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 .
DETAILED ACTION
Response to Amendment
In the amendment dated 01/14/2026, the following occurred: Claims 1, 3-5 and 10-14 have been amended. Claim 2 has been canceled.
Claims 1 and 3-14 are currently pending.
Claim Rejections
Claims 1 and 14 are objected for the following informality: “… results of a plurality of types of analysis applications that analyze a medical image…” should read “… results of a plurality of types of analysis applications that analyze the medical image…”
Claim 12 is objected for the following informality: “…generate a learning model that outputs candidates for analysis applications that analyze a medical image when the medical image is input…” should read “…generate a learning model that outputs candidates for analysis applications that analyze the medical image when the medical image is input…”
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 and 3-14 are rejected under 35 U.S.C. 101 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.
Claims 1, 12, 13 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a device, system and method for medical information processing.
Regarding claims 1, 12, 13 and 14, the limitation of (claim 12 being representative) store report data created by a user in the past regarding analysis results of a plurality of types of analysis applications that analyze a medical image, the report data including information of types of analysis applications that have generated analysis results selected by the user from the analysis results of the plurality of types of analysis applications, generate a learning model that outputs candidates for analysis applications that analyze a medical image when the medical image is input by learning the report data stored; and acquire a new medical image of a subject to be analyzed and input the acquired new medical image of the subject to be analyzed to the generated learning model to select the candidates for the analysis applications output from the learning model as one or more analysis applications that analyze the acquired new medical image of the subject to be analyzed and regarding the limitation of claim 1 - select one or more analysis applications that analyze the acquired medical image on the basis of report data created by a user in the past regarding analysis results of a plurality of types of analysis applications that analyze a medical image as drafted, is a process that, under the broadest reasonable interpretation, covers a method organizing human activity but for the recitation of generic computer components. That is other than reciting (in claim 1) a medical information processing device, processing circuitry, (in claims 12 and 13) a medical information processing system, a medical information processing device, processing circuitry and (in claim 14) a computer, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). For example, but for the medical information processing system, the medical information processing device, the processing circuitry and the computer, the claims encompass acquire clinical data of a subject, select one or more analysis applications that analyze the acquired clinical data, store report data, generate a learning model that outputs candidates for analysis applications that analyze clinical data and use machine learning model to analyze new acquired clinical data in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Note that the broadest reasonable interpretation of “a learning model” encompasses an element that can be executed manually, such as with pen and paper, by a user. For example, a model could be in the form of an equation, formula, or a set of rules applied to the input data and training the model by updating elements, such as weights. As such, the claims encompass a user manually acquiring clinical data, applying a model to select most suitable analysis application. Accordingly, the claim recites an abstract idea. SEE 17/907396
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements of a medical information processing device and processing circuitry. Claims 12 and 13 recite the additional elements of a medical information processing system, a medical information processing device and processing circuitry. Claim 14 recites the additional element of a computer. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic server or generic computer components for enabling access to medical information or for performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including 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. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 12 and 13 further recite the additional element of a report system. This additional element is recited at a high level of generality (i.e. a general means to store report data) and amounts to extra solution activity. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Claim 12 further recites the additional elements of a learning model which represents a mathematical concept as described in the Specification (The learning model MD is generated using various description methods such as a neural network, a support vector machine, and a decision tree. Neural networks include, for example, an auto-encoder, a convolutional neural network (CNN), a recurrent neural network (RNN), and the like.). This mathematical concept is applied to (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the medical information processing system, the medical information processing device, the processing circuitry and the computer to perform the noted steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, 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”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Also as discussed above with respect to integration of the abstract idea into a practical application, the additional element of the report system was considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional activity in the field. Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). Therefore when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of a learning model was determined to be the application of math to the identified abstract idea. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). As such the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 3-11 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 3, 7 and 10 further merely describe(s) selecting the one or more analysis applications. Claim(s) 4 further merely describe(s) supplementary information. Claim(s) 5 further merely describe(s) inputting acquired clinical data. Claim(s) 6 further merely describe(s) generating learning model. Claim(s) 8 further merely describe(s) executing the selected analysis application. Claim(s) 9 further merely describe(s) acquiring report data. Claim(s) 11 further merely describe(s) the learning model. As can be seen, all dependent claims further define the abstract idea. Moreover, claim(s) 9 includes the additional element of “a report system” which is interpreted the same as the report system in claims 12 and 13 and does not provide practical application or significantly more.
Claim Rejections - 35 USC § 103
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 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 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 1 and 3-14 are rejected under 35 U.S.C. 103 as being unpatentable over Rosales (US 2006/0200010) and in further view of Peterson (US 2015/0238692).
