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 .
DETAILED ACTION
This action is response to the communication filed on July 28, 2023. Claims 1-20 are pending.
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. Regarding the claim 1, it recites training a plurality of machine learning models to predict a weight change, the plurality of machine learning models including a first model that generates an initial prediction and a second model that generates a final prediction based on the initial prediction, wherein the plurality of machine learning models is trained based on a collection of historical genetic information, historical activity information, and historical weight information about a plurality of users; receiving genetic information and activity information about a new user; and invoking the plurality of machine learning models to predict a weight change of the new user based on the training, the genetic information, and the activity information.
The claim recited the limitation of “training a plurality of machine learning models to predict a weight change, the plurality of machine learning models including a first model that generates an initial prediction and a second model that generates a final prediction based on the initial prediction, wherein the plurality of machine learning models is trained based on a collection of historical genetic information, historical activity information, and historical weight information about a plurality of users” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. User can train can train their brain based on given information to provide prediction. Therefore, the training limitation is a mental process.
The claim recites two additional elements: receiving genetic information and activity information about a new user; and invoking the plurality of machine learning models to predict a weight change of the new user based on the training, the genetic information, and the activity information. The receiving step as recited amounts to mere data gathering, which is a form of insignificant extra-solution activity, (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Similarly, the invoking step as recited is nothing but data processing which is an insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving and invoking steps amounts to no more than mere instructions to apply the exception using a generic computer component. The courts have recognized these functions as well‐understood, routine, and conventional as they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the plurality of machine learning models comprises a first group of two or more models, including the first model, that generates two or more initial predictions utilized by the second model, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the plurality of machine learning models utilize ensemble learning to predict the weight change, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein each model of the plurality of machine learning models differs by at least one hyperparameter, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of predicting weight change. The claim recites the limitations of determining a portion of the activity information that is a dominant contributor to the weight change; and generating a recommendation based on the determined portion, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 6 is dependent on claim 5 and includes all the limitations of claim 5. Therefore, claim 6 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the recommendation is to increase or decrease consumption of a nutrient for a specified duration., which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the activity information includes a dietary consumption log of the new user, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the activity information includes an exercise log of the new user, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 9 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 9 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the weight change is predicted based on a weekly interval of the activity information, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 10 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the plurality of machine learning models predicts the weight change based on a subset of genetic markers in a Deoxyribonucleic acid (DNA) sequence of the new user, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 11 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 11 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the collection is from the plurality of users over multiple years, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
As to claims 12-13, 17-18, they have similar limitations as of claims 1-2 above. Hence, they are rejected under the same rational as of claims 1-2 above.
Claim 14 is dependent on claim 12 and includes all the limitations of claim 12. Therefore, claim 14 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the processor is further configured to execute instructions stored in the memory to: determine a food item that is a dominant contributor to the weight change; and generate a recommendation based on the food item or nutrient, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 15 is dependent on claim 12 and includes all the limitations of claim 12. Therefore, claim 15 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the processor is further configured to execute instructions stored in the memory to: determine an exercise that is a dominant contributor to the weight change; and generate a recommendation based on the exercise, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 16 is dependent on claim 12 and includes all the limitations of claim 12. Therefore, claim 16 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the weight change is predicted based on a monthly interval of the activity information wherein the activity information includes an exercise log of the new user, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 19 is dependent on claim 17 and includes all the limitations of claim 17. Therefore, claim 19 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein each model of the plurality of machine learning models is implemented by a neural network, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 20 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of predicting weight change. The claim recites the limitations of wherein the activity information is a combination of dietary consumption information and exercise information, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Narayanan et al. (Pub. No. : US 20160081620 A1)
As to claim 1 Narayanan teaches a method, comprising:
training a plurality of machine learning models to predict a weight change, the plurality of machine learning models including a first model that generates an initial prediction and a second model that generates a final prediction based on the initial prediction, wherein the plurality of machine learning models is trained based on a collection of historical genetic information, historical activity information, and historical weight information about a plurality of users (paragraphs [0009], [0011], [0019], [0083], [0093], [0116], [0122]: the method and apparatus may provide personalized models for predicting trends in weight change management, wherein the activity model activity models may include at least one selected from the group consisting of a cardio activity, a non-cardio activity, standing, sitting, walking, climbing/descending stairs, hiking, jogging, sprinting, cycling, a treadmill exercise, and driving including a gender, an age, a height, a weight, and a body mass index (BMI) and a prediction model predict a trend in weight change management of a user);
receiving genetic information and activity information about a new user (paragraphs [0083], [0019], [0137]: receive from the input device 330 user profile information that includes information about at least one selected from the group consisting of a gender, an age, a height, a weight, and a body mass index (BMI) and the activity models may include at least one selected from the group consisting of a cardio activity, a non-cardio activity, standing, sitting, walking, climbing/descending stairs, hiking, jogging, sprinting, cycling, a treadmill exercise, and driving); and
invoking the plurality of machine learning models to predict a weight change of the new user based on the training, the genetic information, and the activity information (paragraphs [0083], [0019], [0116], [0121]-[0122]: a personalized prediction model so as to predict a trend in weight change management of a user).
