DETAILED ACTION
Status of Claims
This action is in reply to the communication filed on 16 January, 2026.
Claims 1, 2, 4 – 8, 10, 11 and 19 have been amended.
Claims 1 – 19 are currently pending and have been examined.
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 .
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 - 19 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), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept – i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
Claim 10 is representative. Claim 10 recites:
A method of operating an electronic device, comprising:
obtaining first initial biometric information regarding oxygen saturation and second initial biometric information regarding electrocardiogram (ECG) data of a sleeping user via at least one sensor device;
obtaining first biometric information as multidimensional data in a form of a first image-based representation by decomposing the first initial biometric information which is a first one-dimensional (1D) time signal, into first frequency components and arranging the first frequency components on a two dimensional (2D) plane;
obtaining a second 1D time signal representing heart rate variability (HRV) based on a plurality of peak points of the ECG and time intervals between the plurality of peak points based on the second initial biometric information:
obtaining second biometric information as multidimensional data in a form of a second image-based representation by decomposing the second 1D time signal representing the HRV into second frequency components and arranging the second frequency components on a 2D plane;
inputting the first biometric information and the second biometric information, each in the form of the first and second image-based representations, into a first artificial intelligence model trained to determine a sleep state to obtain sleep state information of the user; and
transmitting the sleep state information to a display device connected to the electronic device for outputting the sleep state information.
Claim 1 recites a device, and Claim 19 recites a system that executes the steps of the method recited in Claim 10.
STEP 1
The claims are directed to a device, a system, and a method, which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims, as illustrated by Claim 10, recite limitations that encompass an abstract idea within the mathematical formula or relationship grouping; including:
obtaining first biometric information as multidimensional data in a form of a first image-based representation by decomposing the first initial biometric information which is a first one-dimensional (1D) time signal, into first frequency components and arranging the first frequency components on a two dimensional (2D) plane;
obtaining a second 1D time signal representing heart rate variability (HRV) based on a plurality of peak points of the ECG and time intervals between the plurality of peak points based on the second initial biometric information:
obtaining second biometric information as multidimensional data in a form of a second image-based representation by decomposing the second 1D time signal representing the HRV into second frequency components and arranging the second frequency components on a 2D plane.
Claim 10 recites a process operable to “determine the sleep state to obtain sleep state information of the user” by collecting (i.e. obtaining) and analyzing biometric information of a sleeping user. The biometric information of a sleeping user is obtained using a sensor device, which is described in the specification as being purely conventional, and is operated in its normal data collection capacity. For example, the sensor may be a PPG sensor to obtain the user's heart rate, blood pressure, blood sugar, blood volume, or oxygen saturation; and an ECG sensor to obtain ECG information.
The sensor data is converted into a multidimensional form by “decomposing” the data. The specification does not use the term “decomposing”; nonetheless, it’s plain ordinary meaning includes using mathematical relationships such as Fourier Transforms to convert or transform time series data into a frequency domain. (Here, Examiner further notes that converting time series data to the frequency domain is notoriously old and well known).
For example, SPO2 data from the sensor may be converted into a multidimensional form, disclosed in the specification as a two-dimensional graph, by performing various conversion methods including one of: Fourier transform, Wavelet transform, Wavelet spectrogram and MEL-Frequency Cepstral Coefficients (MFCC). (@ 0068, 0115). Each of these techniques are well-known and provide a mathematical manipulation of the raw sensor data.
Similarly, ECG data from the sensor may be converted into a multidimensional form, disclosed in the specification as a two-dimensional graph, using a two-step process. First, ECG data is analyzed to determine peaks and time intervals between peaks, and a tachogram of heart rate variance is obtained. Then, the tachogram is converted by performing various conversion methods including one of: Fourier transform, Wavelet transform, Wavelet spectrogram and MEL-Frequency Cepstral Coefficients (MFCC). (@ 0119). Each of these techniques are well-known and provide a mathematical manipulation of the raw sensor data. As such, the claims recite a mathematical formula or relationship.
