Detailed Notice
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/02/2026 has been entered.
Status of Claims
Claims 1-16, 18-21, and 24-25 are currently pending.
Claims 1, 12, 18, and 24-25 are amended.
Claims 17 and 22-23 are canceled.
Claims 1-16, 18-21, and 24-25 are rejected.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-16, 18-21, and 24-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The specification does not show support for “determining either (a) the rate at which labor is progressing is too slow or (b) the rate at which labor is progressing is too fast; wherein in response to determining that the rate at which labor is progressing is too slow, recommending at least one of: (a) initiating administration of a new medication to the patient to increase the rate at which labor is progressing; (b) increasing an amount of a medication being administered to the patient to increase the rate at which labor is progressing; (c) decreasing an amount of a medication being administered to the patient to increase the rate at which labor is progressing; or (d) terminating administration of a medication to the patient to increase the rate at which labor is progressing; wherein in response to determining that the rate at which labor is progressing is too fast, recommending at least one of: (a) initiating administration of a new medication to the patient to decrease the rate at which labor is progressing; (b) increasing an amount of a medication being administered to the patient to decrease the rate at which labor is progressing; (c) decreasing an amount of a medication being administered to the patient to decrease the rate at which labor is progressing; or (d) terminating administration of a medication to the patient to decrease the rate at which labor is progressing”.
The closest paragraph [0021] discloses predictive models, forecasting future contractions, and predicting the magnitude, duration, and time between contractions. Paragraph [0022] talks about initiating a response based on the stage of labor and whether labor is progressing at an appropriate rate or progressing safely. The response recites in [0021], are notifications, recommendations for modifications of care plan (increase monitoring), scheduling resources relating to the modification of care plan, and recommendation a laboring mother at home go when to go to the hospital. Paragraph [0071] recites similarities to [0021], as well as sending the response to a clinician interface. Paragraph [0081] talks about how the prediction may be based on previous contractions, predicted future contractions, or a combination of both. These cited paragraphs do not recite recommending administrating medication, more specifically, the specification does not show support for initiating a new medication whether that be increasing or decreasing the rate at which labor is progressing, making recommendations to increase or decrease dosage whether to increase or decrease the rate at which labor is progressing, and the specification does not support stopping or terminating medication to increase or decrease labor progressing. At most, paragraph 21 only discusses prompting recommendation for intervention when labor is not progressing as quickly as desired, and even then, the recommendation is broadly “medication”. The specification does not support the claimed actions of initiating a new medication, terminating administration of a medication, or increasing/decreasing an amount of the medication being administered. Instead, paragraph 21 discusses the “recommendations for modification of a care plan, such as increased monitoring, and scheduling resources relating to a recommended modification of a care plan, and the like”.
Therefore, there is no support for any of “recommending at least one of:(a) initiating administration of a new medication to the patient to increase the rate at which labor is progressing;(b) increasing an amount of a medication being administered to the patient to increase the rate at which labor is progressing; (c) decreasing an amount of a medication being administered to the patient to increase the rate at which labor is progressing; or (d) terminating administration of a medication to the patient to increase the rate at which labor is progressing; wherein in response to determining that the rate at which labor is progressing is too fast, recommending at least one of:(a) initiating administration of a new medication to the patient to decrease the rate at which labor is progressing;(b) increasing an amount of a medication being administered to the patient to decrease the rate at which labor is progressing;(c) decreasing an amount of a medication being administered to the patient to decrease the rate at which labor is progressing; or (d) terminating administration of a medication to the patient to decrease the rate at which labor is progressing”.
