependent claims 6 and 16:
Step 2a Prong One: incorporates the rejection of claims 1 and 11.
Step 2a Prong Two: the claims additionally recite wherein the receiving includes receiving at least one Digital Imaging and Communications in Medicine (DICOM) file that includes the electrocardiogram data (insignificant extra-solution activity as discussed in MPEP 2106.05(g) with respect to receiving and a field of use and technological environment as discussed in MPEP 2106.05(h) with respect to the received information being a DICOM file that includes the electrocardiogram data).
Step 2b: the claims additionally recite wherein the receiving includes receiving at least one Digital Imaging and Communications in Medicine (DICOM) file that includes the electrocardiogram data (insignificant extra-solution activity as discussed in MPEP 2106.05(g) such as mere data gathering and outputting with respect to receiving and a field of use and technological environment as discussed in MPEP 2106.05(h) with respect to the received information being a DICOM file that includes the electrocardiogram data).
Regarding dependent claim 7:
Step 2a Prong One: incorporates the rejection of claim 1.
Step 2a Prong Two: the claims additionally recite in response to receiving the electrocardiogram data, storing, by the one or more processors, the electrocardiogram data in the electronic storage device (insignificant extra-solution activity as discussed in MPEP 2106.05(g)).
Step 2b: the claims additionally recite in response to receiving the electrocardiogram data, storing, by the one or more processors, the electrocardiogram data in the electronic storage device (insignificant extra-solution activity as discussed in MPEP 2106.05(g), i.e. outputting/storing received data).
Regarding dependent claims 8, 17, and 19:
Step 2a Prong One: incorporates the rejection of claims 1, 11, and 18.
Step 2a Prong Two: the claims additionally recite wherein the one or more data subsets include an electrocardiogram signal length of eight seconds (a field of use and technological environment as discussed in MPEP 2106.05(h), i.e. the limitation merely provides further description of the data which is processed and which is a result of segmenting).
Step 2b: the claims additionally recite wherein the one or more data subsets include an electrocardiogram signal length of eight seconds (a field of use and technological environment as discussed in MPEP 2106.05(h), i.e. the limitation merely provides further description of the data which is processed and which is a result of segmenting).
Regarding dependent claim 9:
Step 2a Prong One: incorporates the rejection of claim 1. The claim additionally recites wherein the one or more signal classifications include at least one of a normal classification or an abnormal classification (a mental process of observation and determination; i.e. the claim merely provides further description regarding the result of the mental classification; Examiner notes that paragraph 0003 of the specification of the instant application indicates that conventionally, a veterinarian (i.e. a human) is capable of analyzing ECG signals to determine whether to classify them as normal or abnormal).
Step 2a Prong Two: the claim recites no additional limitations other than those discussed above.
Step 2b: the claim recites no additional limitations other than those discussed above.
Regarding dependent claim 10:
Step 2a Prong One: incorporates the rejection of claim 9.
Step 2a Prong Two: the claims additionally recite displaying, by the one or more processors, an alert indicating at least one of the one or more data subsets includes the abnormal classification (insignificant extra-solution activity as discussed in MPEP 2106.05(g)).
Step 2b: the claims additionally recite displaying, by the one or more processors, an alert indicating at least one of the one or more data subsets includes the abnormal classification (insignificant extra-solution activity as discussed in MPEP 2106.05(g), i.e. outputting data, such as through a display, corresponding to the result of the classification).
Regarding dependent claim 20:
Step 2a Prong One: incorporates the rejection of claim 18. The claim further recites wherein the one or more signal classifications include at least one of a normal classification, an abnormal classification, or a non-diagnostic classification (a mental process of observation and determination; i.e. the claim merely provides further description regarding the result of the mental classification; Examiner notes that paragraph 0003 of the specification of the instant application indicates that conventionally, a veterinarian (i.e. a human) is capable of analyzing ECG signals to determine whether to classify them as normal or abnormal).
Step 2a Prong Two: the claim recites no additional limitations other than those discussed above.
Step 2b: the claim recites no additional limitations other than those discussed above.
Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as recited in the dependent claims discussed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components, and limitations describing a field of use or technological environment. The additional elements as discussed above, in combination with the abstract idea, are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea, and limitations describing a field of use or technological environment.
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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102€, (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-5, 7, 9-15, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rossi et al. (US 20170360377 A1) in view of Leib et al. (US 20170095206 A1).
