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 .
Response to Amendment
This Office Action is responsive to the amendment filed on 09 Jul 2026. As directed by the amendment: claims 1-2, 4-5, and 11-13 have been amended, claim 3 has been canceled, and claim 14 has been added. Thus, claims 1-2 and 4-14 are presently pending in this application.
Response to Arguments
Rejections Under 35 U.S.C. § 112
Applicant’s arguments, see Remarks, filed 09 Jul 2026, with respect to the rejections under 35 U.S.C. 112 have been fully considered and are persuasive in light of the claim amendments. The rejections under 35 U.S.C. 112 have been withdrawn.
Rejections Under 35 U.S.C. § 101
Applicant's arguments filed 09 Jul 2026 have been fully considered but they are not persuasive.
Step 1 - Statutory Category of Claim 12 (Resolved)
The previous rejection of claim 12 is withdrawn, as the claim has been amended to recite “non-transitory computer-readable storage medium”, and is now directed to a statutory category of invention. However, claim 12 remains rejected under 35 U.S.C. 101, as explained in further detail below.
Step 2A, Prong One - The Claims Are Not Directed to a Mental Process
Applicant argues that “The ECG delineation model is a trained neural network ( e.g., an autoencoder, as described in paragraph [0068] and Fig. 2 of the specification) that receives input samples and generates corresponding location information. The inference performed by such a trained model cannot practically be carried out in the human mind” (Remarks, page 9).
Examiner respectfully disagrees. The claim does not recite that the ECG delineation model is a trained neural network. Furthermore, a trained neural network encompasses mathematical and logical operations, which are mental processes. The claim places no limitations on how quickly the operations must be performed, nor how much information is processed. Therefore, under the broadest reasonable interpretation, the step of “generating a plurality of ECG delineation information corresponding to a plurality of samples having a predetermined time length, from the one or more received ECG data through an ECG delineation model” is directed to a mental process.
Step 2A, Prong Two - Integration into a Practical Application (Technological Improvement)
Applicant argues that “The claimed invention achieves the technological improvement of stably providing ECG delineation information regardless of the number of leads or the measurement time length" (Remarks, page 10), and that “the claimed invention provides a specific ECG data processing pipeline in which the input ECG data are first transformed into a standardized structure, samples having a uniform input format are generated, inference is performed while maintaining a consistent model input format, and multiple inference results are subsequently integrated to generate the final ECG delineation information.
“This sequence of operations constitutes a computer-implemented signal processing technique that is fundamentally different from a mental process that could be performed by observing or manually analyzing ECG waveforms. Instead, it represents a specific technological improvement in the manner in which ECG data are processed” (Remarks, page 11).
Examiner respectfully disagrees. The ECG data processing pipeline described encompasses nothing more than mental processes and mathematical operations that are performed by generically described computing components. The claim does not recite any tangible, physical outcome of the processing pipeline. Therefore, these steps do not integrate the judicial exception into a practical application.
Step 2B - Significantly More
Applicant argues that “the claimed ordered combination of sample segmentation [Wingdings font/0xE0] model-based generation of ECG delineation information [Wingdings font/0xE0] mode-based integration is neither well-understood, routine, nor conventional in the relevant field” (Remarks, page 11).
Examiner respectfully disagrees. The Kalra reference was cited in the context of the gathering of data from ECG measurement equipment, which is a pre-solution activity. The claimed ECG measurement equipment is well-understood, routine, and conventional in the field of ECG measurement technology.
Therefore, the rejections of claims 1-13 under 35 U.S.C. 101 are maintained.
Rejections Under 35 U.S.C. § 102
Applicant’s arguments, see Remarks, filed 09 Jul 2026, with respect to the rejections of claims 1-13 under 35 U.S.C. 102 have been fully considered and are persuasive in light of the claim amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Li et al. (US 20220031223 A1), hereinafter Li.
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 1-2 and 4-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Determination as to whether a claim satisfies the criteria for subject matter eligibility is a stepwise process (MPEP 2016).
