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 .
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 non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 11 follows.
Regarding claim 11, the claim recites a series of steps or acts, determining, by the one or more processors based on the biometric data, a likelihood that the muscle contraction comprises a true labor uterine contraction. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of determining, by the one or more processors based on the biometric data, a likelihood that the muscle contraction comprises a true labor uterine contraction sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 11 recites outputting, by the one or more processors for display by a user device, the muscle contraction vector indicating the direction of the muscle contraction and the likelihood that the muscle contraction comprises a true labor uterine contraction, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The display of the vector and likelihood does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the displayed vector and likelihood, nor does the method use a particular machine to perform the Abstract Idea.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. The claim recites additional step of determining, by the one or more processors based on the biometric data, a muscle contraction vector indicating a direction of the muscle contraction over the period of time, which is another abstract idea in the form of a mathematical concept. The claim also recites configuring algorithms, by one or more processors, to control an acquisition of maternal and/or fetal biometric data, implemented by a set of sensors, using maternal patient data and/or fetal patient data, wherein the set of sensors are positioned around an abdomen and a back of a patient, which is an abstract idea in the form of a mental process. Besides the Abstract Idea, The claim recites the step of receiving, by one or more processors from a set of sensors, biometric data indicative of a muscle contraction of a patient over a period of time. Obtaining data indicative of a muscle contraction is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the acquiring and receiving step is recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claim 1, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The set of sensors are generic sensors configured to perform pre-solutional data gathering activity, the user device is a generic device configured to perform displaying an output, and the memory and processors are configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to data processing, and the training of machine learning modules. The processing training steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Penders (US 20200196958 A1 - cited by applicant) in view of Greenberg (US 6751498 B1) in view of Berkow (US 20110152651 A1).
In regards to claim 1, Penders teaches a system comprising: a wearable device comprising a set of sensors ([0227]); a memory; and one or more processors in communication with the memory([0227]), wherein the one or more processors are configured to:
receive, from a set of sensors, biometric data indicative of a muscle contraction of a patient over a period of time ([0188-0189]);
determine, based on the biometric data, a muscle contraction vector indicating a direction of the muscle contraction over the period of time ([0199-0200]);
determine, based on the biometric data, a likelihood that the muscle contraction comprises a true labor uterine contraction ([0215]);
and output, for display by a user device, the muscle contraction vector indicating the direction of the muscle contraction and the likelihood that the muscle contraction comprises a true labor uterine contraction ([0214] [0228]).
Penders fails to teach a device configured to configure algorithms to control an acquisition of maternal and/or fetal biometric data, implemented by the set of sensors, using maternal patient data and/or fetal patient data; and wherein the set of sensors are positioned around an abdomen and a back of the patient when the wearable device is worn by the patient.
Greenberg teaches a set of sensors that are positioned around an abdomen and a back of the patient (Figure 4A). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the sensors of Penders to be positioned around the back and abdomen like the device of Greenberg. Doing so would be a mere rearrangement of parts.
Penders in view of Greenberg fails to explicitly teach a device configured to configure algorithms to control an acquisition of maternal and/or fetal biometric data, implemented by the set of sensors, using maternal patient data and/or fetal patient data. However, Penders in view of Greenberg teaches a processor and instructions configured to carry out a method that includes acquiring a plurality of signals from a plurality of sensors during uterine activity (Penders [0188] [0197]). Berkow teaches an controller that initiates data collection from a subject ([0032] controllers inherently carry out algorithms and someone would have had to program said algorithm). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of Penders in view of Greenberg to carry out an algorithm that initiates data collection from the sensors like the controller of Berkow. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of initiating data collection.
In regards to claim 2, modified Penders teaches the system of claim 1, wherein the wearable device comprises: a wearable band configured to be worn about a torso of the patient; wherein each sensor of the set of sensors is configured to collect a respective electrical signal of a set of electrical signals, wherein the biometric data comprises the set of electrical signals, and wherein the set of sensors are configured to output the set of electrical signals to the one or more processors (Penders [0227]).
