DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 14, 16, 18, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 14, the limitation “such as average of the target patient having the health conditions, demographics, and health information based on the actual medical labels of each reference patient identified as a nearest neighbor of the target patient” renders the claim indefinite because it is unclear whether the limitations following the phrase “such as” are part of the claimed invention. See MPEP § 2173.05(d). For the purposes of examination, the limitations following “such as” will not be considered required.
Regarding claim 16, the limitation “the series of one of more machine learning layers” renders the claims indefinite because it lacks antecedent basis in the claims. For the purpose of examination, “the series of one of more machine learning layers” will be interpreted as “a series of one of more machine learning layers”.
Regarding claim 18, the limitation “such as mean, prevalence, or distance-weighted prevalence within the identified multiple nearest neighbors of the patient” renders the claim indefinite because it is unclear whether the limitations following the phrase “such as” are part of the claimed invention. See MPEP § 2173.05(d). For the purposes of examination, the limitations following “such as” will not be considered required.
Regarding claim 20, the limitation “estimating values like demographics or biomarkers” renders the claims indefinite because this limitation is using exemplary language. Similar to the phrases “for example” or “such as,” in this context, “like” renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). For the purposes of examination, the limitations following “like” will not be considered 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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
This analysis in view of 35 U.S.C. § 101 is based on MPEP § 2106, please see
this section of the MPEP for additional information.
Step 1 of the analysis is the question: “Is the claim to a process, machine,
manufacture, or composition of matter?” and the answer is determined to be yes, as the
claims as a whole are directed to a manufacture and a method.
For Step 2, the preliminary question is whether the eligibility of the claim is self-
evident. The answer is determined to be no, as the claim is not immediately self-evident
as statutory.
Step 2A Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
A claim is directed to a judicial exception when a law of nature, a natural
phenomenon, or an abstract idea is recited (i.e., 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, 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.”
Claim 1 (and equivalently in claim 13/14) recites the following limitations:
“receiving a biosignal including a series of data points related to a target patient's health, demographics, or health information”;
“condensing a feature or characteristic of the biosignal related to the target patient's health, demographics, or health information into a target patient embedding vector that correlates to a physiological signature of the target patient”;
“comparing the target patient embedding vector to one or more reference embedding vectors that correlate to one or more reference patients”;
“identifying one or more nearest neighbors of the target patient in the reference patients based on the comparison of the target patient embedding vector to the one or more reference embedding vectors that correlate to the one or more reference patients”
“assessing the patient health risk or predicting the patient health event based on the identified one or more nearest neighbors of the target patient.”
The above identified elements comprise an explicit claim recitation of an abstract idea. Therefore, rather than merely involve a judicial exception, the claims are directed to the identified judicial exception.
This claim language is identified as an abstract idea, because in MPEP §
2106.04(a)(2) III B. this language is similar to concepts relating to organizing or
analyzing information in a way that can be performed mentally or are analogous to
human mental work. For example, Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d
1138, 120 USPQ2d 1473 (Fed. Cir. 2016). In Synopsys, the patentee claimed methods
of logic circuit design, comprising converting a functional description of a level sensitive
latch into a hardware component description of the latch. 839 F.3d at 1140; 120 USPQ2d at 1475. Although the patentee argued that the claims were intended to be
used in conjunction with computer-based design tools, the claims did not include any
limitations requiring computer implementation of the methods and thus do not involve
the use of a computer in any way. 839 F.3d at 1145; 120 USPQ2d at 1478-79. The
court therefore concluded that the claims “read on an individual performing the claimed
steps mentally or with pencil and paper,” and were directed to a mental process of
“translating a functional description of a logic circuit into a hardware component
description of the logic circuit.” 839 F.3d at 1149-50; 120 USPQ2d at 1482-83.
In the instant case, the identified abstract idea is similar to Synopsys because the
language reads on an individual clinician performing the algorithmic steps mentally or with a pencil and paper. Nothing in the claim precludes the steps from practically being performed in the human mind. MPEP 2106.04(a)(2)(III) states that 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. They do not require any specific computer function for implementation and therefore are directed to a mental process of analyzing biosignals, processing them to create a numerical representation of a patient’s physiological state, and comparing to those from a reference population to assess health risks or predict health events.
Yes. The claim is directed to an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
First, the additional elements are identified.
There are no additional elements claimed, as the biosignals are all acquired pre-solution and there is no explicit claim recitation of any additional structural elements
The remaining features in the claims are directed to further specifying the intended use but do not impose further limits to the recited system because they are generally linking the use of the judicial exception to a particular field of use or technological environment.
Step 2B: Does the claim recite additional elements that amount to significantly
more than the judicial exception?
