International Business Machines Corporation

Patent Trials and Appeals Board

Oct 8, 2020

2020001787 (P.T.A.B. Oct. 8, 2020)

UNITED STATES PATENT AND TRADEMARK OFFICE
UNITED STATES DEPARTMENT OF COMMERCE
United States Patent and Trademark Office
Address: COMMISSIONER FOR PATENTS
P.O. Box 1450
Alexandria, Virginia 22313-1450
www.uspto.gov
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
15/045,451 02/17/2016 James S. Cox SVL920150167US1 1030
45725 7590 10/08/2020
Walder Intellectual Property Law PC
445 Crestover Circle
Richardson, TX 75080
EXAMINER
PATEL, JAY M
ART UNIT PAPER NUMBER
3686
MAIL DATE DELIVERY MODE
10/08/2020 PAPER
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte JAMES S. COX
____________

Appeal 2020-001787
Application 15/045,451
Technology Center 3600
____________

Before JAMES P. CALVE, NINA L. MEDLOCK, and
BRADLEY B. BAYAT, Administrative Patent Judges.

MEDLOCK, Administrative Patent Judge.

DECISION ON APPEAL

STATEMENT OF THE CASE
Appellant1 appeals under 35 U.S.C. § 134(a) from the Examiner’s
final rejection of claims 1–4, 6–14, and 16–20. We have jurisdiction under
35 U.S.C. § 6(b).
We REVERSE.

1 We use the term “Appellant” to refer to “applicant” as defined in 37 C.F.R.
§ 1.42. Our decision references Appellant’s Appeal Brief (“Appeal Br.,”
filed September 10, 2019) and Reply Brief (“Reply Br.,” filed January 6,
2020), and the Examiner’s Answer (“Ans.,” mailed November 7, 2019) and
Final Office Action (“Final Act.,” mailed April 15, 2019). Appellant
identifies International Business Machines Corporation as the real party in
interest (Appeal Br. 2).
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CLAIMED INVENTION
Appellant describes that “[t]he present application relates generally to
an improved data processing apparatus and method” and more specifically to
“mechanisms for performing cognitive evaluation of assessment questions
and answers to determine patient characteristics corresponding to a medical
condition” (Spec. ¶ 1).
Claims 1, 11, and 20 are the independent claims on appeal. Claim 1,
reproduced below with bracketed notations added, is illustrative of the
claimed subject matter:
1. A method, in a data processing system comprising
a processor and a memory, for determining values to associate
with medical conditions of a patient, comprising:
[(a)] receiving, by the data processing system, a patient
assessment data structure comprising a natural language question
and a corresponding answer, about a patient, provided in
response to the question, wherein the patient assessment data
structure stores data corresponding to a plurality of natural
language questions and corresponding answers of a
questionnaire administered to the patient or another person about
the patient;
[(b)] performing, by a cognitive assessment question
answering evaluation system of the data processing system,
cognitive natural language processing on the patient assessment
to extract features from the natural language question and
corresponding answer, wherein the cognitive assessment
question answering evaluation system comprises a plurality of
stages of logic for evaluating the natural language question and
the corresponding answer to determine a meaning, veracity, and
accuracy of the answer to the natural language question in the
patient assessment;
[(c)] evaluating, by query generation logic of the
cognitive assessment question answering evaluation system of
the data processing system, the extracted features within a
context of a preexisting electronic medical record of the patient
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at least by generating one or more queries based on the extracted
features and applying the one or more queries to a corpus of
electronic documents specific to the patient;
[(d)] determining, by patient data structure/annotation
generator logic of the cognitive assessment question answering
evaluation system of the data processing system, a value for a
medical condition of the patient based on results of evaluating
the extracted features within the context of the pre-existing
electronic medical record of the patient; and
[(e)] storing, by patient data structure/annotations logic
of the cognitive assessment question answering evaluation
system of the data processing system, the determined value for
the medical condition in the electronic medical record for the
patient, wherein evaluating the extracted features comprises
converting the extracted features from the question and the
corresponding answer into a natural language statement and
generating the one or more queries based on the natural language
statement, and wherein determining the value for the medical
condition comprises associating with the natural language
statement a confidence value that the natural language statement
is correct.
REJECTION
Claims 1–4, 6–14, and 16–20 are rejected under 35 U.S.C. § 101 as
directed to a judicial exception without significantly more.
ANALYSIS
Under 35 U.S.C. § 101, an invention is patent eligible if it claims a
“new and useful process, machine, manufacture, or composition of matter.”
35 U.S.C. § 101. The Supreme Court, however, has long interpreted § 101
to include an implicit exception: “[l]aws of nature, natural phenomena, and
abstract ideas” are not patentable. Alice Corp. v. CLS Bank Int’l, 573 U.S.
208, 216 (2014).
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The Supreme Court, in Alice, reiterated the two-step framework
previously set forth in Mayo Collaborative Services v. Prometheus
Laboratories, Inc., 566 U.S. 66 (2012), “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., 573 U.S. at 217.
The first step in that analysis is to “determine whether the claims at issue are
directed to one of those patent-ineligible concepts.” Id. If the claims are not
directed to a patent-ineligible concept, e.g., an abstract idea, the inquiry
ends. Otherwise, the inquiry proceeds to the second step where the elements
of the claims are considered “individually and ‘as an ordered combination’”
to determine whether there are additional elements that “‘transform the
nature of the claim’ into a patent-eligible application.” Id. (quoting Mayo,
566 U.S. at 79, 78). This is “a search for an ‘inventive concept’ — i.e., an
element or combination of elements that is ‘sufficient to ensure that the
patent in practice amounts to significantly more than a patent upon the
[ineligible concept] itself.’” Id. at 217–18 (alteration in original).
The U.S. Patent and Trademark Office (the “USPTO”) published
revised guidance on January 7, 2019 for use by USPTO personnel in
evaluating subject matter eligibility under 35 U.S.C. § 101. 2019 REVISED
PATENT SUBJECT MATTER ELIGIBILITY GUIDANCE, 84 Fed. Reg. 50, 57
(Jan. 7, 2019) (the “2019 Revised Guidance”).2 That guidance revised the
USPTO’s examination procedure with respect to the first step of the

