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 .
Specification
A preliminary examination of this application reveals that it includes terminology which is so different from that which is generally accepted in the art to which this invention pertains that a proper search of the prior art cannot be made. For example: hyperparameter is generally accepted in the art of machine learning to pertain to parameters specifically for a model or algorithm to control the learning process and are typically determined before a training or retraining phase. Hyperparameters are typically not user-specific, and are typically not .
Applicant is required to provide a clarification of these matters or correlation with art-accepted terminology so that a proper comparison with the prior art can be made. Applicant should be careful not to introduce any new matter into the disclosure (i.e., matter which is not supported by the disclosure as originally filed).
A shortened statutory period for reply to this action is set to expire TWO (2) MONTHS from the mailing date of this letter.
The disclosure is objected to because of the following informalities: in Paragraphs [0098], and [104], retaining should be retraining.
Appropriate correction is required.
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.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “hyperparameter” in claims 1-5, 8-12, and 15-18 is used by the claims to mean “threshold values/conditions” while the accepted meaning is “a parameter whose value is set before the learning process of a machine learning model/algorithm begins.” The term is indefinite because the specification does not clearly redefine the term. For the purposes of speedy examination, the Examiner is interpreting hyperparameter to instead mean parameter.
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. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidelines(“2019 PEG”).
Step 1: Independent claim 1 (A non-transitory computer readable storage medium…), 8 (A method for…), and 15(A computing device for…) are directed towards a manufacture, a method, and a machine respectively. Therefore, these claims, as well as their dependent claims, are directed towards on of the four statutory categories (process, machine, manufacture, or composition of matter)
Claim 1
Step 2A, Prong 1: The claim recites, inter alia:
performing an initial exploration phase by: randomly assigning at least one hyperparameter to each user of a plurality of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to randomly decide a parameter(hyperparameter) for each user. See MPEP 2106.04(a)(2)(III)
performing a training phase by: analyzing training data, wherein the training data comprises contextual data and a value associated with the at least one randomly assigned hyperparameter; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge training data. See MPEP 2106.04(a)(2)(III)
determining a relationship between the contextual data and the value associated with the at least one randomly assigned hyperparameter; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge a relationship between data and values within the training data. See MPEP 2106.04(a)(2)(III)
performing an exploration-exploitation phase by: dividing the plurality of users into an exploration subset of users and an exploitation subset of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge if a user is to be in an exploration subset or an exploitation subset for a plurality of users. See MPEP 2106.04(a)(2)(III)
determining at least one optimal hyperparameter for each user of the exploitation subset of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate an optimal parameter(hyperparameter). See MPEP 2106.04(a)(2)(III)
determining, using the at least one optimal hyperparameter, at least one decision support output for each user of the exploitation subset of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a decision support output for each user. See MPEP 2106.04(a)(2)(III)
randomly assigning at least one hyperparameter to each user of the exploration subset of users; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to randomly decide a parameter(hyperparameter) for each user. See MPEP 2106.04(a)(2)(III)
determining, using the at least one randomly assigned hyperparameter, at least one decision support output for each user of the exploration subset of users.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a decision support output for each user. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
A non-transitory computer readable storage medium storing a program comprising instructions that, when executed by at least one processor of a computing device, cause the at least one processor to perform operations including:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
A non-transitory computer readable storage medium storing a program comprising instructions that, when executed by at least one processor of a computing device, cause the at least one processor to perform operations including:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
Claim 2
Step 2A, Prong 1: The claim recites, inter alia:
determining, using the at least one randomly assigned hyperparameter, a decision support output for each user of the plurality of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a decision support output for each user. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
providing the decision support output to each user of the plurality of users; and
This limitation is recited at a high level of generality and recites simple application of the abstract idea. Mere recitation that a judicial exception is to be provided in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
receiving monitoring data for each user of the plurality of users, wherein the monitoring data provides information to at least one physiological condition.
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
providing the decision support output to each user of the plurality of users; and
This limitation is recited at a high level of generality and recites simple application of the abstract idea. Mere recitation that a judicial exception is to be provided in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
receiving monitoring data for each user of the plurality of users, wherein the monitoring data provides information to at least one physiological condition.
MPEP 2106.05(d)(II)(i) indicates that merely receiving or transmitting data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim)
Claim 3
Step 2A, Prong 1: This claim recites, inter alia:
the training phase is further performed by determining a relationship between the monitoring data, the contextual data, and the value associated with the at least one randomly assigned hyperparameter.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge a relationship between data and values within the training data. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
wherein the training data further comprises the monitoring data
This limitation represents an insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated performed by generic computer equipment. See MPEP 2106.05(g)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the training data further comprises the monitoring data
MPEP 2106.05(d)(II)(iv) indicates that merely receiving or transmitting data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim)
Claim 4
Step 2A, Prong 1: The claim recites, inter alia:
assigning a plurality of experimental hyperparameters to each user of the exploitation subset;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a parameter(hyperparameter) for each user of the exploitation subset. See MPEP 2106.04(a)(2)(III)
determining at least one predicted outcome for each experimental hyperparameter of the plurality of experimental hyperparameters;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to predict an outcome for each parameter (hyperparameter). See MPEP 2106.04(a)(2)(III)
determining a scalarized outcome for each experimental hyperparameter of the plurality of experimental hyperparameters; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate a scalarized outcome for each parameter(hyperparameter). See MPEP 2106.04(a)(2)(III)
determining the at least one optimal hyperparameter based on the scalarized outcome.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge an optimal parameter (hyperparameter) based on the scalarized outcome. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 5
Step 2A, Prong 1: The claim recites, inter alia:
performing a retraining phase by: analyzing training data, wherein the training data comprises contextual data, a value associated with the randomly assigned hyperparameter of the exploration subset of users, and monitoring data for the exploration subset of users; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge training data. See MPEP 2106.04(a)(2)(III)
determining a relationship between the contextual data, the value associated with the at least one randomly assigned hyperparameter of the exploration subset of users, and the monitoring data for the exploration subset of users.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge a relationship between data and values within the training data. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 6
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
wherein the exploration-exploitation phase is performed using a contextual multi-armed bandit algorithm.