REGARDING CLAIM 12
Rosales discloses a medical information processing system comprising a medical information processing device and a report system, wherein the report system is configured to store report data created by a user in the past regarding analysis results of a plurality of types of analysis applications that analyze a medical image, ([0018] teaches clinical data includes images. [0019] teaches proper use of available information from previous patient records may significantly aid the clinician in making patient specific decisions in order to efficiently achieve a proper diagnosis or decision on treatment and [0020] teaches available data (such as previously recorded cases) to provide a probabilistic and sound approach for differential diagnosis. [0047] teaches previously recorded clinical data from other patients (interpreted by examiner as report data created by a user in the past regarding analysis results of a plurality of types of analysis applications that analyze medical image). [0033] and [0034] teach memory 106 that stores patient data, clinical data, output diagnosis and treatment results (interpreted by examiner as the report system is configured to store report data)), and the medical information processing device comprises processing circuitry configured to: generate a learning model that outputs candidates for analysis applications that analyze a medical image when the medical image is input by learning the report data stored in the report system ([0030] teaches a model or diagnostic system (interpreted by examiner as the learning model) programmed with potential diagnoses or treatments and the system determines which are the most appropriate (interpreted by examiner as outputs candidates for analysis applications that analyze medical image when the clinical data is input by learning the report data stored in the report system) based on the given data. The system 100 is implemented using machine learning techniques, such as training a neural network using sets of training data obtained from a database of patient cases with known diagnosis); and acquire a new medical image of a subject to be analyzed and input the acquired new medical image of the subject to be analyzed to the generated learning model ([0026] teaches inputting the patient data 102 into the system 100 including any new data, such as any new symptoms the patient may have and any changes to the existing patient data [0030] teaches in response to patient data 102 determined by a processor or input by a user, the system analyzes existing clinical data 103. The system 100 analyzes patient data 102 with the clinical data 103 (interpreted by examiner as acquire new medical image of a subject to be analyzed and input the acquired new clinical data of the subject to be analyzed to the generated learning model) and outputs a diagnosis. [0034] teaches the output data indicates a selection associated with a given medical condition, probability associated with the one or more selections, or other process related information).
Rosales does not explicitly disclose, however Peterson discloses:
the report data including information of types of analysis applications that have generated analysis results selected by the user from the analysis results of the plurality of types of analysis applications (Peterson at [0108] teaches a report manager that generates report data and [0152] teaches evidences an administrator may consider both the positive and negative evidences collected in one or more artificial intelligence architecture, such as intelligent agents, and to reconcile conflicting evidences reported by different artificial intelligence architectures (interpreted by examiner as the report data including information of types of analysis applications that have generated analysis results selected)) to select the candidates for the analysis applications output from the learning model as one or more analysis applications that analyze the acquired new medical image of the subject to be analyzed (Peterson at [0108] teaches an algorithm manager that monitors and generates reports known as a Report Manager. [0121] teaches selecting a decision task and using artificial intelligence architecture. [0145] teaches the artificial intelligence architecture of this disclosure may select the decision task that provides the protocol for treatment and [0141] teaches the collaborative decision support system explores the decision space hierarchy to pick one that is applicable to the decision task (interpreted by examiner as to select the candidates for the analysis applications output from the learning model as one or more analysis applications that analyze the acquired new medical image of the subject to be analyzed of Rosales above))
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the diagnostic method of Rosales to incorporate the decision support system as taught by Peterson, with the motivation of allowing for adaptive rule improvements and may allow for more sophisticated rules that can look at extended patient history, trends across patients and the population, etc. (Peterson at [0218]).
REGARDING CLAIM 3
Rosales and Peterson disclose the limitation of claim 1.
Peterson does not explicitly disclose, however Rosales further discloses:
The medical information processing device according to claim 1, wherein the processing circuitry is configured to select the one or more analysis applications on the basis of supplementary information associated with the analysis results included in the report data in addition to the types of the analysis applications (Rosales at [0024] teaches patient history, recorded patient visits, family history, demographic information (all interpreted by examiner as supplemental information)).
REGARDING CLAIM 4
Rosales and Peterson disclose the limitation of claim 3.
Peterson does not explicitly disclose, however Rosales further discloses:
The medical information processing device according to claim 3, wherein the supplementary information includes at least one of information indicating diagnosis of the user or order information of the medical image (Rosales at [0024] teaches patient's treatment, or recorded patient visits (all interpreted by examiner as information indicating diagnosis of the user)).
REGARDING CLAIM 6
Rosales and Peterson disclose the limitation of claim 5.
Rosales does not explicitly disclose, however Peterson further discloses:
The medical information processing device according to claim 5, wherein the processing circuitry is further configured to generate the learning model by learning the report data (Peterson at [0108] teaches algorithm manager that monitors and generates reports known as a Report Manager (interpreted by examiner as the report system). [0152] teaches adaptive learning according to this disclosure, an administrator may consider both the positive and negative evidences collected in one or more artificial intelligence architecture, such as intelligent agents, and to reconcile conflicting evidences reported by different artificial intelligence architectures (interpreted by examiner as the processing circuitry is further configured to generate the learning model by learning the report data)).
REGARDING CLAIM 9
Rosales and Peterson disclose the limitation of claim 1.
