DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to an application filed 11 June 2024, which is a continuation of an application issued as U.S. Patent #12,040,092, which claims priority to a provisional application filed 20 December 2019.
The Office notes that the provisional application does not have support for several of the claimed elements, and specifically for the limitations directed to a device containing two cameras or one camera. Accordingly, the elements unsupported by the provisional application have a filing date consisting of 9 December 2000.
Claims 1-13 were originally filed.
Claims 1-3, 5-7, 9-11 were amended by preliminary amendment.
Claims 14-16 were added by amendment.
Claims 1-16 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11 June 2024 has been considered by the Office to the extent indicated.
Drawings
New corrected drawings in compliance with 37 CFR 1.121(d) are required in this application because the drawings contain lines of insufficient weight, text of insufficient size and/or shading that make the drawings illegible, see Figs. 1-8. Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
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 1-15 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.
Step 1
Claims 1-15 are within the four statutory categories. Claims 1-4 and 15 are drawn to a non-transitory computer-readable media storing instructions that, when executed by one or more processors, predict one or more intradialytic hypotensive (IDH) events, which is within the four statutory categories (i.e. manufacture). Claims 5-8 and 16 are drawn to a system, which is within the four statutory categories (i.e. machine). Claims 9-14 are drawn to a method, which is within the four statutory categories (i.e. process).
Prong 1 of Step 2A
Claim 1 recites: One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, predict one or more intradialytic hypotensive (IDH) events by:
using a machine learning model to determine the probability of a future IDH event occurring based on training of the machine learning model; and
using the machine learning model to make a prediction that the future IDH event will occur in a preferred prediction time period based on the training of the machine learning model, the machine learning model being trained by:
obtaining historical hemodialysis treatment data preceding one or more intradialytic hypotension (IDH) events,
segmenting a first portion of the historical hemodialysis treatment data into a first set, the first portion of the historical hemodialysis treatment data being data corresponding to a first time period before the IDH events,
segmenting a second portion of the historical hemodialysis treatment data into a second set, the second portion of the historical hemodialysis treatment data being data corresponding to a second time period before the IDH events and before the first time period, and
in a first stage, training the machine learning model to predict the occurrence of IDH events based on the first set, the machine learning model treating the first set as containing data indicative of a future IDH event occurring within the preferred prediction time period, and
in a second stage, training the machine learning model to predict the occurrence of IDH events based on the second set, the machine learning model treating the second set as containing data not indicative of the future IDH event occurring within the preferred prediction time period; and
obtaining real-time hemodialysis data associated with a hemodialysis patient; and
applying the real-time hemodialysis data to the machine learning model, to predict whether the probability of the future IDH event occurring within the preferred prediction time period is greater than a threshold probability for the hemodialysis patient.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case the step of training a model by segmenting data in order to predict the possibility of a future IDH event), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea(s) are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for claims 5 and 9 are identical as the abstract idea for claim 1, because the only difference between claims 1, 5 and 9 is that claim 1 recites a non-transitory computer-readable media, whereas claim 5 recites a system and claim 9 recites a non-transitory computer-readable media.
Dependent claims 2-4, 6-8 and 10-16 include other limitations, for example claims 2, 6, 10 and 13-16 provide further details on the prediction timeline, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent 2-4, 6-8 and 10-16 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claims 2-4, 6-8 and 10-16 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 5 and 9.
Prong 2 of Step 2A
Claims 1-16 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the structural components of the computer, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs 74-82 of the present Specification, see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language limiting the data to hemodialysis treatment data and intradialytic hypotension events, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application (e.g. see MPEP 2106.05(g)).
Additionally, dependent claims 2-4, 6-8 and 10-16 include other limitations, but these limitations also amount to no more than mere instructions to apply the exception (e.g. the treatment adjustments of claims 3, 4, 7, 8, 11 and 12), and/or do not include any additional elements beyond those already recited in independent claims 1, 5 and 9, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1-16 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the structural components of the computer), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
paragraphs [0059]-[0064] and [0066] of the Specification discloses that the additional elements (i.e. the structural components of the computer) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data ) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
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.");
Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); and
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.
Dependent claims 2-4, 6-8 and 10-16 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 5 and 9, and/or the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply the exception (e.g. the treatment adjustments of claims 3, 4, 7, 8, 11 and 12), and hence do not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-16 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-16 are rejected under 35 U.S.C. 103 as being obvious over Bunu et al. (WO2019008798 A1), hereinafter Bunu, further in view of Attalah et al. (U.S. PG-Pub 2015/0045713 A1), hereinafter Attalah.
