DETAILED ACTION
Notices to Applicant
This communication is a non-final rejection. Claims 21-40, as filed 07/08/2025, are currently pending and have been considered below.
Priority is generally acknowledged as shown on the filing receipt with the earliest priority date being 08/12/2020.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Claim Objections
Claims 26, 29, 35, and 37 are objected to because of the following informalities. Claims 26 and 35 recite “at least one text-based medicine” which appears to refer to “at least one text-based medical record” or similar. Claims 29 and 37 recite “based at least in part on a characteristic affects usability” which appears to omit the word “that” after the word “characteristic”. Claims 29 and 37 further recite “the associated medical data” which appears to refer to “the associated medical metadata”.
Appropriate correction is required.
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 Long!, 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 §§ 706.02(l)(1) - 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-l.jsp
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,380,989 B2. Although the claims at issue are not identical, they are not patentably distinct from each other. For example, the pending claim 21 is substantially similar to claim 1 of US12380989B2 except that the pending claim omits the quality-score limitations.
Instant claims
Patented claims (US12380989B2)
Relationship
21, 30, 38
1, 9, 16
Pending claims are broader than patent claims and omit the quality-score limitations
22, 31, 39
2, 10, 17
Substantially the same
23, 32, 40
3, 11, 18
Substantially the same
24, 33
4, 12
Substantially the same
25, 34
5, 13
Substantially the same
26, 35
6, 14
Substantially the same
27, 36
7, 15
Substantially the same
28
8
Substantially the same
29, 37
19, 20
Substantially the same
Claim 21, 30, and 38 are further rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11,475,989 B2. Although the claims at issue are not identical, they are not patentably distinct from each other. For example, both the pending claim 21 and claim 1 of US11475989B2 determines a quality score, outputs a prediction of the diagnosis from the AI processing, determines whether a condition rule based on a level of confidence, and outputting medical data to a receiver. Claims 21, 30, and 38 are broadened variants of the patented claim 1.
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 21-28, 30-36, and 38-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process, machine, and/or article of manufacture which recite:
A computer-implemented method, the method comprising:
determining, via an artificial intelligence (AI) system, at least one rule corresponding to at least one condition and at least one receiver; (abstract idea – mental process because a person can make a determination by thinking about data; the AI system amounts to merely applying the abstract idea with a computer)
receiving, via the AI system, medical data and associated medical metadata at a digital storage device, wherein the medical data comprises a whole slide image (WSI);
outputting, via the AI system, a predicted assessment based on the medical data and associated medical metadata; (additional element – insignificant extra-solution activity; mere data-gathering and output)
determining, via the AI system, whether the medical data, the associated medical metadata, and/or the predicted assessment satisfies the at least one condition of the at least one rule based at least in part on a level of confidence in an inability of the AI system to make the predicted assessment;
upon determining that the medical data, the associated medical metadata, and/or the predicted assessment satisfies the at least one condition of the at least one rule, executing the at least one rule corresponding to the at least one condition and the at least one receiver; and (abstract idea – mental process because a person can make determinations by thinking about data and metadata and running through rules in his mind; the AI system amounts to merely applying the abstract idea with a computer)
transmitting, to a server associated with the at least one receiver, the medical data and associated medical metadata from an originating institution for review by the at least one receiver, wherein the at least one receiver possesses an expertise related to the predicted assessment of the AI system (additional element – insignificant extra-solution activity; mere data-gathering and output).
Claim 21 is presented as an exemplary claim but the same analysis applies to the other claims 30 and 38.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes. Other than reciting generic computer terms like “computer-implemented” and “via the AI system”, nothing in the claims precludes the italicized portions from practically being performed in the mind by thinking about various data, applying logic, and making decisions.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims. For example, claim 22, 24-25, 31, 33-34, and 39 recite particular aspects of how the decisions are made but for recitation of generic computer components.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements:
amount to mere instructions to apply an exception. For example, the determinations being made by an AI system amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification as published [0036], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea. For example, receiving data of various types amounts to mere data gathering, recitation of the data types of medical data and metadata amounts to selecting a particular data source or type of data to be manipulated, see MPEP 2106.05(g))
generally link the abstract idea to a particular technological environment or field of use such as the data being whole slide image data, see MPEP 2106.05(h))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i), performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii), electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii), and/or storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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, if the differences between the claimed invention and the prior art are 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21-23, 26-27, 30-32, 35-36, and 38-40 are rejected under 35 U.S.C. 103 as being unpatentable over Lindemer (US20200160510A1) in view of Gecer (Gecer, B., et al.. Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern recognition, 84, 345–356) and Valverde (US20170011183A1).
