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
This Office Action is the first action on the merits.
Claims 1-20 are pending
Priority
This Application claims priority to Provisional Application 63690877 filed 05 September 2024.
Information Disclosure Statement
The Information Disclosure Statement(s) (lDS) submitted on 18 August 2025 is/are in compliance with the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
Claim Rejections - 35 USC § 101
Claims 9-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 9 is rejected because it does not sufficiently recite a non-transitory computer readable storage medium. The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893 F.2d 319(Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. §101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. §101, Aug. 24, 2009; p. 2.
The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 U.S.C. §101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. §101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. §101 by adding the limitation "non-transitory" to the claim. Cf. Animals – Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. §101). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473 (Fed. Cir. 1998).
Claims 10-16 depend from Claim 9 and are rejected for the reasons noted in the rejection(s) of Claim 9, above.
For further 101 subject matter eligibility purposes, Examiner interprets “computer readable medium” to instead mean “non-transitory computer readable medium.”
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 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, 9, 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a method, computer readable medium, and system, which are within a statutory category or are interpreted to be within a statutory category for subject matter eligibility analysis purposes.
Step 2A1
The limitations of:
Claims 1, 9 and 17 (Claim 1 being representative)
receiving an identifier of the patient;
searching and retrieving relevant information factors for the patient from publicly available sources using the identifier, configured to identify and fetch medically relevant information of the patient in response to the identifier of the patient;
weighting each of the retrieved factors relative to contributing to a diagnoses of the patient;
and assigning a score to each of the retrieved factors and providing the scores and corresponding diagnoses to ER personnel,
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to diagnose an emergency room patient in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “receiving, search, weighting, assigning and providing” as indicated supra.
The examiner notes that the abstraction in Claim 1 is not performed by any particular technology and is purely directed to an abstract idea employing the additional elements analyzed below.
For Claims 9, 17, other than reciting generic computer components (discussed infra), i.e., a system implemented by a data processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. 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” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recites the additional element of a (Claim 9, 17) cloud based system and one or more processors that implements the identified abstract idea. The (Claim 9, 17) cloud based system and one or more processors is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer. 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 are directed to an abstract idea.
The claim further recites the additional element of using a trained machine learning model to diagnose an emergency room patient. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to diagnose an emergency room patient merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
Step 2B
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 element of using a (Claim 9, 17) cloud based system and one or more processors to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning model to diagnose an emergency room patient was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Claims 2-8,10-16,18-20 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) 2, 10, 18 merely describe(s) training the ML model, which further defines the abstract idea.
The Examiner notes that the training of a machine learning model is recited in the claim. The type of training utilized by the claimed invention is not described by the Applicant. As such the Examiner is required to analyze the training step given the broadest reasonable interpretation. The step(s) performed to train the model/algorithm is/are considered to be part of the abstract idea because it/they fall(s) under data manipulations that humans perform (i.e., fitting a model to data) and thus are interpreted to be part of the abstraction--the rules or instructions that fall under Certain Methods of Organizing Human Activity. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 12 (Fed. Cir. April 18, 2025) (finding that “[i]terative training using selected training material…are incident to the very nature of machine learning.”). As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Claim(s) 3, 11 merely describe(s) publicly available sources, which further defines the abstract idea.
Claim(s) 4, 12 merely describe(s) the patient identifier, which further defines the abstract idea.
Claim(s) 5, 13 merely describe(s) determining whether a patient is registered in a database, which further defines the abstract idea.
Claim(s) 5, 13, also includes the additional element of “electronic medical record database” which generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more.
Claim(s) 6, 14 merely describe(s) predicting a medical diagnosis, which further defines the abstract idea.
Claim(s) 7, 15 merely describe(s) receiving a selection and training the model, which further defines the abstract idea.