This limitation is recited at a high level of generality and recites use of a generic computer algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic computer algorithm in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the exploration-exploitation phase is performed using a contextual multi-armed bandit algorithm.
This limitation is recited at a high level of generality and recites use of a generic computer algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic computer algorithm in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 7
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
wherein the at least one predicted outcome is determined using a tactic assignment algorithm.
This limitation is recited at a high level of generality and recites use of a generic computer algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic computer algorithm in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the at least one predicted outcome is determined using a tactic assignment algorithm.
This limitation is recited at a high level of generality and recites use of a generic computer algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic computer algorithm in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 8
Step 2A, Prong 1: The claim recites, inter alia:
performing an initial exploration phase by: randomly assigning at least one hyperparameter to each user of a plurality of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to randomly decide a parameter(hyperparameter) for each user. See MPEP 2106.04(a)(2)(III)
performing a training phase by: analyzing training data, wherein the training data comprises contextual data and a value associated with the at least one randomly assigned hyperparameter; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge training data. See MPEP 2106.04(a)(2)(III)
determining a relationship between the contextual data and the value associated with the at least one randomly assigned hyperparameter; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge a relationship between data and values within the training data. See MPEP 2106.04(a)(2)(III)
performing an exploration-exploitation phase by: dividing the plurality of users into an exploration subset of users and an exploitation subset of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge if a user is to be in an exploration subset or an exploitation subset for a plurality of users. See MPEP 2106.04(a)(2)(III)
determining at least one optimal hyperparameter for each user of the exploitation subset of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate an optimal parameter(hyperparameter). See MPEP 2106.04(a)(2)(III)
determining, using the at least one optimal hyperparameter, at least one decision support output for each user of the exploitation subset of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a decision support output for each user. See MPEP 2106.04(a)(2)(III)
randomly assigning at least one hyperparameter to each user of the exploration subset of users; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to randomly decide a parameter(hyperparameter) for each user. See MPEP 2106.04(a)(2)(III)
determining, using the at least one randomly assigned hyperparameter, at least one decision support output for each user of the exploration subset of users.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a decision support output for each user. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
A method for determining user-specific hyperparameters for decision support models, the method comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
A method for determining user-specific hyperparameters for decision support models, the method comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
Claim 9
Step 2A, Prong 1: The claim recites, inter alia:
determining, using the at least one randomly assigned hyperparameter, a decision support output for each user of the plurality of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a decision support output for each user. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
providing the decision support output to each user of the plurality of users; and
This limitation is recited at a high level of generality and recites simple application of the abstract idea. Mere recitation that a judicial exception is to be provided in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
receiving monitoring data for each user of the plurality of users, wherein the monitoring data provides information to at least one physiological condition.
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
providing the decision support output to each user of the plurality of users; and
This limitation is recited at a high level of generality and recites simple application of the abstract idea. Mere recitation that a judicial exception is to be provided in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
receiving monitoring data for each user of the plurality of users, wherein the monitoring data provides information to at least one physiological condition.
MPEP 2106.05(d)(II)(i) indicates that merely receiving or transmitting data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim)
Claim 10
Step 2A, Prong 1: This claim recites, inter alia:
the training phase is further performed by determining a relationship between the monitoring data, the contextual data, and the value associated with the at least one randomly assigned hyperparameter.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge a relationship between data and values within the training data. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
wherein the training data further comprises the monitoring data
This limitation represents an insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated performed by generic computer equipment. See MPEP 2106.05(g)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the training data further comprises the monitoring data
MPEP 2106.05(d)(II)(iv) indicates that merely receiving or transmitting data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim)
Claim 11
Step 2A, Prong 1: The claim recites, inter alia:
assigning a plurality of experimental hyperparameters to each user of the exploitation subset;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a parameter(hyperparameter) for each user of the exploitation subset. See MPEP 2106.04(a)(2)(III)
determining at least one predicted outcome for each experimental hyperparameter of the plurality of experimental hyperparameters;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to predict an outcome for each parameter (hyperparameter). See MPEP 2106.04(a)(2)(III)
determining a scalarized outcome for each experimental hyperparameter of the plurality of experimental hyperparameters; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate a scalarized outcome for each parameter(hyperparameter). See MPEP 2106.04(a)(2)(III)
determining the at least one optimal hyperparameter based on the scalarized outcome.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge an optimal parameter (hyperparameter) based on the scalarized outcome. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 12
Step 2A, Prong 1: The claim recites, inter alia:
performing a retraining phase by: analyzing training data, wherein the training data comprises contextual data, a value associated with the randomly assigned hyperparameter of the exploration subset of users, and monitoring data for the exploration subset of users; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge training data. See MPEP 2106.04(a)(2)(III)
determining a relationship between the contextual data, the value associated with the at least one randomly assigned hyperparameter of the exploration subset of users, and the monitoring data for the exploration subset of users.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge a relationship between data and values within the training data. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 13
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
wherein the exploration-exploitation phase is performed using a contextual multi-armed bandit algorithm.
This limitation is recited at a high level of generality and recites use of a generic computer algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic computer algorithm in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the exploration-exploitation phase is performed using a contextual multi-armed bandit algorithm.
This limitation is recited at a high level of generality and recites use of a generic computer algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic computer algorithm in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 14
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
wherein the at least one predicted outcome is determined using a tactic assignment algorithm.
This limitation is recited at a high level of generality and recites use of a generic computer algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic computer algorithm in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the at least one predicted outcome is determined using a tactic assignment algorithm.