Regarding claim 21, Lindemer discloses: A computer-implemented method, the method comprising:
--determining, via an artificial intelligence (AI) system, at least one rule corresponding to at least one condition and at least one receiver (“the method comprises routing, by routing logic associated with the PCC computing system, the one or more medical image data structures and one or more corresponding medical image metadata data structures to one or more downstream patient evaluation computing systems based on the determined patient complexity,” [0007]; “a rules-based engine may be combined with AI mechanisms when implementing the PCC engine of the illustrative embodiments,” [0035]);
--receiving, via the AI system, medical data and associated medical metadata at a digital storage device, (“The method comprises receiving, by the PCC computing system, medical image study data for a patient, wherein the medical image study data comprises one or more medical image data structures and one or more corresponding medical image metadata data structures,” [0007]);
--upon determining that the medical data, the associated medical metadata, and/or the predicted assessment satisfies the at least one condition of the at least one rule, executing the at least one rule corresponding to the at least one condition and the at least one receiver (“the complexity classifier is a rules based computing engine that applies a plurality of predefined rules to the extracted features to determine which rules in the plurality of predefined rules have criteria that are satisfied by the extracted features, and wherein the patient complexity is determined based on which rules have criteria satisfied by the extracted features,” [0010]); and
--transmitting, to a server associated with the at least one receiver, the medical data and associated medical metadata from an originating institution for review by the at least one receiver, wherein the at least one receiver possesses an expertise related to the predicted assessment of the AI system (“Furthermore, the method comprises routing, by routing logic associated with the PCC computing system, the one or more medical image data structures and one or more corresponding medical image metadata data structures to one or more downstream patient evaluation computing systems based on the determined patient complexity,” [0007]).
Lindemer does not expressly disclose, but Gecer teaches:
--wherein the medical data comprises a whole slide image (WSI)…outputting, via the AI system, a predicted assessment based on the medical data and associated medical metadata (“The proposed framework covered the whole workflow from an input whole slide image to its categorization into five diagnostic classes,” page 16);
One of ordinary skill in the art before the effective filing date would have been motivated to expand Lindemer’s AI-based medical image routing to WSIs as taught by Gecer because this would allow for medical diagnosis on a larger range of image types (see Gecer page 13 describing effectiveness of automating pathologist predictions).
Lindemer and Gecer do not expressly disclose but Valverde teaches:
--determining, via the AI system, whether the medical data, the associated medical metadata, and/or the predicted assessment satisfies the at least one condition of the at least one rule based at least in part on a level of confidence in an inability of the AI system to make the predicted assessment (“The integrated medical platform 245 may determine a new set of questions and tests it needs in order to accomplish several things, such as: 1) identify or eliminate any serious conditions (sometime outliers); 2) refine the likelihood of any of the ranked conditions identified; 3) shorten the list of possibilities; 4) arrive at a high likelihood for a diagnosis; 5) decide to provide this case directly to a doctor because the integrated medical platform 245 has no confidence in the diagnosis (statistically), and so forth,” [0052);
One of ordinary skill in the art before the effective filing date would have been motivated to expand Lindemer’s AI-based medical image routing with WSIs as taught by Gecer to include the differential routing of Valverde because this would allow the system to reach an accurate for images of both high and low complexity.
Additionally, it can be seen that each element is taught by Lindemer, Gecer, or Valverde. The features of Gecer and Valverde does not affect the normal functioning of the elements of the claim which are taught by Lindemer. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Gecer and Valverde with the teachings of Lindemer since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Claims 30 and 38 are substantially similar to claim 21 and are rejected with the same reasoning. Note that Lindemer discloses a computer readable storage medium in [0043].
Regarding claims 22, 31, and 39, Lindemer discloses: analyzing the medical data and associated medical metadata to detect at least one unusual measurement or unusual reporting information associated with the medical data and associated medical metadata (“identify or eliminate any serious conditions (sometime outliers),” [0052]; “outliers, in particular dangerous ones, are identified” [0025]). The motivation to combine is the same as in claim 21.
Regarding claims 23, 32, and 40, Lindemer discloses: upon detecting at least one unusual measurement or unusual reporting information associated with the medical data and associated medical metadata, transmitting, from the AI system, the medical data and associated medical metadata. Lindemer discloses transmitting the data structures upon making a determination as described in claim 21. Making this transmission in response to an unusual measurement would have been obvious in view of Valverde’s “outliers” as described in the rejections of claims 21 and 22. The motivation to combine is the same as in claim 21.