Additionally, claims 1, 12, 18, and 24 recite new matter of “retraining the population-based machine learning model to generate a patient-specific machine learning model to predict UA measurements for the patient in a future time interval”, “retraining the patient-specific machine learning model based on feedback corresponding to one or more contractions forecasted for the patient to generate a retrained patient-specific machine learning model; and applying the retrained patient-specific machine learning model to generate a second set of forecasted future UA measurements for the patient”, “retraining the neural net ARIMA ensemble of the population-based machine learning models at least by adjusting the weighting of the neural net ARIMA ensemble of the population-based machine learning models based on the determination of the performance of the ensemble of the population-based machine learning models to generate a neural net ARIMA ensemble of patient-specific machine learning models”, “retraining the neural net ARIMA ensemble of the patient-specific machine learning models based on feedback corresponding to one or more contractions forecasted for the patient to generate a retrained neural net ARIMA ensemble of the patient- specific machine learning models; and applying the neural net ARIMA ensemble of the retrained patient-specific machine learning models to generate a second set of forecasted future UA measurements for the patient”. Claim 24 recites new matter of “forecasting one or more additional contractions in a second future time interval for the patient using the retrained patient-specific machine learning model and a new time series for the patient including next contractions; based on the one or more additional contractions that are forecasted, determining a second rate at which labor is progressing; determining that the second rate at which labor is progressing is changing in response to treatment of the patient; and continuing treating the patient in a same manner”. The closest paragraphs in the specification disclose “the patient's UA time series is used to train an ensemble of predictive models where each model generates a forecast of UA values for a future time interval. The plurality of forecasts generated by the ensemble may be averaged to provide a primary UA forecast. In another aspect, a plurality of models are trained with data from a reference population. The trained models may then be used with a particular patient's UA measurements during labor to forecast features of the next contraction to occur within a future time interval” ([0004]), “Predictive models are trained to forecast future contractions based on the UA time series” ([0020]), and “The patient's UA time series may be used to train the plurality of models, and the input and/or parameters of each model may vary to output a plurality of forecasts for future UA values for the patient. In some embodiments, the primary UA forecast is the mean of the forecasts from at least a portion of the ensemble member models. In alternative embodiments, the contractions are predicted using a plurality of predictive models trained on UA measurements from a reference population. The models are trained to predict a feature of a future contraction based on identifying of previous contractions using a peak threshold and a recovery threshold. In some aspects, there are three predictive models that each are trained to predict a particular feature of the next contraction” ([0021]). The specification does not disclose “retraining” a machine learning model or “generating a retrained” machine learning model. Therefore, the amended claims 1, 12, 18, and 24 recite new matter.
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 1-16, 18-21, and 24-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
In the instant case, claims 1-11, 21, and 24-25 are directed toward a non-transitory computer-readable media (i.e., manufacture), claims 12-16 are directed toward a system (i.e., machine), and claims 18-20 are directed toward a method (i.e., process). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea
Step 2A—Prong 1:
Independent claims 1, 12, and 18 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “One or more non-transitory computer-readable media having computer-executable instructions embodied thereon that when executed, provide a method for a decision support system for patients in labor using uterine activity forecasts, the method comprising: receiving, a plurality of uterine activity (UA) measurements for a patient in labor, the plurality of UA measurements being acquired over a time span; constructing a time series from the plurality of UA measurements for the patient; training a population-based machine learning model, using UA measurements from a reference population, to forecast future UA measurements; applying the population-based machine learning model to the time series from the plurality of UA measurements for the patient to generate a set of forecasted future UA measurements for the patient; based on the set of forecasted future UA measurements and patient-specific data, retraining the population-based machine learning model to generate a patient-specific machine learning model to predict UA measurements for the patient in a future time interval; forecasting one or more contractions in the future time interval for the patient using the patient- specific machine learning model and an updated time series for the patient; based on the one or more contractions that are forecasted for the patient, determining a rate at which labor is progressing; comparing the rate at which labor is progressing to rates at which labor progressed for the reference population to generate a comparison of the rate at which labor is progressing to rates at which labor progressed for the reference population; based on the comparison of the rate at which labor is progressing to rates at which labor progressed for the reference population, determining either (a) the rate at which labor is progressing is too slow or (b) the rate at which labor is progressing is too fast; wherein in response to determining that the rate at which labor is progressing is too slow, recommending at least one of: (a) initiating administration of a new medication to the patient to increase the rate at which labor is progressing; (b) increasing an amount of a medication being administered to the patient to increase the rate at which labor is progressing; (c) decreasing an amount of a medication being administered to the patient to increase the rate at which labor is progressing; or (d) terminating administration of a medication to the patient to increase the rate at which labor is progressing; wherein in response to determining that the rate at which labor is progressing is too fast, recommending at least one of: (a) initiating administration of a new medication to the patient to decrease the rate at which labor is progressing; (b) increasing an amount of a medication being administered to the patient to decrease the rate at which labor is progressing; (c) decreasing an amount of a medication being administered to the patient to decrease the rate at which labor is progressing; or (d) terminating administration of a medication to the patient to decrease the rate at which labor is progressing; retraining the patient-specific machine learning model based on feedback corresponding to one or more contractions forecasted for the patient to generate a retrained patient-specific machine learning model; and applying the retrained patient-specific machine learning model to generate a second set of forecasted future UA measurements for the patient”.
