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
Status of the Application
Claims 1-20 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Claims filed on 09/23/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/23/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 8 is objected to because of the following informalities: Claim 8, line 9 recites “system configured to one or more data”, where the claim does not recite what the system is configured to do. Examiner interprets this to read “system configured to obtain one or more data” as consistent with the limitations of Claims 1 and 15, which are of similar scope. Appropriate correction is required.
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 1-20 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-7 fall within the statutory category of a process. Claims 8-14 fall within the statutory category of an apparatus or system. Claims 15-20 fall within the statutory category of an article of manufacture as a computer-readable medium.
Step 2A, Prong One
As per Claims 1, 8, and 15, the limitations of analyzing one or more visual information associated with the magnetic resonance imaging scans of the one or more second users, indicating cancer symptoms by comparing the medical data with the historical medical records; analyzing textual information associated with one of: the blood test results and the genetic information of the second users, and the visual information associated with the MRI scans being analyzed; detecting a type of the cancer in the precancerous phases based on the analysis of the visual information associated with the MRI scans of the second users, and textual information associated with one of: the blood test results and the genetic information of the second users, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The steps of analyzing visual information associated with the MRI scans, analyzing textual information associated with blood test results and/or genetic information, and detecting a type of the cancer in precancerous phases are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional elements – hardware processors, memory comprising a plurality of subsystems in form of programmable instructions executable by the hardware processors, and a non-transitory computer-readable storage medium executed by the hardware processors. The processors, memory, and non-transitory computer-readable medium in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recite the additional element of the use of a computer vision model and use of an artificial intelligence model. The courts have found mathematical algorithms, where computer vision and AI models are mathematical algorithms, applied on a general purpose computer to be mere instructions to apply the exception, because they do not more than merely invoke computers as a tool to perform the process, as per MPEP 2106.05(f)(2). The claims also recite the additional elements of obtaining data from communication devices and/or databases and providing an output of the type of the cancer in form of reports through one or more interfaces associated with the communication devices which amounts to insignificant extra-solution activity, as in MPEP 2106.05(g), because the steps of obtaining data are mere data gathering and providing an output in conjunction with the abstract idea where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). The obtained data is described as medical data and/or historical medical records, associated with second users and the medical data comprise MRI scans, blood test results and/or genetic information of the second users, which is merely descriptive and does not provide a functional element. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional elements of processors, memory, and non-transitory computer-readable medium to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The system is recited at a high level of generality and are recited as generic computer components by reciting processors as microprocessors, microcomputers, etc. (Specification, [0050]), memory as any suitable elements for storing data ([0058]), and computer-readable medium as any apparatus that can comprise, store, communicate, propagate, or transport the program ([0091]), which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. The claims also recite the use of a computer vision model and use of an artificial intelligence model, which amount to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims also include the additional elements of obtaining data from communication devices and/or databases and providing an output of the type of the cancer in form of reports through one or more interfaces associated with the communication devices which are elements that are well-understood, routine and conventional computer functions in the field of data management because they are claimed at a high level of generality and include receiving or transmitting data, storing and retrieving information from memory, and presenting data which have been found to be well-understood, routine and conventional computer functions by the Court (MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added); (iv) Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; and (iv) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). 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 the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims
Dependent Claims 2-7, 9-14, and 16-20 add further limitations which are also directed to an abstract idea. For example, Claims 2, 9, and 16 include comparing data with trained datasets which can be performed using human mental evaluation, observation, judgment, and opinion. Therefore, this falls into the abstract grouping of mental processes. Claims 3, 10, and 17 include comparing medical data with historical medical records, and analyzing visual information associated with the MRI scans which falls into the abstract grouping of a mental process. The claims also recite obtaining medical data which amounts to mere data gathering that is well-understood, routine, and conventional for the same reasons as the independent claims. Claims 4 and 11 include training a model based on training datasets which is an additional element that amounts to mere instructions to apply the exception because the training is recited at a high-level of generality such that the computer is merely applying an algorithm. Claims 5, 12, and 18 include obtaining training datasets which amounts to mere data gathering similar to the independent claims; assigning training weights and fine-tuning the training weights, and detecting the type of cancer which also describe mental processes similar to the independent claims. Claims 6, 13, and 19 include retraining the AI model which amounts to mere instructions to apply the exception, for the same reasons as Claims 4 and 11. Claims 7, 14, and 20 include obtaining data comprising textual information which amounts to mere data gathering similar to the independent claims. The claims also include analyzing terminologies, concepts, or contexts and generating insights associated with the type of cancer which fall into the abstract grouping of a mental process for the same reasons as the independent claims. Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
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)(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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cohen et al. (US 2018/0068083 A1), hereinafter Cohen.
