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 § 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 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, 3-11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. – (US2021/0125696 A1 – hereinafter referred to as Liu) in view of Spiro et al. – (US2020/0350047 A1 – hereinafter referred to as Spiro) in view of Friedmann et al. (US 2006/0009478 A1) in view of Mir et al. (US 2020/0270699 A1 – hereinafter Mir) in view of Reider et al. (US 2011/0014351 A1 – hereinafter Reider) and further in view of Gizewski (US 7,953,613 B2).
In regards to claim 1, Liu discloses a system for generating a longevity element and an instruction set for a longevity element plan, the system comprising:
at least a computing device, wherein the computing device is designed and configured to:
receive, from a user, a plurality of user-reported data; (Lui para. [0041] teaches "independent variables preparation for adjusted treatment, for example, longitudinal blood pressure measurements, previous medicine type and dose taken in addition to the initial treatment variables.” This is user reported data on their medicine taken and dosages and well blood pressure measurements over time.)
determine a longevity element comprising at least a user-personalized supplement, (Lui claim 1 cites a personalized hypertension treatment wherein the treatment includes type of medicine (supplement) and dosage that is specific to a user, thus it teaches user-personalized supplements.) wherein determining the longevity element further comprises:
receiving training data correlating user-reported data to supplement doses; (Lui para. [0041] teaches getting training data of a user and other users as it relates to medicines and the dosages taken.)
training a first machine-learning process as a function of the training data; (Lui para. [0048] teaches training a machine learning model using training data wherein it states “This model may be trained using the initial treatment training data. The initial treatment model includes a classification model 126 and a regression model 127. The classification model 126 includes feature selection and classification. Training the classification model includes determining based upon the initial treatment training data, which are the best features for predicting what type of medicine(s) to use to initially treat the patient.” Also, paragraph [0049] teaches an adjustment model which adjust dosages or medicines and it also trained wherein it cites “the adjustment treatment model includes a classification model 126 and a regression model 127. The classification model 126 includes feature selection and classification. Training the classification model includes determining based upon the adjustment treatment training data, which are the best features for predicting what type of medicine to use, which might include changing which medicine(s) to use to treat the patient going forward.”)
and
determining the longevity element as a function of the first machine-learning process; (Liu para. [0050] teaches determining a longevity element, medicine of dosage of said medicine using the treatment and adjustment models.) and
train a third machine-learning model using the longevity element to generate an instruction set for a longevity element plan using a third machine learning process, (Liu para. [0057] disclose a treatment plan for the type of medicine and dosage and as well life-stye changes, this is understood to be an instruction set corresponding the longevity element plan.)
However, Liu fails to disclose wherein the system calculates, as a function of the longevity element and a second machine-learning process, a compensatory supplement, wherein calculating the compensatory supplement comprises training the second machine-learning process with second training data correlating longevity elements to compensatory supplements, wherein the second machine-learning process is configured to input the longevity element and output the compensatory supplement, and wherein the compensatory supplement comprises a supplement that alleviates a side effect from the longevity element; user data comprising at least a microbiome body measurement; and wherein the longevity element comprises a quality of grade supplement.
Spiro et al. teaches calculating, as function of the longevity element and a second machine learning process, a compensatory supplement, wherein calculating the compensatory supplement comprises training the second machine-learning process with second training data correlating longevity elements to compensatory supplements, wherein the second machine-learning model is configured to input the longevity element and output the compensatory supplement; and wherein the compensatory supplement comprises a supplement that alleviates a side effect from the longevity element. (Spiro para. [0046] teaches wherein a decision to prescribe a second medicine to counteract an adverse drug reaction from a first medicine is made when the benefits of the first medicine outweigh the risks of additional adverse reactions from the second medicine, this teaches choosing a compensatory supplement to alleviate a side effect (adverse drug reaction) from the longevity element. Also, para figure 1 shows where a person was prescribed drug D1 for symptoms S1, and as result of Drug D1, the person now has Symptom S2 wherein the user was prescribed D2 for S2. Also see para. [0095] wherein machine learning is used in the system to determine the drugs. While Spiro et al. does not explicitly state that machine-learning process is trained with training data correlating input longevity elements to output compensatory supplements, it is implicit and would have been obvious to one of ordinary skill in the art. As Spiro does state that machine learning to implement the functions, then that machine learning model has to be trained using training data related to the input and output of the model. As show in Spiro para. [0046] and fig. 1 it is able to determine a second medicine (compensatory supplement) based on a first one. Thus, it would have been obvious to one or ordinary skill in the art to use training data correlating longevity element input data to compensatory supplement output data.)