In regards to claim 3, modified Penders teaches the system of claim 2, wherein the set of sensors are arranged on the wearable band such that when the wearable band is worn about the torso of the patient, each sensor of the set of sensors is located proximate to a location on the torso of the patient, wherein the set of sensors include a reference sensor, and wherein the one or more processors are configured to:
determine, based on the set of electrical signals, a set of electrical potential vector signals, wherein each electrical potential vector signal of the set of electrical potential vector signals represents a difference between an electrical signal of the set of electrical signals collected by the reference sensor and an electrical signal of the set of electrical signals collected by another sensor of the set of sensors (Penders [0184]);
determine, based on the set of electrical potential vector signals, the muscle contraction vector (Penders [0199-0200]);
and determine, based on the set of electrical potential vector signals, the likelihood that the muscle contraction comprises a true labor uterine contraction (Penders [0197]).
In regards to claim 4, modified Penders teaches the system of claim 3, wherein to determine the muscle contraction vector, the one or more processors are configured to: process the set of electrical potential vector signals to identify a direction of a movement of the muscle contraction over the period of time relative to the torso of the patient; and process the set of electrical potential vector signals to identify a magnitude of a strength of the muscle contraction over the period of time (Penders [0204]).
In regards to claim 5, modified Penders teaches the system of claim 1, wherein the one or more processors are configured to determine the likelihood that the muscle contraction comprises a true labor uterine contraction based on the direction of the muscle contraction over the period of time (Penders [0204]).
In regards to claim 6, modified Penders teaches the system of claim 1, wherein the memory is configured to store a machine learning model, and wherein the processing circuitry is configured to: execute the machine learning model to determine the muscle contraction vector indicating the direction of the muscle contraction over the period of time; and execute the machine learning model to determine the likelihood that the muscle contraction comprises a true labor uterine contraction (Penders [0203]).
In regards to claim 7, modified Penders teaches the system of claim 6, wherein the memory is configured to store training data comprising a plurality of biometric training data samples, wherein the plurality of biometric data training samples comprises a first set of biometric data training samples that each indicate a true labor uterine contraction and a second set of biometric data training samples that each indicate a non-labor uterine contraction, and wherein the one or more processors are configured to: train the machine learning model by identifying one or more patterns associated with the first set of biometric data training samples and identifying one or more patterns associated with the second set of biometric data training samples (Penders [0203 and 0246]).
In regards to claim 8, modified Penders teaches the system of claim 7, wherein to execute the machine learning model to determine the likelihood that the muscle contraction comprises a true labor uterine contraction, the one or more processors are configured to: process the biometric data to identify one or more patterns corresponding to the muscle contraction of the patient over the period of time; and determine the likelihood that the muscle contraction comprises a true labor uterine contraction based on the one or more patterns corresponding to the muscle contraction, the one or more patterns associated with the first set of biometric data training samples, and the one or more patterns associated with the second set of biometric data training samples (Penders [0217-0221]).
In regards to claim 9, modified Penders teaches the system of claim 1, wherein the one or more processors are further configured to: determine, based on the biometric data, a timeseries indicating a strength of the muscle contraction of the patient over the period of time (Penders [0204 and 0209]); and output, for display by the user device, the average frequency of contractions and the average duration of the contractions over time (Penders [0271]). Modified Penders fails to explicitly teach displaying the timeseries indicating a strength of the muscle contraction of the patient over the period of time. However, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to display the amplitude of the contractions in addition to the frequency in order to display the strength of the contractions over time to the patient.
In regards to claim 10, modified Penders teaches the system of claim 1, wherein the one or more processors are further configured to: receive individual data corresponding to the patient; determine, based on the biometric data and the individual data corresponding to the patient, the direction of the muscle contraction over the period of time; and determine, based on the biometric data and the individual data corresponding to the patient, the likelihood that the muscle contraction comprises a true labor uterine contraction (Penders [0241-0242]).
In regards to claim 11, Penders teaches a method comprising:
receiving, by one or more processors from a set of sensors, biometric data indicative of a muscle contraction of a patient over a period of time, wherein the one or more processors are in communication with a memory ([0188-0189]);
determining, by the one or more processors based on the biometric data, a muscle contraction vector indicating a direction of the muscle contraction over the period of time ([0199-0200]);
determining, by the one or more processors based on the biometric data, a likelihood that the muscle contraction comprises a true labor uterine contraction ([0215]);
and outputting, by the one or more processors for display by a user device, the muscle contraction vector indicating the direction of the muscle contraction and the likelihood that the muscle contraction comprises a true labor uterine contraction ([0228]).