As per Claims 1, 8, and 15, Cohen discloses an artificial intelligence based (AI-based) cancer detection system for detecting cancer in precancerous phases ([0035-0036] AI/machine learning system to detect early stage cancer, [0018] method for predicting likelihood of cancer in a patient, [0059], [0063] determine risk level for presence of cancer in asymptomatic person), the artificial intelligence based (AI-based) cancer detection system comprising:
non-transitory computer-readable storage medium having instructions stored therein that when executed by one or more hardware processors, cause the one or more hardware processors to execute operations ([0018], [0059]/[0192] computer-readable media);
one or more hardware processors ([0018], [0192] system comprising processors);
a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors ([0018], processors coupled to a memory storing computer readable instructions, [0195] storage means including memory communicating with the apparatus), and
wherein the plurality of subsystems comprises:
a data obtaining subsystem configured to obtain one or more data ([0241] extracting information from patient records) from at least one of: one or more communication devices associated with one or more first users, and one or more databases ([0241] extracting information from patient records/EMR, [0243] pulling relevant data from data in databases), wherein the one or more data comprise at least one of: one or more medical data and one or more historical medical records, associated with one or more second users ([0018]/[0027] patient data includes patient records including values for a patient, measured biomarkers, clinical parameters, and diagnostic indicator, [0063] data includes patients medical records and history), and wherein the one or more medical data associated with the one or more second users comprise at least one of: one or more magnetic resonance imaging (MRI) scans, one or more blood test results, and one or more genetic information, of the one or more second users ([0241]extract patient medical records including raw images from a data store, [0242] data includes blood test results and biomarkers as well as images, [0265] images include MRI scans);
an information analyzing subsystem configured to: analyze one or more visual information associated with the magnetic resonance imaging (MRI) scans of the one or more second users, indicating cancer symptoms by comparing the one or more medical data with the one or more historical medical records, using a computer vision model ([0241] medical data is identified from images in the patient medical records, [0262] image data includes MRI scans which are analyzed, [0265] analyze MRI scans to extract clinical imaging data which is relevant to determining risk of cancer, [0358-0359] data from images such as MRI are analyzed to determine factors associated with lung cancer risk by comparing patient data to appropriate matched cohort using a decision support application, [0013] where the decision support system is an artificial intelligence/machine learning system for decision making); and
analyze one or more textual information associated with at least one of: the one or more blood test results and the one or more genetic information, of the one or more second users, and the one or more visual information associated with the magnetic resonance imaging (MRI) scans being analyzed by the computer vision model, using an artificial intelligence (AI) model ([0262]-[0263] patient data may include unstructured data which is text information of lab reports and patient histories which is analyzed, [0265] analyze MRI scans to extract clinical imaging data which is relevant to determining risk of cancer, [0303-0304] use neural network to analyze the image data and the unstructured data);
a cancer detection subsystem configured to detect a type of the cancer in the precancerous phases based on the analysis of the one or more visual information associated with the magnetic resonance imaging (MRI) scans of the one or more second users, and one or more textual information associated with at least one of: the one or more blood test results and the one or more genetic information, of the one or more second users, using the artificial intelligence (AI) model ([0366]/[0370] system for pre-cancer screening which uses testing data from patients to determine an array of tumor types, i.e. type of cancer; [0372-0374] detecting risk for developing cancer by category based on analysis of clinical, unstructured and imaging data using a neural network); and
an output subsystem configured to provide an output of the type of the cancer in the precancerous phases in form of one or more reports through one or more user interfaces associated with the one or more communication devices of the one or more first users ([0374-0375] output a report indicating the individual’s risk of patient being classified with type of cancer, [0297-0298] report is generated regarding patient’s risk of cancer and other information which are provided to the physician, [0193-0194] apparatus such as a computer or handheld device displays as a graphical representation the correlation/risk level).