The Examiner notes that the retraining of a machine learning model is recited in the claim. The type of training utilized by the claimed invention is not described by the Applicant. As such the Examiner is required to analyze the retraining step given the broadest reasonable interpretation. The step(s) performed to retrain the model/algorithm is/are considered to be part of the abstract idea because it/they fall(s) under data manipulations that humans perform (i.e., fitting a model to data) and thus are interpreted to be part of the abstraction--the rules or instructions that fall under Certain Methods of Organizing Human Activity. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 12 (Fed. Cir. April 18, 2025) (finding that “[i]terative training using selected training material…are incident to the very nature of machine learning.”). As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Claim(s) 8,16,20 merely describe(s) using cloud infrastructure, which further defines the abstract idea.
Claim(s) 16,20, also includes the additional elements of “a first virtual cloud network, control plane virtual cloud network, and data plane virtual cloud network” which generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 9, 17 are rejected for lack of adequate written description.
Claims 1, 9, 17recite functional steps for which the Applicant has not adequately described the steps in sufficient detail for one of ordinary skill in the art to conclude that the Applicant had possession of the invention at the time of filing.
Specifically, the claims recite (Claim 1 being representative) “receiving an identifier of the patient; searching and retrieving relevant information factors for the patient from publicly available sources using the identifier using a trained machine learning (ML) model, the trained ML model configured to identify and fetch medically relevant information of the patient in response to the identifier of the patient; weighting each of the retrieved factors relative to contributing to a diagnoses of the patient; and assigning a score to each of the retrieved factors and providing the scores and corresponding diagnoses to ER personnel.” The Applicant has provided no disclosure of how a score is arrived at by weighing retrieved factors. The Specification states:
[Para. 0031] In connection with weighing/weightage, system 10 is trained to sift through the information it has collected and assign weightage scores based on their relevance to the present condition of the patient. As an example, when a patient is brought into the ER with a high fever and a semi-comatose state, assume system 10 determines from scouring the Internet or other public sources that the patient (1) Was planning to travel to Africa; (2) Started a new hobby of watercolor painting; (3) Had underwent his annual colonoscopy last week; and (4) Was planning on meeting his parents who lived 23 miles away in a Chicago suburb before his travel to Africa.
[Para. 0032] Among these four pieces of information, the fact that has the most relevancy to his present condition is his travel to Africa. System 10 therefore assigns the highest weightage score to this fact, followed by the colonoscopy (i.e., medical information), and lower scores to his new hobby and his trip to meet his parents, as it probably has least relevance to the patient's present condition because they have the least probability of causation/correlation with his present condition. Specifically, system 10 "knows" from its medical training that his new hobby and visit to parents has the least likelihood of having connected events that could have led to his present condition. It assigns a probability to these, and then finds that the highest probability is with his Africa visit, since medical knowledge dictates that he could have been infected with malaria while in Africa. But water coloring or visiting a Chicago suburb will have the least likelihood of any event happening that could medically lead to his present condition. Therefore, the weightage depends on the probability of likelihood - the higher the probability, higher the weightage.
As can be seen, there is no specific description as to how the scores are calculated, nor what the specific scores are in terms on number ranges. Any calculation of a score could potentially read on the as-claimed invention. For instance, is the score calculated based on the number of relevant factors or are the scores taking the average of each risk score for each factor? The Examiner simply cannot tell. The claimed analysis and determination amounts to a black box into which information is inputted and a result is received; however there is no disclosure as to what occurs in the box. As such, the claimed invention lacks adequate written description. MPEP 2161.01.
The Examiner prospectively notes that this written description rejection is not based on whether one skilled in the art would know how to program a computer to perform any form of analysis and determination (i.e., an enablement rejection), but rather is directed to the Applicant’s lack of specificity as to how the analysis and/or determination is specifically performed with respect to the Applicant’s claimed invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 16 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
It is unclear to the Examiner whether the processor, the cloud infrastructure, or both are diagnosis the patient. The independent claim 9 from which Claim 16 depends claims the processors as diagnosing an emergency room patient. But Claims 16 then recites the cloud infrastructure is diagnosing the patient. The cloud infrastructure of Claim 16 is claimed as a separate element from the processor in Claim 9. It is unclear which element is performing the diagnosis or is the cloud infrastructure is meant to replace the processor.