Step 1: Does the claim fall within a statutory category of invention?
Claim 1 recites a process (method), claim 12 recites a manufacture (computer program stored in a non-transitory computer readable storage medium), and claim 13 recites a machine (device), which are within the four statutory categories. Therefore, claims 1 and 12-13 are directed to a statutory category of invention.
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1 and 12-13 are directed to an abstract idea.
Claim 1 is directed to a method performed by one or more processors of a computing device, the method comprising: receiving one or more ECG data; generating a plurality of ECG delineation information corresponding to a plurality of samples having a predetermined time length, from the one or more received ECG data through an ECG delineation model; and integrating the plurality of ECG delineation information. Claim 12 is directed to a computer program including instructions which cause a computing device to perform the same steps as claim 1. Claim 13 is directed to a computing device configured to perform the same steps as claim 1.
The limitations of generating ECG delineation information and integrating the ECG delineation information, as drafted, under their broadest reasonable interpretations, are merely mental processes, because these steps are akin to having a doctor or other human actor performing these operations with pen and paper. For example, “generating a plurality of ECG delineation information corresponding to a plurality of samples having a predetermined time length, from the one or more received ECG data through an ECG delineation model” encompasses nothing more than a human actor mentally evaluating the ECG data and making conclusions about what the ECG delineation information should be based on the provided model.
The limitations of “receiving one or more ECG data” encompasses nothing more than a human actor collecting these pieces of information by hand.
Therefore, claims 1 and 12-13 recite an abstract idea.
Claims 2 and 4-11 depend on claim 1. These dependent claims only recite additional features of the analysis described in claim 1, which may also be performed by a human actor mentally and using a pen and paper. For example, claim 4 recites confirming a measurement time length and number of ECG leads, and dividing the ECG data, which encompasses nothing more than a human actor evaluating or performing calculations with pen and paper.
Therefore, claims 1-2 and 4-14 recite an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
This judicial exception is not integrated into a practical application.
Claim 12 recites the additional limitations “a non-transitory computer readable storage medium” and “a computing device”. Claim 13 recites the additional limitations “a processor”, “a memory”, and “a network interface”. These additional elements are recited at a high level of generality (i.e. most generic computers would be known to have these components). Paragraphs [0045], [0053], and [0054]-[0056] of the specification describe the processor, memory, and network interface at a high level of generality. These generic processor and memory limitations are 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. Therefore claim 13 does not integrate the judicial exception into a practical application.
Claims 1-2, 4, and 11-13 recite the additional limitation “ECG measurement equipment comprising electrodes”, which amounts to no more than mere pre-solution activity of data gathering. Therefore, the claimed generic ECG measurement equipment element does not integrate the judicial exception into a practical application.
Thus, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea. As described above, dependent claims 2 and 4-11 only recite other limitations of claim 1, which may be done mentally by a human actor and/or with a pen and paper.
Step 2B: Does the claim include additional elements that are sufficient to amount to significantly more than the judicial exception?
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above with respect to the integration of the judicial exception into a practical application (Step 2A, Prong 2), the additional elements of using computer components to perform the process steps amounts to no more than mere instructions to apply the judicial exception using generic computer elements. The structural elements recited in claim 12 are “a non-transitory computer readable storage medium” and “a computing device”. The structural elements recited in claim 13 are “a processor”, “a memory”, and “a network interface”. These additional elements are recited at a high level of generality (i.e. most generic computers would be known to have these components). Paragraphs [0045], [0053], and [0054]-[0056] of the specification describes the processor and non-transitory computer-readable medium at a high level of generality, and only provides conventional, well-known computing functions that do not add meaningful limits to practicing the abstract idea.
Claims 1-2, 4, and 11-13 recite the additional limitation “ECG measurement equipment comprising electrodes”. As discussed above with respect to integration of the abstract idea into a practical application (Step 2A, Prong 2), the additional element of ECG measurement equipment to collect data amounts to no more than mere pre-solution activity of data gathering. This pre-solution activity of data gathering using ECG measurement equipment is well-understood, routine, and conventional in the field of ECG measurement technology. For example, see Kalra et al. (“Critical review of electrocardiography measurement systems and technology”, 2018, previously cited), which describes known methods of ECG measurement. Therefore, the claimed generic ECG measurement equipment and computer processing elements are all well-understood, routine, and conventional in the field of ECG measurement technology.
