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 § 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, 8-17) are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without significantly more.
Claims (1-6, 8-17) are directed to the abstract idea of Mental processes – concepts performed in the human mind (including an observation, evaluation, judgement, opinion).
“generating a finding in radiology reports; obtaining a medical image of a patient; generating a first finding in response to input of the medical image, the machine-learned model trained, at least in part, from training data impressions based on training data findings; and displaying the first finding”; “training to generate findings, defining a first model to receive images and output findings; defining a second model to receive finding and output impressions; machine training the first model, at least in part, based on losses from the output impressions compared to ground truth impressions; and storing the machine-trained first model.”
This judicial exception is not integrated into a practical application. The claims recite additional limitations such “generating a finding in radiology reports; obtaining a medical image of a patient; generating a first finding in response to input of the medical image, the machine-learned model trained, at least in part, from training data impressions based on training data findings; and displaying the first finding”; “training to generate findings, defining a first model to receive images and output findings; defining a second model to receive finding and output impressions; machine training the first model, at least in part, based on losses from the output impressions compared to ground truth impressions; and storing the machine-trained first model.”. However, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide practical application for an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims recite additional limitations which are “machine learning model; vision model, impression model; attention encoder; database; processor; first model; second model”. However, these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of Abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide significantly more to an abstract idea (MPEP 2106.05(f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Therefore, since there are no limitations in the claims (1-6, 8-17) that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as a combination and as an ordered combination adds nothing that is not already present when looking at the elements taken individually, claims (1-6, 8-17) are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims (1-17) are rejected on the ground of nonstatutory double patenting as being unpatentable over claims (1-8, 10-14, 16-18) of U.S. Patent No. 12,183,463 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because of the following:
It is clear that all the elements of the application claims (1, 11, 16) are to be found in patent claims (1, 10, 18) (as the application claim (1, 11, 16) fully encompasses patent claim (1, 11, 18)). The difference between the application claim (1, 11, 16) and the patent claim (1, 10, 18) lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claim (1, 10, 18) of the patent is in effect a “species” of the “generic” invention of the application claim (1, 11, 16). It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claim (1, 11, 16) is anticipated by claim (1, 10, 18) of the patent, it is not patentably distinct from claim (1, 10, 18) of the patent. (Claims (2-10, 12-15, 17) have been analyzed and rejected w/r to claims (2-11, 13-14, 16-17).
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)(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.
Claim(s) (1-17) are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Oktay et al. (hereinafter Oktay)(US Publication 2023/0102428 A1)
Re claim 1, Oktay discloses a method for generating a finding in radiology reports by a machine-learned system, the method comprising: obtaining a medical image of a patient (See fig. 2: S(10); 202; ¶ 32, 35 where it teaches obtaining medical images/scans from a subject); generating a first finding by a machine-learned model in response to input of the medical image to the machine-learned model, the machine-learned model trained, at least in part, from training data impressions based on training data findings (See fig. 2: S(40); 116, 206; fig. 4; ¶ 28, 35-38, 40, 53-58 where it teaches a ML configured to generate findings based on the obtained medical images/scans from the subject; it also teaches training the ML based on training data.); and displaying the first finding. (See fig. 2: 106; ¶ 36 where it teaches displaying the findings to the user.)