Rosales does not explicitly disclose, however Peterson further discloses:
The medical information processing device according to claim 1, wherein the processing circuitry is configured to acquire the report data from a report system (Peterson at [0108] teaches algorithm manager that monitors and generates reports known as a Report Manager (interpreted by examiner as means to acquire the report data from a report system)).
REGARDING CLAIM 11
Rosales and Peterson disclose the limitation of claim 5.
Rosales does not explicitly disclose, however Peterson further discloses:
The medical information processing device according to claim 5, wherein the learning model includes a plurality of learning models having different numbers of pieces or types of input data, and wherein the processing circuitry is configured to select one or more learning models from the plurality of learning models according to the acquired medical image, and select the one or more analysis applications (Peterson at [0063]-[0064] teaches an image of a patient and [0119] teaches the artificial intelligence architecture, such as Intelligent Agents, of this disclosure allow for the path navigated through the decision to be changed based on treatment hierarchies. Hence, the artificial intelligence architecture, such as Intelligent Agents, of this disclosure allows navigation through the decision tree to advantageously change from one branch to another branch. [0121] teaches selecting a decision task and using artificial intelligence architecture. [0145] teaches the artificial intelligence architecture of this disclosure may select the decision task that provides the protocol for treatment and [0141] teaches the collaborative decision support system explores the decision space hierarchy to pick one that is applicable to the decision task (interpreted by examiner as the learning model includes a plurality of learning models having different numbers of pieces or types of input data, and wherein the processing circuitry is configured to select one or more learning models from the plurality of learning models according to the acquired medical image, and select the one or more analysis applications)).
REGARDING CLAIMS 1, 5, 7, 8, 10, 13 and 14
Claims 1, 5, 7, 8, 10, 13 and 14 are analogous to Claims 3, 4, 6, 9, 11 and 12 thus Claims 1, 5, 7, 8, 10, 13 and 14 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 3, 4, 6, 9, 11 and 12.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1 and 3-14, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
The Office Action asserts that, under a broadest reasonable interpretation (BRI), the claims amount to managing personal behavior or interactions between people if generic computer components are disregarded. As amended, however, the claims require report data that includes information identifying types of analysis applications that generated analysis results selected by a user, and further require selecting one or more analysis applications for analysis of an acquired medical image on the basis of that report data. The selection is performed using information derived from prior analysis application outputs recorded in report data and is not directed to organizing human activity.
Regarding 1, The Examiner respectfully disagrees. The claims recite the steps of store report data, generate a learning model, acquire a new medical image and input the acquired new medical image to the generated learning model. Under the broadest reasonable interpretation, this covers a certain method organizing human activity but for the recitation of generic computer components. A person can follow a set of rules/instructions to acquire medical image of a subject, select one or more analysis applications that analyze the acquired medical image, store report data, generate a learning model that outputs candidates for analysis applications that analyze the medical image and use machine learning model to analyze newly acquired medical images. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). The claim falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas.
Even if it were assumed that the claims recite a judicial exception, the claims are integrated into a practical application. The claims recite acquiring a medical image, selecting one or more analysis applications for analysis of the medical image based on stored report data, and, in certain claims, executing the selected analysis applications and providing analysis results. The report data is recited as an input that is used to control selection of analysis applications for newly acquired medical images and is not recited as extra-solution activity. The claims further recite additional processing elements used in selecting analysis applications, including selecting analysis applications using a learning model trained by learning report data, selecting analysis applications on the basis of statistical data derived from report data, and selecting analysis applications on the basis of a reference time or number of references of analysis results by a user. These recitations define how the report data is used by the medical information processing device in selecting analysis applications.
Regarding 2, The Examiner respectfully disagrees. The claims do not provide a practical application. Acquiring a medical image, selecting one or more analysis applications and executing the selected analysis applications and providing analysis results is an abstract idea. The additional element of a learning model does not provide practical application nor significantly more. The learning model represents a mathematical concept as described in the Specification (The learning model MD is generated using various description methods such as a neural network, a support vector machine, and a decision tree. Neural networks include, for example, an auto-encoder, a convolutional neural network (CNN), a recurrent neural network (RNN), and the like.). This mathematical concept is applied to (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application and MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). As such the claim is not patent eligible.
Rejection under 35 U.S.C. § 103
Regarding the rejection of claims 1 and 3-14, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
… Rosales, however, fails to disclose or suggest selecting one or more analysis applications that analyze a medical image on the basis of report data created by a user in the past regarding analysis results of a plurality of types of analysis applications, including selecting analysis applications on the basis of types of analysis applications that have generated analysis results selected by the user from among analysis results of the plurality of types of analysis applications in the report data…
Regarding 1, The Examiner respectfully submits that Peterson is relied upon to teach the above identified limitation. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Given the broadest reasonable interpretation, the cited references teach the argued feature(s)
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office action. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Cucchiara (US 2004/0030586) teaches system and method for patient clinical data management.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIZA TONY KANAAN whose telephone number is (571)272-4664. The examiner can normally be reached on Mon-Thu 9:00am-6:00pm ET.
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/L T K/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683