There are no additional elements claimed, as the biosignals are all acquired pre-solution and there is no explicit claim recitation of any additional structural elements
The remaining features in the claims are directed to further specifying the intended use but do not impose further limits to the recited system because they are generally linking the use of the judicial exception to a particular field of use or technological environment.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1 and 5-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hasan et al. (U.S. Patent Application Publication No. 2023/0352134) hereinafter referred to as Hasan; in view of Van Assel et al. (U.S. Patent Application Publication No. 2021/0233658) hereinafter referred to as Assel.
Regarding claim 1, Hasan teaches a method of assessing a patient health comprising:
receiving a biosignal including a series of data points related to a target patient's
health, demographics, or health information (¶[0080] electronic records, ¶[0115]);
condensing a feature or characteristic of the biosignal related to the target patient's health, demographics, or health information into a target patient embedding vector that correlates to a physiological signature of the target patient (¶[0113] feature embedding engine resulting in feature vector);
Hasan further teaches nearest neighbor learning techniques (¶[0100]) but does not go so far as to explicitly teach comparing the target patient embedding vector to one or more reference embedding vectors that correlate to one or more reference patients; identifying one or more nearest neighbors of the target patient in the reference patients based on the comparison of the target patient embedding vector to the one or more reference embedding vectors that correlate to the one or more reference patients; and assessing the patient health risk or predicting the patient health event based on the identified one or more nearest neighbors of the target patient.
Attention is brought to the Assel reference, which teaches comparing a target patient embedding vector to one or more reference embedding vectors that correlate to one or more reference patients (¶[0330] similarity to neighbors);
identifying one or more nearest neighbors of the target patient in the reference patients based on the comparison of the target patient embedding vector to the one or more reference embedding vectors that correlate to the one or more reference patients (¶[0330] similarity measure higher than a pre-determined similarity threshold); and
assessing the patient health risk or predicting the patient health event based on the identified one or more nearest neighbors of the target patient (¶[0219]).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the AI care plan algorithm of Hasan to include additional nearest neighbor algorithmic steps to determine risks or predictions, as taught by Assel, because it helps mitigate problems encountered by clinicians in evaluating patient history data (Assel ¶¶[0370-0371]).
Regarding claim 5, Hasan as modified teaches the method of claim 1.
Hasan further teaches wherein the series of data points is related to two or more of a patient's health, demographics, and health information (¶[0080] electronic records, ¶[0115]).
Regarding claim 6, Hasan as modified teaches the method of claim 5.
Hasan further teaches wherein the series of data points related to the two or more of the patient's health, demographics, and health information is received from different sources (¶[0080] electronic records, ¶[0115], plurality of sources for the patient history and demographic information).
Regarding claim 7, Hasan as modified teaches the method of claim 1.
Hasan further teaches wherein the series of data points is related to a patient's health, demographics, and health information (¶[0080] electronic records, ¶[0115]).
Regarding claim 8, Hasan as modified teaches the method of claim 7.
Hasan further teaches wherein the series of data points related to the patient's health is condensed to a patient health embedding vector, the series of data points related to the patient's demographics is condensed to a patient demographics embedding vector, and the series of data points related to a patient's health information is condensed to a patient health information embedding vector (¶[0114] the groupings can be based on feature vectors of one or more fields, including health conditions, demographics, and biomarkers, ¶[0113] feature embedding engine may create additional feature vectors).
Assel also teaches an embedding vector for each target concept (¶[0146]) including an additional vector for clinical history (¶[0148]).
Regarding claim 9, Hasan as modified teaches the method of claim 8.
Hasan teaches further comprising combining the patient health embedding vector, the patient demographics embedding vector, and the patient health information embedding vector into the patient embedding vector using either concatenation or aggregation techniques (¶[0113] algebraic average).
Regarding claim 10, Hasan as modified teaches the method of claim 9.
Assel further teaches wherein the combined patient health embedding vector, the patient demographics embedding vector, and the patient health information embedding vector are compared to multiple reference embedding vectors (¶[0351], ¶¶[0354-0359].
Regarding claim 11, Hasan as modified teaches the method of claim 9.
Hasan teaches further comprising ingesting the combined patient health embedding vector, the patient demographics embedding vector, and the patient health information embedding vector into an embedding vector training model for reference patients (¶¶[0089-0093] all of the embedding vectors are optionally used for training, including by individual category).
Regarding claim 12, Hasan as modified teaches the method of claim 11.
Hasan teaches further comprising training the embedding vector training model for reference patients with the combined patient health embedding vector, the patient demographics embedding vector, and the patient health information embedding vector (¶¶[0089-0093] all of the embedding vectors are optionally used for training, including by individual category).
Regarding claim 13, Hasan as modified teaches the method of claim 11.
