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 ("RCE"), 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 9, 2026, has been entered.
Status of Claims
Claims 1-3, 5-13, and 15-20 were previously pending and subject to a Final Office Action having a notification date of September 8, 2025 (“Final Office Action”). Following the Final Office Action, Applicant filed the RCE and an amendment on March 9, 2026 (“Amendment”), amending claims 1 and 11.
The present non-final Office Action addresses pending claims 1-3, 5-13, and 15-20 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §112
These rejections are withdrawn in view of the Amendment.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 17/139,109 (“Prior Application”), fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of the present application.
Specifically, independent claims 1 and 11 (and thus dependent claims 2, 3, and 5-10 and 12, 13, and 15-20 by virtue of their dependency) in the present application recite a “functional signature,” a “conduct indicator,” and a “functional program.” However, the Prior Application does not disclose or support these limitations. Claims 1-3, 5-13, and 15-20 in the present application also recite other limitations not disclosed or supported by the Prior Application.
Accordingly, claims 1-3, 5-13, and 15-20 of the present application are not entitled to the benefit of the Prior Application and thus the effective filing date of claims 1-3, 5-13, and 15-20 of the present application is September 1, 2021, the actual filing date of the present application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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, 5-7, 9, 11, 15-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2023/0047307 to Sinha et al. (“Sinha”; all of the below citations are supported in U.S. Provisional App. No. 62/230,656, filed on August 6, 2021, from which Sinha claims priority) in view of U.S. Patent App. Pub. No. 2012/0315630 to Gong et al. (“Gong”), U.S. Patent App. Pub. No. 2018/0166165 to Warren et al. (“Warren”), U.S. Patent App. Pub. No. 2018/0049675 to Kerber ("Kerber"), and U.S. Patent App. Pub. No. 2019/0286789 to St. Amant et al. (“St. Amant”):
Regarding claim 1, Sinha discloses a system (system 400 in Figure 7A) for generating a digestive disease functional program (personalized intervention plan execution 440 in Figure 7A which can address digestive health issues per [0020]), the system comprising:
a computing device (sample processing subsystem 420 and computing platform 430 in Figure 7A are a “computing device”), the computing device configured to:
receive at least a digestive biomarker relating to a user ([0060]-[0066] discuss receiving data from various biometric tests such as blood, fecal, etc.; additionally or alternatively, [0054], [0080] and [0088] discuss receiving fecal/saliva samples from a subject and performing processing to obtain sequencing information, SNP-based biomarkers, etc. (“digestive biomarkers”));
generate at least a digestive parameter as a function of the at least a digestive biomarker ([0061] discusses determining fecal calprotectin, blood cell counts, etc. (“digestive parameters”) which would be from the fecal/blood data (from the “digestive biomarkers”); still further, [0061] discusses deriving data (“digestive parameters”) from gastrointestinally-derived signals; [0062] discusses deriving data (“digestive parameters”) from blood chemical and biochemical profiles; still further, [0088] discusses using the SNP-based biomarkers (“digestive biomarkers”) to identify traits (“digestive parameters”) such as carbohydrate intake, protein intake, HDL cholesterol levels, etc.; still further, [0092]-[0094] discuss deriving microbiome biomarkers (“digestive parameters”) as a function of the sequencing information, etc. (the “digestive biomarkers”));
determine a digestive profile… as a function of the at least a digestive parameter (the collection of “digestive parameters” discussed in relation to [0061]-[0062], [0088], and/or [0092]-[0094] constitute a “digestive profile”; for instance, [0088] discusses how the various traits can be part of a gut/digestive health report), …;
identify a functional signature as a function of the digestive profile ([0046] and [0073] discusses returning a baseline state and diagnostic/therapeutic signatures (“functional signature”) based on processing the samples and biometric data (which includes the “digestive profile” noted above); additionally or alternatively, [0050] discusses generating multi-omic signatures (“functional signature”) from combinations of genetic biomarkers, microbiome biomarkers, and lifestyle biomarkers (where the microbiome biomarkers can be part of the “digestive profile” as noted above), wherein identifying the functional signature further comprises:
receiving a conduct indicator ([0067]-[0069] discusses receiving a lifestyle dataset including for instance dietary behaviors, exercise behaviors, etc. (“conduct indicators”); also, [0096] discusses how lifestyle biomarkers can include or be derived from dietary behavior, energy levels, sleep behavior, etc. (“conduct indicators”))… ; and
identifying the functional signature as a function of the conduct indicator and the digestive profile ([0046] and [0073] discusses returning the baseline state and diagnostic/therapeutic signatures (“functional signature”) based on processing the samples and biometric data (which includes the “digestive profile” noted above) and the lifestyle dataset (which includes the conduct indicator(s)); additionally or alternatively, [0050] discusses generating multi-omic signatures (“functional signature”) from combinations of genetic biomarkers, microbiome biomarkers (where the microbiome biomarkers can be part of the “digestive profile” as noted above), and lifestyle biomarkers (which include the “conduct indicators” as noted above) using a functional machine-learning model ([0073]-[0074] discusses how generation of the “functional signature” uses transformation operations including applications of supervised, semi-supervised and unsupervised machine or statistical inference methods (a “functional machine-learning model”), which comprises:
receiving a functional training set that correlates inputs comprising a plurality of digestive profiles and a plurality of conduct indicators to outputs comprising a plurality of functional signatures ([0073]-[0074] discusses how the “functional machine-learning model,” which generates the baseline state and diagnostic/therapeutic signatures (“functional signature”) based on processing the samples and biometric data (which includes the “digestive profile” noted above) and the lifestyle dataset (which includes the conduct indicator(s)), can utilize supervised machine inference methods, where supervised methods would include training the ML model with training sets of inputs and known outputs corresponding to the anticipated type of inputs to be received after training for use in generating outputs; also, [0027] discusses how the samples and lifestyle data of patients can be processed to extract signatures for use in generating improved training sets to train the ML algorithms; accordingly, a “functional training set” is received that correlates digestive profiles and conduct indicators to a functional signature for use in supervised training/learning of the functional ML model);
training, iteratively, the functional machine-learning model using the functional training set (as noted above, the functional ML model is trained as a function of the “functional training set,” and the training is iteratively performed per [0027]), wherein the functional machine-learning model is configured to be updated (“iteratively” training the model includes updating the model) based on correlations from previous iterations (as evidenced by NPL “Iterative Machine Learning: A step towards Model Accuracy” to Banerjee, iterative training involves incorporating feedback/errors from previous iterations of training the model which would be based on correlations (inputs and corresponding outputs) from such previous iterations); and
identifying the functional signature using the trained functional machine-learning model (again as noted above, the functional ML model outputs the “functional signature” based on the “conduct indicator” and “digestive profile” inputs); and
produce a functional program as a function of the functional signature ([0046] and [0106] discuss generating a personalized intervention plan (“functional program”) upon processing the “functional signature”); also, [0050] discusses using the multi-omic signatures (“functional signature”) to generate diagnostics and therapeutic pathways (“functional program”)), wherein the functional program comprises recommending a combination of exercise and edibles to modify the functional signature ([0106]-[0108] of Sinha discloses how the functional program/intervention can include personalized diets (edibles) and exercise regimens to transform signatures to improve the health state of the subject).
However, Sinha appears to be silent regarding the digestive profile comprising at least an enzyme level relating to the user.