With respect to claims 1, 11, and 18, Rossi teaches a computer system for classifying electrocardiogram signals, the computer system comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform operations comprising a method; a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for classifying electrocardiogram signals (e.g. paragraphs 0198-0199, embodiments having form of computer program product including computer program adapted to perform described methods included on computer readable medium; implementation on hardware/firmware, including using processors/circuitry/controllers), the operations comprising the method; and the computer-implemented method for classifying electrocardiogram signals, the method comprising:
receiving, by one or more processors, electrocardiogram data, the electrocardiogram data including at least one electrocardiogram signal (e.g. paragraph 0106, Fig. 16, continuous ECG stream input including individual/single heartbeats);
segmenting, by the one or more processors, the electrocardiogram data into one or more data subsets (e.g. paragraph 0109, Fig. 16 step 1608, windowing beats of the stream; heartbeats/beats refers to collection of samples inside chosen window);
pre-processing, by the one or more processors, the one or more data subsets, the pre-processing including excluding the one or more data subsets that include a poor electrocardiogram signal (e.g. paragraph 0111, Fig. 16 step 1612, beats selected so as to exclude beats which may give rise to false positive; beats excluded because highly affected by noise or motion artifacts, failures in sensing process, etc.);
augmenting, by the one or more processors, the one or more data subsets (e.g. paragraph 0113, Fig. 16 step 1614, each beat associated with transforms; applying beat adaptation to beat to account for current user state; current beat adapted using the identified transform);
determining, by the one or more processors and using a trained machine-learning model, one or more signal classifications for the one or more data subsets (e.g. paragraph 0005, generating, using training data, dictionary of sparse approximation model and then detecting anomalous beats using the dictionary of the sparse approximation model; paragraph 0106, Fig. 16, method to detect abnormal heartbeats using trained dictionary; output for each beat is an indication of whether the beat is normal or anomalous; paragraph 0117, Fig. 16 step 1622, performing detection of anomalous beats; classify beat as normal or anomalous);
aggregating, by the one or more processors, the one or more signal classifications to determine a result classification (e.g. paragraph 0106, method of Fig. 16 may be applied sequentially to series of individual beats, or in parallel to sets of individual beats; output for each beat is an indication of whether the beat is normal or anomalous; paragraph 0117, Fig. 16 step 1622, performing detection of anomalous beats; classify beat as normal or anomalous; paragraph 0119, repeating process when additional beats remain to be classified; Examiner notes that this limitation appears to require aggregation, but also appears to only require that the aggregated number of classifications be a single classification (i.e. one or more, but only one is required); therefore, outputting only a single classification in the instance of having only a single beat/signal to classify; alternatively, outputting classifications for a sequential series of beats, or for parallel sets of beats, also appears to be analogous to aggregating beat/signal classifications to determine a result classification); and
outputting, by the one or more processors, the result classification to an electronic storage device and/or a display (e.g. paragraph 0005, in response to classification of beat as anomalous, storing data related to the beat; paragraph 0119, Fig. 16 step 1624, return indication of whether beat is anomalous; in response to beat being classified as anomalous, initiating transmission of data related to the beat classified as anomalous).
Rossi does not explicitly disclose that the electrocardiogram signals are those of a canine. However, Leib teaches that the electrocardiogram signals are those of a canine (e.g. paragraph 0008, vital functions of dogs; paragraph 0011, monitoring vital functions of animals; paragraph 0030, monitored vital functions such as ECG; paragraph 0041, monitoring well-being of dogs).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Rossi and Leib in front of him to have modified the teachings of Rossi (directed to detecting electrocardiogram anomalies), to incorporate the teachings of Leib (directed to monitoring devices for animals) to include the capability to perform the method on electrocardiogram data (i.e. as taught by Rossi), including electrocardiogram data of a canine (as taught by Leib). One of ordinary skill would have been motivated to perform such a modification in order to enable safe and reliable monitoring of the well-being of animals with as little a-priori knowledge as possible as described in Leib (paragraph 0009).
With respect to claims 2 and 12, Rossi in view of Leib teaches all of the limitations of claims 1 and 11 as previously discussed, and Rossi further teaches the method further comprising:
receiving, by the one or more processors, metadata associated with the electrocardiogram data (e.g. paragraph 0110, accelerometer data, synchronized with ECG data stream, provided as input for motion estimation; measuring energy of acceleration associated with accelerometer data for each heartbeat; identifying outliers in accelerometer data); and
analyzing, by the one or more processors and using the trained machine-learning model, the one or more signal classifications and the metadata to determine whether to update the one or more signal classifications (e.g. paragraph 0111, selecting beats to exclude, including those associated with accelerometer data identified as outlier; discussing a condition (i) in which punctual heart rate is determined with respect to local heart rate and a condition (ii) in which accelerometer data is considered as outlier or not; paragraphs 0117-0118, using thresholds/threshold ranges or look-up tables to classify beat as normal or anomalous; determining whether beat is outlier or inlier with respect to conditions (i) and (ii)).
