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 .
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites:
- "transforming, by the computing system, the sequence of events into a sequence of tokens optimized for consumption by a sequence-to-sequence machine learning model, each token reconstitutable into its corresponding event representation";
- "inputting, by the computing system, the sequence of tokens into a trained sequence-to-sequence machine learning model comprising a Transformer architecture trained on historical event sequences of a plurality of patients";
- "receiving, as an output of the trained machine learning model, a sequence of predicted tokens corresponding to future events associated with the patient"; and
- "reconstituting, by the computing system, the sequence of predicted tokens into a sequence of predicted events associated with the patient, the sequence of predicted events including at least one event that is predicted to occur in the future".
First, while Applicant’s specification provides general support for including “tokens” with the sequence of events or representing the events with “tokens” (where the term token is interpreted to be the equivalent of some descriptive term that may accompany the patient’s event data, but does not have a well-defined definition) [see Applicant’s specification as filed on February 24, 2025, paragraphs [0046] and [0047]], Applicant’s specification is silent as to: (1) transforming the sequence of events into a sequence of tokens, where each token is reconstitutable into its corresponding event representation; (2) inputting the sequence of tokens into a trained sequence-to-sequence machine learning model; (3) receiving, as an output of the trained machine learning model, a sequence of predicted tokens corresponding to future events associated with the patient; and (4) reconstituting the sequence of predicted tokens into a sequence of predicted events associated with the patient. Further, Applicant’s specification does not disclose (i) transforming any type of data from one format into a different format, nor does it disclose (ii) receiving a sequence of predicted tokens, or (iii) transforming that data back into its original format after some processing/predictions are performed using that data (i.e., the specification does not describe transforming/reconstituting a sequence of predicted tokens into a sequence of predicted events). Therefore, this disclosure does not provide written description support for (1) “transforming, by the computing system, the sequence of events into a sequence of tokens optimized for consumption by a sequence-to-sequence machine learning model, each token reconstitutable into its corresponding event representation”; (2) “inputting, by the computing system, the sequence of tokens into a trained sequence-to-sequence machine learning model comprising a Transformer architecture trained on historical event sequences of a plurality of patients”; “(3) receiving, as an output of the trained machine learning model, a sequence of predicted tokens corresponding to future events associated with the patient”; and (4) “reconstituting, by the computing system, the sequence of predicted tokens into a sequence of predicted events associated with the patient, the sequence of predicted events including at least one event that is predicted to occur in the future”.
Matter not present on the filing date of the application in the specification, claims, or drawings that is added after the application filing is usually new matter. See MPEP §§ 2163.06 and 2163.07. These amendments (i) change the scope of the claims, and as described above, and (ii) contain subject matter which was not described in the specification in a way as to reasonably convey to one skilled in the relevant art that the inventors had possession of the claimed invention. Therefore, the limitations directed to: (1) “transforming, by the computing system, the sequence of events into a sequence of tokens optimized for consumption by a sequence-to-sequence machine learning model, each token reconstitutable into its corresponding event representation”; (2) “inputting, by the computing system, the sequence of tokens into a trained sequence-to-sequence machine learning model comprising a Transformer architecture trained on historical event sequences of a plurality of patients”; “(3) receiving, as an output of the trained machine learning model, a sequence of predicted tokens corresponding to future events associated with the patient”; and (4) “reconstituting, by the computing system, the sequence of predicted tokens into a sequence of predicted events associated with the patient, the sequence of predicted events including at least one event that is predicted to occur in the future”, are deemed to be new matter, because the specification does not provide written description support for them. As such, claim 1 is rejected for failing to comply with the written description requirement under 35 U.S.C. § 112(a).
Examiner further notes that the steps of: (1) “transforming, by the computing system, the sequence of events into a sequence of tokens optimized for consumption by a sequence-to-sequence machine learning model, each token reconstitutable into its corresponding event representation”; and (2) “reconstituting, by the computing system, the sequence of predicted tokens into a sequence of predicted events associated with the patient, the sequence of predicted events including at least one event that is predicted to occur in the future” (as described in claim 1), are not described by the present Specification in sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed, e.g. see MPEP § 2161.01. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement, e.g. see MPEP § 2161.01. For example, the present claim language discloses transforming the sequence of events into a sequence of tokens, but the specification does not disclose the steps or algorithm (e.g. the necessary steps and/or flowcharts) that explains how the system is able to transform the sequence of events into another form of data (i.e., the sequence of tokens). Further, the specification does not disclose the necessary steps and/or flowcharts that explain how the system is able to reconstitute (transform back) the sequence of predicted tokens into a sequence of predicted events (i.e., what steps does the machine learning model go through in order to convert a sequence of predicted tokens into a sequence of predicted events?). Therefore, Applicant’s specification lacks adequate written description for these computer-implemented functional claim limitations. See MPEP § 2161.01(I).
Claims 2-20 (which individually depend on claim 1) are also rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, for failing to comply with the written description requirement for similar reasons as described in the § 112(a) lack of written description rejections of claim 1 above.
Notice to Applicant Regarding Patent Eligibility under 35 U.S.C. § 101
The inventive concept in the claimed invention is similar to the claims in the related Patent Application Serial No. 18/482,409, filed on October 10, 2024 (now abandoned). The claimed invention was analyzed under § 101 and is deemed to be eligible, because the claims integrate any abstract idea into a practical application. Namely, while the present invention may be interpreted as being directed toward an abstract idea in the Mental Processes category which, under its broadest reasonable interpretation, covers concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, or opinion) (i.e., a method of providing medical data to predict patient events, comprising: accessing patient data comprising a sequence of events associated with a patient; transforming the sequence of events into a sequence of tokens; and receiving a sequence of predicted tokens corresponding to future events associated with the patient), the additional elements are deemed to include other meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP §§ 2106.05(e) (citing Classen Immunotherapies Inc. v Biogen IDEC).
Similar to the claim limitations in the Classen Immunotherapies Inc. v Biogen IDEC case (see MPEP § 2106.05(e)) – where the claim limitations were directed to a method of analyzing immunization schedules on the later development of chronic immune-mediated disorders in mammals in order to identify a lower risk immunization schedule), Applicant’s disclosure provides support for its claims as being directed to a specific and tangible method that utilizes a trained sequence-to-sequence machine learning model to provide a sequence of predicted tokens corresponding to future events associated with the patient. See Applicant’s specification as filed on February 24, 2025, paragraph [0070]. Further, Examiner notes the additional elements recited in claim 1 directed to “reconstituting the sequence of predicted tokens into a sequence of predicted events associated with the patient”, as providing other meaning limitations beyond generally linking the use of the abstract idea to a particular technological environment. All of these features are directly associated with the specific and tangible method that provides the indication of the predicted event to the user interface associated with the healthcare provider, which Applicant discloses as helping to improve patient care and provide more accurate health predictions. See Applicant’s specification as filed on February 24, 2025, paragraph [0003]. Therefore, Applicant’s claimed invention is deemed to be eligible under § 101, because the claimed invention is deemed to integrate any abstract idea into a practical application.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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/N.A.A./Examiner, Art Unit 3686
/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686