DETAILED ACTION
Notice to Applicant
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to the amendment filed 10/16/25. Claims 1-4, and 6-20 are pending.
Drawings
The replacement drawings of Figs. 2, 3A-B and 4A-B were received on 10/16/25 These drawings are acceptable.
Claim Rejections - 35 USC § 112
The prior rejection of claim 18 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, is hereby withdrawn due to the amendment filed on 10/16/25. 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.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 6-10 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claims 6-10 recite a dependence from claim 5. However, claim 5 has been canceled. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claims 6-10 will be interpreted as being dependent from claim 1, instead of claim 5, for the purpose of applying art in the interest of compact prosecution. However, 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 1-4 and 6-20 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.
35 USC 101 enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter. As explained by the courts, these “four categories together describe the exclusive reach of patentable subject matter. If a claim covers material not found in any of the four statutory categories, that claim falls outside the plainly expressed scope of Section 101 even if the subject matter is otherwise new and useful.” In re Nuijten, 500 F.3d 1346, 1354, 84 USPQ2d 1495, 1500 (Fed. Cir. 2007). Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter? Applicant’s claims fall within at least one of the four categories of patent eligible subject matter because claims 1-4, 6-10 are drawn to a system, and 11-20 are drawn to an electronic device.
Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not complete the eligibility analysis. Claims drawn only to an abstract idea, a natural phenomenon, and laws of nature are not eligible for patent protection. As described in MPEP 2106, subsection III, Step 2A of the Office’s eligibility analysis is the first part of the Alice/Mayo test, i.e., the Supreme Court’s “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l,134 S. Ct. 2347, 2355, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. at 77-78, 101 USPQ2d at 1967-68).
In 2019, the United States Patent and Trademark Office (USPTO) prepared revised guidance (2019 Revised Patent Subject Matter Eligibility Guidance) for use by USPTO personnel in evaluating subject matter eligibility. The framework for this revised guidance, which sets forth the procedures for determining whether a patent claim or patent application claim is directed to a judicial exception (laws of nature, natural phenomena, and abstract ideas), is described in MPEP sections 2106.03 and 2106.04.
As explained in MPEP 2106.04(a)(2), the 2019 Revised Patent Subject Matter Eligibility Guidance explains that abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes. Moreover, this guidance explains that a patent claim or patent application claim that recites a judicial exception is not ‘‘directed to’’ the judicial exception if the judicial exception is integrated into a practical application of the judicial exception. A claim that recites a judicial exception, but is not integrated into a practical application, is directed to the judicial exception under Step 2A and must then be evaluated under Step 2B (inventive concept) to determine the subject matter eligibility of the claim.
Step 2A asks: Does the claim recite a law of nature, a natural phenomenon (product of nature) or an abstract idea? (Prong One) If so, is the judicial exception integrated into a practical application of the judicial exception? (Prong Two) A claim recites a judicial exception when a law of nature, a natural phenomenon, or an abstract idea is set forth or described in the claim. While the terms “set forth” and “describe” are thus both equated with “recite”, their different language is intended to indicate that there are different ways in which an exception can be recited in a claim. For instance, the claims in Diehr set forth a mathematical equation in the repetitively calculating step, while the claims in Mayo set forth laws of nature in the wherein clause, meaning that the claims in those cases contained discrete claim language that was identifiable as a judicial exception. The claims in Alice Corp., however, described the concept of intermediated settlement without ever explicitly using the words “intermediated” or “settlement.” A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
In the instant case, claims 1-4 and 6-20 recite(s) a method and system for certain methods of organizing human activities, which is subject matter that falls within the enumerated groupings of abstract ideas described in MPEP 2106.04 (2019 Revised Patent Subject Matter Eligibility Guidance) Certain methods of organizing human activities includes fundamental economic practices, like insurance; commercial interactions (i.e. legal obligations, marketing or sales activities or behaviors, and business relations). Organizing human activity also encompasses managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions.) The recited systems are drawn to determining differential diagnoses based on patient medical information/monitoring data, and determining/selecting next actions to narrow the diagnosis.
In particular, claims 1 and 11 recite systems configured to:
obtain medical information corresponding to the patient from the at least one database;
obtain real time or near real time patient monitoring data corresponding to the patient from a plurality of patient monitoring devices;
generate, in real or near real time, a differential diagnosis list for the patient based on at least one of the medical information and the patient monitoring data, the differential diagnosis list including one or more diagnoses and a predicted probability corresponding to each diagnosis, each predicted probability indicating an estimated accuracy of a corresponding diagnosis;
generate the predicted probabilities and the recommendation weight based on previously input user predictions. (wherein clause)
obtain one or more procedure recommendations for assessing a first diagnosis of the differential diagnosis list;
obtain a recommendation weight for each of the one or more procedure recommendations, the recommendation weight indicating a change in a predicted probability of a diagnosis based on the information corresponding to the procedure recommendation being considered in the corresponding diagnosis;
and
obtain user predictions for one or more of the diagnoses of the differential diagnosis list…
Similarly, claim 12 recites a system configured to:
obtain, medical information corresponding to the patient from the at least one database;
obtain real time or near real time patient monitoring data corresponding to the patient from a plurality patient monitoring devices;
generate:
a differential diagnosis list for the patient based on at least one of the medical information and the patient monitoring data, the differential diagnosis list including one or more diagnoses;
a predicted probability corresponding to each diagnosis, each predicted probability indicating an estimated accuracy of a corresponding diagnosis;
one or more procedure recommendations for assessing a first diagnosis of the differential diagnosis list; and
a recommendation weight for each of the one or more procedure recommendations, the recommendation weight indicating a change in a predicted probability of a diagnosis based on the information corresponding to the procedure recommendation being considered in the corresponding diagnosis;
responsive to a selection of a procedure recommendation of the one or more procedure recommendations, ordering a procedure associated with the procedure recommendation; wherein the one or more procedure recommendations comprise one of an additional parameter to be monitored, a diagnostic test to be performed, and an imagining study to be performed.
