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 Objections
Claims 1 and 9 are objected to because of the following informalities:
As to claim 1, lines 9 -10, “the plan supplement generator module” should be changed to -- the supplement plan generator module --.
As to claim 9, line, “in put” should be changed to -- input --.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a system with structural components (claims 1 - 10) and a method (claims 11 - 20). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claims 1,
Step 2A, Prong One
The claim recites in part:
a plan generator module operating on the computing device, the plan generator module designed and configured to generate a nutrition instruction set as a function of the user physiological history input; and
a supplement plan generator module operating on the computing device, the plan supplement generator module designed and configured to calculate a supplement instruction set utilizing the user physiological history input and the nutrition instruction set, wherein calculating the supplement instruction set comprises generating a customized dose using a machine-learning process, wherein the machine-learning process is configured to utilize the biological extraction and the user physiological history as input and output the customized dose.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, (1) A human can review a person’s physiological history and mentally determine what foods or dietary guidelines (a nutrition plan) that person should follow based on their blood sugar levels, weight, medical conditions, etc. (2) A human reviews physiological history, a nutrition plan, and biological measurements, and recognizes patterns from past experiences, and mentally determines a customized supplement dose appropriate for the person.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a diagnostic engine operating on the computing device, the diagnostic engine designed and configured to receive a user physiological history input;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites a computing device which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of diagnostic engine, a plan generator module, a supplement plan generator module amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
a diagnostic engine operating on the computing device, the diagnostic engine designed and configured to receive a user physiological history input;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites a computing device which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of diagnostic engine, a plan generator module, a supplement plan generator module amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 2,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the computing device is configured to:
transmit the supplement instruction set to an advisor client device;
receive an altered supplement instruction set from the advisor client device.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The advisor client device amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
the computing device is configured to:
transmit the supplement instruction set to an advisor client device;
receive an altered supplement instruction set from the advisor client device.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The advisor client device amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 3,
Step 2A, Prong One
The claim recites in part:
wherein the altered supplement instruction comprises an altered frequency at which a supplement is taken.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a human can mentally determine a customized supplement dose appropriate for a person, including updating/changing the frequency of the dose.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claim 4,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 3, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the computing device is further configured to transmit the altered supplement instruction set to a user client device.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The user client device amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
the computing device is further configured to transmit the altered supplement instruction set to a user client device.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The user client device amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 5,
Step 2A, Prong One
The claim recites in part:
the advisor response is a function of the user query; and
the advisor response comprises an altered supplement instruction set.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, (1) A human advisor can answer another human’s questions by writing said answers done on a sheet of paper. (2) Said human advisor mentally determine an updated customized supplement dose based on the answers.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the computing device is further configured to:
transmit a user query to an advisor client device;
receive an advisor response from the advisor client device,
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
the computing device is further configured to:
transmit a user query to an advisor client device;
receive an advisor response from the advisor client device,
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 6,
Step 2A, Prong One
The claim recites in part:
the supplement plan generator module is further configured to filter supplements with negative interactions with a user's medications from the
supplement instruction set.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a human can mentally remove supplements from the supplement instruction set that may pose a health risk to a user.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 7,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receive a first training set including at least an element of physiological state data and at least a correlated first prognostic label;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