Re claim 2, Oktay discloses wherein generating the first finding comprises generating text describing a first occurrence represented in the medical image, where the training data findings represented the first and a second occurrence and the training data impressions represented diagnostic conclusions based on the training data findings. (See fig. 2, 4; ¶ 28, 37-38, 40, 53-58)
Re claim 3, Oktay discloses wherein generating comprises generating by the machine-learned model comprising a machine-learned vision model configured to receive the medical image and a machine-learned natural language processing model configured to generate the first finding as text from an output of the machine-learned vision model. (See fig. 2, 4-5)
Re claim 4, Oktay discloses wherein the machine-learned model was trained in a sequence where loss from the training data impressions is back propagated to an impressions model in a first training and then the vision model and the natural language processing model are trained in a second training. (See fig. 2, 4; ¶ 28, 37-38, 53-58)
Re claim 5, Oktay discloses wherein generating comprises generating by the machine-learned model having been trained with machine learning by an impression model machine learning to generate output impressions from output findings of the model being trained for the machine-learned model, the training having used loss from the training data impressions relative to the output impressions. (See fig. 2, 4; ¶ 28, 37-38, 53-58)
Re claim 6, Oktay discloses wherein the machine-learned model was trained where values of learnable parameters of the model being trained for the machine-learned model were changed based on backpropagation from the loss of the training data impressions relative to the output impressions. (See fig. 2, 4; ¶ 28, 37-38, 53-58)
Re claim 7, Oktay discloses wherein the machine-learned model was trained where the impression model also received input of patient background information relative to each training data sample, the patient background information encoded with an attentional encoder. (See fig. 2, 4-5)
Re claim 8, Oktay discloses wherein generating the first finding comprises generating the first finding as a paragraph of patient findings including the first finding. (See fig. 2; ¶ 35-38)
Re claim 9, Oktay discloses wherein displaying comprises integrating the first finding into a radiology report including a first impression created by a physician and displaying the radiology report. (See fig. 2; ¶ 35-38)
Re claim 10, Oktay discloses wherein displaying comprises displaying a comparison of the first finding with a physician created finding. (See fig. 2; ¶ 35-38)
Re claim 11, Oktay discloses a method for machine training to generate findings, the method comprising: defining a first model to receive images and output findings (See fig. 4: 402, 406; ¶ 53-55 where it teaches a image model configured to output a summary of the visually relevant information content in the image data.); defining a second model to receive finding and output impressions (See fig. 4: 404, 408; ¶ 53-55 where it teaches a text model configured to output the semantic relevant information in the text.); machine training the first model, at least in part, based on losses from the output impressions compared to ground truth impressions (See fig. 4; ¶ 53-59 where it teaches training the image and text models based on training data.); and storing the machine-trained first model. (See fig. 1, 4; ¶ 58, 53 where it teaches/suggests storing the model.)
Re claim 12, Oktay discloses wherein defining the first model comprises defining the first model as a vision model configured to receive the images and a natural language processing model configured to output the findings as text from an output of the vision model. (See fig. 4-5)
Re claim 13, Oktay discloses wherein machine training comprises training in a sequence where the loss from the output impressions compared to the ground truth impressions is back propagated to the second model in a first training and then the vision model and the natural language processing model are trained in a second training. (See fig. 4; ¶ 53-59)
Re claim 14, Oktay discloses wherein machine training comprises training where values of learnable parameters of the first model are changed based on backpropagation from the losses. (See fig. 4; ¶ 53-59)
Re claim 15, Oktay discloses wherein machine training comprises machine training the second model based on the losses where the second model also receives input of patient background information relative to each training data sample, the patient background information encoded with an attentional encoder. (See fig. 4-5)
Re claim 16, Oktay discloses a system for creating an anatomical observation, the system comprising: a medical records database having stored therein an image of and/or text describing a patient (See fig. 1: 112; fig. 2; ¶ 28 where it teaches storing medical scans and reports.); a processor configured to input the image and/or text to a machine-trained model configured to create a first anatomical observation in response to input of the image and/or the text, the machine-trained model having been trained with a loss based on diagnostic conclusion derived from second anatomical observations (See fig. 2: S(40); 116, 206; fig. 4; ¶ 28, 35-38, 40, 53-58 where it teaches a ML configured to generate findings based on the obtained medical images/scans from the subject; it also teaches training the ML based on training data.); and a display configured to output the first anatomical observation for the patient. (See fig. 2: 106; ¶ 36 where it teaches displaying the findings to the user.)
Re claim 17, Oktay discloses wherein the machine-trained model comprises a vision model configured to receive the image and a natural language processing model configured to output the first anatomical observation as text from an output of the vision model. (See fig. 2; ¶ 28, 35-38, 40, 53-58)
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEON FLORES whose telephone number is (571)270-1201. The examiner can normally be reached M-F 8am - 6pm.
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/LEON FLORES/Primary Examiner, Art Unit 2676 August 25, 2026
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