Therefore, claims 1-2 and 4-14 are not patent-eligible under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2 and 4-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li et al. (US 20220031223 A1), hereinafter Li.
Regarding claim 1, Li discloses a method performed by one or more processors of a computing device (Figs. 3A-B, paragraph [0065]-[0066], processing units 21 and 31), the method comprising:
receiving one or more ECG data (Fig. 4, paragraph [0076], "ECG application 29 may cause system device 14 to receive and/or otherwise obtain raw ECG data 52 from sensing device 13");
generating a plurality of ECG delineation information corresponding to a plurality of samples having a predetermined time length, from the one or more received ECG data through an ECG delineation model (paragraph [0080], "At step 56, ECG platform 37 causes pre-processed ECG data 55 to be applied to delineator 39 for delineation. Delineator 39 applies a first neural network that is a delineation neural network to pre-processed ECG data 55"; paragraph [0082], "The first neural network may provide as an output, values corresponding to the likelihood of the presence of or one or more waves at a plurality of time points in the cardiac signal"),
wherein the plurality of ECG delineation information includes location information of at least one of a P-wave, a QRS-complex, or a T-wave (paragraph [0083], "The wave types may be any well know wave type such as, P-waves, Q-wave, R-wave, S-wave, Q-waves, R-waves, S-waves, QRS complexes, and/or T-waves"; paragraph [0084], "the wave types include (1) P-waves, (2) QRS complexes, and (3) T-waves, and the wave characterizations include (1) premature waves, (2) paced waves, (3) ventricular QRS complexes, (4) junctional QRS complexes, (5) ectopic P waves, and (6) non-conducted P waves. ... Each wave type may be expressed according to certain characteristics of that wave, such as start and end points (i.e., onset and offset))"); and
integrating the plurality of ECG delineation information (paragraph [0097], "The ECG signal is given as input to the network, which aggregates the information locally and then combines it layer by layer to produce a high-level multi-label classification of the ECG"),
wherein the predetermined time length is determined independently of a measurement time length or a number of leads of an ECG measurement equipment comprising electrodes (paragraph [0081], "the number of samples 'n' provides the duration of the cardiac signal 'n/f' with f being the sampling frequency of the cardiac signal. The sample rate is above a predetermined rate and is preferably relatively high, such as, for example, at least 20, at least 250, at least 500 or at least 1000 samples per second, etc."; paragraph [0082], "The first neural network may provide as an output, values corresponding to the likelihood of the presence of or one or more waves at a plurality of time points in the cardiac signal. The time points may be dictated by the raw ECG data, may be selected by the user of system device 14, or may be preprogrammed").
Regarding claim 2, Li discloses the method of claim 1, as explained above. Li further discloses that the receiving of the ECG data includes: receiving the one or more ECG data from one of a plurality of different types of ECG measurement equipment, each comprising electrodes (paragraph [0061], "sensing device 13 may be one or more electrodes that may be disposed on one or more leads. ECG sensing device 13 may be an ECG-dedicated sensing device such as a conventional 12-lead arrangement or may be a multi-purposes device with sensing hardware for sensing electrical activity of the heart for ECG generation").
Regarding claim 4, Li discloses the method of claim 1, as explained above.
confirming a measurement time length and a number of leads of an ECG measurement equipment (paragraph [0081], "The constant 'm' may be a number of leads in sensing device 13, which is typically 12, though any number of leads may be used. In this example, the number of samples 'n' provides the duration of the cardiac signal 'n/f' with f being the sampling frequency of the cardiac signal"),
generating the plurality of samples by dividing the one or more ECG data based on the predetermined time length (paragraph [0082], "The time points may be dictated by the raw ECG data, may be selected by the user of system device 14, or may be preprogrammed"), and
generating respective ECG delineation information corresponding to the plurality of samples through the ECG delineation model (paragraph [0082], "The first neural network may provide as an output, values corresponding to the likelihood of the presence of or one or more waves at a plurality of time points in the cardiac signal").