generate a diagnostic output utilizing a first machine-learning process trained by the first training set, as a function of the user physiological history input pertaining to the user and the user physiological history input;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receive a first training set including at least an element of physiological state data and at least a correlated first prognostic label;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites:
generate a diagnostic output utilizing a first machine-learning process trained by the first training set, as a function of the user physiological history input pertaining to the user and the user physiological history input;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites a data source which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of language model, protein fitness, protein sequence, protein fitness prediction module, training dataset, unlabeled protein sequences, probability distribution, pairwise classifier, relative fitness, protein sequence pairs, loss function, protein sequence, and fitness score amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 8,
Step 2A, Prong One
The claim recites in part:
wherein the supplement plan generator module is further configured
to:
identify a nutrient deficiency contained within the nutrition instruction set; and
locate a supplement intended to remedy the nutrient deficiency contained within the supplement instruction set.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, (1) A human, can identify a nutrient deficiency by reviewing a diet plan and recognizing that Vitamin D is missing or is too low. (2) A human notices a vitamin D deficiency in a diet plan and mentally selects a vitamin D supplement to remedy it.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 8,
Step 2A, Prong One
The claim recites in part:
the plan generator module is further configured to determine the nutrient deficiency, wherein determining the nutrient deficiency comprises:
generating a machine-learning model that utilizes the user physiological history input and available nutrients as in put and outputs the nutrient deficiency; and
determining the nutrient deficiency as a function of the machine-learning model.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a human can review a user’s health history and daily food intake, mentally compares them to known nutritional needs, and determines that the person is likely deficient in vitamin D.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claim 10,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the computing device is configured to transmit the supplement instruction set to a physical performance entity.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
the computing device is configured to transmit the supplement instruction set to a physical performance entity.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 11 depends on claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 12 depends on claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 13 depends on claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 14 depends on claim 4. Therefore, the claim is rejected for the same reasons as above
Claim 15 depends on claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 16 depends on claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 17 depends on claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 18 depends on claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 19 depends on claim 9. Therefore, the claim is rejected for the same reasons as above.
Claim 20 depends on claim 10. Therefore, the claim is rejected for the same reasons as above.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 - 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 1, limitation "the biological extraction" in line 14 is not understood by the examiner. There is insufficient antecedent basis for this limitation in the claim. The examiner is not sure what the biological extraction exactly is or where it is extracted from. The examiner will interpret the claims as if the biological extraction is a biological sample (e.g. blood, urine, etc) extracted from the user.
Claims 2 - 10 depend on claim 1, therefore the claims are also rejected.
As to claim 11, limitation "the biological extraction" in line 11 is not understood by the examiner. There is insufficient antecedent basis for this limitation in the claim. The examiner is not sure what the biological extraction exactly is or where it is extracted from. The examiner will interpret the claims as if the biological extraction is a biological sample (e.g. blood, urine, etc) extracted from the user.
Claims 12 - 20 depend on claim 11, therefore the claims are also rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1 - 4, 8 - 14, and 18 - 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saint et al (US 2018/0353698) in view of Otto (US 2017/0112452).
As to claim 1, Saint et al teaches a system for generating a supplement instruction set using artificial intelligence (paragraph [0029]… intelligent medicine administration systems and methods that provide enhanced medicine dose recommendations for patient health management), the system comprising:
a computing device (paragraph [0050]…companion device 5);
a diagnostic engine operating on the computing device, the diagnostic engine designed and configured to receive a user physiological history input (paragraph [0052]… the software architecture 200 includes a data aggregator module 210 configured to obtain the health data from a device, such as the pen device 10, sensor device 50, and/or other devices or apps in communication with the companion device 5 ; paragraph [0081]…the crowdsourcing of food data associated with users of the system 100 can produce a database of crowdsourced food history data that includes of individual physiological response data to specific medicine doses associated with consumption of specific food items for a plurality of individual users)(Examiner’s Note: “data aggregator module 210 ” reads on “diagnostic engine” ; “a database of crowdsourced food history data that includes of individual physiological response data to specific medicine doses associated with consumption of specific food items for a plurality of individual users” reads on “designed and configured to receive a user physiological history input” );
