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 .
Remarks
In response to communications sent September 18, 2023, claim(s) 1-20 are pending in this application; of these claims 1 and 11 are in independent form.
Drawings
The drawing(s) filed on September 18, 2023 are accepted by the Examiner.
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: September 18, 2023.
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, 4-11, and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a combination of mental processes and mathematics. This judicial exception is not integrated into a practical application because the only additional elements are necessary extra-solution activity and a general-purpose computer to apply the judicial exception on. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the particular extra-solution activity of sending data are well-understood, routine, and conventional according to precedential court cases, including Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; well-understood, routine, and conventional according to OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015). The application of the judicial exception on a general-purpose computer is well-understood, routine, and conventional according to as per MPEP 2106.05(f)).
For further clarification of the grounds for rejection, see below:
1. A system for identifying and ameliorating body degradations, the system comprising:
a computing device (an additional element, “applying it” on a general-purpose computer; “applying it” on a general-purpose computer is not integrated into p a practical application and well-understood, routine, and conventional as per MPEP 2106.05(f)), wherein the computing device is designed and configured to:
receive a user profile pertaining to a user, wherein the user profile comprises at least a biological extraction datum (an additional element of sending and receiving specific data; it is not integrated because it is necessary pre-solution activity to carry out the abstract idea; see Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; well-understood, routine, and conventional according to OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015));
generate a degradation profile including a rate of biological degradation as a function of the user profile (mental process; Figure 3 of Applicant’s specification provides evidence that the degradation profile is a set of tables; the step of generating the data tables using a user profile would be a mental process capable of being performed with paper and pen);
identify, using a rate of biological degradation in the degradation profile, a degradation imbalance, wherein the degradation imbalance is a rate of biological degradation that exceeds a biological degradation rate threshold value (mental process and mathematical comparison, each a judicial exception);
determine, as a function of the degradation imbalance, a degradation antidote strategy to decrease the rate of biological degradation of the user (the determination is a mental process), wherein determining the degradation antidote strategy further comprises:
performing a simulation, wherein the simulation randomly perturbs a parameter, wherein the parameter is an element of numerical data relating to the user profile (Applicant’s specification at Paragraph [0044] provides evidence that simulation is a mathematical simulation), wherein performing the simulation further comprises:
sampling user biological parameters (sampling from a statistical distribution is mathematical, according to MPEP 2106.04(a)(2)(I)(C): resampling would be a mathematical calculation);
performing a simulated degradation function of the user biological parameters, wherein performing the simulation degradation further comprises using a simulation algorithm to sample the biological parameters using a sampling rate based on a user age and generate a degradation function for each sampled user biological parameter (Applicant’s specification at Paragraph [0044] provides evidence that simulation is a mathematical simulation);
measuring a change in biological degradation as a function of the simulated degradation function (mathematical calculation); and
determining a parameter aggregate that results in a maximally decreased degradation rate (mathematical calculation);
determining, as a function of the simulation, which parameters result in a maximal degree of decrease in degradation rate using the parameter aggregate (output of a mathematical calculation);
determining the degradation antidote strategy as a function of the parameters that result in the maximal degree of decrease in the degradation rate (mental process claiming the idea of inventing rather than the invention itself); and
display to the user, as a function of the degradation antidote strategy and a ranking process, a degradation antidote instruction set (an additional element that is not an abstract idea or judicial exception, but is necessary post-solution activity to carry out the judicial exception with out and integration; it is well-understood, routine, and conventional; see Mayo, 566 U.S. at 79, 101 USPQ2d at 1968).
4. The system of claim 1, wherein the user profile comprises stress data (a limitation to the profile data is a limitation to transmitted data that affects the mental process; however, the mental steps remain abstract despite these data-related limitations).
5. The system of claim 1, further comprising:
generating physiological change data as a function of at least the rate of biological degradation (mental process of generation of data using mathematical functions); and
transmitting physiological change data to a user device (extra-solution activity needed to perform the judicial exception; see the precedential case regarding 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).
6. The system of claim 1, wherein:
the user profile comprises lifestyle datum (a limitation to the profile data is a limitation to transmitted data that affects the mental process; however, the mental steps remain abstract despite these data-related limitations); and
the degradation antidote strategy comprises physical activity datum, wherein the physical activity datum is generated as a function of the lifestyle datum (the determination of a strategy is a mental process).