Regarding claims 26 and 35, Lindemer does not expressly disclose but Valverde teaches: wherein the medical data further comprises at least one text-based medicine, at least one text-based note, and/or at least one text-based record (“The second (frontend) parser may read electronic medical records (EMRs) and parse patient input, taking pertinent information and feeding it to the backend structures,” [0022]; “The patient data may include patient input in the form of a natural language. The patient input may include text or speech converted into text,” [0009]). The motivation to combine is the same as in claim 21.
Regarding claims 27 and 36, Lindemer does not expressly disclose but Valverde teaches: wherein the associated medical metadata comprises at least one text-based document, at least one text-based diagnosis, and/or at least one text-based lab result document (“The processor may be configured to parse text associated with one or more medical information sources 205 to obtain medical information, structure the medical information to form structured medical metadata in an intelligent medical database 105, and create a causal network 240 based on the structured medical metadata,” [0039]; “The patient data may include patient input in the form of a natural language. The patient input may include text or speech converted into text,” [0009]). The motivation to combine is the same as in claim 21.
Claims 24, 25, 33, and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Lindemer (US20200160510A1) in view of Gecer (Gecer, B., et al.. Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern recognition, 84, 345–356), Valverde (US20170011183A1), and Mahesh (US20080118119A1).
Regarding claims 24 and 33, Lindemer does not expressly disclose but Mahesh teaches: wherein the at least one rule comprises at least one specific keyword, at least one tissue type, at least one disease condition, at least one submitting clinician, at least one case identifier, and/or at least one accession number (“Other classifications may indicate for example a disease or condition associated with the image and the body part in the image. The classification process may also determine the severity of the condition and assign a severity indicator to the image,” [0017]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand the rule-based medical image routing of Lindemer, Gecer, and Valverdi to include the classifications of Mahesh because this would improve the system’s ability to find the “appropriate medical practitioner,” [0008].
Regarding claims 25 and 34, Lindemer does not expressly disclose but Mahesh teaches: wherein the at least one condition includes at least one disease type, at least one tissue type, at least one location of a sample, and/or at least one physician assigned to review the data at an originating institution (“Another exemplary classification may be the body part in the image such as, for example, the lungs, or kidneys, etc. Other classifications may indicate for example a disease or condition associated with the image and the body part in the image,” [0017]). The motivation to combine is the same as in claim 24.
Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Lindemer (US20200160510A1) in view of Gecer (Gecer, B., et al.. Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern recognition, 84, 345–356.), Valverde (US20170011183A1), and Sharma (US20200160941A1).
Regarding claims 28, Lindemer does not expressly disclose but Sharma teaches: wherein the determining at least one rule comprises a user selecting the at least one rule (“an instruction or rule can be set up to identify an incoming image of a certain type (e.g., a magnetic resonance image of a brain, etc.) with a certain associated clinician (e.g., reporting physician is Dr. Smith, etc.),” [0064]; “The user interface generator 680 facilitates display and selection of instructions (e.g., for a user, location, organization, function, etc.), and the output generator 690 provides results of instruction execution such as case creation, case routing to a PACS, other data storage, other system, etc., and the like,” [0078]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand the rule-based medical image routing of Lindemer, Gecer, and Valverdi to include the rule selection of Sharma because allowing user selection would improve case routing ([0023]).
Subject Matter Free from Prior Art
Claims 29 and 37 are not anticipated or obvious in view of the prior art.
The closest prior art is Lindemer, Gecer, Valverde, and Sharma. Lindemer expands on this to include AI techniques for analyzing the complexity of a case and filtering cases to identify complex patients (e.g., [0025] and [0035]). Sharma discloses various techniques for assembling a clinical case and routing it to appropriate end users (e.g., [0064]). The prior art does not teach or suggest determining the level of confidence in an inability of the AI system to make the predicted assessment “based at least in part on a characteristic affects usability of the medical data and the associated medical data in making an assessment” as required by claims 29 and 37. Lindemer’s classification engine determines the complexity or sufficiency of the metadata it analyzes, not a confidence based on a characteristic affecting the usability of the data for making the assessment.
The instant claims are distinct from the combination of Sharma and Lindemer because Lindemer's classification engine makes a determination about the sufficiency of the metadata which it analyzes, not about its own ability to make an assessment. Lindemer's Al does not teach: "determining, via the Al system, whether the medical data, the associated medical metadata, and/or the predicted assessment satisfies the at least one condition of the at least one rule based at least in part on a level of confidence in an inability of the Al system to make an assessment, wherein the level of confidence is based at least in part on the quality score."
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA BLANCHETTE whose telephone number is (571)272-2299. The examiner can normally be reached on Monday - Thursday 7:30AM - 6:00PM, EST.
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/JOSHUA B BLANCHETTE/Primary Examiner, Art Unit 3624
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