The limitations of receiving, a plurality of uterine activity (UA) measurements for a patient in labor, the plurality of UA measurements being acquired over a time span; constructing a time series from the plurality of UA measurements for the patient; using UA measurements from a reference population, to forecast future UA measurements; generate a set of forecasted future UA measurements for the patient; based on the set of forecasted future UA measurements and patient-specific data, predict UA measurements for the patient in a future time interval; forecasting one or more contractions in the future time interval for the patient and an updated time series for the patient; based on the one or more contractions that are forecasted for the patient, determining a rate at which labor is progressing; comparing the rate at which labor is progressing to rates at which labor progressed for the reference population to generate a comparison of the rate at which labor is progressing to rates at which labor progressed for the reference population; based on the comparison of the rate at which labor is progressing to rates at which labor progressed for the reference population, determining either (a) the rate at which labor is progressing is too slow or (b) the rate at which labor is progressing is too fast; wherein in response to determining that the rate at which labor is progressing is too slow, recommending at least one of: (a) initiating administration of a new medication to the patient to increase the rate at which labor is progressing; (b) increasing an amount of a medication being administered to the patient to increase the rate at which labor is progressing; (c) decreasing an amount of a medication being administered to the patient to increase the rate at which labor is progressing; or (d) terminating administration of a medication to the patient to increase the rate at which labor is progressing; wherein in response to determining that the rate at which labor is progressing is too fast, recommending at least one of: (a) initiating administration of a new medication to the patient to decrease the rate at which labor is progressing; (b) increasing an amount of a medication being administered to the patient to decrease the rate at which labor is progressing; (c) decreasing an amount of a medication being administered to the patient to decrease the rate at which labor is progressing; or (d) terminating administration of a medication to the patient to decrease the rate at which labor is progressing; based on feedback corresponding to one or more contractions forecasted for the patient; and generate a second set of forecasted future UA measurements for the patient, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of receive, using, predict, forecasting, determining, treating, comparing, generating, and administering, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Further, the abstract idea of claims 12 and 18 are identical as the abstract idea of claim 1. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people.
Dependent claims 2-11, 13-16, 19-21, and 24-25 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 12, and 18. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims 1-16, 18-21, and 24-25 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “non-transitory computer-readable media”, “population-based machine learning model”, “patient-specific machine learning model”, “system”, “processors”, and “memory”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1, [0018]-[0019], [0023]-[0025], and [0034]-[0038], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “training a population-based machine learning model”, “applying the population-based machine learning model to the time series from the plurality of UA measurements for the patient to”, “retraining the population-based machine learning model to generate a patient-specific machine learning model to”, “using the patient- specific machine learning model”, “retraining the patient-specific machine learning model”, “to generate a retrained patient-specific machine learning model”, “applying the retrained patient-specific machine learning model to”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receiving, a plurality of uterine activity (UA) measurements for a patient in labor, the plurality of UA measurements being acquired over a time span”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 2-11, 13-16, 19-21, and 24-25 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1, 12, and 18, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements (i.e., “non-transitory computer-readable media”, “population-based machine learning model”, “patient-specific machine learning model”, “system”, “processors”, and “memory”) that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims 2-11, 13-16, 19-21, and 24-25 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 12 and 18, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving, a plurality of uterine activity (UA) measurements for a patient in labor, the plurality of UA measurements being acquired over a time span”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-16, 18-21, and 24-25 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 01/02/2026 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. 112(a) rejection for new matter, Applicant argues the claims have been amended in a manner believed to overcome the rejection.
