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
This action is in reply to the application filed on 12/30/2024.
Claims 1-6 are currently pending and have been examined.
Claim Rejections – 35 § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, recites in part, “obtaining a preliminary diagnosis of a category of similarly-presenting potential lung diseases”. It is unclear who or what is obtaining a preliminary diagnosis of a category of similarly-presenting potential lung diseases. Claim 6 recites similar limitations. Claims 1 and 6 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-5 are rejected for at least the same reason.
Claim 1, recites in part, “obtaining a first data set corresponding to protein counts found in a blood sample from a patient”. It is unclear who or what is obtaining a first data set corresponding to protein counts found in a blood sample from a patient. Claim 6 recites similar limitations. Claims 1 and 6 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-5 are rejected for at least the same reason.
Claim 1, recites in part, “obtaining a second data set corresponding to additional data regarding the patient”. It is unclear who or what is obtaining a second data set corresponding to additional data regarding the patient. Claim 6 recites similar limitations. Claims 1 and 6 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-5 are rejected for at least the same reason.
Claim 1, recites in part, “providing an indication of the preliminary diagnosis, the first data set, and the second data set to a trained machine learning model”. It is unclear who or what is providing an indication of the preliminary diagnosis, the first data set, and the second data set to a trained machine learning model. Claim 6 recites similar limitations. Claims 1 and 6 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-5 are rejected for at least the same reason.
Claim 1, recites in part, “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 machine learning model”. It is unclear who or what is 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 machine learning model. Claim 6 recites similar limitations. Claims 1 and 6 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-5 are rejected for at least the same reason.
Claim 1, recites in part, “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 machine learning model”. It is unclear who or what is 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 machine learning model. Claim 6 recites similar limitations. Claims 1 and 6 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-5 are rejected for at least the same reason.
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.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 and 3-6 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Exarchos et al. (Recent Advances of Artificial Intelligence Applications In Interstitial Lung Diseases).
Claim 1:
obtaining a preliminary diagnosis of a category of similarly-presenting potential lung diseases (see at least Page 5, The database contains high resolution CT scans with annotated regions of pathological lung areas coupled with clinical parameters from patients with pathologically proven diagnoses of ILDs. Overall, the library contains data from 128 patients affected with 1 of the 13 ILD diseases. Applications of AI in ILD research can be roughly summarized in the following broad categories: (i) screening, (ii) diagnosis and classification, (iii) prognosis. The second category, which is the most populated one, contains algorithms that diagnose ILDs …, and some of them further aim to differentiate among various entities );
obtaining a first data set corresponding to protein counts found in a blood sample from a patient (see at least Table 1, Type of Data includes Proteins; Page 7, Axelsson et al. [16], who aimed to identify proteins in circulating blood that are associated with ILAs);
obtaining a second data set corresponding to additional data regarding the patient (see at least Page 3, some impressive examples have been reported in the healthcare industry as well, especially with regard to computer vision tasks, at which DL algorithms are profoundly adept. Such examples include skin lesions [3], Figure 3. Hierarchy of artificial intelligence, machine learning and deep learning algorithms. endoscopic images [4], histopathologic images [5] and radiology images. The latter type of data, especially CT scans, constitutes an integral part in the pursuit of ILDs);
providing an indication of the preliminary diagnosis, the first data set, and the second data set to a trained machine learning model (see at least Fig. 2; The best performing algorithm resulted in 92.46% accuracy, which is significantly higher compared to other machine learning algorithms (e.g., support vector machines, artificial neural networks, random forests, k-nearest neighbors) used in the same study);
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 machine learning model (see at least Page 5, The database contains high resolution CT scans with annotated regions of pathological lung areas coupled with clinical parameters from patients with pathologically proven diagnoses of ILDs. Overall, the library contains data from 128 patients affected with 1 of the 13 ILD diseases. Applications of AI in ILD research can be roughly summarized in the following broad categories: (i) screening, (ii) diagnosis and classification, (iii) prognosis. The second category, which is the most populated one, contains algorithms that diagnose ILDs …, and some of them further aim to differentiate among various entities);
outputting a recommended treatment using the predicted differential diagnosis (see at least Page 4, The majority is focused on the analysis of imaging modalities; nevertheless, other biomarkers, e.g., volatile organic compounds, gene expression, etc., are also being exploited in all aspects of ILD research: from screening to diagnosis and overall prognosis as well as treatment, aiming for more personalized and effective strategies); and
obtaining confirmation of the predicted differential diagnosis and the recommended treatment (see at least Page 3, AI does not substitute medical personnel, but rather works in a decision-support manner, thus facilitating trivial tasks so that doctors can focus more freely and effectively on the patient).
