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 the amendments filed 3/19/26. Claims 1-4, 7-13, 16-18, and 21-22 are pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: patient care system configured to in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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, 7-13, 16-18, and 21-22 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, 7-13, and 21, are drawn to a system, and claims 16-18 and 22 are drawn to methods.
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? If so, is the judicial exception integrated into a practical application of the judicial exception? 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, 7-13, 16-18, and 21-22 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 the 2019 Revised Patent Subject Matter Eligibility Guidance (See MPEP 2106.04) 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 system and method are drawn to analyzing patient data to determine patient risk for illness/infection, and determining a proper course for intervention based on analysis. (managing personal behavior or relationships or interactions between people)
In particular, claim 1, 16, and 21-22 recites:
cause the patient to be treated by a peritoneal dialysis machine…
determine/determining, using the received patient data as input, a patient risk score, the patient risk score comprising an indication of a risk for the patient to develop an infection within a selected time period;
determine/determining, using the received patient data as input, at least one reason associated with the patient risk score, the at least one reason comprising an indication of leading factors for developing the infection; and
determine/determining, using the at least one reason as input responsive to the patient risk score being over a predetermined threshold value, at least one individualized intervention for the patient.
provide the determined at least one individualized intervention for the patient, wherein providing the determined at least one individualized intervention for the patient, wherein providing the determined at least one individualized intervention includes causing the patient to switch from being treated by the peritoneal dialysis machine to being treated by a hemodialysis machine…
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. Claims 1, 16, and 21-22 recites the additional limitation “receive… patient data of the patient while the patient is being treated by the …peritoneal dialysis treatment.” In this instance the additional step(s) amounts/amount 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)
Claim 1 recites: (storing) a plurality of machine learning (ML) models. Claim 16 recites additional limitation(s) also recites: a memory (storing a plurality of machine learning (ML) models and instructions); and a processor (…operative to execute the instructions). The added claim language recites generic computer components performing functions that were well-known, routine and conventional at the time of Applicant’s invention was filed.
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: “Processing circuitry 420 may include and/or may access various logic for performing processes according to some embodiments. Processing circuitry 420, or portions thereof, may be implemented in hardware, software, or a combination thereof… a logic, circuitry, or a layer may be and/or may include, but are not limited to, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and/or magnetic storage medium), an object, an executable, a thread of execution, a program, a computer, hardware circuitry, integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), a system-on-a-chip (SoC), memory units, logic gates, registers, semiconductor device, chips, microchips, chip sets, software components, programs, applications, firmware, software modules, computer code, combinations of any of the foregoing, and/or the like.” (par. 115)
The application explains: “the processor 1210 may be any type of processor, multiprocessor or controller, whether commercially available or specially manufactured. For instance, according to one example, the processor 1210 may include an MPC823 microprocessor manufactured by MOTOROLA….The memory 1220 may include a computer readable and writeable nonvolatile data storage medium configured to store non-transitory instructions and data. In addition, the memory 1220 may include a processor memory that stores data during operation of the processor 1210. In some examples, the processor memory includes a relatively high performance, volatile, random access memory such as dynamic random access memory (DRAM), static memory (SRAM), or synchronous DRAM. However, the processor memory may include any device for storing data, such as a non-volatile memory, with sufficient throughput and storage capacity to support the functions described herein. Further, examples are not limited to a particular memory, memory system, or data storage system. (see par. 130-131) The language of the system description underscores that the applicant's perceived invention/ novelty focuses on the computerized implementation of the abstract idea, not the underlying structure of the additional (generic) components.