As per Claims 2, 9, and 16, Cohen discloses the limitations of Claims 1, 8, and 15. Cohen also teaches comparing the one or more data obtained from at least one of: the one or more communication devices associated with the one or more first users and the one or more databases, with one or more trained datasets obtained from the artificial intelligence (AI) model ([0035] blood test data from an individual is compared to that which is determined for a cohort population, [0062-0063] train a model using cohort of patient data including blood test which measure biomarkers and compare patient data to the cohort to determine a risk level).
As per Claims 3, 10, and 17, Cohen discloses the limitations of Claims 1, 8, and 15. Cohen also discloses obtaining the one or more medical data comprising the one or more magnetic resonance imaging (MRI) scans of the one or more second users, from the one or more communication devices associated with the one or more first users and the one or more databases ([0241]extract patient medical records including raw images from a data store, [0242] data includes blood test results and biomarkers as well as images, [0265] images include MRI scans, [0341] nodes of the system are in a distributed computing environment and perform remote processing linked through a communication network, [0344] nodes include client devices which communicate with the server);
comparing the one or more medical data comprising the one or more magnetic resonance imaging (MRI) scans of the one or more second users, with the one or more historical medical records associated with the one or more second users, using the computer vision model ([0241] medical data is identified from images in the patient medical records, [0262] image data includes MRI scans which are analyzed, [0265] analyze MRI scans to extract clinical imaging data which is relevant to determining risk of cancer, [0358-0359] data from images such as MRI are analyzed to determine factors associated with lung cancer risk by comparing patient data to appropriate matched cohort using a decision support application, [0013] where the decision support system is an artificial intelligence/machine learning system for decision making); and
analyzing the one or more visual information associated with the magnetic resonance imaging (MRI) scans of the one or more second users, indicating the cancer symptoms, based on comparison of the one or more medical data comprising the one or more magnetic resonance imaging (MRI) scans of the one or more second users, with the one or more historical medical records associated with the one or more second users, using the computer vision model ([0241] medical data is identified from images in the patient medical records, [0262] image data includes MRI scans which are analyzed, [0265] analyze MRI scans to extract clinical imaging data which is relevant to determining risk of cancer, [0358-0359] data from images such as MRI are analyzed to determine factors associated with lung cancer risk by comparing patient data to appropriate matched cohort using a decision support application, [0013] where the decision support system is an artificial intelligence/machine learning system for decision making).
As per Claims 4 and 11, Cohen discloses the limitations of Claims 3 and 10. Cohen also discloses training the computer vision model based on one or more first training datasets associated with the one or more visual information of at least one of: the one or more medical data and the one or more historical medical records ([0025] training the neural net with retrospective patient data, [0062] model is trained using retrospective data associated with known cancer diagnosis, [0241] extracted patient data includes raw images).