Assel further teaches wherein the embedding vector training model for reference patients also compares the target patient embedding vector to the one or more reference embedding vectors that correlate to the physiological parameter of the one or more reference patients (¶[0285]).
Regarding claim 14, Hasan as modified teaches the method of claim 13.
Assel teaches further comprising:
for each of the reference patient's health conditions, demographics, and health information, producing a respective actual medical label (¶[0117] each concept has an actual medical label);
comparing the target patient embedding vector to the one or more reference patient embedding vectors to identify at least one nearest neighbor of the target patient from the one or more reference patients (¶[0330] similarity measure higher than a pre-determined similarity threshold);
calculate a prevalence or aggregated metric
Regarding claim 15, Hasan as modified teaches the method of claim 1.
Assel further teaches wherein assessing the patient health risk or predicting the patient health event is further based on an actual medical label of one or more of the reference patients (¶[0219], ¶[0353] for example).
Regarding claim 16, Hasan as modified teaches the method of claim 15.
Hasan further teaches wherein the series of one of more machine learning layers includes one or more of a logistic regression operating on the patient embedding vector or multiple linear deep learning layers (¶¶[0102-0105]).
Regarding claim 17, Hasan as modified teaches the method of claim 1.
Assel teaches further comprising identifying multiple nearest neighbors of the patient from the reference patients (¶¶[0354-0359]).
Regarding claim 18, Hasan as modified teaches the method of claim 17.
Assel teaches further comprising calculating the value of a health quantity, including as biomarker or demographic, health risk, or health event, by using an aggregated value
Regarding claim 19, the claim is directed to substantially the same subject matter as claims 8-18 and is rejected under substantially the same sections of Hasan and Assel.
Regarding claim 20, Hasan as modified teaches the method of claim 19.
Assel teaches further comprising assessing multiple patient health risks, predicting multiple patient health events, or estimating values
Regarding claim 21, Hasan as modified teaches the method of claim 1.
Assel further teaches wherein the one or more nearest neighbors of the target patient are calculated using one or more of a dot product, cosine similarity (¶[0298]), metric distance learning, linear probe, non-linear probe distance function (¶[0351]).
Claim(s) 2-4 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hasan and Assel as applied to claim 1 above, and further in view of Poltorak (U.S. Patent Application Publication No. 2022/0160309) hereinafter referred to as Poltorak.
Regarding claims 2-4, Hasan as modified teaches the method of claim 1.
Hasan as modified does not teach wherein the biosignal is a photoplethysmography (PPG) signal or a pseudo PPG signal, a video signal or a vocal signal.
Attention is drawn to the Poltorak reference, which teaches wherein the biosignal is a photoplethysmography (PPG) signal or a pseudo PPG signal (¶[0638]), a video signal (¶[0787]) or a vocal signal (¶[0678]).
It would have been obvious to one of ordinary skill in the art to modify the sensor system and data of Hasan as modified to include addition biosensor parameters, as taught by Poltorak, because Poltorak teaches sensors that monitor changes in a patient’s stable state and therefore enables monitoring of health decline and healing, disease, and recovery (Poltorak ¶[0747]).
Regarding claim 22, Hasan as modified teaches the method of claim 1.
Hasan as modified further teaches wherein the target patient embedding vector is a first target patient embedding vector at a first time (see rejection of claim 1).
Assel teaches further comprising:
receiving a biosignal including a series of data points related to the target patient's health, demographics, or health information at a second time (¶[0224]);
condensing the feature or characteristic of the biosignal related to the target patient's health, demographics, or health information into a second target patient embedding vector that correlates to a physiological signature of the target patient at the second time (¶[0295]);
determining a temporal pattern or temporal embedding vector for the target patient (¶[0262]); and
identifying nearest neighbors of the target patient based on the temporal pattern or the temporal embedding vector (¶[0330]).
Hasan as modified does not teach determining a temporal pattern or temporal embedding vector for the target patient based on the first embedding vector and the second embedding vector; and
Hasan as modified does not teach determining a temporal pattern or temporal embedding vector for the target patient based on the first embedding vector and the second embedding vector.
Attention is drawn to the Poltorak reference, which teaches determining a temporal pattern or temporal embedding vector for the target patient based on the first embedding vector and the second embedding vector (¶[0678], ¶[0692]).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the health risk assessment algorithm of Hasan as modified to include updating a user’s model with new information, as taught by Poltorak, to ensure optimize and ensure reliability (Poltorak ¶[0738]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 2020/0151519 to Anushiravani et al. teaches measuring biomarkers and creating embedding vectors, concatenating features into a feature vector in a time sequence, including a differencing step between a reference and an example.
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/AMANDA L STEINBERG/ Examiner, Art Unit 3792