a plan generator module operating on the computing device, the plan generator module designed and configured to generate a nutrition instruction set as a function of the user physiological history input (paragraph [0052]… the food identification module 230 receives data from the data aggregation module 210… the food identification module 230 is configured to receive and store at least some meal information provided from the meal information database 235 (e.g., which may be via the data aggregation module 210) ; paragraph [0091]…the food identification module 230 includes a most-likely food selection module 232, a food-photo recognition module 236 and a meal-nutritional content module 238 in data communication to process food data inputs, e.g., including user entries on nutritional information about food, current time and date, current location of the user, and/or images of food or a menu item, to produce a food data output (output of Food Identification Module 230) associated with meal nutrition content)(Examiner’s Note: “food identification module 230” reads on “plan generator module” ; the food identification module 230…includes a meal-nutritional content module 238.. to produce a food data output (output of Food Identification Module 230) associated with meal nutrition content” reads on “designed and configured to generate a nutrition instruction set” ; “the food identification module 230 is configured to receive and store at least some meal information provided from the meal information database 235 (e.g., which may be via the data aggregation module 210)” reads on “a function of the user physiological history input”) ;
a supplement plan generator module operating on the computing device, the plan supplement generator module designed and configured to calculate a supplement instruction set utilizing the user physiological history input and the nutrition instruction set, wherein calculating the supplement instruction set comprises generating a customized dose using a machine-learning process, wherein the machine-learning process is configured to utilize the biological monitoring and the user physiological history as input and output the customized dose (paragraph [0034]… he sensor device 50 is a wearable sensor device such as a continuous glucose monitor (CGM) to obtain transcutaneous or blood glucose measurements; paragraph [0074]… The learning dose calculator module 220 includes an organization of sub-modules and processing engines to manage intake, processing, storage and output of data. In the example embodiment shown in FIG. 4A, the learning dose calculator module 220 includes a context aggregator configured to receive health data and/or user contextual data, e.g., via the data aggregator module 210… The learning dose calculator module 220 includes a dose calculator engine to receive parameters (e.g., from the dose calculator parameter selector) and/or data and make a particular calculation for a medicine dose which can be recommended for the patient at a particular point in time, based on the received information. In some implementations, the dose calculator engine can receive data from the food identification module 230, e.g., output from the module 230, and/or data directly from the data aggregator module 210, such as current and/or forecasted analyte values (e.g., from the sensor device 50, such as CGM or SMBG devices). The learning dose calculator module 220 includes a dose recommendation engine, in data communication with the dose calculator engine, to process the calculated dose of the medicine and determine whether to generate a recommendation of the calculated dose and produce an output of the recommendation accordingly. In some implementations, for example, the recommended calculated dose output can be displayed on an output of the companion device 5 or pen device 10 ; paragraph [0075]… The learning dose calculator module 220 includes a learning module in communication with the results score database and configured to perform machine learning to optimize dose calculation parameters, e.g., stored by the dose calculator parameter set database, and update accordingly ; paragraph [0078]… [0078] cloud aggregator module and cloud content correlation module, resident on a cloud server as part of the system 100, is in communication with the learning dose calculator module 220 to inform learning module of processed parameters which may be associated with the patient's historical data, and/or data of other users with similar medical conditions, demographics, activities patterns, general health, etc) (Examiner’s Note: “learning dose calculator module 220” reads on “supplement plan generator module” ; “a dose calculator engine to receive data (from the food identification module 230 data directly from the data aggregator module 210) and make a particular calculation for a medicine dose” reads on “designed and configured to calculate a supplement instruction set utilizing the user physiological history input and the nutrition instruction set” ; “dose recommendation engine, in data communication with the dose calculator engine, to process the calculated dose o the medicine and generate a recommendation of the calculated dose” reads on “wherein calculating the supplement instruction set comprises generating a customized dose” ; The learning dose calculator module 220 includes a machine learning module” reads on “machine learning process” ; “ inform learning module of processed parameters which may be associated with the patient's historical data, and/or data of other users with similar medical conditions, demographics, activities patterns, general health, etc” reads on “the machine-learning process is configured to utilize the biological monitoring and the user physiological history as input”)
Saint et al fails to explicitly show/teach that the biological monitoring includes biological extraction.