MENTAL PROCESS-ANALYSIS
Moreover, the language of claims 1 and 11, encompasses performance of the limitations(s) in the mind, but for the recitation of generic computer components.
In particular, the limitations of obtaining, medical information; obtaining real time or near real time patient monitoring data; generating a differential diagnosis list, a predicted probability and a recommendation weight for each of the one or more recommendations; and obtaining from the user, user predictions for one or more of the diagnoses of the differential diagnosis list; obtaining one or more procedure recommendations for assessing a first diagnosis of the differential diagnosis list; and obtaining a recommendation weight for each of the one or more procedure recommendations…is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “through the communication interface,” “at least one processor,“ and “through the human-machine interface,” nothing in the claim elements precludes the step from practically being performed in the mind.
For example, but for the generic computer component language, the obtaining and generating steps in the context of this claim encompasses the user reading/ reviewing and determining differential diagnoses mentally, and estimating the likelihood of a diagnosis.
If a claim limitation, under its broadest reasonable interpretation, covers 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.
As explained in MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). (emphasis added) As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
Moreover, courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
This judicial exception is not integrated into a practical application because the claim language does not recite any improvements to the functioning of a computer, or to any other technology or technical field (See MPEP 2106.04(d)(1); see also MPEP 2106.05(a)(I-II)). Moreover, the claims do not integrate the judicial exception into a practical application because the claimed invention does not: apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)); effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); or apply or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment see MPEP 2106.05(e). (Considerations for integration into a practical application in Step 2A, prong two and for recitation of significantly more than the judicial exception in Step 2B)
While abstract ideas, natural phenomena, and laws of nature are not eligible for patenting by themselves, claims that integrate these exceptions into an inventive concept are thereby transformed into patent-eligible inventions. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2354, 110 USPQ2d 1976, 1981 (2014) (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71-72, 101 USPQ2d 1961, 1966 (2012)). Thus, the second part of the Alice/Mayo test is often referred to as a search for an inventive concept. Id. An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting “the Government’s invitation to substitute Sections 102, 103, and 112 inquiries for the better established inquiry under Section 101”). As made clear by the courts, the “‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the Section 101 categories of possibly patentable subject matter.” Intellectual Ventures I v. Symantec Corp.,838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9).
As described in MPEP 2106.05, Step 2B of the Office’s eligibility analysis is the second part of the Alice/Mayo test, i.e., the Supreme Court’s “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. _, 134 S. Ct. 2347, 2355, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. 66, 101 USPQ2d 1961 (2012)). Step 2B asks: Does the claim recite additional elements that amount to significantly more than the judicial exception? The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The additional steps amount to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Examples of insignificant extra-solution activity include mere data gathering, selecting a particular data source or type of data to be manipulated, and insignificant application. In the instant case the additional step of “provide, through the human-machine interface, the differential diagnosis list and predicted probabilities to the user” 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)
Claims 1, 11, and 12 recites additional limitation(s), including a plurality of patient monitoring devices …configured to generate patient monitoring data by monitoring a physiological parameter of a patient; at least one database storing medical information corresponding to the patient; a power source; a communication interface configured to communicate with the one or more patient monitoring devices; the at least one database over a network; and a human-machine interface configured to provide information to a user and obtain information from the user; a memory configured to store instructions; and at least one processor. The added claim language recites a computer/network structure and database features performing generic functions well-understood, routine and conventional activities that amount to no more than implementing the abstract idea with a computerized system.
The generic nature of the computer system used to carryout steps of the recited method is underscored by the system description in the instant application, which discloses: “The DAM assistant may be deployed, partially deployed, operated, or accessed on electronic device 600 of FIG. 6 in a network environment 700 as shown in FIG. 7, according to an embodiment. FIGS. 6 and 7 are for illustration only, and other embodiments of the electronic device and environment could be used without departing from the scope of this disclosure” (par. 61 of PG-Pub for the instant application) and “Processor 620 may include one or more of a central processing unit (CPU), a graphics processor unit (GPU), an accelerated processing unit (APU), many integrated core (MIC), a field-programmable gate array (FPGA), or a digital signal processing (DSP). Processor 620 may control at least one of other components of electronic device 600, and/or perform an operation or data processing relating to communication. " (par. 64 of PG-Pub for the instant application)
Furthermore, “Electronic device 600 of FIG. 6 may be connected with a first external electronic device 602, a second external electronic device 604, or a server 606 through network 710. Electronic device 600 may be wearable device, an electronic device-mountable wearable device (such as an FIMD), etc…. The first and second external electronic devices 602 and 604 and server 506 may each be a device of a same or a different type than electronic device 600. According to some embodiments, server 606 may include a group of one or more servers. Also, according to some embodiments, all or some of the operations executed on electronic device 600 may be executed on another or multiple other electronic devices (such as electronic devices 602 and 604 or server 606).” (par. 71-72)
The disclosure further explains: “Memory 630 may include a volatile and/or a non-volatile memory….” (par. 65); “Interface 640 may include input/output (I/O) interface 641, communication interface 642, and/or one or more sensors 643. I/O interface 641 serves as an interface that can, for example, transfer commands or data between a user or other external devices and other component(s) of electronic device 600. The I/O interface 641 may provide a human-machine user interface which provides information to a user and receives information from the use” (par. 68) and “communication interface 642 may be connected with a network 710 through wireless or wired communication architecture to communicate with an external electronic device. Communication interface 642 may be a wired or wireless transceiver or any other component for transmitting and receiving signals.” (par. 70).”
The disclosure also states: “The patient care units 120 may include one or more patient monitoring devices that monitor physiological parameters of a patient. Nonlimiting embodiments of patient monitoring devices include a blood pressure monitor, a blood oxygen monitor, an electrocardiogram, an electroencephalogram, a temperature monitor, a heart rate monitor, a respiration rate monitor, a carboxyhemoglobin monitor, an end-tidal carbon dioxide monitor, a heart rhythm monitor, a cardiac output monitor, and a hear rate variability monitor.” (par. 23)
The language describing the different possible computer structures and system implementations underscores that the applicant's perceived invention/ novelty focuses on the computerized implementation of the abstract idea, not the underlying structure of generic system components.