7. The system of claim 5, further comprising:
receiving a plurality of physiological change data from a degradation database (an additional element of sending and receiving specific data; it is not integrated because it is necessary pre-solution activity to carry out the abstract idea; see Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; well-understood, routine, and conventional according to OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)); and
transmitting the plurality of physiological change data to the user device (an additional element of sending and receiving specific data; it is not integrated because it is necessary pre-solution activity to carry out the abstract idea; see Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; well-understood, routine, and conventional according to OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).
8. The system of claim 6, wherein the physical activity datum comprises a temporal aspect (in this instance, the nature of the data do not alter whether the mental process and mathematics are judicial exceptions).
9. The system of claim 1, wherein determining the degradation antidote strategy as a function of the parameters comprises:
determining one or more degradation antidote strategies (mental process);
receiving a selection of at least one degradation antidote strategy of the one or more degradation antidote strategies (this element is necessary extra-solution activity for inputting to trigger and complete the judicial exception; the output is well-understood, routine, and conventional because it amounts to controlling a general-purpose computer); and
determining an effect on a biological profile as a function of the selection (mental process).
10. The system of claim 1, wherein:
the user profile comprises a sleep assessment (limitations to a mental process are still a mental process); and
the parameter comprises at least a sleep parameter (limitations to a mental process are still a mental process).
11. A method for identifying and ameliorating body degradations, the method comprising:
generating, by a computing device (an additional element, “applying it” on a general-purpose computer; “applying it” on a general-purpose computer is not integrated into p a practical application and well-understood, routine, and conventional as per MPEP 2106.05(f)), a degradation profile including a rate of biological degradation as a function of a user profile (mental process; Figure 3 of Applicant’s specification provides evidence that the degradation profile is a set of tables; the step of generating the data tables using a user profile would be a mental process capable of being performed with paper and pen), wherein the user profile comprises at least a biological extraction datum (an additional element of sending and receiving specific data; it is not integrated because it is necessary pre-solution activity to carry out the abstract idea; see Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; well-understood, routine, and conventional according to OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015));
identifying, by the computing device, using a rate of biological degradation in the degradation profile, a degradation imbalance, wherein the degradation imbalance is a rate of biological degradation that exceeds a biological degradation rate threshold value (mental process and mathematical comparison, each a judicial exception);
determining, by the computing device, as a function of the degradation imbalance, a degradation antidote strategy to decrease the rate of biological degradation of the user (the determination is a mental process), wherein determining the degradation antidote strategy further comprises:
performing a simulation, wherein the simulation randomly perturbs a parameter, wherein the parameter is an element of numerical data relating to the user profile (Applicant’s specification at Paragraph [0044] provides evidence that simulation is a mathematical simulation), wherein performing the simulation further comprises:
sampling user biological parameters (sampling from a statistical distribution is mathematical, according to MPEP 2106.04(a)(2)(I)(C): resampling would be a mathematical calculation);
performing a simulated degradation function of the user biological parameters, wherein performing the simulation degradation further comprises using a simulation algorithm to sample the biological parameters using a sampling rate based on a user age and generate a degradation function for each sampled user biological parameter (Applicant’s specification at Paragraph [0044] provides evidence that simulation is a mathematical simulation);
measuring a change in biological degradation as a function of the simulated degradation function (mathematical calculation); and
determining a parameter aggregate that results in a maximally decreased degradation rate (mathematical calculation);
determining, as a function of the simulation, which parameters result in a maximal degree of decrease in degradation rate using the parameter aggregate (output of a mathematical calculation);
determining the degradation antidote strategy as a function of the parameters that result in the maximal degree of decrease in the degradation rate (mental process claiming the idea of inventing rather than the invention itself); and
displaying to the user, by the computing devices, as a function of the degradation antidote strategy and a ranking process, a degradation antidote instruction set (an additional element that is not an abstract idea or judicial exception, but is necessary post-solution activity to carry out the judicial exception with out and integration; it is well-understood, routine, and conventional; see Mayo, 566 U.S. at 79, 101 USPQ2d at 1968).
14. The method of claim 11, wherein the user profile comprises stress data (a limitation to the profile data is a limitation to transmitted data that affects the mental process; however, the mental steps remain abstract despite these data-related limitations).
15. The method of claim 11, further comprising:
generating, by the computing device, physiological change data as a function of at least the rate of biological degradation (mental process of generation of data using mathematical functions); and
transmitting, by the computing device, physiological change data to a user device (extra-solution activity needed to perform the judicial exception; see the precedential case regarding 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).