This limitation is recited at a high level of generality and recites use of a generic computer algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic computer algorithm in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 15
Step 2A, Prong 1: The claim recites, inter alia:
perform an initial exploration phase by: randomly assigning at least one hyperparameter to each user of a plurality of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to randomly decide a parameter(hyperparameter) for each user. See MPEP 2106.04(a)(2)(III)
perform a training phase by: analyzing training data, wherein the training data comprises contextual data and a value associated with the at least one randomly assigned hyperparameter; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge training data. See MPEP 2106.04(a)(2)(III)
determining a relationship between the contextual data and the value associated with the at least one randomly assigned hyperparameter; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge a relationship between data and values within the training data. See MPEP 2106.04(a)(2)(III)
perform an exploration-exploitation phase by: dividing the plurality of users into an exploration subset of users and an exploitation subset of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge if a user is to be in an exploration subset or an exploitation subset for a plurality of users. See MPEP 2106.04(a)(2)(III)
determining at least one optimal hyperparameter for each user of the exploitation subset of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate an optimal parameter(hyperparameter). See MPEP 2106.04(a)(2)(III)
determining, using the at least one optimal hyperparameter, at least one decision support output for each user of the exploitation subset of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a decision support output for each user. See MPEP 2106.04(a)(2)(III)
randomly assigning at least one hyperparameter to each user of the exploration subset of users; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to randomly decide a parameter(hyperparameter) for each user. See MPEP 2106.04(a)(2)(III)
determining, using the at least one randomly assigned hyperparameter, at least one decision support output for each user of the exploration subset of users.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a decision support output for each user. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
A computing device for determining user-specific hyperparameters for decision support models, the computing device comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
a network interface;
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
a processor operatively connected to the network interface;
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
a memory storing a program comprising instructions that, when executed by the processor, cause the computing device to:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
A computing device for determining user-specific hyperparameters for decision support models, the computing device comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
a network interface;
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
a processor operatively connected to the network interface;
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
a memory storing a program comprising instructions that, when executed by the processor, cause the computing device to:
This limitation is recited at a high level of generality and recites use of generic computer equipment to apply the abstract idea. Mere recitation that a judicial exception is to be performed using generic computer equipment in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
Claim 16
Step 2A, Prong 1: The claim recites, inter alia:
determining, using the at least one randomly assigned hyperparameter, a decision support output for each user of the plurality of users;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a decision support output for each user. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
providing the decision support output to each user of the plurality of users using a decision support model; and
This limitation is recited at a high level of generality and recites simple application of the abstract idea using a generic decision support model. Mere recitation that a judicial exception is to be provided using a generic decision support model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
receiving monitoring data for each user of the plurality of users, wherein the monitoring data provides information to at least one physiological condition.
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
providing the decision support output to each user of the plurality of users using a decision support model; and
This limitation is recited at a high level of generality and recites simple application of the abstract idea using a generic decision support model. Mere recitation that a judicial exception is to be provided using a generic decision support model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106. 05(f)
receiving monitoring data for each user of the plurality of users, wherein the monitoring data provides information to at least one physiological condition.
MPEP 2106.05(d)(II)(i) indicates that merely receiving or transmitting data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim)
Claim 17
Step 2A, Prong 1: This claim recites, inter alia:
the training phase is further performed by determining a relationship between the monitoring data, the contextual data, and the value associated with the at least one randomly assigned hyperparameter.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge a relationship between data and values within the training data. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
wherein the training data further comprises the monitoring data
This limitation represents an insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated performed by generic computer equipment. See MPEP 2106.05(g)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the training data further comprises the monitoring data
MPEP 2106.05(d)(II)(iv) indicates that merely receiving or transmitting data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim)
Claim 18
Step 2A, Prong 1: The claim recites, inter alia:
assigning a plurality of experimental hyperparameters to each user of the exploitation subset;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a parameter(hyperparameter) for each user of the exploitation subset. See MPEP 2106.04(a)(2)(III)
determining a predicted outcome for each experimental hyperparameter of the plurality of experimental hyperparameters;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to predict an outcome for each parameter (hyperparameter). See MPEP 2106.04(a)(2)(III)
determining a scalarized outcome for each experimental hyperparameter of the plurality of experimental hyperparameters; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate a scalarized outcome for each parameter(hyperparameter). See MPEP 2106.04(a)(2)(III)
determining the at least one optimal hyperparameter based on the scalarized outcome.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to judge an optimal parameter (hyperparameter) based on the scalarized outcome. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 19
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
wherein the exploration-exploitation phase is performed using a contextual multi-armed bandit algorithm.
This limitation is recited at a high level of generality and recites use of a generic computer algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic computer algorithm in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the exploration-exploitation phase is performed using a contextual multi-armed bandit algorithm.
This limitation is recited at a high level of generality and recites use of a generic computer algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic computer algorithm in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 20
Step 2A, Prong 1: The claim recites, inter alia:
perform a retraining phase by: analyzing training data, wherein the training data comprises contextual data, a value associated with the randomly assigned hyperparameter of the exploration subset of users, and monitoring data for the exploration subset of users; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge training data. See MPEP 2106.04(a)(2)(III)
determining at least one predicted outcome for each user of the exploration subset of users based on a relationship between the contextual data, the value associated with the at least one randomly assigned hyperparameter of the exploration subset of users, and the monitoring data for the exploration subset of users.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to evaluate and judge a relationship between data and values within the training data. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach by Zhou et al., hereafter Zhou, in view of US 20220374944 A1 by Newnham et al., hereafter Newnham.