However, Otto teaches biological monitoring includes biological extraction (paragraph [0029]…patient tests their blood sugar using a SMBG or CGM device. This process can involve the sampling of blood and/or interstitial fluid of the patient, and then measuring glucose concentration using electro-chemical, infrared, photoelectric, or other means. New blood glucose information can be added to a database of pre-existing blood glucose data. If the database is not accessible, the system can retrieve blood glucose data to have a window of data for evaluating risk of hypoglycemia. This window of retrieved data can be as little as approximately twenty-four hours or as much as a year or more, but is preferably approximately two to four weeks of data. In a preferred embodiment, the CGM or SMBG data can be collected via real-time wireless communication with a medical device system or glucose monitoring device).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made, for Saint et al’s biological monitoring to include biological extraction, as in Otto, for the purpose of measuring glucose.
As to claim 2, Saint et al teaches the system, wherein the computing device is configured to:
transmit the supplement instruction set to an advisor client device;
receive an altered supplement instruction set from the advisor client device.
(paragraph [0107]…after a physician gets a new patient started and estimates insulin parameters for that person, the results of the first days or weeks can be sent to the physician for review, and they may opt to modify the parameters based on the outcome. These modifications could be pushed wirelessly to the patient user's companion device 5 for the app, and they may confirm the updates.. The first days or weeks could include normal use, as well as possibly controlled experiments with specific foods or other behaviors. For example, security features associated with parameter settings of a dose calculator, such as passcode or fingerprint detection, may be required for physician making changes. An example method for parameter setting can include setting initial parameters on the app; collecting data by the app; calculating parameters for optimal conditions specific to the patient user in the dose calculator; providing updated recommendation associated with the parameters to physician, in which the physician approves or modifies the parameter setting; receiving, at the app, the approval and/or modification to the patient's companion device 5; and obtaining approval by the patient user ; paragraph [0108]…FIGS. 6A-6D show diagrams illustrating example implementations of parameter settings by the learning dose calculator module 220 and which can be remotely updated, e.g., via physician or caregiver, or fully or semi-automatically updated. The example shown in FIG. 6A shows a feedback loop of a fully manual dose calculator, in which operation of the learning dose calculator module 220 allows an external user, such as a HCP (e.g., physician, nurse, parent or qualified caregiver) or a patient, to remotely update dose calculator parameters. The example shown in FIG. 6B shows a feedback loop of a semi-automatic dose calculator with manual review nodes, in which operation of the learning dose calculator module 220 allows an external user, such as HCP or patient, to remotely update dose calculator parameters based on evaluation of patient outcomes and the previously recommended parameters that may affect those outcomes)(Examiner’s Note: “learning dose calculator module 220 allows an external user to remotely update dose calculator parameters and the previously recommended parameters based on evaluation of patient outcomes and the previously recommended parameters that may affect those outcomes” reads on “transmit the supplement instruction set to an advisor client device” ; “modifications/updates could be pushed wirelessly to the patient user's companion device 5 for the app” reads on “receive an altered supplement instruction set from the advisor client device”).
As to claim 3, Saint et al teaches the system, wherein the altered supplement instruction comprises an altered frequency at which a supplement is taken. (paragraph [0083]…method 400 includes a process 430 to calculate, by the learning dose calculator module 220, a medicine dose value using the selected parameter settings ; [0107]… An example method for parameter setting can include setting initial parameters on the app; collecting data by the app; calculating parameters for optimal conditions specific to the patient user in the dose calculator; providing updated recommendation associated with the parameters to physician, in which the physician approves or modifies the parameter setting; receiving, at the app, the approval and/or modification to the patient's companion device 5; and obtaining approval by the patient user ) (Examiner’s Note: “medicine dose value using the selected/updated parameter settings” reads on “altered frequency at which a supplement is taken).
As to claim 4, Saint et al teaches the system, wherein the computing device is further configured to transmit the altered supplement instructions set to a user client device (paragraph [0032]… The companion device 5 includes an app associated with the pen device 10 of the intelligent medicine administering system 100, which can monitor and/or control functionalities of the pen device 10 and to provide a dose calculator and/or decision support modules that can calculate and recommend a dose of the medicine for the patient user to administer using the pen device 10)(Examiner’s Note: “companion device 5 includes an app associated with the pen device 10…to provide a dose calculator and/or decision support modules that can calculate and recommend a dose of the medicine for the patient user” reads on “computing device is further configured to transmit the altered supplement instructions set” ; “pen device 10” reads on “user client device”).