It would have been obvious to one ordinary skill in the art before effective filing of the claimed invention to modify the teachings of Liu with that of Spiro in order to allow for prescribing a second drug or compensatory supplement to alleviate the symptom from the first drug or longevity element as both references deal with prescribing users drugs and to deal with ailments or symptoms and the benefit of doing so it creates a system that check for and assist the physician in prescribing drugs as well as the dosages while preventing a drug cascade.
However, Liu in view of Spiro does not discloses retrieving the compensatory supplement in a database for calculation of a subsequent compensatory supplement calculation; and user data comprising at least a microbiome body measurement.
Friedmann et al. discloses retrieving the compensatory supplement in a database for calculation of a subsequent compensatory supplement calculation. (Friedmann para. [0075] teaches knowing that patient takes a particular medicine or supplement and the dosage, then the system can calculate replacement medicine and dosage based on the medicine and dosage being replaced. This teaches calculating a subsequent compensatory supplement based on the other compensatory supplement.)
It would have been obvious to one ordinary skill in the art before effective filing of the claimed invention to modify the teachings of Liu in view of Spiro with that of Friedmann et al. in order to allow for calculating a subsequent drug or compensatory supplement as all the prior art references deal with drug or supplement calculations and the benefit of doing so it allows calculating equivalent or replacement drugs/supplement as the first one may not be also be available.
However, Liu in view of Spiro in view of Friedmann et al. does not explicitly disclose user data comprising at least a microbiome body measurement.
Mir discloses wherein user data comprises a microbiome body measurement. (Mir para. [0008] providing a gut microbiome sample from a person, analyzing it to determine quantitative measure of microbes, predicting the state of health of the person (diseased or healthy) and recommending or administering to the person an invention of food, supplements, probiotics, lifestyle change or pharmaceutical drugs. Also, para. [0009] teaches data being saved in a database. Also, para. [0008, 0010 and 0049-0051] teaches the use of machine learning models.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Liu in view of Spiro in view of Friedmann et al. with the teachings of Mir in order to allow the system consider the microbiome body measurement of subjects as all the ref references deal the use of longevity elements (drugs, vitamins and supplements) and the Lui, Spiro and Mir references use machine learning. The benefit of doing so it creates a robust and efficient system that account for a variety of wellness issues and recommending or administering to a person an invention of food, supplements, probiotics, lifestyle change or pharmaceutical drugs,
However, Liu in view of Spiro in view of Friedmann et al. in view of Mir does not explicitly disclose receiving a user-preference for supplements and a frequency of delivery.
Reider discloses receiving a user-preference for supplements and a frequency of delivery. (Reider para. [0099] teachers user preference for and against certain supplements is received and used for determining supplements for users wherein it cites “In an exemplary embodiment, the recommended regimen may be reduced and/or corrected to adjust for the maximum daily allowable limits of certain vitamins and minerals, user allergies, user dietary preferences, or duplicate nutritional supplements and or nutritional supplement dosage forms. Examples of user dietary preferences are, for example, user likes and dislikes of certain supplements and or dosage forms such as fish oil, a user's wish to avoid animal products and/or to obtain organic products.”. Reider in para. [0058] teaches receiving a user-preference for frequency of delivery wherein it cites “The customer may also select the number and frequency of auto shipments on the shopping cart screen.”.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Liu in view of Spiro in view of Friedmann et al. in view of Mir with that of Reider in order to allow for creating a customized or personalized supplements for a user based on user preferences and frequency of delivery as the Liu, Spiro, and Reider references deal with determining medicines or supplements for user based on user data and the benefit of doing so it creates a more efficient and user friendly system by delivering to a user the supplements they prefer and on a schedule that prefer.
However, Liu in view of Spiro in view of Friedmann et al in view of Mir in view of Reider does not disclose wherein the longevity element comprises a quality grade of supplement.
Gizewski discloses wherein the longevity element comprises a quality grade of supplement. (Gizewski column 34 line 53-58 teaches a database or knowledgebase of supplement (longevity element) that has quality, purity and rate of absorption of particular brands and types of supplements to ensure patients get the highest quality supplements wherein it cites “he DASM 490 may also reference authoritative standards/laboratory tests/scientific evaluations to assess the quality, purity, and rate of absorption of particular brands and types of supplements in order to ensure only highest quality products are used and to alert subscribers to inferior and possible dangerous products.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Liu in view of Spiro in view of Friedmann et al. in view of Mir in view of Reider with the Gizewski in order to allow using supplement quality with recommending supplements to uses as the Liu, Spiro, Reider and Gizewki references deal with determining medicines or supplements for user based and the benefit of doing so it creates a more efficient and user friendly system by delivering to a user the supplements they prefer and ensure quality and grade of the supplements are good.
