DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 8/7/23 has/have been acknowledged and is/are being considered by the Examiner.
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.
Claim(s) 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1- Claim 1
Claim 1 and dependent claims 2-7 are drawn to a method and thus meet the requirements for step 1.
Step 2a (prong 1) - Claim 1
Claims 1 recites the step of “generating a final prediction result” Under the broadest reasonable interpretation, this step covers a concept capable of being performed in the human mind, and thus falls within the mental processes grouping of abstract ideas. Other than reciting the method is “computer-implemented” in the preamble, nothing in the claim precludes the step from practically being performed in the mind.
Accordingly, claim 1 recites an abstract idea.
Step 2a (prong 2) – Claim 1
The judicial exception is not integrated into a practical application. Claim 1 recites the additional elements of:
Generating an integrated feature vector is insignificant extra-solution activity (i.e., data gathering),
Generating a dynamic feature vector is insignificant extra-solution activity (i.e., data gathering), and
Generating a final prediction result is recited at a high level of generality (i.e., as generic devices, a “computer-implemented” method, performing generic computer functions like sending, receiving, and visually displaying data) is insignificant extra-solution activity (i.e., data output).
These steps do not integrate the abstract idea into a practical application because they are insignificant extra solution activity.
Step 2b- Claim 1
The additional elements when considered individually and in combination are not enough to qualify as significantly more than the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, generating a final prediction result is recited at a high level of generality (i.e., as generic devices, a “computer-implemented” method, performing generic computer functions like sending, receiving, and visually displaying data). Further, It is noted that using neural networks is recited at a high level of generality.
The additional elements that were considered insignificant extra solution activity have been re-analyzed and do not amount to anything more than what is well-understood, routine and conventional when considered individually and in combination with evidence provided. Specifically:
Generating an integrated feature vector … using an artificial neural network is well understood, routine, and conventional (i.e., receiving data MPEP 2106.05(d)(II)).
Generating a dynamic feature vector using an artificial neural network is well-understood routine and conventional (i.e., gathering data/statistics MPEP 2106.05(d)(II)).
Generating a final prediction result is considered to be well-understood, routine, and conventional (i.e., presenting data MPEP 2106.05(d)(II)).
Claim 1 is thus consider to be directed to an abstract idea without significantly more.
Claims 2-7 depend from claim 1. The type of data analyzed as stated in claims 2-7 are considered extra solution activity. Thus, the dependent claim do not change the overall analysis that claims 2-7 are also directed to an abstract idea.
Claim 8
Independent claim 8 contains limitations similar to claim 1 and are similarly rejected as patent ineligible subject matter based on the same reasoning as above.
Claim 9
Independent claim 9 is directed to a system containing limitations similar to that for claim 1 and further includes a processor with memory. Analyzing the processor and memory of claim 9 under step 2a, prong 1, the processor and memory are recited at a high level of generality and merely use the computer elements (the processor and memory) as a tool. When analyzed under step 2a, prong 2, the processor and memory perform generic computer functions like storing and processing data. Further, when the analysis is extended to step 2b, the processor and memory are considered to use the computer elements as tools, MPEP 2106.05(d)(II). Thus, claim 9 is considered to be patent ineligible subject matter.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-2 and 7-9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hadley et al. (U.S. Pub. 2022/0061710).
Regarding claim 1, Hadley discloses a method performed by a computing device to generate a prediction result by using static data and dynamic data (e.g. Abstract), the method comprising: generating an integrated feature vector from static data and dynamic data of input data by using an artificial neural network model (825; ¶¶108, 114; [static data]); generating a dynamic feature vector from the dynamic data of the input data by using the artificial neural network model (830; ¶¶119-121; [dynamic data]); and generating a final prediction result of the artificial neural network model based on the integrated feature vector and the dynamic feature vector (e.g. 866; ¶¶126-127).
Regarding claim 2, Hadley further discloses wherein the dynamic data includes time-series biometric data (e.g. ¶108; [step counts, time based interactive feature]), and wherein the static data includes information related to a patient other than the time-series biometric data (e.g. ¶108; [static lab test data]).
Regarding claim 7, Hadley further discloses wherein the generating of the dynamic feature vector from the dynamic data of the input data by using the artificial neural network model includes: preprocessing the dynamic data of the input data (e.g. ¶100; [cleaned and filtered]); and generating a dynamic feature vector based on the preprocessed dynamic data (830; ¶¶119-121).
Regarding claim 8, Hadley discloses a computer program stored in a non-transitory computer-readable storage medium, wherein the computer program causes at least one processor (e.g. 205) to perform operations of generating a prediction result by using static data and dynamic data (e.g. Abstract), and the operations include: an operation of generating an integrated feature vector from static data and dynamic data of input data by using an artificial neural network model (825; ¶¶108, 114); an operation of generating a dynamic feature vector from the dynamic data of the input data by using the artificial neural network model (830; ¶¶119-121); and an operation of generating a final prediction result of the artificial neural network model based on the integrated feature vector and the dynamic feature vector (e.g. 866; ¶¶126-127).
Regarding claim 9, Hadley discloses a computing device comprising: at least one processor (e.g. 205); and a memory (e.g. 215) coupled to the at least one processor (e.g. Fig. 2), wherein the at least one processor is configured to: generate an integrated feature vector from static data and dynamic data of input data by using an artificial neural network model (825; ¶¶108, 114); generate a dynamic feature vector from the dynamic data of the input data by using the artificial neural network model (830; ¶¶119-121); and generate a final prediction result of the artificial neural network model based on the integrated feature vector and the dynamic feature vector (e.g. 866; ¶¶126-127).
Conclusion
It is noted that claims 3-6 are rejected under 35 USC 101 only.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Basu et al. (U.S. Pub. 2018/0203978) – teaches using neural networks to predict outcomes using both static and dynamic data (e.g. ¶77).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REX R HOLMES whose telephone number is (571)272-8827. The examiner can normally be reached Monday-Thursday 7:00AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer McDonald can be reached at (571) 270-3061. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REX R HOLMES/Primary Examiner, Art Unit 3796
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