As to claim 8, Saint et al teaches the system, wherein the supplement plan generator module is further configured to:
identify a nutrient deficiency contained within the nutrition instruction set; an
locate a supplement intended to remedy the nutrient deficiency contained within the supplement instruction set (paragraph [0064]… learning dose calculator module 220 in calculations to calculate dose or predicted glucose, rather than just relying on an instantaneous glucose value. In some implementations, the learning dose calculator can calculate based on glucose trend and current glucose value that includes knowledge of carbs, insulin, exercise, etc., to produce a dose calculator based on a forecasted trend using such information. In some implementations, the learning dose calculator can adjust an insulin recommendation based on recent glucose trend. For example, if glucose is rising within a certain range, a set amount of additional insulin may be added to the recommendation)(Examiner’s Note: “the learning dose calculator can calculate based on glucose trend and current glucose value” reads on “identify a nutrient deficiency contained within the nutrition instruction set” ; “the learning dose calculator can adjust an insulin recommendation based on recent glucose trend” reads on “locate a supplement intended to remedy the nutrient deficiency contained within the supplement instruction set”)
As to claim 9, Saint et al teaches the system, wherein the plan generator module is further configured to determine the nutrient deficiency, wherein determining the nutrient deficiency comprises:
generating a machine-learning model that utilizes the user physiological history input and available nutrients as input and outputs the nutrient deficiency; and
determining the nutrient deficiency as a function of the machine-learning model.
(paragraph [0077]…The learning dose calculator module 220 is able to learn from past dosing outcomes (and/or food consumption outcomes and behavioral outcomes, like exercise) that are specific to each patient user of the system 100 by processing, e.g., statistically analyzing, at the learning module, (i) the results of a particular dosing and/or food consumption and/or activity event with (ii) the past dose setting parameters and (iii) the user's analyte state, health state and context data that were current at the time of the particular dosing and/or food consumption and/or activity event. From this analysis, the patient user's individual and holistic physiological response to the medicinal dosing (and/or food consumption or activity behavior) is quantified so that the system 100 can continuously optimize parameters that govern dose determination for specific situations the patient user finds himself/herself in, while also adapting to the changes that every person undergoes in life (e.g., such as a change in health through acute and chronic disease, stress level, diet, lifestyle changes, etc.) ; [0078] In some implementations, for example, a cloud aggregator module and cloud content correlation module, resident on a cloud server as part of the system 100, is in communication with the learning dose calculator module 220 to inform learning module of processed parameters which may be associated with the patient's historical data, and/or data of other users with similar medical conditions, demographics, activities patterns, general health, etc.)(Examiner’s Note: “at the learning module, (i) the results of a particular dosing and/or food consumption and/or activity event with (ii) the past dose setting parameters and (iii) the user's analyte state, health state and context data that were current at the time of the particular dosing and/or food consumption and/or activity event” reads on “generating a machine-learning model that utilizes the user physiological history input and available nutrients as input and outputs the nutrient deficiency” ; “the patient user's individual and holistic physiological response to the medicinal dosing (and/or food consumption or activity behavior) is quantified so that the system 100 can continuously optimize parameters that govern dose determination for specific situations the patient user finds himself/herself in” reads on “determining the nutrient deficiency as a function of the machine-learning model”).
As to claim 10, Saint et al teaches the system, wherein the computing device is configured to transmit the supplement instruction set to a physical performance entity (paragraph [0107]… the results can be sent to the physician for review)(Examiner’s Note: “physician” reads on “physical performance entity”).
Claim 11 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 12 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 18 has similar limitations as claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 19 has similar limitations as claim 9. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 10. Therefore, the claim is rejected for the same reasons as above.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saint et al (US 2018/0353698) in view of ---Otto (US 2017/0112452), as applied to claim 1, and in further view of Davis et al (US 2003//0028399).