The claims, as illustrated by Claim 10, recite limitations that encompass an abstract idea including:
obtaining first biometric information regarding oxygen saturation and second initial biometric information regarding electrocardiogram (ECG) data of a sleeping user; obtaining a second 1D time signal representing heart rate variability (HRV) based on a plurality of peak points of the ECG and time intervals between the plurality of peak points based on the second initial biometric information;
(analyzing) the first biometric information and the second biometric information to determine a sleep state to obtain sleep state information of the user; and
outputting the sleep state information.
The claims, as illustrated by Claim 10, recite limitations that encompass an abstract idea within the “mental processes” grouping – concepts performed in the human mind including observation, evaluation, judgment and opinion. Claim 10 recites a process operable to “determine the sleep state to obtain sleep state information of the user” by collecting (i.e. obtaining) and analyzing biometric information of a sleeping user, and displaying the information. The biometric information of a sleeping user is obtained using a sensor device, which is described in the specification as being purely conventional, and is operated in its normal data collection capacity. For example, the sensor may be a PPG sensor to obtain the user's heart rate, blood pressure, blood sugar, blood volume, or oxygen saturation; and an ECG sensor to obtain ECG information. The sensor data is converted using mathematical relationships discussed below.
The collected data is analyzed, using a trained model. The specification discloses that using the trained model replaces a prior art analysis by a professional of a sleeping user’s bio-signals to determine the user’s sleep state. (see published specification US PGPUB 2025/0140397 A1 @ 0007) Using a trained model, instead of a human, may “reduce cost and time required for sleep state diagnosis”. (@ 0015 – 0016). Analyzing bio-signals to determine a sleeping user’s sleep state information, using well-known techniques as disclosed in the specification, is a process that, except for generic computer implementation steps, can be performed in the human mind.
Collecting information, including when limited to particular content, is within the realm of abstract ideas, and analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, are mental processes within the abstract idea category (Electric Power Group v. Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016).
As such, the claims recite an abstract idea within the mental process grouping.
The claims, as illustrated by Claim 10, also recite limitations that encompass an abstract idea within the “certain methods of organizing human activity” grouping –
managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
Claim 10 recites determining a sleep state (i.e. a diagnosis) based on biometric information, by applying a trained model to the information and displaying the results. This process is typical in medicine, even according to the specification, where a professional analyzes bio-signals to determine a sleeping user’s sleep state, and is process that merely organizes this human activity. (See MPEP 2016.04 (a)(2) II C finding that “a mental process that a neurologist should follow when testing a patient for nervous system malfunctions” is a method of organizing human activity, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). As such, the claims recite an abstract idea within the certain methods of organizing human activity grouping.
STEP 2A PRONG TWO
The claims recite limitations that include additional elements beyond those that encompass the abstract idea above including:
an electronic device;
at least one sensor device;
inputting the first biometric information and the second biometric information, each in the form of the first and second image-based representations, into a first artificial intelligence model trained to determine a sleep state to obtain sleep state information of the user;
transmitting the sleep state information to a display device connected to the electronic device for outputting the sleep state information.
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with the MPEP. (see MPEP 2106.05)
The electronic device and artificial intelligence model are recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using a generic computer components. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment.
In particular, the claims replace the knowledge and experience of a medical professional by applying established methods of machine learning to an abstract diagnostic process in a new data environment – i.e. applying a trained model to the first and second biometric information. The specification teaches that the learning model may be trained to output sleep state information using the biometric information; using any suitable model and correctly labelled biological information acquired in the clinical server (@ 0061, 0095 - 0097).
Machine learning limitations reciting broad, functionally described, well-known techniques executed by generic and conventional computing devices, as the claims do here, does not provide a practical application of the abstract diagnostic process. “Today we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under §101.” (Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025)).
Obtaining SPO2 and ECG data using conventional devices is an insignificant extra-solution activity – i.e. a data gathering step. Similarly, transmitting and displaying the results of the abstract process does not improve the computer itself, or any other technology, nor does the display of results provide a meaningful limitation beyond generally linking the abstract idea to a particular technological environment.