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 Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 1,3-4,6,9,11-12,14,17,19 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Ahmed et al (US Publication No. 20210319889) in view of BARNES et al (US Publication No. 20220044812) in view of DERRICK et al (US Publication No. 20210319887).
Regarding Claim 1
Ahmed teaches a method of diagnosing an emergency room (ER) patient, the method comprising:
receiving an identifier of the patient [Ahmed at Para. 0032 teaches the method may include obtaining, by the server computing system, data identifying a patient from a plurality of patients based on a social media handle associated with the patient];
searching and retrieving relevant information factors for the patient from publicly available sources using the identifier [Ahmed at Para. 0032] … [ … ]
Ahmed does not teach [ … ] … using a trained machine learning (ML) model, the trained ML model configured to identify and fetch medically relevant information of the patient in response to the identifier of the patient;
weighting each of the retrieved factors relative to contributing to a diagnoses of the patient;
and assigning a score to each of the retrieved factors and providing the scores and corresponding diagnoses to ER personnel.
BARNES teaches [ … ] … using a trained machine learning (ML) model, the trained ML model configured to identify and fetch medically relevant information of the patient in response to the identifier of the patient [BARNES at Para. 0077 teaches the learning system can include, for example, a rule-based extraction system, a machine learning (ML) model (which may include a deep learning neural network or other machine learning models), a natural language processor (NLP), etc., which can extract data elements from the unstructured patient data and determine their data categories based on a trained language extraction model, such as language extraction model 312 of FIG. 3B. Some of the data elements can also be mapped to pre-defined data representations (e.g., codes, fields, etc.) to form structured data, based on data table 330 of FIG. 3C. Moreover, as part of a normalization process, the learning system can also detect and correct data errors in the extracted data elements, and convert the extracted data elements to standardized data formats (interpret to combine with sources and identifier of Ahmed)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine patient identifier of Ahmed with the model of BARNES with the motivation to improve personalized healthcare.
Ahmed/BARNES do not teach weighting each of the retrieved factors relative to contributing to a diagnoses of the patient;
and assigning a score to each of the retrieved factors and providing the scores and corresponding diagnoses to ER personnel.
DERRICK teaches weighting each of the retrieved factors relative to contributing to a diagnoses of the patient [DERRICK at Para. 0217 teaches a rules engine may execute instructions to perform indexing or scoring and to arrive at one or more indices or scores associating and/or correlating an index or score with a Social Determinant Of Health and/or a plurality of indices or scores with a plurality of Social Determinants Of Health. One index or score associated with a Social Determinant Of Health, and/or one index or score associated with a plurality of Social Determinants Of Health, and/or a plurality of indices or scores associated with a plurality of Social Determinants Of Health may represent a quantitative contribution to an overall Social Determinant Of Health index or score (“Social Determinant Index”).];
and assigning a score to each of the retrieved factors and providing the scores and corresponding diagnoses to ER personnel [DERRICK at Para. 0217; DERRICK at Para. 0357 teaches some embodiments may be directed to reporting the predicted indicator scores to the patient and to the healthcare professional. The report may include a graphic and/or a numerical presentation of one or more of such scores].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Ahmed, BARNES with the scores of DERRICK with the motivation to improve health outcomes and reduce costs
Regarding Claim 3
Ahmed/BARNES/DERRICK teach the method of claim 1,
Ahmed/BARNES/DERRICK further teach wherein the publicly available sources comprises at least one of social media sources, blogs, video repositories and publications [Ahmed at Para. 0032 teaches the method may include obtaining, by the server computing system, data identifying a patient from a plurality of patients based on a social media handle associated with the patient].
Regarding Claim 4
Ahmed/BARNES/DERRICK teach the method of claim 1,
Ahmed/BARNES/DERRICK further teach wherein the identifier comprises at least one of a drivers license, passport, social security number, state identifier, military identifier, alien resident card, or a photograph [Ahmed at Para. 0066 teaches for example, the patient identification data 510 may include social security number 525, date of birth 526 and gender 527, as shown in diagram 520 of FIG. 5B.].