Claim 6 recites substantially similar system limitations to those of method claim 1 and, as such, are rejected for similar reasons as given above.
Claim 3:
Exarchos discloses the limitations as shown in the rejections above. Exarchos further discloses the following limitations:
further comprising entering a background monitoring state, the background monitoring state comprising (see at least Page 5, The objective of this resource is to pinpoint potential genetic targets related to the pathogenesis, diagnosis, monitoring and treatment of ILDs):
monitoring a patient database for a new data set (see at least Page 5, The objective of this resource is to pinpoint potential genetic targets related to the pathogenesis, diagnosis, monitoring and treatment of ILDs; Page 10, These findings are included in new datasets processed using AI in order to maximize the significance of the results; … large databases were developed especially for imaging, and there has been significant progress in AI pertaining to radiology. Respiratory medicine follows the broad use of AI in medicine, depicting the need for accurate, correct and quick solutions);
rerunning the machine learning model using the new data set (see at least Page 2, Machine learning (ML) is a subfield of AI where statistical models are trained to “learn” patterns from complex data in order to perform a specific task. Figure 2 shows a flowchart of the learning process; Page 10, These findings are included in new datasets processed using AI in order to maximize the significance of the results; … large databases were developed especially for imaging, and there has been significant progress in AI pertaining to radiology. Respiratory medicine follows the broad use of AI in medicine, depicting the need for accurate, correct and quick solutions);
obtaining an updated diagnosis (see at least Page 5, Applications of AI in ILD research can be roughly summarized in the following broad categories: (i) screening, (ii) diagnosis and classification, (iii) prognosis);
alerting a clinician if the updated diagnosis differs from the predicted differential diagnosis (see at least Page 3, AI does not substitute medical personnel, but rather works in a decision-support manner, thus facilitating trivial tasks so that doctors can focus more freely and effectively on the patient; Page 5, The second category, which is the most populated one, contains algorithms that diagnose ILDs … and some of them further aim to differentiate among various entities. The vast majority of studies in the last category (i.e., prognosis) deal with fibrosis and aim to assess its progression over time and subsequent prognosis); and
storing an anonymized data set based on the new data set if the updated diagnosis matches the predicted differential diagnosis (see at least Page 4, a critical issue for the exploration of ILDs, especially with AI algorithms, is the production of high-quality data that are well characterized and annotated. Same as in all healthcare-related repositories, anonymization and data privacy issues are of utmost importance and should be considered thoroughly, even in small-scale private datasets; Page 5, The vast majority of studies in the last category (i.e., prognosis) deal with fibrosis and aim to assess its progression over time and subsequent prognosis).
Claim 4:
Exarchos discloses the limitations as shown in the rejections above. Exarchos further discloses the following limitations:
wherein the new data set comprises at least one of: an updated protein count, an age of the patient, a sex of the patient, a race of the patient, or a symptom experienced by the patient (see at least Table 1, Type of Data includes Proteins; Page 7, Axelsson et al. [16], who aimed to identify proteins in circulating blood that are associated with ILAs).