Nothing in the claim recites specific limitations directed to an improved technology or technological process. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception by use of conventional computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a generic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claim do not integrate the abstract sleep state determining process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract sleep state determining process.
Machine learning limitations reciting broad, functionally described, well-known techniques executed by generic and conventional computing devices, as the claims do here, does not provide an inventive concept for the abstract diagnostic process. “Today we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under §101.” (Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025)). Displaying the results of the abstract process is an ancillary part of the abstract process itself as in Electric Power Group.
The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure (i.e. an electronic device, a sensor device, a display device, a server). Each of the above components are disclosed in the specification as being purely conventional and/or known in the industry. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting well-understood, routine and conventional computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently well-known that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination the limitations recited in the claims add nothing that is not already present when the steps are considered individually. As such, the additional elements recited in the claim do not provide significantly more than the abstract sleep state determining process, or an inventive concept.
The dependent claims add additional features including:
those that merely serve to further narrow the abstract idea above such as:
further limiting the sleep state information a particular type (Claim 3, 12);
further limiting the type of sensor (Claim 9, 18);
those that recite additional abstract ideas such as:
obtaining and converting a tachogram of HRV (Claim 2, 11);
a training guide related to acquisition of labelled data (Claim 7, 16);
converting training information into a multidimensional form (Claim 8, 17);
those that recite well-understood, routine and conventional activity or computer functions such as:
storing first/second biometric information (Claim 4, 13);
obtaining and labeling training information and training a model using the labeled data; (Claims 5, 6, 14, 15);
displaying information, including a training guide (Claim 7, 16);
re-training a model using updated data (Claim 8, 17);
those that recite insignificant extra-solution activities such as:
obtaining labeled databased based on a guide; (Claim 7, 16);
or those that are an ancillary part of the abstract idea.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. As such, the additional element do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention.
The apparatus claims are no different from the method claims in substance. “The equivalence of the method, system and media claims is readily apparent.” “The only difference between the claims is the form in which they were drafted.” (Bancorp). The method claims recite the abstract idea implemented on a generic computer, while the apparatus claims recite generic computer components configured to implement the same idea. Specifically, Claims 1 – 9 and 19 merely add the generic hardware noted above that nearly every computer will include. The apparatus claim’s requirement that the same method be performed with a programmed computer does not alter the method’s patentability under U.S.C. 101 (In re Grams). Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
Claims 1, 3 - 10 and 12 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al.: (US PGPUB 2021/0275087 A1) in view of Scharf et al.: (US PGPUB 2016/0007864 A1).
CLAIMS 1, 10 and 19
Huang discloses a system and method for monitoring a patient with a sleep disorder that includes the following limitations:
obtaining first initial biometric information regarding oxygen saturation and second initial biometric information regarding electrocardiogram (ECG) data of a sleeping user via at least one sensor device; obtaining first biometric information which is a first one-dimensional (1D) time signal; (Huang 0012, 0025, 0026, 0028, 0034, 0047, 0050, 0051, 0057, 0075);
obtaining a second 1D time signal representing heart rate variability (HRV) based on biometric information based on a plurality of peak points of the ECG and time intervals between the plurality of peak points based on the second initial biometric information; (Huang 0012, 0026, 0028, 0057).
Huang discloses a system and method for monitoring a patient with a sleep disorder including obtaining information regarding parameters of a sleeping user including blood oxygen levels, (i.e. oxygen saturation), and electrocardiogram data. Oxygen saturation is obtained via a pulse oximeter, and the ECG data is obtained via a heart monitor (i.e. at least one sensor device). Both oxygen saturation and HRV are one-dimensional time signals. Huang discloses detecting a heart rate, heart rhythm and heart rate variability. Detecting heart rate variability inherently includes obtaining time intervals between peak points, as is well-known in the art. Huang further discloses:
inputting the first biometric information and the second biometric information into a first artificial intelligence model trained to determine a sleep state to obtain sleep state information of the user; (Huang 0037, 0063, 0098 – 0103, 0106, 0107); and
transmitting the sleep state information to a display device connected to the electronic device for output the sleep state information; (Huang 0031, 0032, 0080 – 0082).