Claims 1 has been amended to recite a system further comprising: an at-home peritoneal dialysis machine configured to infuse the patient's peritoneal cavity with dialysate; and a hemodialysis machine at a dialysis clinic configured to pass the patient's blood through a dialyzer while also passing dialysate through the dialyzer. The recitation of dialysis machines is not significantly more. Neither of the machines performs in improved or unconventional manner. For example, Updyke (US 20140276371 A1) describes the standard process of blood passing through a dialyzer and dialysate for hemodialysis and (par. 4-5)
Claims 16, 21 and 22 were previously amended to further recite “via peritoneal dialysis device.” However, peritoneal dialysis devices were well-known, routine and conventional in the art at the time of filing. In particular, Dadson (WO 9906082 A1) discloses the use of PD machines for institutional and home use, more than 15 years prior to applicant’s filing date. (pg. 3-4)
Claims 1,16 and 21-22 further recite performing steps “via a machine learning model” and/ or “via one of a plurality of ML models.” Claims 1 and 16 also recite the following limitations “generating the one or more ML models comprises: extracting patient historical data from one or more databases corresponding to the pool of patients; generating a training data set based on the extracted patient historical data; and training the one or more ML models based in part on the training data set.” These additional elements do not add amount to “significantly more” than the abstract idea, and do not integrate the abstract idea into a practical application. The recitation of machine learning is at high level of generality, and the use of machine learning in the claims amounts to adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, 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 performing a step “via one of the plurality of ML models…” and “generating the plurality of ML models comprises…” in the claims invokes the use of the machine learning model(s) merely as a tool to perform an existing process. More specifically, the claims are drawn using or applying a machine learning model/ models to determine a patient’s risk of developing an infection based upon patient parameters and risk factors, then determining an intervention for the patient based upon the determined risk. But for the recitation of “via one of the plurality of ML models” the recited process is analogous to a health care profession reviewing patient parameters to assess risk and determining how to mitigate risk to the patient.
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, are analogous to the steps performed in generating and training a machine learning model, as recited in which are recited in claims 1,16, and 21-22:
- 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)) (i.e. analogous to receiving data from a care coordination system);
- 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;
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.
Claims 2-4; 7-13 are dependent from Claim 1 and include(s) all the limitations of claim(s) 1. However, the additional limitations of the claims 2-4; 7-13 fail to recite significantly more than the abstract idea, and only serve to further define the abstract idea. Therefore, claim(s) 2-4; 7-13 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claims 17-18 are dependent from Claim 16 and include(s) all the limitations of claim(s) 16. However, the additional limitations of the claims 17-18 fail to recite significantly more than the abstract idea, and only serve to further define the abstract idea. Therefore, claim(s) 17-18 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Regarding Prior Art/Allowable Subject Matter
Amarasingham et al (US 20150213224 A1) discloses a system and method substantially as recited in claims 1, 16, 21, and 22 . For example, Amarasingham teaches a system for determining a patient's risk of developing an infection, the system comprising:
a memory storing a plurality of machine learning (ML) models and instructions; and a processor coupled to the memory and operative to execute the instructions, (par. 29; par. 40)- which instructions when executed cause the system to:
receive, from a care coordination system, patient data of a patient (par. 71-The method 80 receives structured and unstructured clinical and non-clinical data related to specific patients from a variety of sources and in a number of different formats, as shown in block 82; Fig. 6);
determine, via one of the plurality of ML models using the received patient data as input, a patient risk score, (Fig. 6; par. 71-by analyzing the pre-processed data, one or more potential diseases or adverse events of interest as related to each patient are identified. In block 90, the method 80 applies one or more predictive models to further analyze the data and calculate one or more risk scores for each patient as related to the identified diseases or adverse events; par. 104- adverse events includes healthcare-associated infections (HAIs)) ;