Furthermore, the courts have recognized certain computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05 (d) (II)). Among these are the following features, which are recited in or analogous to limitations recited in claims 1, 11, and 12:
- 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));
- Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
- 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;
Claims 1, 11 and 12 further recite: “by an artificial intelligence (AI) model …” Claims 1 and 11 also recite: “ wherein the Al model includes a first neural network that is trained to generate the predicted probabilities and the recommendation weight based on previously input user predictions.” However, as currently drafted, the recitation of AI models, including a trained neural network, fails to recite significantly more, and amounts to adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea “using an artificial intelligence model”, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984. (See MPEP 2106.05(A))
The recitation of “generate, by an artificial intelligence (AI) model,” in the claims invokes the use of an AI model and trained neural network merely as a tool to perform an existing process. More specifically, the claims are drawn to a medical provider using a tool (i.e. AI) to determine differential diagnoses, and to evaluate/predict the probability of a given diagnosis, a known process which has been performed by medical providers many years.
Claims 2-4 and 6-10 are dependent from Claim 1 and include(s) all the limitations of claim(s) 1. However, the additional limitations of the claims 2-4 and 6-10 fail to recite significantly more than the abstract idea. More specifically, the additional limitations further define the abstract idea with additional steps or details regarding data types; or additional steps which amount to insignificant extra solution activities. Therefore, claim(s) 2-4 and 6-10 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claims 13-20 are dependent from Claim 12 and include(s) all the limitations of claim(s) 12. However, the additional limitations of the claims 13-20 fail to recite significantly more than the abstract idea. More specifically, the additional limitations further define the abstract idea with additional steps or details regarding data types; or additional steps which amount to insignificant extra solution activities. Therefore, claim(s) 13-20 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Because Applicant’s claimed invention recites a judicial exception that is not integrated into a practical application and does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself, the claimed invention is not patent eligible.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claim(s) 12-14 and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kenig et al (US 20200160995 A1).
Claim 12. Kenig is an electronic device comprising:
a power source; (par. 19, Fig. 1)
a communication interface configured to communicate with one or more patient monitoring devices and at least one database over a network; (Fig. 1-2; par. 15, par. 19; par. 34-35)
a human-machine interface configured to provide information to a user and obtain information from the user; (par. 20- When reviewing diagnosis record 104 via a machine-human interface, such as a display or audio interface of a care provider device, a care provider may enter an input (e.g., via the user input device, which may include a keyboard, mouse, microphone, touch screen, stylus, or other device) that may be processed by the care provider device and sent to the server system 102.)
a memory configured to store instructions; (par. 19-20) and
at least one processor (par. 24; par. 37-38) configured to execute the instructions to:
obtain, through the communication interface, medical information corresponding to the patient from the at least one database; (par. 17, par. 20-access patient diagnosis record; par. 48-49-receiving historical data; par. 43- The EMR data 210 may be obtained from the EMR module 110 and/or EMR database 122 shown in FIG. 1, and may include past and current medical data for the patient, including health conditions, health statistics (weight, height, age, sex, nationality, or the like), past diagnostic test results, medications, family history, and the like. )
obtain, through the communication interface, real time or near real time patient monitoring data corresponding to the patient from a plurality of patient monitoring devices; (par. 15-multiple monitoring devices; par. 43- Monitoring device data 217 may include data processed by the monitoring module 117 and/or received directly from the monitoring devices 120, and may include real-time data from pulse oximeters, heart rate monitors, blood glucose monitors, ECGs, and the like, which may be attached to the patient in the medical facility. In this way, the monitoring device data 217 may be continuously received at the server system (e.g., at monitoring module 117) and continuously input into the digital twin 108.)
generate, in real or near real time, by an artificial intelligence (AI) model:
a differential diagnosis list for the patient based on at least one of the medical information and the patient monitoring data, the differential diagnosis list including one or more diagnoses; (fig. 3; par. 44: real time--The resulting digital twin 108 may be a digital compilation of all available health data on the patient and may be continuously updated, in real-time, as data is received by the processor(s) of the system, to reflect the current health of the patient; par. 46- the supervisory algorithm may include code adapted to take the outputs of each of algorithms 220, 222, 224, 226, and 228 (which may include a different diagnosis and/or disease indications, in one example) and formulate a differential diagnosis of the patient, including a list of possible diagnoses of the patient. par. 49- The meta-analysis results may include a differential diagnosis of the patient, including a finite list of most likely, possible diagnoses (e.g., a list of two, three, four, five, or the like, diagnoses) based on the outputs of the algorithms 135…; See also par. 55)
a predicted probability corresponding to each diagnosis, each predicted probability indicating an estimated accuracy of a corresponding diagnosis; (par. 49- the diagnosis record may present the percentage likelihood of a plurality of possible diagnoses output by the algorithms and/or a percentage likelihood of a diagnosis,)
one or more procedure recommendations for a first diagnosis of the differential diagnosis list; (par. 28; par. 49; par. 55-57-recommended procedure/next actions: The suggested next actions may include one or more diagnostic tests to run, such a blood tests, urine tests, imaging procedures (e.g., ultrasound, Mill, x-ray, and the like), and the like; Fig. 3) and
a recommendation weight for each of the one or more recommendations, the recommendation weight indicating a change in a predicted probability of a diagnosis based on the information corresponding to the recommendation being considered in the corresponding diagnosis; and (par. 49- the diagnosis record may present the percentage likelihood of a plurality of possible diagnoses output by the algorithms and/or a percentage likelihood of a diagnosis, which may have been from an output list; par. 50- system prioritizes results of certain algorithms as more trusted- the system may mark (e.g., tag) the particular algorithm as “more trusted” and, as a result, prioritize the results/outputs from that algorithm over the other algorithms for future updates to the diagnosis record 104 and/or for alternate diagnosis records of different patients (seen by the same healthcare provider). In this way, the system (e.g., server system 102) may include machine learning capabilities for learning algorithm preferences of a certain healthcare provider. )
provide, through the human-machine interface, the differential diagnosis list, the predicted probabilities, the one or more recommendations, and the one or more recommendation weights to the user; (par. 49-differential diagnosis and percentage likelihood data presented to and confirmed by a healthcare provider: diagnosis record may present the percentage likelihood of a plurality of possible diagnoses output by the algorithms and/or a percentage likelihood of a diagnosis, which may have been from an output list, as confirmed by a healthcare provider.; par. 55-57; par. 63; Fig. 3) and
responsive to a selection of a procedure recommendation of the one or more procedure recommendations, ordering a procedure associated with the procedure recommendation; (par. 4-6: treatment recommendations; par. 44-ordering system for imaging or medical resources; par. 50- if the physician relies on or selects a diagnosis or result output from a particular algorithm more frequently than other algorithms, or takes actionable results based on the diagnosis output from the particular algorithm (such as listing that diagnosis in the medical record of the patient, ordering diagnostic tests or labs to confirm the particular diagnosis, or the like), the system may mark (e.g., tag) the particular algorithm as “more trusted” and, as a result, prioritize the results/outputs from that algorithm over the other algorithms for future updates to the diagnosis record 104 and/or for alternate diagnosis records of different patients (seen by the same healthcare provider).
