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 Objections
Claims 8 and 16 are objected to because of the following informalities: in claim 8 the word “receiving” on line 9 should be replaced by the word “receive” for grammatical purposes; in claim 16, the word “to” should be inserted prior to the word “train” on line 3. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) an abstract idea including the mentally performable actions of encoding text and audio data as respective text vectors and audio vectors; calculating a distance between the vectors; and determining diagnosis results based on the calculated distance between the text and audio vectors. These aspects require observations, evaluations, judgements and opinions that can be performed within the human mind and/or with the aid of pen and paper. Encoding data into vectors and calculating vector distances represent algorithms and linear algebraic operations that can be performed by humans/mathematicians, with diagnosis of patient condition representing a physician’s judgement and opinion based on observation and evaluation.
This judicial exception is not integrated into a practical application because there are no improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a); there is no application or use of
a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo; there is no application of the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b); there is no transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and there is no application or use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to the particular technological environment of cardiac auscultation diagnosis, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use of a generic processor (and generic computer-readable storage media such as recited in claims 8 and 15) functions in the usual capacity as a tool upon which the abstract idea is run (and stored). The functioning of the computer or technical field is not improved. The disclosed invention relies upon known machine learning techniques (e.g., unsupervised, contrastive learning known to be useful in not requiring labeling of data) and employs the techniques in the usual manner. The additional element of receiving text and audio data represents insignificant data gathering activity required to execute the abstract idea. Such data would be required in any implementation of a system attempting to diagnose based on heart sounds, where physicians desiring to make an informed diagnosis, would require not only the audio data associated with the individual’s heart, but also knowledge of the patient’s medical records and medical history as is standard in any medical examination (e.g., standard patient pre-examination questionnaires covering medical history, as well as any issues the patient is currently experiencing, demographics such as age, sex, etc.). The reference to how the diagnosis is determined (i.e., by a machine learning model) merely indicates the field-of-use in much the same way the phrase “by use of a generic computer” does. Additionally, the combination of a processor and data collection would be required in any computerized implementation of a diagnostic technique.
The use of a processor and the data receiving step alone and in combination is further WURC in the medical diagnostics art (along with the use of one or more computer-readable storage media devices as in the case of claims 8 and 15). The applicant discloses that the computer/processor may take any form now known or to be developed in the future that is capable of running a program (pars. 0014, 0015, 0051, 0054, 0055, etc.). Related comments are made with respect to the computer-readable media as well (see pars. 0016, 0018). It is noted that the broadest reasonable interpretation of “receiving text data and audio data” may include data that has already been obtained and stored on a storage media for later access, and does not necessarily invoke any particular sensor structure. In any event, the collection of audio data by stethoscopes is traditional (see par. 0002) as is the storage of patient medical records in text form.
Claims 2-4, 10, 11 and 16-18 do not contain any additional elements beyond those already discussed above.
Regarding claims 5, 6, 12, 13, 19 and 20, reference to how the text or audio is encoded represents a tangential or nominal field-of-use limitation. The applicant states that a variety of generic text encoders may be used (par. 0035), with a variety of architectures utilized to encode the audio data (par. 0036). The encoders function in their usual capacity to encode and are thus insignificant. The function of encoding is further considered mentally performable. The recited encoding architectures are further WURC in the machine learning art. The applicant does not discuss any unconventional architecture and merely lists the options in an exemplary fashion.
Regarding claims 7 and 14, the additional element of transmitting the diagnosis results to a user represents insignificant post-solution activity in the form of routine data outputting. Any diagnostic invention would require such a feature in order to convey the results of the analysis in human perceivable form.
Related comments to those made above with regards to claim 1 apply equally to patentably indistinct claim 8, where the various codes for encoding, calculating and determining represent features that are mentally performable.
Regarding claim 9, the reference to a generic code for training a machine learning model represents an insignificant field-of-use limitation attempting to nominally associate the invention to the field of machine learning in the same manner as saying, “apply the abstract idea on a computer.” Training machine learning models based on input data is further WURC in the machine learning arts as training is a basic goal of any machine learning system.
Regarding patentably indistinct claim 15, note the comments made above for substantially similar limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kim ‘843 discloses software and a system that takes electrical biosignals and turns them into compact numerical vectors. The resulting vector can be used on its own or combined with other patient data like age, gender, vitals, lab results, or text-derived data. A downstream model then uses that information to predict disease, diagnose conditions, or provide clinical support. Yuan ‘033 discloses a related abstract idea wherein the invention moves a first stage of feature extraction onto the local terminal device, so only compact feature vectors are sent to the server for second-stage labeling and recommendation, in order to reduce the amount of transmitted data while still allowing the server to perform deeper analysis. A server then applies a classification model to label each vector and produce a diagnosis or treatment plan.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNEDY SCHAETZLE whose telephone number is (571)272-4954. The examiner can normally be reached 2nd Monday of the biweek and W-F.
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/KENNEDY SCHAETZLE/Primary Examiner, Art Unit 3796
KJS
September 19, 2026
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