16. The method of claim 11, wherein:
the user profile comprises lifestyle datum (a limitation to the profile data is a limitation to transmitted data that affects the mental process; however, the mental steps remain abstract despite these data-related limitations); and
the degradation antidote strategy comprises physical activity datum, the physical activity datum generated as a function of the lifestyle datum (the determination of a strategy is a mental process).
17. The method of claim 15, further comprising:
receiving, by the computing device a plurality of physiological change data from a degradation database (an additional element of sending and receiving specific data; it is not integrated because it is necessary pre-solution activity to carry out the abstract idea; see Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; well-understood, routine, and conventional according to OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)); and
transmitting, by the computing device, the plurality of physiological change data to the user device (an additional element of sending and receiving specific data; it is not integrated because it is necessary pre-solution activity to carry out the abstract idea; see Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; well-understood, routine, and conventional according to OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).
18. The method of claim 16, wherein the physical activity datum comprises a temporal aspect (in this instance, the nature of the data do not alter whether the mental process and mathematics are judicial exceptions).
19. The method of claim 11, wherein determining, by the computing device, the degradation antidote strategy as a function of the parameters comprises:
determining one or more degradation antidote strategies (mental process);
receiving a selection of at least one degradation antidote strategy of the one or more degradation antidote strategies (this element is necessary extra-solution activity for inputting to trigger and complete the judicial exception; the output is well-understood, routine, and conventional because it amounts to controlling a general-purpose computer); and
determining an effect on a biological profile as a function of the selection (mental process).
20. The method of claim 11, wherein:
the user profile comprises a sleep assessment (limitations to a mental process are still a mental process); and
the parameter comprises at least a sleep parameter (limitations to a mental process are still a mental process).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 5, 11-13, and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 11 of U.S. Patent No. 11,798,652. Although the claims at issue are not identical, they are not patentably distinct from each other because the claimed genus of the instant application is broader and encompasses the species of the reference patent.
Instant Application 18/369,357
Reference Patent: 11,798,652
1. A system for identifying and ameliorating body degradations, the system comprising:
a computing device, wherein the computing device is designed and configured to:
receive a user profile pertaining to a user, wherein the user profile comprises at least a biological extraction datum;
generate a degradation profile including a rate of biological degradation as a function of the user profile;
identify, using a rate of biological degradation in the degradation profile, a degradation imbalance, wherein the degradation imbalance is a rate of biological degradation that exceeds a biological degradation rate threshold value;
determine, as a function of the degradation imbalance, a degradation antidote strategy to decrease the rate of biological degradation of the user, wherein determining the degradation antidote strategy further comprises:
performing a simulation, wherein the simulation randomly perturbs a parameter, wherein the parameter is an element of numerical data relating to the user profile, wherein performing the simulation further comprises:
sampling user biological parameters;
performing a simulated degradation function of the user biological parameters, wherein performing the simulation degradation further comprises using a simulation algorithm to sample the biological parameters using a sampling rate based on a user age and generate a degradation function for each sampled user biological parameter;
measuring a change in biological degradation as a function of the simulated degradation function; and
determining a parameter aggregate that results in a maximally decreased degradation rate;
determining, as a function of the simulation, which parameters result in a maximal degree of decrease in degradation rate using the parameter aggregate;
determining the degradation antidote strategy as a function of the parameters that result in the maximal degree of decrease in the degradation rate; and
display to the user, as a function of the degradation antidote strategy and a ranking process, a degradation antidote instruction set.
1. A system for identifying and ameliorating body degradations, the system comprising:
a computing device, wherein the computing device is designed and configured to:
receive a biological extraction datum pertaining to a user;
generate a degradation profile including a rate of biological degradation, wherein generating the degradation profile further comprises:
training a degradation machine-learning model using a training data and a degradation machine-learning process, wherein the training data correlates biological extraction data and biological degradation data; and generating the rate of biological degradation as a function of the degradation machine-learning model, wherein the degradation machine-learning model uses the biological extraction datum as an input to output the rate of biological degradation;
identify, using a rate of biological degradation in the degradation profile, a degradation imbalance, wherein the degradation imbalance is a rate of biological degradation that exceeds a biological degradation rate threshold value, wherein identifying the degradation imbalance further comprises: training a standard rate machine-learning model using a training data set and a classifier, wherein training the standard machine-learning model further comprises selecting the training data set as a function of similarity between a physiology of the user and physiologies of other individuals; and generating the threshold value as a function of the standard rate machine-learning model, wherein the standard rate machine-learning model uses the physiology of the user as an input to output the biological degradation rate threshold value corresponding to the user;
determine, as a function of the degradation imbalance, a degradation antidote strategy to decrease the rate of biological degradation of the user, wherein determining the degradation antidote strategy further comprises:
performing a simulation, wherein the simulation randomly perturbs a parameter, wherein the parameter is an element of numerical data relating to the biological extraction datum; wherein performing the simulation further comprises:
sampling user biological parameters;
performing a simulated degradation function of the user biological parameters, wherein performing the simulation degradation further comprises using a simulation algorithm to sample the biological parameters using a sampling rate based on a user age and generate a degradation function for each sampled user biological parameter;
measuring a change in biological degradation as a function of the simulated degradation function; and
determining a parameter aggregate that results in a maximally decreased degradation rate; determining, as a function of the simulation, which parameters result in a maximal degree of decrease in degradation rate using the parameter aggregate; generating, as a function of the simulation, a degradation curve for the parameters that result in the maximum degree of decrease in the degradation; and
determining the degradation antidote strategy as a function of the parameters that result in the maximal degree of decrease in the degradation rate and the degradation curve; and
display to the user, as a function of the degradation antidote strategy and a ranking process, a degradation antidote instruction set.