wherein the one or more recommendations comprise one of an additional parameter to be monitored, a diagnostic test to be performed, and an imaging study to be performed. (Fig. 3; par. 56-58: The user interface display 400 may also include a visualization of suggested next actions to confirm the selected diagnosis at 406. The suggested next actions may include one or more diagnostic tests to run, such a blood tests, urine tests, imaging procedures (e.g., ultrasound, Mill, x-ray, and the like), and the like.)
claim 13 Kenig teaches the electronic device of claim 12, wherein the at least one processor is further configured to obtain, from the user and through the human-machine interface, a user prediction for a diagnosis of the differential diagnosis list. (par. 63- The user interface displays may provide an organized and instructive visualization of the diagnosis record of the patient and enable a health care provider to more quickly and accurately perform a differential diagnosis on the patient…one or more of the user interface displays may provide the user, via one or more user inputs (such as buttons or displayed, selectable elements, as described above, or via audio inputs), the ability to specify the final selected diagnosis.
Claim 14 Kenig teaches the electronic device of claim 13, wherein the at least one processor is further configured to input the user prediction into the AI model to generate one or more of new recommendations and new recommendation weights. (par. 43-44:the monitoring device data 217 may be continuously received at the server system (e.g., at monitoring module 117) and continuously input into the digital twin 108. As described further below, the digital twin 108 may be continuously updated as input data is received and/or updated; par. 71-72- updating the differential diagnoses list based on updated information: the updated first user interface including a visualization (or audio representation) of an updated, second list of a plurality of possible diagnoses of the patient based on outputs of the plurality of different algorithms. For example, the second list may include different and/or fewer diagnoses than the first list. In this way, the additionally received medical data may help to narrow the differential diagnosis presented to the user to a smaller number. In some example, the second list may have more diagnoses than the first list, but second list may contain more accurate diagnosis)
Claim 17. Kenig teaches the electronic device of claim 12, wherein the at least one processor is further configured to: continuously input the real time or near real time patient monitoring data corresponding to the patient into the AI model; (par. 43-44the monitoring device data 217 may be continuously received at the server system (e.g., at monitoring module 117) and continuously input into the digital twin 108. As described further below, the digital twin 108 may be continuously updated as input data is received and/or updated) and autonomously update the predicted probabilities based on the patent monitoring data. (par. 66-68- to automatically update the digital twin of the patient in response to receiving new and/or updated health data on the patient. For example, if new imaging or lab results are uploaded to the system, the processor(s) may then automatically update the digital twin based on the newly received data; par. 71-72- updating the differential diagnoses list based on updated information: the updated first user interface including a visualization (or audio representation) of an updated, second list of a plurality of possible diagnoses of the patient based on outputs of the plurality of different algorithms. For example, the second list may include different and/or fewer diagnoses than the first list. In this way, the additionally received medical data may help to narrow the differential diagnosis presented to the user to a smaller number. In some example, the second list may have more diagnoses than the first list, but second list may contain more accurate diagnosis)
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.
Claim(s) 1-4, 6, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kenig et al (US 20200160995 A1) in view of Roh et al (US 20230215560 A1).
Claims 1 and 11. Kenig teaches a medical system comprising:
one or more patient monitoring devices, each patient monitoring device being configured to generate patient monitoring data by monitoring a physiological parameter of a patient; (par. 43- monitoring device data 217… Monitoring device data 217 may include data processed by the monitoring module 117 and/or received directly from the monitoring devices 120, and may include real-time data from pulse oximeters, heart rate monitors, blood glucose monitors, ECGs, and the like, which may be attached to the patient in the medical facility. In this way, the monitoring device data 217 may be continuously received at the server system (e.g., at monitoring module 117) and continuously input into the digital twin 108.)
at least one database storing medical information corresponding to the patient; (par. 65- method 900 includes obtaining medical data of a patient. Obtaining medical data of the patient may include obtaining past (e.g., historical or previously obtained medical data on the patient, such as from an EMR system or database, as described herein) and present (e.g., current data obtained in real-time from one or more patient monitoring devices or via inputs from a user) medical data of the patient. )
and
an electronic device (Fig. 1) comprising:
a power source; (par. 19, Fig. 1)
a communication interface configured to communicate with the one or more patient monitoring devices and the at least one database over a network; (Fig. 1-2; par. 15, par. 19; par. 34-35)
a human-machine interface configured to provide information to a user and obtain information from the user; (par. 20- When reviewing diagnosis record 104 via a machine-human interface, such as a display or audio interface of a care provider device, a care provider may enter an input (e.g., via the user input device, which may include a keyboard, mouse, microphone, touch screen, stylus, or other device) that may be processed by the care provider device and sent to the server system 102.)