Regarding independent claim 1, Zhou teaches:
performing an initial exploration phase by:
randomly assigning at least one hyperparameter to each user of a plurality of users; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Input: prior mean m and prior variance v for each parameter theta, Step 1 Random Draw. Each parameter theta is a parameter(hyperparameter). In the initial iteration, step 1 of the algorithm randomly assigns each hyperparameter as an initial exploration phase.}
performing a training phase by:
analyzing training data, wherein the training data comprises contextual data {(Zhou) Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings and the average challenge embeddings of users’ choice as the input variables and users’ weight-loss status as the prediction target.” Average challenge embeddings of users’ choice are contextual data} and a value associated with the at least one randomly assigned hyperparameter; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, expected reward function; Section 5.2 Paragraph 3, “Finally, in light of previous theoretical MAB studies, we examine our model performance through a simulated environment. Specifically, we construct a logistic predictor for users’ binary challenge-selection decisions, that is [logistic reward function]” The logistic predictor uses the parameters(hyperparameters) to simulate a reward value.} and
determining a relationship {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum parameter(hyperparameter) values based on at least a combination of user embeddings and the average challenge embeddings of users’ choice} between the contextual data {(Zhou) Section 5.7 Paragraph 1} and the value associated with the at least one randomly assigned hyperparameter; {(Zhou) Section 5.2 Paragraph 3} and
determining at least one optimal hyperparameter for each user; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum, which is equivalent to optimal, parameters(hyperparameters) for all users.}
determining, using the at least one optimal hyperparameter, at least one decision support output for each user; {(Zhou) Section 3.2 Paragraph 3, “Our algorithm extends an ordinary TS by integrating a constrained optimization problem to solve for the optimal recommendation decisions subject to the diversity constraint” Optimal recommendation decisions are decision support outputs}
randomly assigning at least one hyperparameter to each user; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “Step 1 (Random Draw)”. The Random Draw draws parameters randomly from a set of parameters to each user which is equivalent to randomly assigning the parameters(hyperparameters)} and
determining, using the at least one randomly assigned hyperparameter, at least one decision support output for each user. {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “For i=1,2,…I do … Offer item k to individual i …” Offering a recommendation item to an individual is equivalent to determining a decision support for that individual user.}
Zhou does not explicitly teach:
A non-transitory computer readable storage medium storing a program comprising instructions that, when executed by at least one processor of a computing device, cause the at least one processor to perform operations including:
performing an exploration-exploitation phase by:
dividing the plurality of users into an exploration subset of users and an exploitation subset of users;
Newnham does teach:
A non-transitory computer readable storage medium storing a program comprising instructions that, when executed by at least one processor of a computing device, cause the at least one processor to perform operations including: {(Newnham) Paragraph [0135], “Any logic or application described herein that comprises software or code can be embodied in any non-transitory computer-readable medium for use by or in connection with an instruction execution system in a computer system or other system.”}
performing an exploration-exploitation phase by:
dividing the plurality of users into an exploration subset of users and an exploitation subset of users; {(Newnham) Paragraph [0040], “The exploration controller 125 is further configured randomly to assign the advertising request 122 received by the exploration controller 125 into one of three advertising request groups, 1) an exploration group comprising exploration advertising requests, the exploration group usable by the exploration controller 125 to gather a useful set of training data regarding the advertising requests; 2) an exploitation group comprising exploitation advertising requests, the exploitation group usable by the exploration controller 125 to exploit one or more of the optimized bid floor and the optimized shading factor” Assignment to an exploration group or an exploitation group is dividing the users into an exploration subset of users and an exploitation subset of users.}
Newnham and Zhou are analogous art because they are in the same area of invention, that being performing exploration and exploitation learning.
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date, having the teachings of Newnham and Zhou in front of them, to have combined the non-transitory computer-readable medium and the division of users into an exploration subset of users and an exploitation subset of users, as taught by Newnham, with the determining an optimal parameter(hyperparameter) and a decision support output for each user based on the optimal parameter(hyperparameter), as taught by Zhou, so that only the exploitation subset of users are considered for this part of the algorithm, and the random assignment of a parameter(hyperparameter) and determining a decision support output for each user based on the randomly assigned parameter(hyperparameter), as taught by Zhou, so that only the exploration subset of users are considered for this part of the algorithm. The motivation for this would be to separate the exploration and exploitation members for better comparison of exploration and exploitation reward for the division of users, and to have a medium to run the method on for the non-transitory computer-readable medium. This application of the division of the users technique and addition of a non-transitory computer readable medium taught by Newnham to the method of exploration and exploitation taught by Zhou would yield the predictable result of the improved system that is equivalent to the material specified in claim 1 of the instant application. The Examiner notes that this motivation applies to all dependent claims of this claim.
Regarding dependent claim 2, Zhou, in view of Newnham, teaches the material disclosed in claim 1 and additionally Zhou teaches:
determining, using the at least one randomly assigned hyperparameter{(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “Step 1 (Random Draw)”}, a decision support output for each user of the plurality of users; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “For i=1,2,…I do … Offer item k to individual i …”}
providing the decision support output to each user of the plurality of users; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “For i=1,2,…I do … Offer item k to individual i …” Offering a recommendation item to an individual is equivalent to providing a decision support output to a user} and
receiving monitoring data for each user of the plurality of users, wherein the monitoring data provides information to at least one physiological condition. {(Zhou) Section 4.2 Paragraph 2, “The third dataset contains auxiliary information for each user, such as... initial weight when first joining the platform, online weigh-in activities...”; Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings …” Initial weight is information of physiological condition and is included in the auxiliary information for each user and user embeddings which are equivalent to monitoring data.}
Regarding dependent claim 3, Zhou, in view of Newnham, teaches the material disclosed in claim 2, and additionally teaches:
wherein the training data further comprises the monitoring data, {(Zhou) Section 4.2 Paragraph 2} and the training phase is further performed by determining a relationship {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum parameter(hyperparameter) values based on at least a combination of the user embeddings and the average challenge embeddings of users’ choice} between the monitoring data, the contextual data, and the value associated with the at least one randomly assigned hyperparameter{(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, expected reward function; Section 5.2 Paragraph 3, “Finally, in light of previous theoretical MAB studies, we examine our model performance through a simulated environment. Specifically, we construct a logistic predictor for users’ binary challenge-selection decisions, that is [logistic reward function]” The logistic predictor uses the parameters(hyperparameters) to simulate a reward value}. {(Zhou) Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings and the average challenge embeddings of users’ choice as the input variables and users’ weight-loss status as the prediction target.” User embeddings contain monitoring data. Average challenge embeddings of users’ choice are contextual data.}