As to claim 5, Saint et al teaches the computing device, an advisor client device, and a supplement instruction set.
Saint et al fails to explicitly show/teach the computing device is further configured to: transmit a user query to an advisor client device; and receive an advisor response from the advisor client device, wherein: the advisor response is a function of the user query; and the advisor response comprises an altered supplement instruction
However, Davis et al teaches the computing device is further configured to: transmit a user query to an advisor client device; and receive an advisor response from the advisor client device, wherein: the advisor response is a function of the user query; and the advisor response comprises an altered supplement instruction
(Paragraph [0009]… An improved system could allow the patient to initiate appropriate interaction and, therefore, care at the appropriate time. An improved system and method could allow the doctor or clinician (or their agents) to collaborate with and provide information to the patient outside of the traditional crisis-based, office driven contact. Such a system and method could allow the doctor or clinician (or their agents) to effectively prescribe and provide educational materials or courses that may benefit the patient's condition, to identify gaps in patient understanding that would increase the risk of adverse events or lower chances of improved outcomes, and would likewise allow the patient to immediately ask questions of the doctor or clinician in confidence and in a convenient manner. The patient would preferably be able to ask questions regarding not only a disease or treatment, but also about that particular patient's gaps in knowledge or execution. The clinician could preferably support and monitor a patient's learning, and health progress in real time rather than in discrete, oftentimes episodic random office visits. Such a system and method would further allow for disease management and preventative care by providing a schedule for educational courses, and a prescribed health routine including medications, self-monitoring events, lab tests with reminders, and for symptom history collection, verification and treatment modification ; Paragraph [0085]…FIG. 1 shows two integrated care modules 20 and 30. The first module 20 is the doctor's or clinician's module, hereinafter referred to as "the Provider's Module." The second module is the patient's module, hereinafter referred to as "the Patient's Module.") (Examiner’s Note: “Patient’s Module 30” reads on “the computing device” ; “patient to immediately ask questions of the doctor or clinician” reads on “transmit a user query” ; “Providers Module 20” reads on “advisor client device” ; “providing a schedule for educational courses, and a prescribed health routine including medications, self-monitoring events, lab tests with reminders, and for symptom history collection, verification and treatment modification” reads on “the advisor response is a function of the user query; and the advisor response comprises an altered supplement instruction”).
Therefore, it would have been obvious for one having ordinary skill in the art at the time the invention was made, for Saint et al’s computing device to be further configured to: transmit a user query to an advisor client device; and receive an advisor response from the advisor client device, wherein: the advisor response is a function of the user query; and the advisor response comprises an altered supplement instruction, as in Davis et al, for the purpose of having interactive clinical collaboration between the physician or clinician and the patient in a manner that enables the physician, clinician or groups thereof to direct and supervise patients' healthy behavior between traditional episodes of care.
Claim 15 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saint et al (US 2018/0353698) in view of --- Otto (US 2017/0112452), as applied to claim 1, and in further view of Hanina et al (US 2011/0119073).
As to claim 6, Saint et al teaches the supplement plan generator module.
Saint et al and Otto both fails to explicitly show/teach wherein the supplement plan generator module is further configured to filter supplements with negative interactions with a user's medications from the supplement instruction set.
However, Hanina et al teaches a supplement plan generator module is further configured to filter supplements with negative interactions with a user's medications from the supplement instruction set (paragraph [0025]… actions after taking medication may give insight into patient responses. Notice of fainting, falling down, lack of motion, facial gestures, gastrointestinal distress or the like may all be logged as adverse reactions to a particular medication regimen, and may allow for adjustment of dosage or prescription instructions in the future for the patient. If adverse reactions are severe, an immediate medication review and contact from a medical professional may be provided to cure the issue. Additionally, the system in accordance with the present invention may be directly tied and be interoperable with a pharmacy or medical provider's systems, thus allowing such recommendations for dosage changes, regimen changes and the like to be forwarded to these professionals automatically. Through such links, reordering medication, dosage changes, medication changes and the like may be automatically provided. Furthermore, ease of providing additional prescriptions can be enhanced as patient, medication and regimen information will already be available to the pharmacist or medical service provider) (Examiner’s Note: “adverse reactions to a particular medication regimen, and may allow for adjustment of dosage or prescription instructions in the future for the patient” reads on “a supplement plan generator module is further configured to filter supplements with negative interactions with a user's medications from the supplement instruction set”).