a memory configured to store instructions; and (par. 19-20)
at least one processor configured to execute the instructions (par. 24; par. 37-38) to:
obtain, through the communication interface, medical information corresponding to the patient from the at least one database; (par. 17, par. 20-access patient diagnosis record; par. 48-49-receiving historical data; par. 43- The EMR data 210 may be obtained from the EMR module 110 and/or EMR database 122 shown in FIG. 1, and may include past and current medical data for the patient, including health conditions, health statistics (weight, height, age, sex, nationality, or the like), past diagnostic test results, medications, family history, and the like. )
obtain, through the communication interface, real time or near real time patient monitoring data corresponding to the patient from the one or more patient monitoring devices; (par. 43- Monitoring device data 217 may include data processed by the monitoring module 117 and/or received directly from the monitoring devices 120, and may include real-time data from pulse oximeters, heart rate monitors, blood glucose monitors, ECGs, and the like, which may be attached to the patient in the medical facility. In this way, the monitoring device data 217 may be continuously received at the server system (e.g., at monitoring module 117) and continuously input into the digital twin 108.)
generate, by an artificial intelligence (AI) model, a differential diagnosis list for the patient based on at least one of the medical information and the patient monitoring data, (fig. 3; par. 46- the supervisory algorithm may include code adapted to take the outputs of each of algorithms 220, 222, 224, 226, and 228 (which may include a different diagnosis and/or disease indications, in one example) and formulate a differential diagnosis of the patient, including a list of possible diagnoses of the patient. par. 49- The meta-analysis results may include a differential diagnosis of the patient, including a finite list of most likely, possible diagnoses (e.g., a list of two, three, four, five, or the like, diagnoses) based on the outputs of the algorithms 135…; See also par. 55) the differential diagnosis list including one or more diagnoses and a predicted probability corresponding to each diagnosis, each predicted probability indicating an estimated accuracy of a corresponding diagnosis; (par. 49- the diagnosis record may present the percentage likelihood of a plurality of possible diagnoses output by the algorithms and/or a percentage likelihood of a diagnosis,)
provide, through the human-machine interface, the differential diagnosis list and predicted probabilities to the user; and (par. 49-differential diagnosis and percentage likelihood data presented to and confirmed by a healthcare provider: diagnosis record may present the percentage likelihood of a plurality of possible diagnoses output by the algorithms and/or a percentage likelihood of a diagnosis, which may have been from an output list, as confirmed by a healthcare provider.; par. 63)
obtain, from the user and through the human-machine interface, user predictions for one or more of the diagnoses of the differential diagnosis list, (par. 63- The user interface displays may provide an organized and instructive visualization of the diagnosis record of the patient and enable a health care provider to more quickly and accurately perform a differential diagnosis on the patient…one or more of the user interface displays may provide the user, via one or more user inputs (such as buttons or displayed, selectable elements, as described above, or via audio inputs), the ability to specify the final selected diagnosis.
Kenig discloses a system in which one or more trained machine learning algorithms are used to generate the predicted probabilities based on previously input user predictions. (par. 23; par. 33; par. 47-50; par. 63). Kenig does not expressly disclose, but Roh teaches that the machine learning algorithms include a (trained) neural network(s). (wherein the AI model includes a first neural network that is trained to generate the predicted probabilities based on previously input user predictions.) (par. 119-120) At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system of Kenig with the teaching of Roh to include trained neural networks to determine likelihood of a diagnosis with the motivation of generating a model which can generate more and more accurate predictions based on new inputs over time. (Roh: par. 119)
Claim 1 has been amended to recite: obtain one or more procedure recommendations for assessing a first diagnosis of the differential diagnosis list; obtain a recommendation weight for each of the one or more procedure recommendations, the recommendation weight indicating a change in a predicted probability of a diagnosis based on the information corresponding to the procedure recommendation being considered in the corresponding diagnosis.
Kenig discloses a method and system configured to:
obtain one or more procedure recommendations for assessing a first diagnosis of the differential diagnosis list; (par. 49-differential diagnosis and percentage likelihood data presented to and confirmed by a healthcare provider: diagnosis record may present the percentage likelihood of a plurality of possible diagnoses output by the algorithms and/or a percentage likelihood of a diagnosis, which may have been from an output list, as confirmed by a healthcare provider.; par. 50-if the physician relies on or selects a diagnosis or result output from a particular algorithm more frequently than other algorithms, or takes actionable results based on the diagnosis output from the particular algorithm (such as listing that diagnosis in the medical record of the patient, ordering diagnostic tests or labs to confirm the particular diagnosis, or the like), the system may mark (e.g., tag) the particular algorithm as “more trusted” and, as a result, prioritize the results/outputs from that algorithm over the other algorithms for future updates to the diagnosis record 104 and/or for alternate diagnosis records of different patients (seen by the same healthcare provider; par. 55-57; par. 63; Fig. 3)
obtain a recommendation weight for each of the one or more procedure recommendations, the recommendation weight indicating a change in a predicted probability of a diagnosis based on the information corresponding to the procedure recommendation being considered in the corresponding diagnosis. (par. 49- the diagnosis record may present the percentage likelihood of a plurality of possible diagnoses output by the algorithms and/or a percentage likelihood of a diagnosis, which may have been from an output list; par. 50- system prioritizes results of certain algorithms as more trusted- the system may mark (e.g., tag) the particular algorithm as “more trusted” and, as a result, prioritize the results/outputs from that algorithm over the other algorithms for future updates to the diagnosis record 104 and/or for alternate diagnosis records of different patients (seen by the same healthcare provider). In this way, the system (e.g., server system 102) may include machine learning capabilities for learning algorithm preferences of a certain healthcare provider. )
Claim 2. Kenig teaches the medical system wherein the plurality of patient monitoring devices comprises at least two from the following list: a blood pressure monitor, a blood oxygen monitor, an electrocardiogram, an electroencephalogram, a temperature monitor, a heart rate monitor, a respiration rate monitor, a carboxyhemoglobin monitor, an end-tidal carbon dioxide monitor, a heart rhythm monitor, a cardiac output monitor, and a hear rate variability monitor. (par. 21- The monitoring devices 120 may include traditional medical devices monitoring respective patients, such as pulse oximeters, heart rate monitors, blood glucose monitors, ECGs, as well as microphones, cameras, continuous blood pressure bracelets)