Regarding Claim 6
Ahmed/BARNES/DERRICK teach the method of claim 1,
Ahmed/BARNES/DERRICK further teach the trained ML model further configured to predict medical diagnoses in response to the identifier of the patient [Ahmed at Para. 0073 teaches for some implementations, a health diagnosis may be generated for the patient using the health-related data included in the social media content and the data related to the health history of the patient; Ahmed at Para. 0074 teaches for some implementations, the health diagnosis may be performed using machine learning].
Regarding Claim 9
Ahmed teaches a computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to diagnose an emergency room (ER) patient, the diagnosing comprising:
receiving an identifier of the patient [Ahmed at Para. 0032 (see Claim 1 for explanation)];
searching and retrieving relevant information factors for the patient from publicly available sources using the identifier [Ahmed at Para. 0032 (see Claim 1 for explanation)] … [ … ]
Ahmed does not teach [ … ] … using a trained machine learning (ML) model, the trained ML model configured to identify and fetch medically relevant information of the patient in response to the identifier of the patient;
weighting each of the retrieved factors relative to contributing to a diagnoses of the patient;
and assigning a score to each of the retrieved factors and providing the scores and corresponding diagnoses to ER personnel.
BARNES teaches [ … ] … using a trained machine learning (ML) model, the trained ML model configured to identify and fetch medically relevant information of the patient in response to the identifier of the patient [BARNES at Para. 0077 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine patient identifier of Ahmed with the model of BARNES with the motivation to improve personalized healthcare.
Ahmed/BARNES do not teach weighting each of the retrieved factors relative to contributing to a diagnoses of the patient;
and assigning a score to each of the retrieved factors and providing the scores and corresponding diagnoses to ER personnel.
DERRICK teaches weighting each of the retrieved factors relative to contributing to a diagnoses of the patient [DERRICK at Para. 0217 (see Claim 1 for explanation)];
and assigning a score to each of the retrieved factors and providing the scores and corresponding diagnoses to ER personnel [DERRICK at Para. 0217, 0357 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Ahmed, BARNES with the scores of DERRICK with the motivation to improve health outcomes and reduce costs
Regarding Claim 11
Claim(s) 11 is/are analogous to Claim(s) 3, thus Claim(s) 11 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Regarding Claim 12
Claim(s) 12 is/are analogous to Claim(s) 4, thus Claim(s) 12 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4.
Regarding Claim 14
Claim(s) 14 is/are analogous to Claim(s) 6, thus Claim(s) 14 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 6.
Claims 2,7,10,15,18 rejected under 35 U.S.C. 103(a) as being unpatentable over Ahmed, BARNES, DERRICK as applied to claim 1, 9, 17 above, and further in view of Eleftherou et al (US Publication No. 20190348178) in view of LI et al (US Publication No. 20220093257).
Regarding Claim 2
Ahmed/BARNES/DERRICK teach the method of claim 1,
Ahmed/BARNES/DERRICK do not teach further comprising: training the ML model with medical information and validating the trained machine learning model using standardized medical examinations.
Eleftherou teaches further comprising: training the ML model with medical information [Eleftherou at Para. 0024 teaches for example, the medical professional may dictate notes describing the evaluation of the patient, from which the diagnosis system 102 may extract concepts, grammatical features, and the like. At block 203, results of one or more patient examinations are received (e.g., lab results, ultrasounds, scans, etc.) by the diagnosis system 102. The diagnosis system 102 may generate (or refine) a model 103 for the patient based on the input data, the professional evaluation, and the examination results] … [ … ]
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Ahmed, BARNES, DERRICK with the model training of Eleftherou with the motivation to improve clinical diagnosis systems.
Ahmed/BARNES/DERRICK/Eleftherou do not teach [ … ] … and validating the trained machine learning model using standardized medical examinations.