Regarding claim 5, Li discloses the method of claim 1, as explained above. Li further discloses that the processor derives, from the ECG delineation information, amplitude information of the T-wave (paragraph [0089], ST elevation).
Regarding claim 6, Li discloses the method of claim 1, as explained above. LI further discloses that the integrating of the plurality of ECG delineation information includes:
generating smoothed ECG delineation information for the plurality of samples (paragraph [0104], "ECG platform 37 may cause labels for each time window (i.e., labels) to be aggregated by post-processor 43 to generate processed labels 60"), and
integrating the smoothed ECG delineation information for the plurality of samples (paragraph [0104], "Post-processor 43 may also aggregate the delineation-based labels with the classification labels corresponding to the same time points").
Regarding claim 7, Li discloses the method of claim 6, as explained above. Li further discloses that the generating of the smoothed ECG delineation information for the plurality of samples further includes: deriving a representative value among result values of the ECG delineation information for the plurality of samples within a fixed time interval (paragraph [0104], "ECG platform 37 may cause labels for each time window (i.e., labels) to be aggregated by post-processor 43 to generate processed labels 60").
Regarding claim 8, Li discloses the method of claim 6, as explained above. Li further discloses that the integrating of the smoothed ECG delineation information for the plurality of samples further includes: deriving a representative value among the result values of the smoothed ECG delineation information (paragraph [0105], "Post-processor 43 may also filter the labels to remove redundant labels, assemble labels according to a known hierarchy of labels, or ignore labels that are known to be of lesser importance according to a hierarchy or weighted values. Post-processor 43 may also aggregate the labels through time so as to compute the start (onset) and end (offset) times of each abnormality").
Regarding claim 9, Li discloses the method of claim 1, as explained above. Li further discloses:
extracting ECG characteristics from the integrated ECG delineation information, wherein the extracting of ECG characteristics from the integrated ECG delineation information includes:
dividing the integrated ECG delineation information based on one heart beat time (paragraph [0132], "delineator 39 may determine the onset and offset of one or more waves and/or beats. At step 200, clusterer 42 may use the onsets and offsets of beats in wave information 201 to extract the beats from the ECG data (e.g., pre-processed ECG data). This may involve determining portions of the ECG data starting at an onset of a beat and ending at an offset. These portions may be extracted from the ECG data and designated as a beat"),
extracting each ECG characteristic from the ECG delineation information divided based on the one heart beat time (paragraph [0133], "Embedding data 203 may be one or more vectors that represent the beat portions of ECG data"), and
deriving a representative value of each extracted ECG characteristic (paragraph [0135], "Grouper 49 may process the embedding data 203 corresponding to beats using one or more algorithms, resulting in group data 205 corresponding to a similarity between two or more beats. The one or more algorithms may be one or more neural networks (e.g., grouping neural networks). Group data 205 may include one or more scores or values corresponding to a similarity and/or distance between vectors of embedding data 203. A similarity may involve similar characteristics and/or features such as similar t-waves, p-waves, QRS complexes, or other waves, or a similar distance between any of the forgoing, for example. In this manner, grouper 49 may quantify a similarity between two or more beats of ECG data. Based on group data 205, clusterer 49 may determine if group data 205 (e.g., scores or values) is within a threshold differential or satisfies a threshold value and thus if the corresponding beats are similar enough to one another (e.g., have similar characteristics and/or features) such they should be grouped or matched together").
Regarding claim 10, Li discloses the method of claim 9, as explained above. Li further discloses that the ECG characteristics include at least one of: an RR-interval, a QRS duration, a QT interval, or a QT-corrected interval (paragraph [0135], "A similarity may involve similar characteristics and/or features such as similar t-waves, p-waves, QRS complexes, or other waves").