Regarding dependent claim 4, Zhou, in view of Newnham, teaches the material disclosed in claim 1, and additionally teaches:
assigning a plurality of experimental hyperparameters to each user of the exploitation subset; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves assigning the parameters (hyperparameters).}
determining at least one predicted outcome for each experimental hyperparameter of the plurality of experimental hyperparameters; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves a logistic expected reward which results in an outcome.}
determining a scalarized outcome for each experimental hyperparameter of the plurality of experimental hyperparameters; and {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. The reward value is logistically scalarized.}
determining the at least one optimal hyperparameter based on the scalarized outcome. {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum, which is equivalent to optimal, parameters(hyperparameters) for all users.}
Regarding dependent claim 5, Zhou, in view of Newnham, teaches the material disclosed in claim 4, and additionally Zhou teaches:
performing a retraining phase by: {(Zhou) Section 3.1 Paragraph 2, “At the end of each period, the platform receives users’ feedback on the recommendations, that is, whether they have adopted or engaged in the recommended interventions… Users’ feedback serves as a reward for the platform’s recommendation decisions; the platform may use the information of users’ feedback to update its knowledge about users’ preferences and adjust its subsequent recommendations” Updating its knowledge about users’ preferences and adjusting subsequent recommendations means it is retraining the algorithm.}
analyzing training data, wherein the training data comprises contextual data, {(Zhou) Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings and the average challenge embeddings of users’ choice as the input variables and users’ weight-loss status as the prediction target.” User embeddings contain monitoring data. Average challenge embeddings of users’ choice are contextual data.} a value associated with the randomly assigned hyperparameter of the exploration subset of users {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, expected reward function; Section 5.2 Paragraph 3, “Finally, in light of previous theoretical MAB studies, we examine our model performance through a simulated environment. Specifically, we construct a logistic predictor for users’ binary challenge-selection decisions, that is [logistic reward function]” The logistic predictor uses the parameters(hyperparameters) to simulate a reward value.}, and monitoring data for the exploration subset of users {(Zhou) Section 5.7 Paragraph 1, “…user embeddings…”}; and
determining a relationship between the contextual data, the value associated with the at least one randomly assigned hyperparameter of the exploration subset of users, and the monitoring data for the exploration subset of users. {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum parameter(hyperparameter) values based on at least a combination of the user embeddings and the average challenge embeddings of users’ choice}
Regarding dependent claim 6, Zhou, in view of Newnham, teaches the material disclosed in claim 1, and additionally Zhou teaches:
wherein the exploration-exploitation phase is performed using a contextual multi-armed bandit algorithm. {(Zhou) Section 3.1 Paragraph 3, “We formulate the above recommendation problem as a contextual MAB.” MAB stands for multi-armed bandit.}
Regarding dependent claim 7, Zhou, in view of Newnham, teaches the material disclosed in claim 4, and additionally Zhou teaches:
wherein the at least one predicted outcome is determined using a tactic assignment algorithm. {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, expected reward function; Section 5.2 Paragraph 3, “Finally, in light of previous theoretical MAB studies, we examine our model performance through a simulated environment. Specifically, we construct a logistic predictor for users’ binary challenge-selection decisions” A logistic predictor predicts an outcome and is a tactic assignment algorithm.}
Regarding independent claim 8, Zhou teaches:
A method for determining user-specific hyperparameters for decision support models, the method comprising:
performing an initial exploration phase by:
randomly assigning at least one hyperparameter to each user of a plurality of users; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Input: prior mean m and prior variance v for each parameter theta, Step 1 Random Draw. Each parameter theta is a parameter(hyperparameter). In the initial iteration, step 1 of the algorithm randomly assigns each hyperparameter as an initial exploration phase.}
performing a training phase by:
analyzing training data, wherein the training data comprises contextual data {(Zhou) Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings and the average challenge embeddings of users’ choice as the input variables and users’ weight-loss status as the prediction target.” Average challenge embeddings of users’ choice are contextual data} and a value associated with the at least one randomly assigned hyperparameter; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, expected reward function; Section 5.2 Paragraph 3, “Finally, in light of previous theoretical MAB studies, we examine our model performance through a simulated environment. Specifically, we construct a logistic predictor for users’ binary challenge-selection decisions, that is [logistic reward function]” The logistic predictor uses the parameters(hyperparameters) to simulate a reward value.} and
determining a relationship {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum parameter(hyperparameter) values based on at least a combination of user embeddings and the average challenge embeddings of users’ choice} between the contextual data {(Zhou) Section 5.7 Paragraph 1} and the value associated with the at least one randomly assigned hyperparameter; {(Zhou) Section 5.2 Paragraph 3} and
determining at least one optimal hyperparameter for each user; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum, which is equivalent to optimal, parameters(hyperparameters) for all users.}
determining, using the at least one optimal hyperparameter, at least one decision support output for each user; {(Zhou) Section 3.2 Paragraph 3, “Our algorithm extends an ordinary TS by integrating a constrained optimization problem to solve for the optimal recommendation decisions subject to the diversity constraint” Optimal recommendation decisions are decision support outputs}
randomly assigning at least one hyperparameter to each user; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “Step 1 (Random Draw)”. The Random Draw draws parameters randomly from a set of parameters to each user which is equivalent to randomly assigning the parameters(hyperparameters)} and
determining, using the at least one randomly assigned hyperparameter, at least one decision support output for each user. {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “For i=1,2,…I do … Offer item k to individual i …” Offering a recommendation item to an individual is equivalent to determining a decision support for that individual user.}
Zhou does not explicitly teach:
performing an exploration-exploitation phase by:
dividing the plurality of users into an exploration subset of users and an exploitation subset of users;
Newnham does teach:
performing an exploration-exploitation phase by:
dividing the plurality of users into an exploration subset of users and an exploitation subset of users; {(Newnham) Paragraph [0040], “The exploration controller 125 is further configured randomly to assign the advertising request 122 received by the exploration controller 125 into one of three advertising request groups, 1) an exploration group comprising exploration advertising requests, the exploration group usable by the exploration controller 125 to gather a useful set of training data regarding the advertising requests; 2) an exploitation group comprising exploitation advertising requests, the exploitation group usable by the exploration controller 125 to exploit one or more of the optimized bid floor and the optimized shading factor” Assignment to an exploration group or an exploitation group is dividing the users into an exploration subset of users and an exploitation subset of users.}
Newnham and Zhou are analogous art because they are in the same area of invention, that being performing exploration and exploitation learning.