Penders fails to teach configuring algorithms, by one or more processors, to control an acquisition of maternal and/or fetal biometric data, implemented by a set of sensors, using maternal patient data and/or fetal patient data, wherein the set of sensors are positioned around an abdomen and a back of a patient. Greenberg teaches a set of sensors that are positioned around an abdomen and a back of the patient (Figure 4A). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method Penders so that the sensors are positioned around the back and abdomen like the device of Greenberg. Doing so would be a mere rearrangement of parts.
Penders in view of Greenberg fails to teach configuring algorithms, by one or more processors, to control an acquisition of maternal and/or fetal biometric data, implemented by a set of sensors, using maternal patient data and/or fetal patient data. However, Penders in view of Greenberg instructions configured to carry out a method that includes acquiring a plurality of signals from a plurality of sensors during uterine activity (Penders [0188] [0197]). Berkow teaches initiating data collection from a subject ([0032] controllers inherently carry out algorithms and someone would have had to program said algorithm). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Penders in view of Greenberg to carry out an algorithm that initiates data collection from the sensors like the controller of Berkow. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of initiating data collection.
In regards to claim 12, modified Penders teaches the method of claim 11, further comprising outputting, by the set of sensors, the set of electrical signals to the one or more processors, wherein a wearable device comprises: a wearable band configured to be worn about a torso of the patient; and the set of sensors affixed to the wearable band, wherein each sensor of the set of sensors is configured to collect a respective electrical signal of a set of electrical signals, wherein the biometric data comprises the set of electrical signals (Penders [0227]).
In regards to claim 13, modified Penders teaches the method of claim 12, wherein the set of sensors are arranged on the wearable band such that when the wearable band is worn about the torso of the patient, each sensor of the set of sensors is located proximate to a location on the torso of the patient, wherein the set of sensors include a reference sensor, and wherein the method further comprises:
determining, by the one or more processors based on the set of electrical signals, a set of electrical potential vector signals, wherein each electrical potential vector signal of the set of electrical potential vector signals represents a difference between an electrical signal of the set of electrical signals collected by the reference sensor and an electrical signal of the set of electrical signals collected by another sensor of the set of sensors (Penders [0184]);
determining, by the one or more processors based on the set of electrical potential vector signals, the muscle contraction vector (Penders [0199-0200]);
and determining, by the one or more processors based on the set of electrical potential vector signals, the likelihood that the muscle contraction comprises a true labor uterine contraction (Penders [0197]).
In regards to claim 14, modified Penders teaches the method of claim 13, wherein determining the muscle contraction vector comprises: processing the set of electrical potential vector signals to identify a direction of a movement of the muscle contraction over the period of time relative to the torso of the patient; and processing the set of electrical potential vector signals to identify a magnitude of a strength of the muscle contraction over the period of time (Penders [0204]).
In regards to claim 15, modified Penders teaches the method of claim 11, further comprising determining, by the one or more processors, the likelihood that the muscle contraction comprises a true labor uterine contraction based on the direction of the muscle contraction over the period of time (Penders [0204]).
In regards to claim 16, modified Penders teaches the method of claim 11, wherein the memory is configured to store a machine learning model, and wherein the method further comprises: executing, by the one or more processors, the machine learning model to determine the muscle contraction vector indicating the direction of the muscle contraction over the period of time; and executing, by the one or more processors, the machine learning model to determine the likelihood that the muscle contraction comprises a true labor uterine contraction (Penders [0203]).
In regards to claim 17, modified Penders teaches the method of claim 16, wherein the memory is configured to store training data comprising a plurality of biometric training data samples, wherein the plurality of biometric data training samples comprises a first set of biometric data training samples that each indicate a true labor uterine contraction and a second set of biometric data training samples that each indicate a non-labor uterine contraction, and wherein the method further comprises: training, by the one or more processors, the machine learning model by identifying one or more patterns associated with the first set of biometric data training samples and identifying one or more patterns associated with the second set of biometric data training samples (Penders [0203 and 0246]).