As to claim 2 Narayanan teaches wherein the plurality of machine learning models comprises a first group of two or more models, including the first model, that generates two or more initial predictions utilized by the second model (paragraphs [0009], [0019], [0093]).
As to claim 3 Narayanan teaches wherein the plurality of machine learning models utilize ensemble learning to predict the weight change (paragraphs [0009], [0019], [0093], [0137]).
As to claim 4 Narayanan teaches wherein each model of the plurality of machine learning models differs by at least one hyperparameter (paragraph [0017], [0031], [0132]).
As to claim 5 Narayanan teaches determining a portion of the activity information that is a dominant contributor to the weight change; and generating a recommendation based on the determined portion (paragraph [0019], [0079]).
As to claim 6 Narayanan teaches wherein the recommendation is to increase or decrease consumption of a nutrient for a specified duration (paragraph [0015]-[0016], [0122]).
As to claim 7 Narayanan teaches wherein the activity information includes a dietary consumption log of the new user (paragraph [0030], [0075]).
As to claim 8 Narayanan teaches wherein the activity information includes an exercise log of the new user (paragraph [0130]).
As to claim 9 Narayanan teaches wherein the weight change is predicted based on a weekly interval of the activity information (paragraphs [0016], [0079]).
As to claim 10 Narayanan teaches wherein the plurality of machine learning models predicts the weight change based on a subset of genetic markers in a Deoxyribonucleic acid (DNA) sequence of the new user (paragraphs [0083]-[0084]).
As to claim 11 Narayanan teaches wherein the collection is from the plurality of users over multiple years (paragraphs [0016], [0079], [0120]).
As to claims 12-13, 17-18, they have similar limitations as of claims 1-2 above. Hence, they are rejected under the same rational as of claims 1-2 above.
As to claim 14 Narayanan teaches wherein the processor is further configured to execute instructions stored in the memory to: determine a food item that is a dominant contributor to the weight change and generate a recommendation based on the food item or nutrient (paragraph [0030]).
As to claim 15 Narayanan teaches wherein the processor is further configured to execute instructions stored in the memory to: determine an exercise that is a dominant contributor to the weight change; and generate a recommendation based on the exercise (paragraphs [0015]-[0016]).
As to claim 16 Narayanan teaches wherein the weight change is predicted based on a monthly interval of the activity information (paragraphs [0016], [0079]).
As to claim 19 Narayanan teaches wherein each model of the plurality of machine learning models is implemented by a neural network (paragraph [0119]).
As to claim 20 Narayanan teaches wherein the activity information is a combination of dietary consumption information and exercise information (paragraphs [0030], [0019]).
Examiner's Note: Examiner has cited particular columns and line numbers or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in its entirety as potentially teaching of all or part of the claimed invention, as well as the context.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MD I UDDIN whose telephone number is (571)270-3559. The examiner can normally be reached M-F, 8:00 am to 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MD I UDDIN/Primary Examiner, Art Unit 2169