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date, having the teachings of Newnham and Zhou in front of them, to have combined the division of users into an exploration subset of users and an exploitation subset of users, as taught by Newnham, with the determining an optimal parameter(hyperparameter) and a decision support output for each user based on the optimal parameter(hyperparameter), as taught by Zhou, so that only the exploitation subset of users are considered for this part of the algorithm, and the random assignment of a parameter(hyperparameter) and determining a decision support output for each user based on the randomly assigned parameter (hyperparameter), as taught by Zhou, so that only the exploration subset of users are considered for this part of the algorithm. The motivation for this would be to separate the exploration and exploitation members for better comparison of exploration and exploitation reward for the division of users. This application of the division of the users technique taught by Newnham to the method of exploration and exploitation taught by Zhou would yield the predictable result of the improved system that is equivalent to the material specified in claim 8 of the instant application. The Examiner notes that this motivation applies to all dependent claims of this claim.
Regarding dependent claim 9, Zhou, in view of Newnham, teaches the material disclosed in claim 8 and additionally Zhou teaches:
determining, using the at least one randomly assigned hyperparameter{(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “Step 1 (Random Draw)”}, a decision support output for each user of the plurality of users; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “For i=1,2,…I do … Offer item k to individual i …”}
providing the decision support output to each user of the plurality of users; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “For i=1,2,…I do … Offer item k to individual i …” Offering a recommendation item to an individual is equivalent to providing a decision support output to a user} and
receiving monitoring data for each user of the plurality of users, wherein the monitoring data provides information to at least one physiological condition. {(Zhou) Section 4.2 Paragraph 2, “The third dataset contains auxiliary information for each user, such as... initial weight when first joining the platform, online weigh-in activities...”; Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings …” Initial weight is information of physiological condition and is included in the auxiliary information for each user and user embeddings which are equivalent to monitoring data.}
Regarding dependent claim 10, Zhou, in view of Newnham, teaches the material disclosed in claim 9, and additionally teaches:
wherein the training data further comprises the monitoring data, {(Zhou) Section 4.2 Paragraph 2} and the training phase is further performed by determining a relationship {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum parameter(hyperparameter) values based on at least a combination of the user embeddings and the average challenge embeddings of users’ choice} between the monitoring data, the contextual data, and the value associated with the at least one randomly assigned hyperparameter{(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, expected reward function; Section 5.2 Paragraph 3, “Finally, in light of previous theoretical MAB studies, we examine our model performance through a simulated environment. Specifically, we construct a logistic predictor for users’ binary challenge-selection decisions, that is [logistic reward function]” The logistic predictor uses the parameters(hyperparameters) to simulate a reward value}. {(Zhou) Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings and the average challenge embeddings of users’ choice as the input variables and users’ weight-loss status as the prediction target.” User embeddings contain monitoring data. Average challenge embeddings of users’ choice are contextual data.}
Regarding dependent claim 11, Zhou, in view of Newnham, teaches the material disclosed in claim 8, and additionally teaches:
assigning a plurality of experimental hyperparameters to each user of the exploitation subset; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves assigning the parameters (hyperparameters).}
determining at least one predicted outcome for each experimental hyperparameter of the plurality of experimental hyperparameters; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves a logistic expected reward which results in an outcome.}
determining a scalarized outcome for each experimental hyperparameter of the plurality of experimental hyperparameters; and {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. The reward value is logistically scalarized.}
determining the at least one optimal hyperparameter based on the scalarized outcome. {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum, which is equivalent to optimal, parameters(hyperparameters) for all users.}
Regarding dependent claim 12, Zhou, in view of Newnham, teaches the material disclosed in claim 11, and additionally Zhou teaches:
performing a retraining phase by: {(Zhou) Section 3.1 Paragraph 2, “At the end of each period, the platform receives users’ feedback on the recommendations, that is, whether they have adopted or engaged in the recommended interventions… Users’ feedback serves as a reward for the platform’s recommendation decisions; the platform may use the information of users’ feedback to update its knowledge about users’ preferences and adjust its subsequent recommendations” Updating its knowledge about users’ preferences and adjusting subsequent recommendations means it is retraining the algorithm.}
analyzing training data, wherein the training data comprises contextual data, {(Zhou) Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings and the average challenge embeddings of users’ choice as the input variables and users’ weight-loss status as the prediction target.” User embeddings contain monitoring data. Average challenge embeddings of users’ choice are contextual data.} a value associated with the randomly assigned hyperparameter of the exploration subset of users {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, expected reward function; Section 5.2 Paragraph 3, “Finally, in light of previous theoretical MAB studies, we examine our model performance through a simulated environment. Specifically, we construct a logistic predictor for users’ binary challenge-selection decisions, that is [logistic reward function]” The logistic predictor uses the parameters(hyperparameters) to simulate a reward value.}, and monitoring data for the exploration subset of users {(Zhou) Section 5.7 Paragraph 1, “…user embeddings…”}; and
determining a relationship between the contextual data, the value associated with the at least one randomly assigned hyperparameter of the exploration subset of users, and the monitoring data for the exploration subset of users. {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum parameter(hyperparameter) values based on at least a combination of the user embeddings and the average challenge embeddings of users’ choice}
Regarding dependent claim 13, Zhou, in view of Newnham, teaches the material disclosed in claim 8, and additionally Zhou teaches:
wherein the exploration-exploitation phase is performed using a contextual multi-armed bandit algorithm. {(Zhou) Section 3.1 Paragraph 3, “We formulate the above recommendation problem as a contextual MAB.” MAB stands for multi-armed bandit.}
Regarding dependent claim 14, Zhou, in view of Newnham, teaches the material disclosed in claim 11, and additionally Zhou teaches:
wherein the at least one predicted outcome is determined using a tactic assignment algorithm. {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, expected reward function; Section 5.2 Paragraph 3, “Finally, in light of previous theoretical MAB studies, we examine our model performance through a simulated environment. Specifically, we construct a logistic predictor for users’ binary challenge-selection decisions” A logistic predictor predicts an outcome and is a tactic assignment algorithm.}
Claim(s) 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou, in view of Newnham, in further view of US 20150095271 A1 by Ioannidis et al., hereafter Ioannidis.