Claim 3. Kenig teaches the medical system of claim 1, wherein the at least one processor is further configured to: continuously input the real time or near real time patient monitoring data corresponding to the patient into the AI model; (par. 43-44the monitoring device data 217 may be continuously received at the server system (e.g., at monitoring module 117) and continuously input into the digital twin 108. As described further below, the digital twin 108 may be continuously updated as input data is received and/or updated) and autonomously update the predicted probabilities in response to the patient monitoring data indicating that one or more of the differential diagnosis list and the predicted probabilities should be adjusted. (par. 66-68- to automatically update the digital twin of the patient in response to receiving new and/or updated health data on the patient. For example, if new imaging or lab results are uploaded to the system, the processor(s) may then automatically update the digital twin based on the newly received data; par. 71-72- updating the differential diagnoses list based on updated information: the updated first user interface including a visualization (or audio representation) of an updated, second list of a plurality of possible diagnoses of the patient based on outputs of the plurality of different algorithms. For example, the second list may include different and/or fewer diagnoses than the first list. In this way, the additionally received medical data may help to narrow the differential diagnosis presented to the user to a smaller number. In some example, the second list may have more diagnoses than the first list, but second list may contain more accurate diagnosis)
Claim 4 Kenig and Roh in combination disclose the medical system of claim 1, as explained in the rejection of claim 1. Kenig does not expressly disclose how the AI model is trained. Roh discloses a system wherein the first neural network/machine learning model is trained to obtain predicted probabilities by: comparing one or more predicted probabilities output by the first neural network to one or more user predictions; (par. 125- The patient data may be input into the model and the prediction may be compared against the prior diagnosis from a medical care provider. If the prediction matches the diagnosis, then the prediction is correct. If, however, the prediction does not match the diagnosis, the model may be trained to associate the patient data with a particular diagnosis and the model is updated and trained. In this way, the machine learning system can compare its predicted data against real-world data and modify the one or more machine learning algorithms based upon this comparison.) and adjusting parameters of the first neural network based on a difference obtained from the comparison. (par. 125)
At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system of Kenig with the teaching of Roh, with the motivation of continuously improving the model’s prediction accuracy. (par. 125)
Claim 6. Kenig teaches the medical system of claim 1, wherein the one or more recommendations comprise one of an additional parameter to be monitored, a diagnostic test to be performed, and an imaging study to be performed. (Fig. 3; par. 57- user interface display 300 may additionally include a visualization of actions to narrow down the diagnosis of the patient at 316. For example, the actions to narrow down the diagnosis 316 may include one or more suggested diagnostic tests (e.g., labs, imaging procedures, and the like) to run on the patient in order to narrow down the list of possible diagnoses at 312; par. 57; par. 68; par. 71)
Claim 18. Kenig discloses an electronic device in which one or more trained machine learning algorithms are used to generate the predicted probabilities based on previously input user predictions. (par. 23; par. 33; par. 47-50; par. 63). Kenig does not expressly disclose that the machine learning algorithms include a (trained) neural network(s). (wherein the AI model includes a first neural network that is trained to generate the predicted probabilities based on previously input user predictions.) Kenig also does not expressly disclose how the AI model is trained.
Roh teaches that the machine learning algorithms include a (trained) neural network(s). (wherein the AI model includes a first neural network that is trained to generate the predicted probabilities based on previously input user predictions.) (par. 119-120). Roh also discloses an electronic device wherein the first neural network/machine learning model is trained to obtain predicted probabilities by: comparing one or more predicted probabilities output by the first neural network to one or more user predictions; (par. 125- The patient data may be input into the model and the prediction may be compared against the prior diagnosis from a medical care provider. If the prediction matches the diagnosis, then the prediction is correct. If, however, the prediction does not match the diagnosis, the model may be trained to associate the patient data with a particular diagnosis and the model is updated and trained. In this way, the machine learning system can compare its predicted data against real-world data and modify the one or more machine learning algorithms based upon this comparison.) and adjusting parameters of the first neural network based on a difference obtained from the comparison. (par. 125)
At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system of Kenig with the teaching of Roh, with the motivation of generating a model which can generate increasingly more accurate predictions based on new inputs over time. (Roh: par. 119)
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kenig et al (US 20200160995 A1) in view of Roh et al (US 20230215560 A1), as applied to claim 1, and in further view of Andbloom et al (US 20220020493 A1)
Claim 7. Kenig and Roh teach the medical system of claim 1, as explained. Kenig further discloses providing an indication of a change in the diagnostic predicted probability based on the first recommendation (par. 57- Test A, listed at 316 may be used to rule out on or more of the diagnoses listed at 312. In some examples, the actions to narrow down the diagnosis 316 may include additional instructions, such as if the result of Test A is X, you should perform Test B to further narrow the possible diagnosis list 312, or, if the result of Test A is Y, you should perform Test B to further narrow the possible diagnosis list 312. In this way, user interface display 300 may provide instructions to the user (e.g., care provider) on how to narrow the diagnosis of the patient in the most efficient manner possible; par. 68; par. 71)
Kenig and Roh in combination do not disclose, but Andbloom teaches wherein the at least one processor is further configured to, for a first recommendation of the first diagnosis, generate a predicted probability increase based on the first recommendation indicating that the first diagnosis is accurate; generate a predicted probability decrease based on the first recommendation indicating that the first diagnosis is inaccurate; and generate a weight for the first recommendation by combining the predicted probability increase and the predicted probability decrease. (par. 92-98: These expressions may be evaluated by any of the methods proposed herein. Assume that the evaluation of (Z) yields the optimal value for d.sub.3=0.12 and that the evaluation of (W) yields the optimal value for d.sub.3=0.03, we could therefore conclude that a biological test geared towards finding rashes would increase our confidence in neglecting C as the most probable diagnosis and the recommended test to perform would therefore be a test for finding rashes.)