As per claims 1, 5 and 9, Bunu discloses a method, a system and One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, predict one or more … health events (Bunu, Figs. 1 and 3.) by:
using a machine learning model to determine the probability of a future … health event occurring based on training of the machine learning model (Bunu, Fig. 6, 10 and 12.); and
using the machine learning model to make a prediction that the future … health event will occur in a preferred prediction time period based on the training of the machine learning model, the machine learning model being trained (Bunu, Figs. 3, 6, 10 and 12.) by:
obtaining historical … patient treatment data preceding one or more… health events (System obtains sample data, see Bunu paragraphs 30-33.),
segmenting a first portion of the historical … patient data into a first set, the first portion of the historical ... patient data being data corresponding to a first time period before the … health events (Bunu segments of various sample data into multiple sets, including positive and negative correlations, occurring at various timepoints before the actual onset of an event, see paragraphs 33-38 and Figs. 3 and 4.),
segmenting a second portion of the historical ... patient treatment data into a second set, the second portion of the historical ... patient treatment data being data corresponding to a second time period before the … health events and before the first time period (Bunu segments of various sample data into multiple sets, including positive and negative correlations, occurring at various timepoints before the actual onset of an event, see paragraphs 33-38 and Figs. 3 and 4.), and
in a first stage, training the machine learning model to predict the occurrence of … health events based on the first set, the machine learning model treating the first set as containing data indicative of a future … health event occurring within the preferred prediction time period (Various segmented samples with positive and negative correlation are used as training data, see Bunu paragraphs 30 and 34. System analyzes data in accordance with the alert target time period, see paragraphs 40 and 68-69.), and
in a second stage, training the machine learning model to predict the occurrence of … health events based on the second set, the machine learning model treating the second set as containing data not indicative of the future … health event occurring within the preferred prediction time period (Various segmented samples with positive and negative correlation are used as training data, see Bunu paragraphs 30 and 34. System analyzes data in accordance with the alert target time period, see paragraphs 40 and 68-69.); and
obtaining real-time ... patient data associated with a … patient (Bunu collects real time patient data, see Fig. 12 and corresponding text.); and
applying the real-time … patient data to the machine learning model, to predict whether the probability of the future … health event occurring within the preferred prediction time period is greater than a threshold probability for the … patient (Bunu predicts a health event occurring within a threshold probability by applying real time patient data to the trained machine learning model, see paragraph 87.).
Bunu fails to explicitly disclose predicting an intradialytic hypotension (IDH) event using hemodialysis treatment data.
Attalah teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to predict an intradialytic hypotension (IDH) event using hemodialysis treatment data (Attalah, paragraphs 64, 65).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the disease onset prediction method of Bunu to include predict an intradialytic hypotension (IDH) event using hemodialysis treatment data, as taught by Attalah, in order to arrive at a disease onset prediction method that can treat a greater variety of patient conditions using historical and real time patient data.
Both Bunu and Attalah are directed to the electronic processing of patient healthcare data and specifically to the prediction of future healthcare states. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
As per claims 2, 6, 10 and 13-16, Bunu/Attalah discloses the method of claim 1, detailed above. Bunu/Attalah also discloses:
2,6,10. wherein the preferred prediction time period is within a maximum duration before IDH events and a minimum duration before the IDH events, wherein the minimum duration is at least long enough to medically intervene before an IDH event (Bunu discloses predicting within the alert target time period, see paragraphs 40 and 68-69. Attalah discloses predicting IDH events, as shown above.);
13. wherein the machine learning model makes the prediction each time the hemodialysis patient's intradialytic systolic blood pressure is measured (Bunu discloses making a prediction each time the vital signs are measured, see Fig. 12. 9. Attalah discloses predicting IDH events, as shown above.); and
14,15,16. wherein the minimum amount of time is at least fifteen minutes and the maximum about of time is seventy-five minutes (Bunu, Fig. 12.).
As per claims 2, 6, 10 and 13-16, Bunu/Attalah discloses the method of claim 1, detailed above.
Bunu fails to explicitly disclose:
3,7,11. responsive to making the prediction that the future IDH event will occur in the preferred prediction time period, adjusting without human intervention a treatment of the hemodialysis patient to prevent the future IDH event; and
4,8,12. wherein adjusting without human intervention the treatment of the hemodialysis patient comprises one or more of reducing an ultrafiltration rate, decreasing a dialysate temperature, or mechanically repositioning the hemodialysis patient.
Attalah teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide:
3,7,11. responsive to making the prediction that the future IDH event will occur in the preferred prediction time period, adjusting without human intervention a treatment of the hemodialysis patient to prevent the future IDH event (Attalah, paragraphs 4, 32, 66 and 68.); and
4,8,12. wherein adjusting without human intervention the treatment of the hemodialysis patient comprises one or more of reducing an ultrafiltration rate, decreasing a dialysate temperature, or mechanically repositioning the hemodialysis patient (Attalah, paragraphs 4, 32, 66 and 68.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify disease onset prediction method of Bunu/Attalah with the adjustment of parameters to avoid a predicted IDH event, as taught by Attalah, in order to arrive at a disease onset prediction method that can treat a greater variety of patient conditions using historical and real time patient data. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Regarding claims 3, 7 and 11, Attalah discloses manual adjustment of parameters, not automated adjustment. However, it was known at the time of the invention that merely providing an automatic means to replace a manual activity which accomplishes the same result is not sufficient to distinguish over the prior art, In re Venner , 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958). For example, simply automating the adjustment of parameters gives you just what you would expect from the manual step as shown in Attalah. In other words there is no enhancement found in the claimed step. The claimed adjusting step only provides automating the manual activity. The end result is the same as compared to the manual method. A computer can simply iterate the steps faster. The result is the same.
It would have been obvious to a person of ordinary still in the art at the time of the invention to automate the adjusting steps because this would more reliably provide quicker parameter adjustment, which is purely known, and an expected result from automation of what is known in the art.
Conclusion
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382. The Examiner can normally be reached on Monday-Friday (8-5). If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Kambiz Abdi, can be reached at 571.272.6702.
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/MARK HOLCOMB/
Primary Examiner, Art Unit 3685
11 February 2026