Huang discloses a learning process for training a machine learning model to detect an abnormal sleep state, and an operating process where the trained model is applied to data of a monitored patient. The results are transmitted to a display device for display.
With respect to the following limitations:
obtaining first biometric information as multidimensional data in a form of a first image-based representation by decomposing the first initial biometric information which is a first one-dimensional (1D) time signal, into first frequency components and arranging the first frequency components on a two dimensional (2D) plane; obtaining second biometric information as multidimensional data in a form of a second image-based representation by decomposing the second 1D time signal representing the HRV into second frequency components and arranging the second frequency components on a 2D plane; (Scharf 0003 – 0013, 0068 – 0070, 0093 – 0095, 0132 – 0135, Figure 7a, 7b).
Huang discloses detecting a time duration of change in the parameters, such as the number of occurrences or frequency of sleep disorder events over time. The time duration of change is displayed as a graph (i.e. a multi-dimensional form). A two-dimensional graph is embodied in the present specification as a “multi-dimensional form”. However, Huang does not disclose “decomposing” the first and second signal from a time signal into respective frequency components arranged on a 2D plane.
Scharf discloses a physiological monitoring system that includes obtaining blood oxygen saturation data and HRV data by collecting and analyzing sensor signals. Scharf uses a signal analyzer to convert the data from the time domain to a frequency domain, which are illustrated as 2D graphs. The converted data is analyzed by an algorithm to obtain a diagnostic result. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the sleep monitoring system of Huang so as to have included converting oxygen saturation and HRV data from the time domain to the frequency domain, in accordance with the teaching of Scharf, in order to “accurately determine certain physical variables” (Scharf Abstract).
With respect to CLAIM 1, Huang discloses the following structure:
An electronic device comprising: a communication device; a storage device storing a first artificial intelligence model trained to determine a sleep state; and at least one processor; (Huang 0027 – 0034, 0076).
With respect to CLAIM 19, Huang discloses the following structure:
A health condition diagnosis system comprising: at least one sensor device configured to obtain biometric information, including oxygen saturation and electrocardiogram (ECG) data of a user, over a period of time; (Huang 0025, 0026, 0028, 0050);
a display device; (Huang 0032, 0080 – 0082); and
a server storing a first artificial intelligence model trained to determine a sleep state, 0077, 0098 – 0104).
CLAIMS 3, 4, 9, 12, 13 and 18
The combination of Huang/Scharf discloses the limitations above relative to Claims 1 and 10. Additionally, Huang discloses the following limitations:
wherein the sleep state information is information related to a sleep disorder, and the sleep disorder includes at least one of sleep apnea syndrome, obstructive sleep apnea syndrome, sleep apnea, and snoring; (Huang 0007, 0008, 0047, 0050, 0065);
controlling a storage device connected to the electronic device to store a first biometric information set regarding oxygen saturation and a second biometric information set regarding ECG data for each of at least one user obtained through the at least one sensor device; (Huang 0034 – 0036);
wherein the at least one sensor device includes at least one of a fiber optic oxygen sensor, a membrane oxygen sensor, an ECG sensor, an EKG sensor, a PPG sensor, a bio-impedance sensor, or an ultrasound sensor; (Huang 0028, 0075).
Huang discloses detecting sleep disorders, including OSA, using sensor devices. Huang discloses storing information including the sensor data for analysis.
CLAIMS 5, 6, 8, 14, 15 and 17
The combination of Huang/Scharf/Olde discloses the limitations above relative to Claims 4 and 13. With respect to the following limitations:
obtaining a first training information set by converting the first biometric information set into a multidimensional form; obtaining a second training information set by converting a tachogram of heart rate variability, obtained based on the second biometric information set, into a multidimensional form; wherein the first artificial intelligence model is trained based on the first training information set and the second training information set; (Huang 0037, 0098).