Nevertheless, Gong teaches (claim 123) that it was known in the healthcare informatics art to produce a diagnostic marker profile including a presence or level of an IBS marker (which can be serine protease per [0017] which includes e.g., tryspin per [0294]-[0295], where tryspin is a digestive enzyme as evidenced by NPL “Tryspin” to Wikipedia) from a sample of a patient and analyze the profile with a statistical technique (e.g., ML per [0188]) to advantageously classify the sample as an IBS sample or a non-IBS sample thereby facilitating rapid and accurate diagnosis and effective early treatment ([0051]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the digestive profile of Sinha to include at least an enzyme level relating to the user as taught by Gong to advantageously facilitate rapid and accurate diagnosis of a patient as having IBS or not and effective early treatment. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Furthermore, Sinha appears to be silent regarding the digestive profile comprising a digestive health score generated as a function of a relationship between the digestive parameter and a digestive threshold, wherein generating the digestive health score comprises: determining, for each of one or more digestive parameters, a respective deviation relative to a corresponding digestive threshold; applying a respective weight to a respective deviation and; aggregating weighted deviations to produce the digestive health score.
Nevertheless, Warren teaches that it was known in the healthcare informatics art to develop a MAGI score based on an aggregation of weighted gut/digestive parameters ([0059], [0078]), compare the MAGI score to one or more thresholds/ranges (digestive threshold), and determine a gut inflammation risk level (digestive health score) such as low, moderate, or high based on the comparison ([0030]-[0031], [0066]) to advantageously facilitate determination of personalized protocols/interventions/health plans including procedure/treatments for microbe-based disease or diseases, such as gut inflammation, based on the health scores/risk levels ([0032]-[0033]). Furthermore, Kerber teaches ([0034]) that it was known in the healthcare informatics art to determine when patient physiological parameters exceed corresponding physiological parameter thresholds (determine deviations relative to corresponding thresholds) and to weight such threshold deviations/exceedances more than others to allow the system to prioritize and quickly respond to unforeseen life-threatening patient situations or context.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the digestive profile of Sinha to include a digestive health score generated as a function of a relationship between the digestive parameter and a digestive threshold as taught by Warren to advantageously facilitate determination of personalized protocols/interventions/health plans including procedure/treatments for microbe-based disease or diseases, such as gut inflammation, based on the health scores/risk levels. Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for generating the digestive health score to include determining, for each of one or more digestive parameters, a respective deviation relative to a corresponding digestive threshold and applying a respective weight to a respective deviation, similar to as taught by Kerber to advantageously allow the system to prioritize and quickly respond to unforeseen life-threatening patient situations or context. As Warren already discloses aggregating weighted gut/digestive parameters ([0059], [0078]), then such aggregation would thus be an aggregation of the weighted deviations to produce the digestive health score per the combination with Kerber. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Finally, while [0070] of Sinha discloses how a sampling kit facilitates self-sampling of genetic and/or microbiome samples from the subject including elements configured to facilitate reception of the lifestyle dataset, Sinha might be silent regarding the receiving of the conductor indicator/lifestyle dataset specifically being as a function of an exposure element comprising at least one epigenetic factor.
Nevertheless, St. Amant teaches that it was known in the healthcare informatics and machine learning art that epigenetic changes (epigenetic factors) can occur due to climate, diet, exercise, environmental toxins, etc. (behavioral/exposure elements) ([0035]) and that machine learning can be used to generate recommendations to improve an epigenetic state of particular individuals including their biological, behavioral, and/or emotional states ([0031], [0037]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the receiving of the conductor indicator/lifestyle dataset specifically being as a function of an exposure element comprising at least one epigenetic factor in the system of Sinha as taught by St. Amant to facilitate generation of recommendations to improve an epigenetic state of particular individuals including their biological, behavioral, and/or emotional states. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 5, the Sinha/Gong/Warren/Kerber/St. Amant combination discloses the system of claim 1, further including wherein the conduct indicator includes a dimensional element ([0068]-[0069] and [0096] of Sinha discuss data/behaviors in relation to diet, sleep, stress, exercise, meditation, work determinants of health, etc. (various “dimensional elements”).