In regards to claim 18, modified Penders teaches the method of claim 17, wherein executing the machine learning model to determine the likelihood that the muscle contraction comprises a true labor uterine contraction comprises: processing the biometric data to identify one or more patterns corresponding to the muscle contraction of the patient over the period of time; and determining, the likelihood that the muscle contraction comprises a true labor uterine contraction based on the one or more patterns corresponding to the muscle contraction, the one or more patterns associated with the first set of biometric data training samples, and the one or more patterns associated with the second set of biometric data training samples (Penders [0217-0221]).
In regards to claim 19, modified Penders teaches the method of claim 11, wherein the method further comprises: determining, by the one or more processors based on the biometric data, a timeseries indicating a strength of the muscle contraction of the patient over the period of time; and determining, by the one or more processors for display by the user device, the average frequency of contractions and the average duration of the contractions over time (Penders [0271]). Modified Penders fails to explicitly teach displaying the timeseries indicating a strength of the muscle contraction of the patient over the period of time. However, It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to display the amplitude of the contractions in addition to the frequency in order to display the strength of the contractions over time to the patient.
In regards to claim 20, Penders teaches a computer readable medium comprising instructions that when executed cause one or more processors to: receive, from a set of sensors, biometric data indicative of a muscle contraction of a patient over a period of time; determine, based on the biometric data, a muscle contraction vector indicating a direction of the muscle contraction over the period of time; determine, based on the biometric data, a likelihood that the muscle contraction comprises a true labor uterine contraction; and output, for display by a user device, the muscle contraction vector indicating the direction of the muscle contraction and the likelihood that the muscle contraction comprises a true labor uterine contraction ([0241-0242]).
Penders fails to teach configuring algorithms, by one or more processors, to control an acquisition of maternal and/or fetal biometric data, implemented by a set of sensors, using maternal patient data and/or fetal patient data, wherein the set of sensors are positioned around an abdomen and a back of a patient. Greenberg teaches a set of sensors that are positioned around an abdomen and a back of the patient (Figure 4A). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the instructions Penders so that the sensors are positioned around the back and abdomen like the device of Greenberg. Doing so would be a mere rearrangement of parts.
Penders in view of Greenberg fails to teach configuring algorithms, by one or more processors, to control an acquisition of maternal and/or fetal biometric data, implemented by a set of sensors, using maternal patient data and/or fetal patient data. However, Penders in view of Greenberg instructions configured to carry out a method that includes acquiring a plurality of signals from a plurality of sensors during uterine activity (Penders [0188] [0197]). Berkow teaches initiating data collection from a subject ([0032] controllers inherently carry out algorithms and someone would have had to program said algorithm). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Penders in view of Greenberg to carry out an algorithm that initiates data collection from the sensors like the controller of Berkow. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of initiating data collection.
Response to Arguments
Applicant’s arguments, see remarks, filed 10/31/2025, with respect to the 35 U.S.C. 112(b) rejection of claim 6 have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection of claim 6 has been withdrawn.
Applicant’s arguments, see remarks, filed 10/31/2025, with respect to the 35 U.S.C. 101 rejection of claims 1-20 have been fully considered, but are not persuasive. Upon further consideration, it has been determined that the amended language does not overcome the rejection under 35 U.S.C. 101. The step of “configuring algorithms, by one or more processors, to control an acquisition of maternal and/or fetal biometric data, implemented by a set of sensors, using maternal patient data and/or fetal patient data”, is merely an abstract idea in the form of a mental process. A human could configure an algorithm to acquire data. The acquisition of the data itself is mere data gathering.
Applicant’s arguments, see remarks, filed 10/31/2025, with respect to the 35 U.S.C. 102 and 35 U.S.C. 103 rejection of claims 1-20 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Penders (US 20200196958 A1 - cited by applicant) in view of Greenberg (US 6751498 B1) in view of Berkow (US 20110152651 A1).
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 LUCY EPPERT whose telephone number is (571)270-0818. The examiner can normally be reached M-F 7:30-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Robertson can be reached at (571) 272-5001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LUCY EPPERT/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791