Regarding independent claim 15, Zhou teaches:
perform an initial exploration phase by:
randomly assigning at least one hyperparameter to each user of a plurality of users; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Input: prior mean m and prior variance v for each parameter theta, Step 1 Random Draw. Each parameter theta is a parameter(hyperparameter). In the initial iteration, step 1 of the algorithm randomly assigns each hyperparameter as an initial exploration phase.}
perform a training phase by:
analyzing training data, wherein the training data comprises contextual data {(Zhou) Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings and the average challenge embeddings of users’ choice as the input variables and users’ weight-loss status as the prediction target.” Average challenge embeddings of users’ choice are contextual data} and a value associated with the at least one randomly assigned hyperparameter; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, expected reward function; Section 5.2 Paragraph 3, “Finally, in light of previous theoretical MAB studies, we examine our model performance through a simulated environment. Specifically, we construct a logistic predictor for users’ binary challenge-selection decisions, that is [logistic reward function]” The logistic predictor uses the parameters(hyperparameters) to simulate a reward value.} and
determining a relationship {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum parameter(hyperparameter) values based on at least a combination of user embeddings and the average challenge embeddings of users’ choice} between the contextual data {(Zhou) Section 5.7 Paragraph 1} and the value associated with the at least one randomly assigned hyperparameter; {(Zhou) Section 5.2 Paragraph 3} and
determining at least one optimal hyperparameter for each user; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum, which is equivalent to optimal, parameters(hyperparameters) for all users.}
determining, using the at least one optimal hyperparameter, at least one decision support output for each user; {(Zhou) Section 3.2 Paragraph 3, “Our algorithm extends an ordinary TS by integrating a constrained optimization problem to solve for the optimal recommendation decisions subject to the diversity constraint” Optimal recommendation decisions are decision support outputs}
randomly assigning at least one hyperparameter to each user; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “Step 1 (Random Draw)”. The Random Draw draws parameters randomly from a set of parameters to each user which is equivalent to randomly assigning the parameters(hyperparameters)} and
determining, using the at least one randomly assigned hyperparameter, at least one decision support output for each user. {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “For i=1,2,…I do … Offer item k to individual i …” Offering a recommendation item to an individual is equivalent to determining a decision support for that individual user.}
Zhou does not explicitly teach:
A computing device for determining user-specific hyperparameters for decision support models, the computing device comprising:
a network interface;
a processor operatively connected to the network interface;
a memory storing a program comprising instructions that, when executed by the processor, cause the computing device to:
perform an exploration-exploitation phase by:
dividing the plurality of users into an exploration subset of users and an exploitation subset of users;
Newnham does teach:
perform an exploration-exploitation phase by:
dividing the plurality of users into an exploration subset of users and an exploitation subset of users; {(Newnham) Paragraph [0040], “The exploration controller 125 is further configured randomly to assign the advertising request 122 received by the exploration controller 125 into one of three advertising request groups, 1) an exploration group comprising exploration advertising requests, the exploration group usable by the exploration controller 125 to gather a useful set of training data regarding the advertising requests; 2) an exploitation group comprising exploitation advertising requests, the exploitation group usable by the exploration controller 125 to exploit one or more of the optimized bid floor and the optimized shading factor” Assignment to an exploration group or an exploitation group is dividing the users into an exploration subset of users and an exploitation subset of users.}
Newnham and Zhou are analogous art because they are in the same area of invention, that being performing exploration and exploitation learning.
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date, having the teachings of Newnham and Zhou in front of them, to have combined the division of users into an exploration subset of users and an exploitation subset of users, as taught by Newnham, with the determining an optimal parameter(hyperparameter) and a decision support output for each user based on the optimal parameter(hyperparameter), as taught by Zhou, so that only the exploitation subset of users are considered for this part of the algorithm, and the random assignment of a parameter(hyperparameter) and determining a decision support output for each user based on the randomly assigned parameter (hyperparameter), as taught by Zhou, so that only the exploration subset of users are considered for this part of the algorithm. The motivation for this would be to separate the exploration and exploitation members for better comparison of exploration and exploitation reward for the division of users. This application of the division of the users technique taught by Newnham to the method of exploration and exploitation taught by Zhou would yield the predictable result of an improved system.
Zhou, in view of Newnham, still does not teach:
A computing device for determining user-specific hyperparameters for decision support models, the computing device comprising:
a network interface;
a processor operatively connected to the network interface;
a memory storing a program comprising instructions that, when executed by the processor, cause the computing device to:
Ioannidis does teach:
A computing device for determining user-specific hyperparameters for decision support models, the computing device comprising:
a network interface;
a processor operatively connected to the network interface;
a memory storing a program comprising instructions that, when executed by the processor, cause the computing device to: {(Ioannidis) Paragraph [0048], “Processor 220 utilizes program memory instructions to execute a method, such as method 400 of FIG. 4, to interpret received requests and data as well as to and to produce arm/selection data for transmission across the network interface 210.”}
Ioannidis, Newnham, and Zhou are analogous art because they are in the same area of invention, that being performing exploration and exploitation learning.