Leib teaches that the metadata is canine metadata associated with the electrocardiogram data (e.g. paragraph 0008, vital functions of dogs; paragraph 0011, monitoring vital functions of animals; paragraph 0030, monitored vital functions such as ECG; paragraph 0041, monitoring well-being of dogs; paragraph 0010, autonomously learning which values vital functions should have according to target state of animal; paragraph 0048, evaluation unit comparing actual state with target state to detect whether there is a deviation of the actual state from the target state; paragraph 0096-0101, invention advantageously enhanced by providing specific information about the animal, such as breed, age, weight, etc.; if such information available, taking into account in evaluation and comparison of target state with the actual state; interface for data transmission with database comprising vital function comparison data of other animals, data on animal species or breed, etc.; retrieving and evaluating relevant data from the database; paragraph 0135, classifying pattern of vital functions as suitable for target state; i.e. the system determines whether actual state of animal data, such as vital functions including ECG data of a dog (analogous to electrocardiogram data of a canine), is normal or deviates from a target state (analogous to classification of the signal), taking into account additional data, such as specific information about the animal/dog (analogous to analyzing the signal classification and canine metadata to determine whether to update the classification)).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Rossi and Leib in front of him to have modified the teachings of Rossi (directed to detecting electrocardiogram anomalies), to incorporate the teachings of Leib (directed to monitoring devices for animals) to include the capability to further take into account, when classifying the ECG data (beat/signal, such as from a dog) as normal or anomalous, additional information/metadata about the dog/canine corresponding to the beat/signal (as taught by Leib). One of ordinary skill would have been motivated to perform such a modification in order to enable safe and reliable monitoring of the well-being of animals with as little a-priori knowledge as possible as described in Leib (paragraph 0009).
With respect to claims 3 and 13, Rossi in view of Leib teaches all of the limitations of claims 2 and 12 as previously discussed, and Leib further teaches wherein the canine metadata includes at least one of a breed, an age, a gender, and a weight (e.g. paragraph 0096-0101, invention advantageously enhanced by providing specific information about the animal, such as breed, age, weight, etc.; if such information available, taking into account in evaluation and comparison of target state with the actual state; interface for data transmission with database comprising vital function comparison data of other animals, data on animal species or breed, etc.; retrieving and evaluating relevant data from the database).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Rossi and Leib in front of him to have modified the teachings of Rossi (directed to detecting electrocardiogram anomalies), to incorporate the teachings of Leib (directed to monitoring devices for animals) to include the capability to further take into account, when classifying the ECG data (beat/signal, such as from a dog) as normal or anomalous, additional information/metadata about the dog/canine corresponding to the beat/signal, including information about the dog’s breed, age, weight, etc. (as taught by Leib). One of ordinary skill would have been motivated to perform such a modification in order to enable safe and reliable monitoring of the well-being of animals with as little a-priori knowledge as possible as described in Leib (paragraph 0009).
With respect to claims 4 and 14, Rossi in view of Leib teaches all of the limitations of claims 1 and 11 as previously discussed, and Rossi further teaches wherein the pre-processing the one or more data subsets includes at least one of a baseline wander removal, a signal normalization, a frequency removal, a heart rate computation, or an unsuitable signal removal (e.g. paragraph 0108, Fig. 16 step 1606, performing heart rate estimation; identifying outliers, which may be removed prior to anomaly detection; paragraph 0111, Fig. 16 step 1612, beats selected so as to exclude beats which may give rise to false positive; beats excluded because highly affected by noise or motion artifacts, failures in sensing process, etc.).
With respect to claims 5 and 15, Rossi in view of Leib teaches all of the limitations of claims 1 and 11 as previously discussed, and Rossi further teaches the augmenting including: applying, by the one or more processors, at least one transformation to the one or more data subsets (e.g. paragraph 0113, Fig. 16 step 1614, each beat associated with transforms; applying beat adaptation to beat to account for current user state; current beat adapted using the identified transform).
With respect to claim 7, Rossi in view of Leib teaches all of the limitations of claim 1 as previously discussed, and Rossi further teaches the method further comprising: in response to receiving the electrocardiogram data, storing, by the one or more processors, the electrocardiogram data in the electronic storage device (e.g. paragraph 0003, continuously acquiring and storing physiological signals, such as ECG data; paragraph 0005, in response to classification of beat as anomalous, storing data related to the beat; paragraph 0094, ECG data may be stored data; paragraph 0119, Fig. 16 step 1624, return indication of whether beat is anomalous; in response to beat being classified as anomalous, initiating transmission of data related to the beat classified as anomalous).