2. The system of claim 1, wherein generating the degradation profile further comprises:
training a degradation machine-learning model using a training data and a degradation machine-learning process, wherein the training data correlates biological extraction data and biological degradation data; and
generating the rate of biological degradation as a function of the degradation machine-learning model, wherein the degradation machine-learning model uses the biological extraction datum as an input to output the rate of biological degradation.
See claim 1.
3. The system of claim 1, wherein identifying the degradation imbalance further comprises:
training a standard rate machine-learning model using a training data set, wherein training the standard machine-learning model further comprises selecting the training data set as a function of similarity between a physiology of the user and physiologies of other individuals; and
generating the threshold value as a function of the standard rate machine-learning model, wherein the standard rate machine-learning model uses the physiology of the user as an input to output the biological degradation rate threshold value corresponding to the user.
See claim 1.
5. The system of claim 1, further comprising:
generating physiological change data as a function of at least the rate of biological degradation; and
transmitting physiological change data to a user device.
See claim 1.
11. A method for identifying and ameliorating body degradations, the method comprising:
generating, by a computing device, a degradation profile including a rate of biological degradation as a function of a user profile, wherein the user profile comprises at least a biological extraction datum;
identifying, by the computing device, using a rate of biological degradation in the degradation profile, a degradation imbalance, wherein the degradation imbalance is a rate of biological degradation that exceeds a biological degradation rate threshold value;
determining, by the computing device, as a function of the degradation imbalance, a degradation antidote strategy to decrease the rate of biological degradation of the user, wherein determining the degradation antidote strategy further comprises:
performing a simulation, wherein the simulation randomly perturbs a parameter, wherein the parameter is an element of numerical data relating to the user profile, wherein performing the simulation further comprises:
sampling user biological parameters;
performing a simulated degradation function of the user biological parameters, wherein performing the simulation degradation further comprises using a simulation algorithm to sample the biological parameters using a sampling rate based on a user age and generate a degradation function for each sampled user biological parameter;
measuring a change in biological degradation as a function of the simulated degradation function; and
determining a parameter aggregate that results in a maximally decreased degradation rate;
determining, as a function of the simulation, which parameters result in a maximal degree of decrease in degradation rate using the parameter aggregate;
determining the degradation antidote strategy as a function of the parameters that result in the maximal degree of decrease in the degradation rate; and
displaying to the user, by the computing devices, as a function of the degradation antidote strategy and a ranking process, a degradation antidote instruction set.