determine, via one of the plurality of ML models using the received patient data as input, at least one reason associated with the patient risk score, the at least one reason comprising an indication of leading factors for developing the infection; (providing contributing factors that influence risk: par. 73- The risk score (with specific regard to high risk) computed for each patient for a disease of interest is compared to a disease high risk threshold in block 110. Each disease is associated with its own high risk threshold. If the risk score is less than the high risk threshold, then the process determines if the patient's risk score falls into the medium or low risk categories, otherwise the process returns to data integration and is repeated when new data associated with a patient become available. If the risk score is greater than or equal to the high risk threshold, then the identified patient having the high risk score is identified as `high risk` and included in a patient list in block 112; par. 84- identification of factors that influence risk scores: The dashboard user interface 75 may also indicate a change in the level of risk. For example, upon return of recent lab results (e.g., slightly elevated creatinine and tox screen positive for cocaine) and other (updated) social factors that influence risk (e.g., noncompliance with sodium restriction due to homelessness) as well as medical pathway language queues, and prior admission history, the system may identify a patient initially evaluated to be at medium risk of readmission to currently be at high risk for readmission. A reviewer can follow these changes in real-time and to validate the change in risk level and take any additional appropriate action; models used-par. 58; par. 61-62, par. 64-65; par. 71-the method 80 applies one or more predictive models to further analyze the data and calculate one or more risk scores for each patient as related to the identified diseases or adverse events. In blocks 92 and 94, one or more lists showing those patients with the highest risks for each identified disease or adverse event are generated, transmitted, and otherwise presented to designated medical staff, such as members of an intervention coordination team.) and
determine, via one of the plurality of ML models using the at least one reason as input responsive to the patient risk score being over a predetermined threshold value at least one individualized interventional treatment for the patient (par. 75; par. 84- identification of factors that influence risk scores: The dashboard user interface 75 may also indicate a change in the level of risk. For example, upon return of recent lab results (e.g., slightly elevated creatinine and tox screen positive for cocaine) and other (updated) social factors that influence risk (e.g., noncompliance with sodium restriction due to homelessness) as well as medical pathway language queues, and prior admission history, the system may identify a patient initially evaluated to be at medium risk of readmission to currently be at high risk for readmission. A reviewer can follow these changes in real-time and to validate the change in risk level and take any additional appropriate action; par. 104-105: having a reliable warning tool using the predictive model, patients with risk factors … can be treated in a timely manner by the appropriate clinical treatment team to avoid serious and potentially life-threatening adverse clinical outcomes);
wherein each of the plurality of ML models is generated from a training set representative of a pool of patients (par. 64- the artificial intelligence model tuning process 72 is adapted to reconfigure or adjust the predictive model based on the specific clinical setting or population in which it is applied. Further, no manual reconfiguration or modification of the predictive model is necessary. The artificial intelligence model tuning process 72 may also be useful to scale the predictive model to different health systems, populations, and geographical areas in a rapid timeframe); wherein generating the plurality of ML models comprises: extracting patient historical data from one or more databases corresponding to the pool of patients (par. 34- Data may be extracted from numerous sources);
generating a training data set based on the extracted patient historical data; and training the plurality of ML models based in part on the training data set. (par. 34; par. 64 training and retraining model)
Amarasingham does not expressly disclose that the patient data is for patients receiving an the at-home peritoneal dialysis treatment; and does not disclose wherein each of the plurality of ML models is generated from a training set representative of a pool of patients receiving an the at-home peritoneal dialysis treatment; wherein the pool of patients comprises a first pool of patients diagnosed with an infection within the selected time period and a second pool of patients that did not develop an infection within the selected time period, and the training set is representative of both the first pool of patients and the second pool of patients.
Chang et al (Chang YJ, Yeh ML, Li YC, Hsu CY, Lin CC, et al.; August 24, 2011; PLOS ONE “Predicting Hospital-Acquired Infections by Scoring System with Simple Parameters.” 6(8): e23137) discloses a method of assessing patient risk of developing/acquiring an infection but does not disclose that the patient data is for patients receiving an the at-home peritoneal dialysis treatment; and does not disclose wherein each of the plurality of ML models is generated from a training set representative of a pool of patients receiving an the at-home peritoneal dialysis treatment; wherein the pool of patients comprises a first pool of patients diagnosed with an infection within the selected time period and a second pool of patients that did not develop an infection within the selected time period, and the training set is representative of both the first pool of patients and the second pool of patients.
Response to Arguments
Applicant's arguments filed 3/19/26 have been fully considered but they are not persuasive.
(A) Applicant argues the amended language of claim 1 renders the claim patent eligible because the claims recite peritoneal dialysis and hemodialysis machines.