Huang/Olde disclose determining a sleep state of a user by applying a trained model to first and second biometric information, including the recited converting into a multidimensional form. Huang further discloses obtaining training data training the model during a learning process. With respect to the following limitations:
obtaining first labeled data by labeling the first training information set based on classification of the sleep state of the user; obtaining second labeled data by labeling the second training information set based on the classification of the sleep state of the user; and training the first artificial intelligence model based on the first labeled data and the second labeled data; (Huang 0099, 0100).
Huang discloses that the training dataset is “correlated with” a sleep disorder and used to train the model. This fairly teaches “labeling” the dataset. With respect to the following limitations:
obtaining a request for retraining of the first artificial intelligence model; obtaining, based on the retraining request, the first biometric information set and the second biometric information set from a storage device connected to the electronic device; obtaining the first training information set by converting the first biometric information set into a multidimensional form; obtaining the second training information set by converting a tachogram of heart rate variability, obtained based on the second biometric information set, into a multidimensional form; and obtaining a second artificial intelligence model trained based on the first training information set and the second training information set; (Huang 0098, 0102, 0106, 0107).
The claims require re-training the model using data obtained from the monitored patient. Huang fairly teaches re-training using current data such as patient feedback using button.
CLAIMS 7 and 16
The combination of Huang/Olde discloses the limitations above relative to Claims 6 and 15. With respect to the following limitations:
controlling the display device to display a training guide screen related to the acquisition of the first labeled data and the second labeled data; and obtaining the first labeled data and the second labeled data based on an input received via the display device in association with the training guide screen.
The claims recite instructions presented on a display, which is directed towards “printed matter”. Printed matter carries no patentable weight, and are obvious over the art of record. (See MPEP 2111.05)
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al.: (US PGPUB 2021/0275087 A1) in view of Scharf et al.: (US PGPUB 2016/0007864 A1) in view of Olde et al.: (US PGPUB 2013/0023776 A1).
CLAIMS 2 and 11
The combination of Huang/Scharf discloses the limitations above relative to Claims 1 and 10. With respect to the following limitations:
obtaining a tachogram of heart rate variability based on the plurality of peak points and the time intervals between the plurality of peak points; and obtaining the second biometric information by converting the tachogram of heart rate variability into a multidimensional form; (Olde 0039, 0071 – 0078).
Huang/Scharf discloses obtaining heart rate variability information for an ECG signal, and converting the HRV into a multidimensional form; but does not disclose obtaining a tachogram of HRV; however, Olde does. Olde discloses a system and method for monitoring a property of the cardiovascular system of a subject including heart rate variability. Olde discloses obtaining heart rate variability information and a tachogram of HRV. Olde performs further analysis of the heart rate variability tachogram to obtain a HRV parameter using a discrete Fourier transform (i.e. converting the tachogram into a multidimensional form). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the sleep monitoring system of Huang/Scharf so as to have included using tachograms of HRV and commonly used data analysis techniques such as DFT or MEL to determine cardiac parameters, in accordance with the teaching of Olde, in order to provide a “simple characterization of HRV”.
Response to Arguments
Applicant's arguments filed 16 January, 2026 have been fully considered but they are not persuasive.
The U.S.C. §101 Rejection
Applicant asserts that the claims are not directed to an abstract idea under Step 2A because the claims are “no longer directed to any concept that can be performed in the human mind.” In particular, the claim recites operations that cannot be practically performed mentally, including “decomposing, arranging and inputting information into a trained model to perform inference.” The trained model replaces a prior art analysis by a professional of a sleeping user’s bio-signals to determine the user’s sleep state (i.e. inference). (see published specification US PGPUB 2025/0140397 A1 @ 0007) Using a trained model, instead of a human, may “reduce cost and time required for sleep state diagnosis”. (@ 0015 – 0016). Analyzing bio-signals to determine a sleeping user’s sleep state information, using well-known techniques as disclosed in the specification, is a process that, except for generic computer implementation steps, can be performed in the human mind.