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date, having the teachings of Ioannidis, Newnham, and Zhou in front of them, to have combined the instructions for the method taught by Zhou, in view of Newnham, with the processor utilizing program memory instructions to execute a method and a network interface. The motivation for this would be to be able to actually run and transmit the results of the method. The structure taught by Ioannidis and the method taught by Zhou, in view of Newnham, when combined, perform the same function as they do separately. Thus, this combination of the method taught by Zhou, in view of Newnham, and the structure to run the method taught by Ioannidis would yield the predictable result that is equivalent to the invention disclosed in claim 15 of the instant application. The Examiner notes that this motivation applies to all dependent claims of this claim.
Regarding dependent claim 16, Zhou, in view of Newnham and Ioannidis, teaches the material disclosed in claim 15 and additionally Zhou teaches:
determining, using the at least one randomly assigned hyperparameter{(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “Step 1 (Random Draw)”}, a decision support output for each user of the plurality of users using a decision support model; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “For i=1,2,…I do … Offer item k to individual i …” The algorithm is a decision support model.}
providing the decision support output to each user of the plurality of users; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, “For i=1,2,…I do … Offer item k to individual i …” Offering a recommendation item to an individual is equivalent to providing a decision support output to a user} and
receiving monitoring data for each user of the plurality of users, wherein the monitoring data provides information to at least one physiological condition. {(Zhou) Section 4.2 Paragraph 2, “The third dataset contains auxiliary information for each user, such as... initial weight when first joining the platform, online weigh-in activities...”; Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings …” Initial weight is information of physiological condition and is included in the auxiliary information for each user and user embeddings which are equivalent to monitoring data.}
Regarding dependent claim 17, Zhou, in view of Newnham and Ioannidis, teaches the material disclosed in claim 16, and additionally teaches:
wherein the training data further comprises the monitoring data, {(Zhou) Section 4.2 Paragraph 2} and the training phase is further performed by determining a relationship {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum parameter(hyperparameter) values based on at least a combination of the user embeddings and the average challenge embeddings of users’ choice} between the monitoring data, the contextual data, and the value associated with the at least one randomly assigned hyperparameter{(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, expected reward function; Section 5.2 Paragraph 3, “Finally, in light of previous theoretical MAB studies, we examine our model performance through a simulated environment. Specifically, we construct a logistic predictor for users’ binary challenge-selection decisions, that is [logistic reward function]” The logistic predictor uses the parameters(hyperparameters) to simulate a reward value}. {(Zhou) Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings and the average challenge embeddings of users’ choice as the input variables and users’ weight-loss status as the prediction target.” User embeddings contain monitoring data. Average challenge embeddings of users’ choice are contextual data.}
Regarding dependent claim 18, Zhou, in view of Newnham and Ioannidis, teaches the material disclosed in claim 15, and additionally teaches:
assigning a plurality of experimental hyperparameters to each user of the exploitation subset; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves assigning the parameters (hyperparameters).}
determining a predicted outcome for each experimental hyperparameter of the plurality of experimental hyperparameters; {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves a logistic expected reward which results in an outcome.}
determining a scalarized outcome for each experimental hyperparameter of the plurality of experimental hyperparameters; and {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. The reward value is logistically scalarized.}
determining the at least one optimal hyperparameter based on the scalarized outcome. {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum, which is equivalent to optimal, parameters(hyperparameters) for all users.}
Regarding dependent claim 19, Zhou, in view of Newnham and Ioannidis, teaches the material disclosed in claim 15, and additionally Zhou teaches:
wherein the exploration-exploitation phase is performed using a contextual multi-armed bandit algorithm. {(Zhou) Section 3.1 Paragraph 3, “We formulate the above recommendation problem as a contextual MAB.” MAB stands for multi-armed bandit.}
Regarding dependent claim XX, Zhou, in view of Newnham and Ioannidis, teaches the material disclosed in claim XY, and additionally Zhou teaches:
perform a retraining phase by: {(Zhou) Section 3.1 Paragraph 2, “At the end of each period, the platform receives users’ feedback on the recommendations, that is, whether they have adopted or engaged in the recommended interventions… Users’ feedback serves as a reward for the platform’s recommendation decisions; the platform may use the information of users’ feedback to update its knowledge about users’ preferences and adjust its subsequent recommendations” Updating its knowledge about users’ preferences and adjusting subsequent recommendations means it is retraining the algorithm.}
analyzing training data, wherein the training data comprises contextual data, {(Zhou) Section 5.7 Paragraph 1, “First, we use the logistic predictor described in Section 5.2 to simulate whether users will choose a particular item.…We use users’ weigh-in data to train a logistic predictor for this simulation, which takes user embeddings and the average challenge embeddings of users’ choice as the input variables and users’ weight-loss status as the prediction target.” User embeddings contain monitoring data. Average challenge embeddings of users’ choice are contextual data.} a value associated with the randomly assigned hyperparameter of the exploration subset of users {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, expected reward function; Section 5.2 Paragraph 3, “Finally, in light of previous theoretical MAB studies, we examine our model performance through a simulated environment. Specifically, we construct a logistic predictor for users’ binary challenge-selection decisions, that is [logistic reward function]” The logistic predictor uses the parameters(hyperparameters) to simulate a reward value.}, and monitoring data for the exploration subset of users {(Zhou) Section 5.7 Paragraph 1, “…user embeddings…”}; and
determining at least one predicted outcome for each user of the exploration subset of users {(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves a logistic expected reward which results in an outcome.} based on a relationship between the contextual data, the value associated with the at least one randomly assigned hyperparameter of the exploration subset of users, and the monitoring data for the exploration subset of users.{(Zhou) Section 3.2 A TS Algorithm with Diversity Constraint, Update the posterior mean. Updating the posterior mean involves finding minimum parameter(hyperparameter) values based on at least a combination of the user embeddings and the average challenge embeddings of users’ choice which is a relationship between them. }
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Patents and/or related publications are cited in the Notice of References Cited (Form PTO-892) attached to this action to further show the state of the art with respect to physiological conditions, contextual multi-armed bandits, and exploration and exploitation learning.
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D. H. L.
Examiner
Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
Prosecution Insights