LI teaches [ … ] … and validating the trained machine learning model using standardized medical examinations [LI at Para. 0049 teaches (3) Learning parameters: according to the physical examination data of a sample medical institution, several models M1, M2, . . . ML are constructed by matrix hyperparameter scanning (i.e., hyperparameters for scanning: the number K∈{3, 4, 5,6} of the layers of the network, the number n2, . . . nK−1∈{50, 30, 20, 10} of nodes in middle layers of the network, and activation functions ∈{ReLU, sigmoid, Tanh} between two adjacent layers). Parameters of each of the models are learned based on a mini-batch gradient descent (MBGD). Optimal parameters are determined via 10-fold cross validation. An optimal model is used as a basic predicting model Mbest for migration to other medical institutions. Mbest is solidified into the basic predicting model constructing module.].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Ahmed, BARNES, DERRICK, Eleftherou with the model validation of LI with the motivation to improve a control effect of chronic diseases and greatly reduce a medical burden.
Regarding Claim 7
Ahmed/BARNES/DERRICK/Eleftherou/LI teach the method of claim 2,
Ahmed/BARNES/DERRICK/Eleftherou/LI further teach further comprising:
receiving a selection of one of the diagnoses [Eleftherou at Para. 0030 teaches at block 256, the medical professional selects a diagnosis from the candidate diagnoses, and provides an indication of the selected diagnosis to the diagnosis system 102];
and retraining the ML model based on the selection [Eleftherou at Para. 0036 teaches at block 630, the diagnosis system 102 retrains the models 103 based on the diagnoses, treatments, and the outcomes of the treatments].
Regarding Claim 10
Claim(s) 10 is/are analogous to Claim(s) 2, thus Claim(s) 10 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
Regarding Claim 15
Claim(s) 15 is/are analogous to Claim(s) 7, thus Claim(s) 15 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
Regarding Claim 18
Claim(s) 18 is/are analogous to Claim(s) 2, thus Claim(s) 18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
Claims 5, 13 rejected under 35 U.S.C. 103(a) as being unpatentable over Ahmed, BARNES, DERRICK as applied to claim 1, 9, 17 above, and further in view of Kimak et al (US Publication No. 20050187794).
Regarding Claim 5
Ahmed/BARNES/DERRICK teach the method of claim 1,
Ahmed/BARNES/DERRICK do not teach further comprising: determining whether the patient is registered in an electronic medical records (EMR) database.
Kimak teaches further comprising: determining whether the patient is registered in an electronic medical records (EMR) database [Kimak at Para. 0094 teaches FIG. 8 is a patient search screen in one embodiment of the invention. The GUI 204 first allows the point of service care provider to search for electronic medical records from the main registry database 104. Once the patient's name is entered 801, registry entries with similar information will appear 801. Its own records 802 will appear to the point of service care provider in white boxes also allowing them to edit those specific entries. If the electronic medical records appear in a gray shaded box 804, then the source is from another point of service care provider and the amount of information presented is limited as well as the ability to edit the shaded fields. The GUI will also tell you how many electronic medical records matched your searching criteria 806].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Ahmed, BARNES, DERRICK with the registration of Kimak with the motivation to improve record management.
Regarding Claim 13
Claim(s) 13 is/are analogous to Claim(s) 5, thus Claim(s) 13 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 5.
Claims 8, 16, 20 rejected under 35 U.S.C. 103(a) as being unpatentable over Ahmed, BARNES, DERRICK as applied to claim 1, 9, 17 above, and further in view of Kasso et al (US Publication No. 20220200819).
Regarding Claim 8
Ahmed/BARNES/DERRICK teach the method of claim 1,
Ahmed/BARNES/DERRICK do not teach further comprising: using a cloud infrastructure for the diagnosing, the cloud infrastructure comprising a first virtual cloud network (VCN) comprising a local peering gateway (LPG) communicatively coupled to a secure shell (SSH) VCN via the LPG; wherein the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN.