Claim 5:
Exarchos discloses the limitations as shown in the rejections above. Exarchos further discloses the following limitations:
obtaining a set of disease state classes belonging to the category of similarly-presenting potential lung diseases (see at least Page 5, The database contains high resolution CT scans with annotated regions of pathological lung areas coupled with clinical parameters from patients with pathologically proven diagnoses of ILDs. Overall, the library contains data from 128 patients affected with 1 of the 13 ILD diseases. Applications of AI in ILD research can be roughly summarized in the following broad categories: (i) screening, (ii) diagnosis and classification, (iii) prognosis. The second category, which is the most populated one, contains algorithms that diagnose ILDs …, and some of them further aim to differentiate among various entities);
obtaining a training dataset of patient records in which all of the patient records include a confirmed diagnosis of one of the set of disease state classes and no patient records correspond to patients who had none of the set of disease state classes (see at least Table 1; Page 5, The database contains high resolution CT scans with annotated regions of pathological lung areas coupled with clinical parameters from patients with pathologically proven diagnoses of ILDs. Overall, the library contains data from 128 patients affected with 1 of the 13 ILD diseases);
determining features of the training dataset that are relevant to differential diagnoses as among the set of disease state classes (see at least Table 1; Page 5, ILDs are an ideal candidate for encompassing AI algorithms. In the current literature review, we describe the most prominent and recent applications of AI algorithms in ILD research. The majority is focused on the analysis of imaging modalities; nevertheless, other biomarkers, e.g., volatile organic compounds, gene expression, etc., are also being exploited in all aspects of ILD research: from screening to diagnosis and overall prognosis as well as treatment, aiming for more personalized and effective strategies. Applications of AI in ILD research can be roughly summarized in the following broad categories: (i) screening, (ii) diagnosis and classification, (iii) prognosis. The second category, which is the most populated one, contains algorithms that diagnose ILDs …, and some of them further aim to differentiate among various entities);
eliminating features of at least a portion of the training dataset that may be relevant to diagnosis of one or more of the disease state classes, but are not relevant to differential diagnosis as between the disease state classes, to create a reduced training dataset (see at least Table 1; Page 5, In the current literature review, we describe the most prominent and recent applications of AI algorithms in ILD research. The majority is focused on the analysis of imaging modalities; nevertheless, other biomarkers, e.g., volatile organic compounds, gene expression, etc., are also being exploited in all aspects of ILD research: from screening to diagnosis and overall prognosis as well as treatment, aiming for more personalized and effective strategies. Applications of AI in ILD research can be roughly summarized in the following broad categories: (i) screening, (ii) diagnosis and classification, (iii) prognosis. The second category, which is the most populated one, contains algorithms that diagnose ILDs …, and some of them further aim to differentiate among various entities); and
training at least one machine learning model using the reduced training dataset to create the trained machine learning model (see at least Table 1; Page 5, Applications of AI in ILD research can be roughly summarized in the following broad categories: (i) screening, (ii) diagnosis and classification, (iii) prognosis. The second category, which is the most populated one, contains algorithms that diagnose ILDs …, and some of them further aim to differentiate among various entities).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art axe such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Exarchos et al., Recent Advances of Artificial Intelligence Applications In Interstitial Lung Diseases in view of Reicher et al., U.S. Patent Application Publication US 2024/0020825 A1.
Claim 2:
Exarchos discloses the limitations as shown in the rejections above. Exarchos may not specifically disclose the following limitations, but Reicher as shown does:
wherein the second data set comprises at least one of: an age of the patient, a sex of the patient, or a race of the patient (Reicher, see at least Paragraph 52, The demographic data 118 may include any suitable demographic data associated with patients, such as a patient name, address, date of birth, age, race, ethnicity, phone number, employment status, medical and prescription drug insurance coverage, social media information, and so on).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the intelligent healthcare of Exarchos with the data of Reicher with the motivation of providing the benefit to “… reduce morbidity and mortality through improved non-invasive diagnosis” (Reicher, see at least Paragraph 38).
Conclusion
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-Friday, 9:00am-5:00pm.
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.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866.217.9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Joy Chng/
Primary Examiner, Art Unit 3686