With respect to claim 9, Rossi in view of Leib teaches all of the limitations of claim 1 as previously discussed, and Rossi further teaches wherein the one or more signal classifications include at least one of a normal classification or an abnormal classification (e.g. paragraph 0117, Fig. 16 step 1622, performing detection of anomalous beats; classify beat as normal or anomalous).
With respect to claim 10, Rossi in view of Leib teaches all of the limitations of claim 9 as previously discussed, and Leib further teaches the method further comprising: displaying, by the one or more processors, an alert indicating at least one of the one or more data subsets includes the abnormal classification (e.g. paragraph 0021, in the event deviation leaves tolerance range/correspondence between actual state and target state, outputting output signal signaling the deviation or correspondence; paragraph 0029, autonomously triggering output signal in response to deviation, giving feedback addressed to user; paragraph 0048, signaling deviation to animal owner via output unit).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Rossi and Leib in front of him to have modified the teachings of Rossi (directed to detecting electrocardiogram anomalies), to incorporate the teachings of Leib (directed to monitoring devices for animals) to include the capability to output an alert to a user indicating that the beat/signal has an abnormal/anomalous/deviation classification (as taught by Leib). One of ordinary skill would have been motivated to perform such a modification in order to enable safe and reliable monitoring of the well-being of animals with as little a-priori knowledge as possible as described in Leib (paragraph 0009).
With respect to claim 20, Rossi in view of Leib teaches all of the limitations of claim 18 as previously discussed, and Rossi further teaches wherein the one or more signal classifications include at least one of a normal classification, an abnormal classification, or a non-diagnostic classification (e.g. paragraph 0117, Fig. 16 step 1622, performing detection of anomalous beats; classify beat as normal or anomalous).
Claims 8, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Rossi in view of Leib, further in view of Ma et al. (US 20210022633 A1).
With respect to claims 8, 17, and 19, Rossi in view of Leib teaches all of the limitations of claims 1, 11, and 18 as previously discussed, and Rossi further teaches wherein the one or more data subsets include an electrocardiogram signal length of a number of seconds (e.g. paragraph 0108, reference windows having fixed size such as 10 seconds; paragraph 0109, windowing beats using reference window size; other/various window sizes may be selected).
Rossi and Leib do not explicitly disclose wherein the one or more data subsets include an electrocardiogram signal length of eight seconds. However, Ma teaches wherein the one or more data subsets include an electrocardiogram signal length of eight seconds (e.g. paragraph 0062, extracting eight-second signal segment from original ECG signal for preprocessing).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Rossi, Leib, and Ma in front of him to have modified the teachings of Rossi (directed to detecting electrocardiogram anomalies) and Leib (directed to monitoring devices for animals), to incorporate the teachings of Ma (directed to determining arrhythmia type) to include the capability to utilize, for the ECG data subset signal length (as taught by Rossi), eight-second segments (as taught by Ma). One of ordinary skill would have been motivated to perform such a modification in order to detect and classify different types of arrhythmia quickly and accurately as described in Ma (paragraph 0003).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Rossi in view of Leib, further in view of Li et al. (US 20220095930 A1).
With respect to claims 6 and 16, Rossi in view of Leib teaches all of the limitations of claims 1 and 11 as previously discussed. Rossi and Lieb do not explicitly disclose wherein the receiving includes receiving at least one Digital Imaging and Communications in Medicine (DICOM) file that includes the electrocardiogram data.
However, Li teaches wherein the receiving includes receiving at least one Digital Imaging and Communications in Medicine (DICOM) file that includes the electrocardiogram data (e.g. paragraph 0092, ECG signal waveforms generated in any known format, including DICOM waveform supplement 30; paragraph 0094, cardiac data transmitted from cardiac monitor to computing device).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Rossi, Leib, and Li in front of him to have modified the teachings of Rossi (directed to detecting electrocardiogram anomalies) and Leib (directed to monitoring devices for animals), to incorporate the teachings of Li (directed to non-invasive cardiac monitoring) to include the capability to utilize, as the format for the ECG signal, a DICOM file format (as taught by Li). One of ordinary skill would have been motivated to perform such a modification in order to provide real-time, non-invasive monitoring of cardiac parameters, allowing insight into vital signs of a user on a continuous or semi-continuous, real-time basis as described in Li (paragraph 0004).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain,” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting in re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (GCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co, v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert, denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F,3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir, 2005): Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
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/JEREMY L STANLEY/
Primary Examiner, Art Unit 2127
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