Regarding claim 11, Li discloses the method of claim 1, as explained above. Li further discloses:
generating a user interface (UI) including information on the ECG delineation information (paragraph [0141], "Some or all of the analyzed ECG data may be displayed on a graphic user interface"),
wherein the user interface includes:
a first area displaying ECG data measured by an ECG measurement equipment (paragraph [0077], "System device 14 may optionally display raw ECG data 52 or a portion thereof on display 17"; paragraph [0107], "ECG application 29 may receive data (e.g., raw ECG data, pre-processed ECG data, wave information, labels and any other data generated during steps 54, 56, 58, 61, and/or 63) and cause system device 14 to display"),
a second area displaying the ECG delineation information (paragraph [0107], "ECG application 29 may receive data (e.g., raw ECG data, pre-processed ECG data, wave information, labels and any other data generated during steps 54, 56, 58, 61, and/or 63) and cause system device 14 to display"), and
a third area displaying a value of an ECG characteristic (paragraph [0107], "ECG application 29 may receive data (e.g., raw ECG data, pre-processed ECG data, wave information, labels and any other data generated during steps 54, 56, 58, 61, and/or 63) and cause system device 14 to display").
Regarding claim 12, Li discloses a computer program stored in a non-transitory computer readable storage medium (Fig. 3A, paragraph [0066], memory 22 and storage 27; Fig. 3B, paragraph [0071], memory 32 and storage 35), the computer program including instructions which cause a computing device to perform operations (paragraph [0068], "ECG application 29 may be stored in storage 27 and executed on processing unit 21. ECG application 29 may be a software application and/or software modules having one or more sets of instructions suitable for performing the operations of system device 14"), wherein the operations comprise:
an operation of receiving one or more ECG data (Fig. 4, paragraph [0076], "ECG application 29 may cause system device 14 to receive and/or otherwise obtain raw ECG data 52 from sensing device 13");
an operation of generating a plurality of ECG delineation information corresponding to a plurality of samples having a predetermined time length, from the one or more received ECG data through an ECG delineation model (paragraph [0080], "At step 56, ECG platform 37 causes pre-processed ECG data 55 to be applied to delineator 39 for delineation. Delineator 39 applies a first neural network that is a delineation neural network to pre-processed ECG data 55"; paragraph [0082], "The first neural network may provide as an output, values corresponding to the likelihood of the presence of or one or more waves at a plurality of time points in the cardiac signal"),
wherein the plurality of ECG delineation information includes location information of at least one of a P-wave, a QRS-complex, or a T-wave (paragraph [0083], "The wave types may be any well know wave type such as, P-waves, Q-wave, R-wave, S-wave, Q-waves, R-waves, S-waves, QRS complexes, and/or T-waves"; paragraph [0084], "the wave types include (1) P-waves, (2) QRS complexes, and (3) T-waves, and the wave characterizations include (1) premature waves, (2) paced waves, (3) ventricular QRS complexes, (4) junctional QRS complexes, (5) ectopic P waves, and (6) non-conducted P waves. ... Each wave type may be expressed according to certain characteristics of that wave, such as start and end points (i.e., onset and offset))"); and
an operation of integrating the plurality of ECG delineation information (paragraph [0097], "The ECG signal is given as input to the network, which aggregates the information locally and then combines it layer by layer to produce a high-level multi-label classification of the ECG"),
wherein the predetermined time length is determined independently of a measurement time length or a number of leads of an ECG measurement equipment comprising electrodes (paragraph [0081], "the number of samples 'n' provides the duration of the cardiac signal 'n/f' with f being the sampling frequency of the cardiac signal. The sample rate is above a predetermined rate and is preferably relatively high, such as, for example, at least 20, at least 250, at least 500 or at least 1000 samples per second, etc."; paragraph [0082], "The first neural network may provide as an output, values corresponding to the likelihood of the presence of or one or more waves at a plurality of time points in the cardiac signal. The time points may be dictated by the raw ECG data, may be selected by the user of system device 14, or may be preprogrammed").