At the time of filing, it would have been obvious to one of ordinary skill in the art to further modify the system of Kenig and Roh in combination with the teaching of Andbloom, with the motivation of supporting the analysis of complex biological systems while enabling a reduction of the number of measurements/test that needs to be conducted to reach an accurate conclusion. (Andbloom: par. 6)
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kenig et al (US 20200160995 A1) in view of Roh et al (US 20230215560 A1), as applied to claim 1, and in further view of Duggirala et al (US 20040147840 A1).
Claim 8. Kenig and Roh disclose the medical system of claim 1, as explained but do not disclose wherein the at least one processor is further configured to autonomously adjust the predicted probabilities in response to information obtained from implementation of a recommendation being input into the AI model.
Duggirala discloses a system wherein the at least one processor is further configured to autonomously adjust the predicted probabilities in response to information obtained from implementation of a recommendation being input into the AI model. (Figs. 3-4; par. 41; par. 70-74-probabilities are adjusted based on analysis of one or more further acts) At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system of Kenig and Roh in combination with the teaching of Duggirala, to include automatically adjusted probabilities based upon information obtained from implementation of a recommendation being input into the AI model. As suggested by Duggirala, one would have been motivated to include this feature to clearly communicate to the user information minimizing the additional number of acts needed to increase diagnostic certainty. (par. 74)
Claim 9. Kenig and Roh disclose the medical system of claim 1 as explained. Roh further discloses that the AI model includes neural networks. Kenig and Roh do not expressly disclose wherein the AI model includes two neural networks, the two neural networks comprising: the first neural network trained to generate the differential diagnosis list; and a second neural network trained to generate the one or more recommendations.
Duggirala discloses wherein the AI model includes two neural networks, the two neural networks (par. 24) comprising: the first neural network trained to generate the differential diagnosis list (Fig. 3; par. 49- A list of associated or differential diagnosis of particular pathological classes or subclasses may be provided) ; and a second neural network trained to generate the one or more recommendations. (par. 70-71: In act 42, a further act for diagnosis is recommended based on the CAD analysis. The further act is recommended automatically by the same or different processor used to perform the CAD analysis. For example, a processor and a medical sensor are used to jointly implement the CAD analysis and the automatic recommendation.) At the time of filing, it would have been obvious to one of ordinary skill in the art to further modify the system of Kenig and Roh in combination with the teaching of Duggirala. One would have been motivated to include this feature to more easily identify the minimal number of additional acts needed to increase diagnostic certainty and narrow the differential diagnosis list.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kenig et al (US 20200160995 A1) in view of Roh et al (US 20230215560 A1), as applied to claim 1, and in further view of Feder et al (US 20090259494 A1 ).
Claim 10 Kenig and Roh disclose the medical system of claim 1 as explained. Kenig and Roh do not disclose wherein the at least one processor is further configured to determine a relative benefit to cost for each recommended procedure based on a cost of the recommendation and a weight of the recommendation.
Feder teaches a system wherein the at least one processor is further configured to determine a relative benefit to cost for each recommended procedure based on a cost of the recommendation and a weight of the recommendation. (par. 26-27; par. 29-3; par. 186-187) At the time of applicant’s invention, it would have been obvious to one of ordinary skill in the art to modify the system of Kenig and Roh in combination with the teaching of Feder to include a cost-benefit analysis for further investigation and diagnostic recommendations. One would have been motivated to include this feature to recommend a best cost-benefit clinical datum to investigate next in a patient while precluding search for low-priority or irrelevant clinical data. (Feder: par. 18)
Claim(s) 15-16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kenig et al (US 20200160995 A1) as applied to claim 12, and in further view of Duggirala et al (US 20040147840 A1).
Claim 15. Kenig teaches the electronic device of claim 12, as explained and further discloses providing information on which data points are contributing to each of the suggested diagnoses (par. 55- display a corresponding user interface display which may be a detailed diagnosis summary page that includes additional details (e.g., which data points of the digital twin contributed to each of the suggested diagnoses) on the selected possible diagnosis.) Kenig does not expressly disclose generating or obtaining a value of a parameter that is being monitored, the value indicating an amount that the parameter is contributing to a corresponding diagnosis.
Duggirala discloses a system wherein the at least one processor is further configured to obtain a value of a parameter that is being monitored, the value indicating an amount that the parameter is contributing to a corresponding diagnosis. (par. 72-74The database is used to determine that a left ventricle end diastolic dimension measured at 7.6 cm indicate a probability of a DCM at 86.2 percent. A 95 percentile indication of non DCM is provided by a measurement of less than 5.8 cm. The missing feature vectors are output to the user to provide a checklist of information associated with the current examination that may increase or decrease the diagnosis probability. Recommendations may additionally include performance of previously performed acts for increased accuracy associated with a feature vector. The recommendations prompt the user to acquire additional information to refine the diagnosis. ) At the time of filing, it would have been obvious to on one ordinary skill in the art to modify the system of Kenig with the teaching of Duggirala to obtain/provide a value associating a given parameter’s contribution to a likely diagnosis. One would have been motivated to include this feature to aid healthcare providers in reaching a more accurate medical diagnosis by performing the best or most likely significant measurements or other acts using the fewest number of acts. (Duggirala: par. 74)
Claim 16. Kenig does not disclose, but Duggira teaches wherein the at least one processor is further configured to recommend ending monitoring of the parameter that is being monitored based on the value being below a predefined threshold. (par. 74: Based on the resulting probability of diagnoses, the sonographer may determine whether further recommended measurements are desired; par. 75- the recommendations output in act 42 are associated with a sensitivity. The sensitivity indicates the likely effect on diagnosis of the missing recommended action. In one embodiment of indicating sensitivity, the recommended further acts are provided in rank or order of importance. For example, a left ventricle end systolic dimension, wall thickness, ejection fraction and other possible measurements are provided in the above order. The order then indicates that left ventricle end systolic dimension measurement is most important or most significant for confirming or indicating as a DCM versus non-DCM diagnosis.) At the time of filing, it would have been obvious to on one ordinary skill in the art to modify the system of Kenig with the teaching of Duggirala with the motivation of aiding healthcare providers in reaching a more accurate medical diagnosis by performing the best or most likely significant measurements or other acts using the fewest number of acts. (Duggirala: par. 74)
Claim 20. Kenig discloses the device of claim 12, as explained but does not disclose wherein the at least one processor is further configured to autonomously adjust the predicted probabilities in response to information obtained from implementation of a recommendation being input into the AI model.