Regarding claim 6, the Sinha/Gong/Warren/Kerber/St. Amant combination discloses the system of claim 1, further including wherein receiving the conduct indicator further comprises obtaining an exposure element and receiving the conduct indicator as a function of the exposure element ([0069] of Sinha discloses medication use (“exposure element”), where the “conduct indicator” indicates that the patient/user takes the medication).
Regarding claim 7, the Sinha/Gong/Warren/Kerber/St. Amant combination discloses the system of claim 1, further including wherein identifying the functional signature further comprises:
producing an indicator index as a function of the conduct indicator ([0068] of Sinha discusses how food photos (conduct indictor) of the subject can be scored (an “indicator index” can be produced); furthermore, [0068]-[0070] and [0096] discuss receiving other lifestyle data such as stress levels, energy levels, sleep behavior, etc. (conduct indicators) from the subject; in order for the functional ML model of [0073]-[0074] to process the conduct indictors of the lifestyle dataset, the conduct indictors would have to be converted into a digital representation (indicator index) usable by the functional ML model to generate the baseline state and signatures (“functional signature”); and
identifying the functional signature as a function of the indicator index (the above indicator indices, which correspond to respective conduct indicators, are processed by the “functional ML model” of [0073]-[0074] of Sinha to generate the “functional signature”).
Regarding claim 9, the Sinha/Gong/Warren/Kerber/St. Amant combination discloses the system of claim 1, further including wherein producing the functional program further comprises:
determining a holistic prospect ([0084] of Sinha discusses how the baseline state (functional signature) can be updated over time); and
producing the functional program as a function of the holistic prospect ([0084] of Sinha discusses how the subject can be lead into a different personalized intervention program (functional program) based on the updated baseline state).
Claims 11, 15-17, and 19 are rejected in view of the Sinha/Gong/Warren/Kerber/St. Amant combination as respectively discussed above in relation to claims 1, 5-7, and 9.
Claims 2, 3, 12, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2023/0047307 to Sinha et al. (“Sinha”) in view of U.S. Patent App. Pub. No. 2012/0315630 to Gong et al. (“Gong”), U.S. Patent App. Pub. No. 2018/0166165 to Warren et al. (“Warren”), U.S. Patent App. Pub. No. 2018/0049675 to Kerber ("Kerber"), and U.S. Patent App. Pub. No. 2019/0286789 to St. Amant et al. (“St. Amant”), and further in view of UK Patent App. No. 2,438,931 to El-Tawil (“El-Tawil”):
Regarding claim 2, the Sinha/Gong/Warren/Kerber/St. Amant combination discloses the system of claim 1, but appears to be silent regarding wherein determining the digestive profile further comprises identifying a positive impact on an absorption condition.
Nevertheless, El-Tawil teaches (see Advantages and Claims) that it was known in the healthcare informatics art to utilize for instance zinc to reduce increased intestinal permeability in cases with inflammatory bowel diseases thereby modulating the immune-response and improving the quality of life of patients (identifying positive impact on an absorption condition).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for determining the digestive profile of the Sinha/Gong/Warren/Kerber/St. Amant combination to include identifying a positive impact on an absorption condition as taught by El-Tawil to advantageously modulate the immune-response and improve the quality of life of patients with inflammatory bowel diseases. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 3, the Sinha/Gong/Warren/Kerber/St. Amant/El-Tawil combination discloses the system of claim 2, further including wherein the absorption condition is correlated to increased intestinal permeability (the absorption condition in El-Tawil corresponds to increased intestinal permeability as noted above; similar to as discussed previously, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for determining the digestive profile of the Sinha/Gong/Warren/Kerber/St. Amant combination to include identifying a positive impact on an absorption condition as taught by El-Tawil to advantageously modulate the immune-response and improve the quality of life of patients with inflammatory bowel diseases. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Claims 12 and 13 are rejected in view of the Sinha/Gong/Warren/Kerber/St. Amant/El-Tawil combination as respectively discussed above in relation to claims 2 and 3.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2023/0047307 to Sinha et al. (“Sinha”) in view of U.S. Patent App. Pub. No. 2012/0315630 to Gong et al. (“Gong”), U.S. Patent App. Pub. No. 2018/0166165 to Warren et al. (“Warren”), U.S. Patent App. Pub. No. 2018/0049675 to Kerber ("Kerber"), and U.S. Patent App. Pub. No. 2019/0286789 to St. Amant et al. (“St. Amant”), and further in view of U.S. Patent App. Pub. No. 2017/0024771 to Flitsch et al. (“Flitsch”):
Regarding claim 8, the Sinha/Gong/Warren/Kerber/St. Amant combination discloses the system of claim 1, but appears to be silent regarding wherein identifying the functional signature further comprises determining a root cause and identifying the functional signature as a function of the root cause.