As per Claims 5, 12, and 18, Cohen discloses the limitations of Claims 1, 8, and 15. Cohen also discloses training, by the one or more hardware processors, the artificial intelligence (AI) model, by:
obtaining one or more second training datasets associated with at least one of: the one or more medical data and the one or more historical medical records, wherein the one or more second training datasets comprise the one or more first training datasets being processed by the computer vision model ([0018] set of data includes patient records with retrospective data, i.e. historical medical records, and the training data set is made up of a portion of the data set);
assigning one or more training weights to the one or more second training datasets based on priority of each data associated with at least one of: the one or more medical data and the one or more historical medical records ([0018] each input of the medical data/records has an associated weight, [0350] assigning weights to the training data);
fine-tuning the one or more training weights based on the one or more second training datasets indicating information associated with tumor lifecycle to determine whether a training subsystem configured with the artificial intelligence (AI) model analyzes one or more cancer patterns without bias from tumor progression stages ([0020] iteratively regenerating the classifier when the predetermined statistic for correctly classifying patients by adjusting the associated weights of the inputs until the classifier meets the predetermined statistic, [0278] using feedback, adjust the weight of the inputs, [0352] when performance criteria is not met, retrain the classifier by adjusting the parameters, [0266] model used to determine patterns in data associated with cancer stages/progression, [0388]); and
detecting the type of the cancer comprising at least one of: benign tumor lifecycle and malignant tumor lifecycle based on the fine-tuned one or more training weights assigned to the one or more second training datasets ([0446-0447] the data used to generate the model is for classifying lung nodules as benign or malignant).
As per Claims 6, 13, and 19, Cohen discloses the limitations of Claims 5, 12, and 18. Cohen also discloses performing a reinforcement refinement process by assigning one or more tokens for each second training dataset to analyze each medical data, upon failure of detection of the malignant tumor lifecycle ([0018] determining whether the classifier/model has correctly classified patients, [0280] retraining the classifier until prediction of the correct classification for the patient as determining correct risk of cancer, i.e. malignant tumor),
wherein performing the reinforcement refinement process comprises retraining, by the one or more hardware processors, the artificial intelligence (AI) model with one or more corrected training weights integrating one or more feedback, to optimize an accuracy of the detection of the type of the cancer ([0020] iteratively regenerating the classifier when the predetermined statistic for correctly classifying patients by adjusting the associated weights of the inputs until the classifier meets the predetermined statistic, [0070] the AUC is calculated to determine the accuracy of the classifier, [0270] utilize additional information, i.e. feedback, to retrain the classifier to improve the accuracy of the classifier to determine cancer risk, also see [0280]).
As per Claims 7, 14, and 20, Cohen discloses the limitations of Claims 1, 8, and 15. Cohen also discloses obtaining the one or more data comprising the one or more textual information associated with at least one of: the one or more blood test results and the one or more genetic information, of the one or more second users ([0262]-[0263] patient data may include unstructured data which is text information of lab reports and patient histories which is analyzed, [0241-0243] extracting information from patient records/EMR/databases including blood test results, [0018]/[0027] patient data includes patient records including values for a patient, measured biomarkers, clinical parameters, and diagnostic indicator);
analyzing, by the one or more hardware processors, at least one of: one or more terminologies, one or more concepts, and one or more contexts, associated with the type of the cancer from the one or more textual information associated with at least one of: the one or more blood test results and the one or more genetic information, of the one or more second users, using the artificial intelligence (AI) model, wherein the artificial intelligence (AI) model comprises one or more natural language processing (NLP) models ([0262] natural language processing model and other known techniques are used to analyze image data as well as concepts and textual information of the patient medical records, [0263] NLP analyzes unstructured data including lab reports); and
generating one or more insights associated with the type of the cancer from at least one of: the one or more medical data and the one or more historical medical records, based on the analysis of the at least one of: the one or more terminologies, the one or more concepts, and the one or more contexts, associated with the type of the cancer, using the artificial intelligence (AI) model ([0263] determine textual data from analysis of patient medical data, [0292] data processed by natural language processing (NN9) is provided to a neural network to determine if patient is at increased risk for cancer).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Van Der Baan et al. (US 2022/0044762 A1) teaches machine learning to assess patient’s biomarkers to assess patient’s risk of cancer.
El-Baz et al. (US 2020/0285714 A9) teaches analyzing MRI images and biological values using machine learning for early detection of cancer.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682