Therefore, it would have been obvious for one having ordinary skill in the art at the time the invention was made, for Saint et al’s supplement plan generator module to be further configured to filter supplements with negative interactions with a user's medications from the supplement instruction set, as in Hanina et al, for the purpose of adjusting of dosage or prescription instructions in the future for the patient to keep the patient healthy.
Claim 16 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saint et al (US 2018/0353698) in view of ---Otto (US 2017/0112452), as applied to claim 1, and in further view of CONROY et al (US 2019/0133480).
As to claim 7, Saint et al teaches the diagnostic engine.
Saint et al and Otto both fail to explicitly show/teach wherein the diagnostic engine is further configured to: receive a first training set including at least an element of physiological state data and at least a correlated first prognostic label; and generate a diagnostic output utilizing a first machine-learning process trained by the first training set, as a function of the user physiological history input pertaining to the user and the user physiological history input.
However, CONROY et al teaches the diagnostic engine is further configured to: receive a first training set including at least an element of physiological state data and at least a correlated first prognostic label; and generate a diagnostic output utilizing a first machine-learning process trained by the first training set, as a function of the user physiological history input pertaining to the user and the user physiological history input. (paragraph [0010]…a method may include: obtaining a first continuous stream of samples measured by one or more physiological sensors; discretizing the first continuous stream of samples to generate a training sequence of quantized beats; determining a training sequence of vectors corresponding to the training sequence of quantized beats, wherein each vector of the training sequence of vectors is determined based on a respective quantized beat of the training sequence of quantized beats and an embedding matrix; associating a label with each vector of the training sequence of vectors, wherein each label is indicative of a medical condition that is evidenced by samples of the first continuous stream obtained during a time interval associated with the respective vector of the training sequence of vectors; applying the training sequence of vectors as input across a neural network to generate corresponding instances of training output; comparing each instance of training output to the label that is associated with the corresponding vector of the training sequence of vectors; based on the comparing, training the neural network and the embedding matrix; obtaining a second continuous stream of samples from one or more of the physiological sensors; paragraph [0027]… generate output 216. Output 216 may be, for instance, an indication or prediction of a health condition ; paragraph [0028]…This first continuous stream of samples may be obtained from physiological sensor 104 in real time or may be obtained from a log of previously-recorded streams of samples.) (Examiner’s Note: “associating a label with each vector of the training sequence of vectors, wherein each label is indicative of a medical condition that is evidenced by samples of the first continuous stream obtained during a time interval associated with the respective vector of the training sequence of vectors” reads on “receive a first training set including at least an element of physiological state data and at least a correlated first prognostic label” ; “applying the training sequence of vectors as input across a neural network to generate corresponding instances of training output” reads on “generate a diagnostic output utilizing a first machine-learning process trained by the first training set” ; “generate output 216. Output 216 may be, for instance, an indication or prediction of a health condition…the first continuous stream of samples may be obtained from physiological sensor 104 in real time or may be obtained from a log of previously-recorded streams of samples” reads on “generate a diagnostic output utilizing a first machine-learning process trained by the first training set, as a function of the user physiological history input pertaining to the user and the user physiological history input” )
Therefore, it would have been obvious for one having ordinary skill in the art at the time the invention was made, for Saint et al’s diagnostic engine is further configured to: receive a first training set including at least an element of physiological state data and at least a correlated first prognostic label; and generate a diagnostic output utilizing a first machine-learning process trained by the first training set, as a function of the user physiological history input pertaining to the user and the user physiological history input, as in CONROY et al, for the purpose of predicting a health condition.
Claim 17 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Conclusion
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128