Nevertheless, Flitsch teaches ([0055], [0062]-[0063]) that it was known in the healthcare informatics art to utilize machine learning to determine root causes of lifestyle choices and predict their effects on a health indicator (functional signature) to advantageously facilitate predictions of future events and behavior and suggest changes to current habits/behaviors to improve user health.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have identified the functional signature of the Sinha/Gong/Warren/Kerber/St. Amant combination by determining a root cause and identifying the functional signature as a function of the root cause as taught by Flitsch to advantageously facilitate predictions of future events and behavior and suggest changes to current habits/behaviors to improve user health. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim 18 is rejected in view of the Sinha/Gong/Warren/Kerber/St. Amant/Flitsch combination as discussed above in relation to claim 8.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2023/0047307 to Sinha et al. (“Sinha”) in view of U.S. Patent App. Pub. No. 2012/0315630 to Gong et al. (“Gong”), U.S. Patent App. Pub. No. 2018/0166165 to Warren et al. (“Warren”), U.S. Patent App. Pub. No. 2018/0049675 to Kerber ("Kerber"), and U.S. Patent App. Pub. No. 2019/0286789 to St. Amant et al. (“St. Amant”), and further in view of U.S. Patent App. Pub. No. 2021/0287368 to Sa et al. (“Sa”):
Regarding claim 10, the Sinha/Gong/Warren/Kerber/St. Amant combination discloses the system of claim 1, further including wherein producing the functional program further comprises:
obtaining a digestive functional goal ([0114] and [0126] of Sinha discuss weight loss goals (digestive functional goal)); and
producing the functional program as a function of the functional signature and the digestive functional goal using a goal machine-learning model ([0046] and [0106] of Sinha discuss generating the personalized intervention plan (“functional program”) upon processing the “functional signature” with a multi-omic model which can be an ML model per [0212]-[0213]; furthermore, as [0114] discusses how the model can generate the personalized intervention plan/functional program based on the goal, then it is a “goal machine-learning model”), wherein the goal machine-learning model (the multi-omic ML model as noted above) is trained using a goal training set comprising functional signatures correlated to functional programs (Figure 1B, [0073], and [0204]-[0206] of Sinha illustrate/discuss training of the multi-omic model using a training set that includes microbiome/biometric/lifestyle data (which is transformed into functional signatures) and therapeutic information (functional programs), …, and wherein the goal machine-learning model uses the functional signature and the digestive functional goal as inputs and outputs the functional program (as the “goal-machine learning model” generates the personalized intervention plan/functional program based on the “functional signature” and the goal, it uses the “functional signature” and the goal as inputs and outputs the personalized intervention plan/functional program).
However, the Sinha/Gong/Warren/Kerber/St. Amant combination might be silent regarding the goal training set being received from one or more remote devices.
Nevertheless, Sa teaches ([0041]) that it was known in the healthcare informatics art to receive training data for use in training an ML model either from a remote computer or from a local memory.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the goal training set of the Sinha/Gong/Warren/Kerber/St. Amant combination to be received from a remote computer as taught by Sa. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim 20 is rejected in view of the Sinha/Gong/Warren/Kerber/St. Amant/Sa combination as discussed above in relation to claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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