Regarding claim 13, Li discloses a computing device (Fig. 2, paragraph [0060], system 10) comprising:
a processor including one or more cores (Figs. 3A-B, processing units 21 and 31);
a network interface receiving one or more ECG data (paragraph [0066], "Communication unit 23 may receive and/or transmit information to and from other components in ECG processing system 10 including, but not limited to, sensing device 13 and server 15"; paragraph [0068], "ECG application 29 may cause system device 14 to receive ECG data from sensing device 13, to record ECG data from sensing device 13, to communicate ECG data to server 15"); and
a memory (Fig. 3A, paragraph [0066], memory 22 and storage 27; Fig. 3B, paragraph [0071], memory 32 and storage 35),
wherein the processor is configured to:
receive one or more ECG data (Fig. 4, paragraph [0076], "ECG application 29 may cause system device 14 to receive and/or otherwise obtain raw ECG data 52 from sensing device 13");
generate a plurality of ECG delineation information corresponding to a plurality of samples having a predetermined time length, from the one or more received ECG data through an ECG delineation model (paragraph [0080], "At step 56, ECG platform 37 causes pre-processed ECG data 55 to be applied to delineator 39 for delineation. Delineator 39 applies a first neural network that is a delineation neural network to pre-processed ECG data 55"; paragraph [0082], "The first neural network may provide as an output, values corresponding to the likelihood of the presence of or one or more waves at a plurality of time points in the cardiac signal"),
wherein the plurality of ECG delineation information includes location information of at least one of a P-wave, a QRS-complex, or a T-wave (paragraph [0083], "The wave types may be any well know wave type such as, P-waves, Q-wave, R-wave, S-wave, Q-waves, R-waves, S-waves, QRS complexes, and/or T-waves"; paragraph [0084], "the wave types include (1) P-waves, (2) QRS complexes, and (3) T-waves, and the wave characterizations include (1) premature waves, (2) paced waves, (3) ventricular QRS complexes, (4) junctional QRS complexes, (5) ectopic P waves, and (6) non-conducted P waves. ... Each wave type may be expressed according to certain characteristics of that wave, such as start and end points (i.e., onset and offset))"); and
integrate the plurality of ECG delineation information (paragraph [0097], "The ECG signal is given as input to the network, which aggregates the information locally and then combines it layer by layer to produce a high-level multi-label classification of the ECG"),
wherein the predetermined time length is determined independently of a measurement time length or a number of leads of an ECG measurement equipment comprising electrodes (paragraph [0081], "the number of samples 'n' provides the duration of the cardiac signal 'n/f' with f being the sampling frequency of the cardiac signal. The sample rate is above a predetermined rate and is preferably relatively high, such as, for example, at least 20, at least 250, at least 500 or at least 1000 samples per second, etc."; paragraph [0082], "The first neural network may provide as an output, values corresponding to the likelihood of the presence of or one or more waves at a plurality of time points in the cardiac signal. The time points may be dictated by the raw ECG data, may be selected by the user of system device 14, or may be preprogrammed").
Regarding claim 14, Li discloses the method of claim 1, as explained above. Li further discloses that each sample of the plurality of samples corresponds one-to-one to a respective one of the plurality of ECG delineation information, such that a number of the plurality of ECG delineation information is equal to a number of the plurality of samples (paragraph [0084], "The output of the first neural network may be a matrix Y, which may be a matrix of real numbers. For example, matrix Y may be a matrix of the size p×n. Matrix Y may include scores for each type of wave at each time point of the cardiac signal. In matrix Y, 'n' is the number of samples, as discussed above with respect to Matrix X, and 'p' is the number of wave types plus the number of wave characterizations"; if p = 1, then the size of matrix Y = n).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTINE SISON whose telephone number is (703)756-4661. The examiner can normally be reached 8 am - 5 pm PT, Mon - Fri.
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/CHRISTINE SISON/Examiner, Art Unit 3796
/PAMELA M. BAYS/Primary Examiner, Art Unit 3796
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