In regards to claim 3, Liu in view of Spiro in view of Friedmann et al. in view of Mir in view of Reider in view of Gizewski discloses the system of claim 2, wherein generating the instruction set further comprises calculating an attenuation schedule using a fourth machine-learning process, wherein the attenuation schedule is configured to wean a user off the longevity element. (Friedmann et al. para. [0568 and 0569] disclose a calculated attenuation schedule for a longevity element. In particular is a tapering schedule (attenuation schedule) for an opioid (longevity element).)
In regards to claim 4, Liu in view of Spiro in view of Friedmann et al. in view of Mir in view of Reider in view of Gizewski disclose the system of claim 3, wherein generating the instruction set corresponding to the longevity element plan comprises adding attenuation schedule to the instruction set. (Friedmann et al. para. [0568 and 0569] disclose a calculated attenuation schedule for a longevity element. In particular is a tapering schedule (attenuation schedule) for an opioid (longevity element). As this is the plan for weaning a patient then is add to their instructions.)
In regards to claim 5, Liu in view of Spiro in view of Friedmann et al. in view of Mir in view of Reider in view of Gizewski disclose the system of claim 3, wherein the attenuation schedule comprises a supplement dose and dose frequency. (Friedmann et al. para. [0568] gives a dose and frequency wherein it prescribes 30mg/day.)
In regards to claim 6, Liu in view of Spiro in view of Friedmann et al. in view of Mir in view of Reider in view of Gizewski discloses the system of claim 1, wherein the plurality of user-reported data further comprises at least a symptom. (Liu para. [0058] teaches a user interface wherein a patient enters various types of data such as blood pressure, patient characteristics, how the patient is feeling, any complications or side-effects, historical blood press and so. Where in side-effects are symptoms.)
In regards to claim 7, Liu in view of Spiro in view of Friedmann et al. in view of Mir in view of Reider in view of Gizewski discloses the system of claim 1, wherein the plurality of user reported data includes a diagnosis. (Liu para. [0041] teaches patient medical conditions (e.g., diabetes, pregnancy, renal failure, etc.), wherein this are diagnosis.)
In regards to claim 8, Liu in view of Spiro in view of Friedmann et al. in view of Mir in view of Reider in view of Gizewski discloses the system of claim 1, wherein calculating a compensatory supplement further comprises:
retrieving at least a second element of data from a database, wherein a second element of data is at least a side-effect from a first longevity element; and calculating, a compensatory supplement amount to alleviate a side-effect of the longevity element. (Liu para. [0041 and 0058] teaches user entering data about themselves such as blood pressure, patient characteristics, how the patient is feeling, any complications or side-effects, historical information and etc. This information is used along with the machine learning models to determine a medicine and dosage for the patient (Liu para. [0050]). Also, Spiro [0046] teaches prescribing a compensatory supplement. It would obvious to combine Liu and Spiro to not only determine a compensatory drug but also the amount of the drug to use as Liu does that in determining treatment plans and amounts of medicines.)
In regards to claim 9, Liu in view of Spiro in view of Friedmann et al. in view of Mir in view of Reider in view of Gizewski discloses the system of claim 1, wherein receiving a plurality of user-reported data comprises receiving the plurality of user-reported data from a user database. (Liu para. [0041] teaches extracting patient data from a patient database.)
In regards to claim 10, Liu in view of Spiro in view of Friedmann et al. in view of Mir in view of Reider in view of Gizewski discloses the system of claim 1, wherein the instruction set comprises a delivery duration, wherein the delivery duration reflects a complete dosage frequency for weaning a user off a supplement. (Friedmann et al. para. [0568] cites “Weaning a patient from opioids may proceed as follows: one-half of the previous daily dose may be given in q6 hr. doses for the first 2 days and reduced by 25% every 2 days. This schedule is continued until a total daily dose of 30 mg/day of oral morphine in the adult is reached. After 2 days at this minimum dose, the analgesic may be discontinued.”. This teaches a complete dosage frequency for weaning a patient off opioids.)
In regards to claim 11, it is method embodiment of claim 1 and thus rejected using the same reasoning found in claim 1.
In regards to claim 13, it is method embodiment of claim 3 and thus rejected using the same reasoning found in claim 3.
In regards to claim 14, it is method embodiment of claim 4 and thus rejected using the same reasoning found in claim 4.
In regards to claim 15, it is method embodiment of claim 5 and thus rejected using the same reasoning found in claim 5.
In regards to claim 16, it is method embodiment of claim 6 and thus rejected using the same reasoning found in claim 6.
In regards to claim 17, it is method embodiment of claim 7 and thus rejected using the same reasoning found in claim 7.
In regards to claim 18, it is method embodiment of claim 8 and thus rejected using the same reasoning found in claim 8.
In regards to claim 19, it is method embodiment of claim 9 and thus rejected using the same reasoning found in claim 9.
In regards to claim 20, it is method embodiment of claim 10 and thus rejected using the same reasoning found in claim 10.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127