In response, the examiner disagrees. The recitation of dialysis machines is not significantly more. Neither of the machines performs in improved or unconventional manner. For example, Updyke (US 20140276371 A1) describes the standard process of blood passing through a dialyzer and dialysate for hemodialysis and (par. 4-5)
Claims 16, 21 and 22 were previously amended to further recite “via peritoneal dialysis device.” However, peritoneal dialysis devices were well-known, routine and conventional in the art at the time of filing. In particular, Dadson (WO 9906082 A1) discloses the use of PD machines for institutional and home use, more than 15 years prior to applicant’s filing date. (pg. 3-4)
Moreover, the involvement of the dialysis machines, as recited, is in an extra-solution capacity. The claims are drawn to the decision process for determining whether a patient will be switched from hemodialysis to at-home peritoneal dialysis. The “intervention” is the provision of this decision; not the function of the either type of dialysis machine which remains conventional.
(B) Applicant argues the rejection of the claims under 35 USC 101, in light of the claim amendments. More specifically, applicant argues that the claims as amended, recites “a particular treatment.”
In response, the applicant’s arguments have been considered, but are not persuasive. Examiner notes the claim amendments and newly added claims. However, the amended language is not sufficient to overcome the claim rejections under 35 USC 101 for the reasons set forth in the current rejection.
The claims, even with the newly added claim amendments, do not recite a particular treatment. It is noted that applicant’s arguments rely heavily on describing the details of peritoneal versus hemodialysis. However, the claims do not recite administering a particular treatment, but rather “cause the patient to be treated by a peritoneal dialysis machine…” and providing the determined individualized intervention by “causing the patient to switch from being treated by the peritoneal dialysis machine to being treated by a hemodialysis machine.” The amended language is much broader than a recitation of administering a particular treatment, and could include generating, receiving or transmitting a prescription or medical order.
Moreover, the claims do not recite a “particular” treatment. As previously explained, the treatment or prophylaxis limitation must be “particular,” i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). For example, consider a claim that recites mentally analyzing information to identify if a patient has a genotype associated with poor metabolism of beta blocker medications. This falls within the mental process grouping of abstract ideas enumerated in Section I of the 2019 PEG. The claim also recites “administering a lower than normal dosage of a beta blocker medication to a patient identified as having the poor metabolizer genotype.” This administration step is particular, and it integrates the mental analysis step into a practical application. Conversely, consider a claim that recites the same abstract idea and “administering a suitable medication to a patient.” This administration step is not particular, and is instead merely instructions to “apply” the exception in a generic way. Thus, the administration step does not integrate the mental analysis step into a practical application.
As explained, the claims are drawn to analyzing patient data to determine patient risk for illness/infection, and determining a proper course for intervention based on analysis. (managing personal behavior or relationships or interactions between people). Contrary to applicant’s arguments, the abstract idea identified in the rejection under 35 USC 101 does not recite extraction of data. Moreover, “the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings” (See MPEP 2106.04(a)(2)(II))
The recitation of performing a step “via one of the plurality of ML models…” and “generating the plurality of ML models comprises…” in the claims invokes the use of the machine learning model(s) merely as a tool to perform an existing process. More specifically, the claims are drawn using or applying a machine learning model/ models to determine a patient’s risk of developing an infection based upon patient parameters and risk factors, then determining an intervention for the patient based upon the determined risk. But for the recitation of “via one of the plurality of ML models” the recited process is analogous to a health care profession reviewing patient parameters to assess risk and determining how to mitigate risk to the patient.
Therefore the rejections of claims 1-4; 7-13; 16-18 and 21-22 have been maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Peipert et al (Peipert JD, Hays RD. “Methodological considerations in using patient reported measures in dialysis clinics.” J Patient Rep Outcomes. 2017;1(1):11. doi: 10.1186/s41687-017-0010-9. Epub 2017 Nov 5. PMID: 29757314)-discusses the factors considered for determining treatment in dialysis patients.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachel L Porter whose telephone number is (571)272-6775. The examiner can normally be reached M-F, 10-6:30.
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/Rachel L. Porter/Primary Examiner, Art Unit 3626