Examiner respectfully disagrees. The independent claims still recite “determining either (a) the rate at which labor is progressing is too slow or (b) the rate at which labor is progressing is too fast; wherein in response to determining that the rate at which labor is progressing is too slow, recommending at least one of: (a) initiating administration of a new medication to the patient to increase the rate at which labor is progressing; (b) increasing an amount of a medication being administered to the patient to increase the rate at which labor is progressing; (c) decreasing an amount of a medication being administered to the patient to increase the rate at which labor is progressing; or (d) terminating administration of a medication to the patient to increase the rate at which labor is progressing; wherein in response to determining that the rate at which labor is progressing is too fast, recommending at least one of: (a) initiating administration of a new medication to the patient to decrease the rate at which labor is progressing; (b) increasing an amount of a medication being administered to the patient to decrease the rate at which labor is progressing; (c) decreasing an amount of a medication being administered to the patient to decrease the rate at which labor is progressing; or (d) terminating administration of a medication to the patient to decrease the rate at which labor is progressing”, which is not disclosed in the specification. Additionally, the specification does not show support for “retraining” or “generating a retrained” machine learning model as shown in the 112(a) Rejection above. Applicant also provides no support (paragraphs from the specification), that would prove otherwise. Therefore, the 35 U.S.C. 112(a) Rejection is maintained.
Regarding the 35 U.S.C. 101 Rejection, Applicant argues forecasting contractions for an individual in labor is not the same as or similar to budgeting, filtering content, considering historical usage, nor a mental process followed by a neurologist, and therefore is not a personal behavior. Applicant argues for the claims to be classified as a certain method of organizing human activity, there must be person behavior or relationships or interactions between people to manage.
Examiner respectfully disagrees. MPEP 2106.04(a)(2)(II) states the phrase “methods of organizing human activity” also encompasses “managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions)”. The limitations that fall within this subgrouping do not need to be the same or similar to budgeting, filtering content, considering historical usage, nor a mental process followed by a neurologist. Instead, MPEP 2106.04(a)(2) states that “the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping”. Therefore, a person is capable of predicting or forecasting future uterine contractions.
Applicant argues the claims recite a similar improvement to Desjardins by reciting a technological improvement to the machine learning model through patient-specific model generation, iterative retraining based on targeted feedback, and efficient forecasting of physiological event from time-series data.
Examiner respectfully disagrees. Desjardins was eligible by improving the function of the machine learning model by reducing storage requirements, reducing system complexity, and preserving previously learned tasks while learning new tasks. However, the current claims do not recite similar features. Additionally, the machine learning model is recited at a high level such that they amount to generic computer components that are being linked or applied to the abstract idea. See MPEP 2106.05(f) recites “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)”.
Applicant argues the additional elements integrate the abstract idea by reciting an improvement in the function of the machine learning model. The managing time-series data ingestion, model retraining, and forecast generation improves the function of the computer itself because it allows the system to operate on a compact, structured representation of US measurements rather than repeatedly scanning raw physiological data streams.
Examiner respectfully disagrees. Managing time-series data ingestion and forecast generation are not improvement to the technology (i.e., machine learning model), but a business practice improvement. Additionally, they would be part of the abstract idea and the abstract idea cannot integrate itself into a practical application. Furthermore, the model retraining is recited at a high level and is being used within its ordinary capacity. Again, under MPEP 2106.05(f) is not an improvement, but merely applying the abstract idea to the additional elements.
Applicant argues the additional elements that amount to significantly more than any abstract idea. Applicant also argues the claims improve computer capabilities and the technological field of machine-learning based physiological forecasting and clinical decision-support. Applicant also argues the claims recite limitations that are not-well understood, routine, or conventional and that meaningfully restrict the claim to a particular useful application.
Examiner respectfully disagrees. As stated above, physiological forecasting and clinical decision-support is not an improvement to the technology or technical field, but a business practice improvement. The additional elements are recited at a high level such that they amount to apply it to the abstract idea. Examiner has also provided evidence by the MPEP, court decisions, etc. as required by MPEP 2106.05(a) (“Appropriate forms of support include one or more of the following: (a) A citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s); (b) A citation to one or more of the court decisions discussed in Subsection II below as noting the well-understood, routine, conventional nature of the additional element(s); (c) A citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and (d) A statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional element(s)”). Applicant also provides no support of how the claims recite limitations that are not-well understood, routine, or conventional and that meaningfully restrict the claim to a particular useful application. Therefore, the 35 U.S.C. 101 Rejection is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHAEL SOJIN STONE whose telephone number is (571)272-8798. The examiner can normally be reached Monday-Friday 7 AM - 7 PM (EST).
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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