11. A method for identifying and ameliorating body degradations, the method comprising:
receiving, by a computing device, a biological extraction datum pertaining to a user;
generating, by the computing device, a degradation profile including a rate of biological degradation, wherein generating the degradation profile further comprises:
training a degradation machine-learning model using a training data and a degradation machine-learning process, wherein the training data correlates biological extraction data and biological degradation data; and generating the rate of biological degradation as a function of the degradation machine-learning model, wherein the degradation machine-learning model uses the biological extraction datum as an input to output the rate of biological degradation;
identifying, by the computing device, using the rate of biological degradation in the degradation profile, a degradation imbalance, wherein the degradation imbalance is a rate of biological degradation that exceeds a biological degradation rate threshold value, wherein identifying the degradation imbalance further comprises: training a standard rate machine-learning model using a training data set and a classifier, wherein training the standard machine-learning model further comprises selecting the training data set as a function of similarity between a physiology of the user and physiologies of other individuals; and generating the threshold value as a function of the standard rate machine-learning model, wherein the standard rate machine-learning model uses the physiology of the user as an input to output the biological degradation rate threshold value corresponding to the user;
determining, by the computing device, as a function of the degradation imbalance, a degradation antidote strategy to decrease the rate of biological degradation of the user, wherein determining the degradation antidote strategy further comprises: performing a simulation, wherein the simulation randomly perturbs a parameter, wherein the parameter is an element of numerical data relating to the biological extraction datum; wherein performing the simulation further comprises:
sampling user biological parameters;
performing a simulated degradation function of the user biological parameters, wherein
performing the simulation degradation further comprises using a simulation algorithm to sample the biological parameters using a sampling rate based on a user age and generate a degradation function for each sampled user biological parameter; measuring a change in biological degradation as a function of the simulated degradation function; and
determining a parameter aggregate that results in a maximally decreased degradation rate;
determining, as a function of the simulation, which parameters result in a maximal degree of decrease in degradation rate using the parameter aggregate;
determining, as a function of the simulation, which parameters result in a maximal decrease in degradation rate; generating, as a function of the simulation, a degradation curve for the parameters that result in the maximum degree of decrease in the degradation; and determining the degradation antidote strategy as a function of the parameters that result in the maximal degree of decrease in the degradation rate and the degradation curve; and
displaying to the user, by the computing device, as a function of the degradation antidote strategy and a ranking process, a degradation antidote instruction set.
12. The method of claim 11, wherein generating, by the computing device, the degradation profile further comprises:
training a degradation machine-learning model using a training data and a degradation machine-learning process, wherein the training data correlates biological extraction data and biological degradation data; and
generating the rate of biological degradation as a function of the degradation machine-learning model, wherein the degradation machine-learning model uses the biological extraction datum as an input to output the rate of biological degradation.
See claim 11.
13. The method of claim 1, wherein identifying, by the computing device, the degradation imbalance further comprises:
training a standard rate machine-learning model using a training data set and a classifier, wherein training the standard machine-learning model further comprises selecting the training data set as a function of similarity between a physiology of the user and physiologies of other individuals; and
generating the threshold value as a function of the standard rate machine-learning model, wherein the standard rate machine-learning model uses the physiology of the user as an input to output the biological degradation rate threshold value corresponding to the user.
See claim 11.
15. The method of claim 11, further comprising:
generating, by the computing device, physiological change data as a function of at least the rate of biological degradation; and
transmitting, by the computing device, physiological change data to a user device.
See claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220058520 A1: pertinent because it is by the same applicant
US 20210212647 A1: pertinent because of bone mineral density analysis by deep learning
US 20110208033 A1: pertinent because of fracture risk assessment by machine learning
US-20180247020-A1 (“”Itu”): pertinent because of assessment of bone health using multiple features as set forth in the parent application 17000929; For example, Itu teaches a system for identifying (Itu Para [0068]: identifying osteoporosis using machine learning) and ameliorating (Itu Para [0100]: therapy planning for osteoporosis based on the machine learning-based workflow) body degradations (Itu Para [0037]: disease evolution including decreases in bone strength in time), the system comprising: a computing device (Itu Para [0133]: computing device), wherein the computing device is designed and configured to: receive a user profile pertaining to a user, wherein the user profile comprises at least a biological extraction datum (Itu Para [0037]: receive patient data about a patient); generate a degradation profile including a rate of biological degradation as a function of the user profile (Itu Para [0037]: generate an osteoporosis profile including a disease evolution measured as decrease in bone strength in time); identify, using a rate of biological degradation in the degradation profile (Itu Para [0037]: a predictive disease evolution model using features extracted from patients to predict the decrease in bone strength in time), a degradation imbalance, wherein the degradation imbalance is a rate of biological degradation that exceeds a biological degradation rate threshold value (Itu Para [0105]: identifying, using the measurement of interest, an risk that is out-of-range); determine, as a function of the degradation imbalance, a degradation antidote strategy to decrease the rate of biological degradation of the user (Itu Para [0102]: determine an optimal treatment drug based on the machine-learning model), wherein determining the degradation antidote strategy further comprises: performing a simulation, wherein the simulation randomly perturbs a parameter, wherein the parameter is an element of numerical data relating to the user profile (Itu Para [0042]: perturbing by determining whether to use a particular drug or combinations of treatment drugs).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jesse P Frumkin whose telephone number is (571)270-1849. The examiner can normally be reached Monday - Friday, 10-5 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 September 5, 2026
Prosecution Insights