Duggirala discloses a system wherein the at least one processor is further configured to autonomously adjust the predicted probabilities in response to information obtained from implementation of a recommendation being input into the AI model. (Figs. 3-4; par. 41; par. 70-74-probabilities are adjusted based on analysis of one or more further acts) At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system of Kenig with the teaching of Duggirala, to include automatically adjusted probabilities based upon information obtained from implementation of a recommendation being input into the AI model. As suggested by Duggirala, one would have been motivated to include this feature to clearly communicate to the user information minimizing the additional number of acts needed to increase diagnostic certainty. (par. 74)
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kenig et al (US 20200160995 A1) as applied to claim 12, and in further view of Andbloom et al (US 20220020493 A1)
Claim 19. Kenig teaches the medical system of claim 12, as explained. Kenig further discloses providing an indication of a change in the diagnostic predicted probability based on the first recommendation (par. 57- Test A, listed at 316 may be used to rule out on or more of the diagnoses listed at 312. In some examples, the actions to narrow down the diagnosis 316 may include additional instructions, such as if the result of Test A is X, you should perform Test B to further narrow the possible diagnosis list 312, or, if the result of Test A is Y, you should perform Test B to further narrow the possible diagnosis list 312. In this way, user interface display 300 may provide instructions to the user (e.g., care provider) on how to narrow the diagnosis of the patient in the most efficient manner possible; par. 68; par. 71)
Kenig does not disclose, but Andbloom teaches wherein the at least one processor is further configured to, for a first recommendation of the first diagnosis, generate a predicted probability increase based on the first recommendation indicating that the first diagnosis is accurate; generate a predicted probability decrease based on the first recommendation indicating that the first diagnosis is inaccurate; and generate a weight for the first recommendation by combining the predicted probability increase and the predicted probability decrease. (par. 92-98: These expressions may be evaluated by any of the methods proposed herein. Assume that the evaluation of (Z) yields the optimal value for d.sub.3=0.12 and that the evaluation of (W) yields the optimal value for d.sub.3=0.03, we could therefore conclude that a biological test geared towards finding rashes would increase our confidence in neglecting C as the most probable diagnosis and the recommended test to perform would therefore be a test for finding rashes.)
At the time of filing, it would have been obvious to one of ordinary skill in the art to further modify the system of Kenig with the teaching of Andbloom, with the motivation of supporting the analysis of complex biological systems while enabling a reduction of the number of measurements/test that needs to be conducted to reach an accurate conclusion. (Andbloom: par. 6)
Response to Arguments
Applicant's arguments filed 10/16/25 have been fully considered but they are not persuasive.
(A) Applicant argues that the amended claim language overcomes the rejection of the claims under 35 USC 101.
In response, the examiner disagrees. The claims remain drawn to abstract idea (certain methods of organizing human activities.) The amendments are noted. However, claims 1-4 and 6-20 recite systems are drawn to determining differential diagnoses based on patient medical information/monitoring data, and determining/selecting next actions to narrow the diagnosis. Claim 12 has been removed from the “mental process” analysis based upon the amended claim language.
The recited judicial exception is not integrated into a practical application because the claim language does not recite any improvements to the functioning of a computer, or to any other technology or technical field (See MPEP 2106.04(d)(1); see also MPEP 2106.05(a)(I-II)). Moreover, the claims do not integrate the judicial exception into a practical application because the claimed invention does not: apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)); effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); or apply or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment see MPEP 2106.05(e). (Considerations for integration into a practical application in Step 2A, prong two and for recitation of significantly more than the judicial exception in Step 2B)
(B) Applicant argues that the prior art does not teach the newly added claim limitations.
In response, the examiner disagrees, and has updated the prior art rejections and citations to address the newly added limitations.
Applicant seems to emphasize that the recommendation is for a “procedure.” However, it is not clear from the language how the argued “procedure” is distinct over the lab tests, imaging, or other suggested next steps disclosed by the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Stoval III(US 20190189268 A1)- Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure, by applying a model developed using computer vision and machine learning techniques based on deep learning methodology to classify image studies, a classification assigned to the image study using the model, automatically generate a differential diagnosis for the patient based on the classification assigned by the model, and automatically adjust triaging of the image study based on the differential diagnosis.
Kabir (US 20240296955 A1)- discloses a high probability differential diagnosis generator and smart electronic medical record, including an artificial intelligence system.
Tulley et al ( US 20220059224 A1)-teaches a system suggesting next best action for patient care (see par. 152)
Chiofolo et al (US 20200221990 A1)-discloses a prediction algorithm that employs a neural network structure (Fig. 7) Each neuron 162 in the input layer represents an input value shown as a value X (e.g., an assumed, estimated, or measured, or known input such as UO, SCr, body weight, GFR). One value is output from each neuron 162, and each output is given a weight coefficient (or simply a weight). The weighted sum of each output (represented by an arrow) forms an input to each neuron 164 in a first intermediate (hidden) layer H. (par. 108)
Tagaki (US 20230335278 A1) discloses a diagnosis assistance apparatus and a diagnosis assistance method for using learning models for assisting a doctor in making a diagnosis of a cardiac disease. The learning model generation unit identifies the disease estimation model having the highest accuracy rate for each patient, and determines the identified disease estimation model as the disease estimation model suitable for the patient.
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RACHEL L. PORTER
Primary Examiner
Art Unit 3684
/Rachel L. Porter/Primary Examiner, Art Unit 3684