Kasso teaches further comprising: using a cloud infrastructure for the diagnosing, the cloud infrastructure comprising a first virtual cloud network (VCN) comprising a local peering gateway (LPG) communicatively coupled to a secure shell (SSH) VCN via the LPG; wherein the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN [Kasso at Para, 0095 teaches the VCN 1006 can include a local peering gateway (LPG) 1010 that can be communicatively coupled to a secure shell (SSH) VCN 1012 via an LPG 1010 contained in the SSH VCN 1012. The SSH VCN 1012 can include an SSH subnet 1014, and the SSH VCN 1012 can be communicatively coupled to a control plane VCN 1016 via the LPG 1010 contained in the control plane VCN 1016. Also, the SSH VCN 1012 can be communicatively coupled to a data plane VCN 1018 via an LPG 1010].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Ahmed, BARNES, DERRICK with the network of Kasso with the motivation to improve security of the secure shell instance 150 and core cloud resources.
Regarding Claim 16
Claim(s) 16 is/are analogous to Claim(s) 8, thus Claim(s) 16 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 8.
Regarding Claim 17
Ahmed teaches a cloud based system for diagnosing an emergency room (ER) patient, the system comprising:
receive an identifier of the patient [Ahmed at Para. 0032 (see Claim 1 for explanation)];
search and retrieve relevant information factors for the patient from publicly available sources using the identifier [Ahmed at Para. 0032 (see Claim 1 for explanation)] … [ … ]
Ahmed does not teach a trained machine learning (ML) model [BARNES at Para. 0077 (see Claim 1 for explanation)];
one or more processors coupled to the trained ML model and configured to:
[ … ] … using the trained ML model, the trained ML model configured to identify and fetch medically relevant information of the patient in response to the identifier of the patient;
weight each of the retrieved factors relative to contributing to a diagnoses of the patient;
and assign a score to each of the retrieved factors and provide the scores and corresponding diagnoses to ER personnel.
BARNES teaches a trained machine learning (ML) model [BARNES at Para. 0077 (see Claim 1 for explanation)];
one or more processors coupled to the trained ML model and configured to [BARNES at Para. 0076 teaches in operation 704, patient data processor 200 can process the patient data using a learning system with Artificial Intelligence (AI)-assisted clinical extraction tool (e.g., AI-assisted clinical extraction tool 302). The processing may include extracting, based on a trained language extraction model that reflects language semantics and a user's prior habit of entering other patient data, data elements from the patient data and data categories represented by the data elements, and mapping the extracted data elements to pre-determined data representations based on the data categories]:
[ … ] … using the trained ML model, the trained ML model configured to identify and fetch medically relevant information of the patient in response to the identifier of the patient [BARNES at Para. 0077 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine patient identifier of Ahmed with the model of BARNES with the motivation to improve personalized healthcare.
Ahmed/BARNES do not teach weight each of the retrieved factors relative to contributing to a diagnoses of the patient;
and assign a score to each of the retrieved factors and provide the scores and corresponding diagnoses to ER personnel.
DERRICK teaches weight each of the retrieved factors relative to contributing to a diagnoses of the patient [DERRICK at Para. 0217 (see Claim 1 for explanation)];
and assign a score to each of the retrieved factors and provide the scores and corresponding diagnoses to ER personnel [DERRICK at Para. 0217, 0357 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Ahmed, BARNES with the scores of DERRICK with the motivation to improve health outcomes and reduce costs
Regarding Claim 19
Claim(s) 19 is/are analogous to Claim(s) 3, thus Claim(s) 19 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Regarding Claim 20
Claim(s) 20 is/are analogous to Claim(s) 8, thus Claim(s) 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 8.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
RAI et al (Foreign Publication WO-2022245863-A1) discloses a method for aggregating data.
BALAKRISHNAN et al (US Publication No. 20240289586) discloses methods and systems for using diagnostic feedback loops as an integrated mechanism to generate or improve machine learning classification models.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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