Applicant asserts that transforming (i.e. decomposing) involves mathematical algorithms and cannot be done in a person’s mind. Examiner agrees. In particular, the specification discloses that transforming the sensor data from a time domain to a frequency domain uses mathematical techniques such as a Fourier transform. These limitations are directed to mathematical relationships.
Applicant asserts that the claim “integrate any abstract concept into a practical application by virtue of their specific, structured data processing flow on an electronic device.” The device “produces useful information through non-generic processing of sensor signals, which is a practical technical implementation, not an abstract mental exercise.” Nonetheless, the “technical implementation” performs an abstract process on a generic computer. The electronic device is generic – i.e. a smartphone, computer, etc. - and the sensors are generic – i.e. PPG sensor, ECG sensor. These sensor operate in their normal capacity to obtain the recited data.
Applicant further asserts that the recited converting is accomplished using “non-conventional signal processing” that is “far outside the realm of routine, conventional activity”. Applicant asserts that “more accurate detection of sleep states” is a “technical improvement in system performance.” Examiner disagrees.
Initially, whether decomposing is conventional or not is irrelevant, since this feature is included in the scope of the abstract mathematical relationships in the claims. As such, decomposing is not an additional feature to be considered under Step 2A Prong Two, or Step 2B. (see Berkheimer) Further, decomposing is purely conventional, including specific disclosures in the prior art teaching converting time-measures of blood oxygen saturation and ECG signals into a frequency domain for further analysis. Corenman et al.: (US4,911,167) issued on 27 March, 1990 teaches these specific functions on the specific data recited in the claims.
Applicant relies on the reasoning in Example 39, asserting that the example “transformations” are similar to “converting” – i.e. decomposing. Examiner disagrees. A mathematical concept may be expressed in symbols or “words used in a claim operating on data to solve a problem can serve the same purpose as a formula” (MPEP 2106.04(a)(2) I.) Other data conversions have been found to be mathematical relations including “converting BCD to Binary”; “performing a resampled statistical analysis to generate a resampled distribution”; “using a formula to convert geospatial coordinates into natural numbers”. Here, converting time-domain data to frequency-domain is a mathematical relationship.
Further, Example 39 is directed to collecting training data by performing the image transformations, using the data to train the model and re-training. The pending claim do not recite such training/re-training, since the model is already trained – it is merely applied to the manipulated data.
The U.S.C. §102/103 Rejections
Applicant asserts that Huang, and Olde, do not disclose “converting” as claimed. Examiner agrees. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Scharf et al..
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 4,911,167 to Corenman et al. discloses a system and method for improving the calculation of blood oxygen concentrations using pulse oximeters that includes processing in either the time domain or the frequency domain, where in the frequency domain, the time-measure is Fourier transformed into its spectral components.
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 of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to John A. Pauls whose telephone number is (571) 270-5557. The Examiner can normally be reached on Mon. - Fri. 8:00 - 5:00 Eastern. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773.
Official replies to this Office action may now be submitted electronically by registered users of the EFS-Web system. Information on EFS-Web tools is available on the Internet at: http://www.uspto.gov/patents/process/file/efs/guidance/index.jsp. An EFS-Web Quick-Start Guide is available at: http://www.uspto.gov/ebc/portal/efs/quick-start.pdf.
Alternatively, official replies to this Office action may still be submitted by any one of fax, mail, or hand delivery. Faxed replies should be directed to the central fax at (571) 273-8300. Mailed replies should be addressed to “Commissioner for Patents, PO Box 1450, Alexandria, VA 22313-1450.” Hand delivered replies should be delivered to the “Customer Service Window, Randolph Building, 401 Dulany Street, Alexandria, VA 22314.”
/JOHN A PAULS/Primary Examiner, Art Unit 3683
Date: 4 March, 2026