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 .
Status of Claims
In the response filed 04/27/2026, the following occurred: Claims 1 and 3 were amended.
Claims 1-6 are pending and have been examined.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-6: Step 1
Claims 1-5 are drawn to a method for distinguishing among similarly-presenting lung diseases (i.e. process). Claim 6 is drawn to a system for classifying among a defined set of similarly-presenting diseases (i.e. machine).
Claims 1-6: Step 2A Prong One
Claim 1 recites a method for distinguishing among similarly-presenting lung diseases, comprising: obtaining a preliminary diagnosis of a category of similarly-presenting potential lung diseases; obtaining a first data set corresponding to protein counts found in a blood sample from a patient; obtaining a second data set corresponding to additional data regarding the patient; providing an indication of the preliminary diagnosis, the first data set, and the second data set to a trained model; determining a predicted differential diagnosis of a given lung disease of the category of similarly-presenting potential lung diseases, based upon an output of the trained model; outputting a recommended treatment using the predicted differential diagnosis; and obtaining confirmation of the predicted differential diagnosis and the recommended treatment. Claim 6 recites similar limitations.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior by manually following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. But for the recitation of generic computer components, these limitations encompass a user obtaining a preliminary diagnosis of a category of similarly-presenting potential lung diseases; obtaining a first data set corresponding to protein counts found in a blood sample from a patient; obtaining a second data set corresponding to additional data regarding the patient; providing an indication of the preliminary diagnosis, the first data set, and the second data set to a trained model; determining a predicted differential diagnosis of a given lung disease of the category of similarly-presenting potential lung diseases, based upon an output of the trained model; outputting a recommended treatment using the predicted differential diagnosis; and obtaining confirmation of the predicted differential diagnosis and the recommended treatment. These steps could be carried out manually by a user following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. Claim 6 recites similar limitations.
Claims 2-5 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, but for the recitation of generic computer components, Claim 2 further defines the second data set. Claim 3 further defines entering a background monitoring state. Claim 4 further defines a new data set. Claim 5 further defines obtaining set of disease state classes, training dataset of patient records, determining features in the training dataset that are relevant to differential diagnoses and steps to reduce training dataset. Therefore, these claims are similarly drawn to Certain Methods of Organizing Human Activity.
Claims 1-6: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with insignificant, extra-solution data gathering activity, and adding limitations similar to adding the words “apply it” to the abstract idea. Claim 1 recites the additional elements that the computer-implemented method steps are performed by at least one processor. Claim 6 recites additional elements of a computer-implemented system comprising at least one processor.
Claims 1-6, directly or indirectly, recite the following generic computer components: “electronic processor,” and a “non-transitory computer-readable medium” which are similar to adding the words “apply it” to the abstract idea. The written description discloses that the recited computer components encompass generic components including “The system may include an electronic processor and a non-transitory computer-readable medium storing machine-executable instructions” (see at least Paragraph [0013]) and “the computing device 310 can be a device, network, or other resource that includes an integrated circuit (IC) or processor for computation, such as a server, cloud resource, or any suitable computing resource“ (see at least Paragraph [0048]). Although the additional element “machine learning model” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning), and thus fails to add an inventive concept to the claims. See MPEP 2106.05 (h). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-6: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements (for example, machine learning) are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). As explained above, the generic computer components and machine learning are at best the equivalent of merely adding the words “apply it” to the judicial exception.
Receiving and transmitting data over a network (i.e. receiving and communicating data or signals) has been recognized as well-understood, routine, and conventional activity of a general-purpose computer (see MPEP 2106.05(d) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
Gathering and analyzing information using conventional techniques and displaying the result has also been found to be insufficient to show an improvement to technology, (see MPEP 2106.05(a) and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48).
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). Therefore, the high-level recitation of an output of results also fails to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Response To Arguments
Applicant’s arguments from the response filed on 04/27/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appeared.
In the remarks, Applicant asserts that (1) Claim 1, as amended recites ”providing by the at least one processor, an indication of the primary diagnosis, the first data set, and the second data set to a trained machine learning model” and “determining, by the at least one processor, a predicted differential diagnosis of a given lung disease of the category of similarly-presenting potential lung diseases, based upon an output of the trained machine learning model”. Applicant asserts that these steps require computation processing of protein count data through a trained machine learning model, which is an operation that cannot be practically performed manually by a human. Therefore, the pending claims do not merely “falls within the Certain Methods of Organizing Human Activity group of abstract ideas”. Even assuming the claims recite an abstract idea, the claims integrate any such idea into a practical application by providing a specific technological improvement in medical diagnostics.
In response to applicant’s arguments (1) as listed above, the examiner respectfully disagrees. The claim does not provide any details about how the trained machine learning model operates and how the output is generated by the trained machine learning model, merely that the trained machine learning model produces an output. In addition, “computation processing of protein count data through the trained machine learning model” is not recited in the claim limitations. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. While the disclosure states that “the capability to provide … aid in healthcare providers’ efforts to differentiate IPF-type disorders and CTD-ILD-type disorders for specific patients”, there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement in the abstract idea of differentiating certain disorders for specific patients. As such, Applicant’s arguments have been considered but are not found to be persuasive.
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joy Chng whose telephone number is 571.270.7897. The examiner can normally be reached on Monday-Thursday and every other Friday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, JASON DUNHAM can be reached on 571.272.8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Joy Chng/
Primary Examiner, Art Unit 3686
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