2 The USPTO issued an update on October 17, 2019 (the “October 2019
Update: Subject Matter Eligibility,” available at https://www.uspto.gov/sites/
default/files/documents/peg_oct_2019_update.pdf) clarifying the
2019 Revised Guidance in response to public comments.
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Mayo/Alice framework by (1) “[p]roviding groupings of subject matter that
[are] considered an abstract idea”; and (2) clarifying that a claim is not
“directed to” a judicial exception if the judicial exception is integrated into a
practical application of that exception. Id. at 50.
The first step, as set forth in the 2019 Revised Guidance (i.e.,
Step 2A), is, thus, a two-prong test. In Step 2A, Prong One, we look to
whether the claim recites a judicial exception, e.g., one of the following
three groupings of abstract ideas: (1) mathematical concepts; (2) certain
methods of organizing human activity, e.g., fundamental economic
principles or practices, commercial or legal interactions; and (3) mental
processes. 2019 Revised Guidance, 84 Fed. Reg. at 54. If so, we next
determine, in Step 2A, Prong Two, whether the claim as a whole integrates
the recited judicial exception into a practical application, i.e., whether the
additional elements recited in the claim beyond the judicial exception, 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. Id. at 54–55. Only if
the claim (1) recites a judicial exception and (2) does not integrate that
exception into a practical application do we then conclude that the claim is
“directed to” the judicial exception, e.g., an abstract idea. Id.
If the claim is determined to be directed to a judicial exception under
revised Step 2A, we evaluate the additional elements, individually and in
combination, in Step 2B, to determine whether they provide an inventive
concept, i.e., whether the additional elements or combination of elements
amounts to significantly more than the judicial exception itself; only then, is
the claim patent eligible. 2019 Revised Guidance, 84 Fed. Reg. at 56.
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The Examiner determined here that the claims recite “determin[ing]
values to associate with medical conditions of patients in medical records,”
i.e., a method of organizing human activity and, therefore, an abstract idea,
and that the judicial exception, i.e., the abstract idea, is not integrated into a
practical application (Final Act. 2–3). The Examiner also determined that
the claims do not include additional elements sufficient to amount to
significantly more than the abstract idea itself, i.e., that “[t]he use of a
computer or processor to merely automate or implement the abstract idea
cannot provide significantly more than the abstract idea itself” (id. at 3–4).
Independent Claim 1 and Dependent Claims 2–4 and 6–10
The Federal Circuit has explained that “the ‘directed to’ inquiry
applies a stage-one filter to claims, considered in light of the specification,
based on whether ‘their character as a whole is directed to excluded subject
matter.’” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir.
2016) (quoting Internet Patents Corp. v. Active Network, Inc., 790 F.3d
1343, 1346 (Fed. Cir. 2015)). It asks whether the focus of the claims is on a
specific improvement in relevant technology or on a process that itself
qualifies as an “abstract idea” for which computers are invoked merely as a
tool. See id. at 1335–36.
Here, the Specification is titled “COGNITIVE EVALUATION OF
ASSESSMENT QUESTIONS AND ANSWERS TO DETERMINE
PATIENT CHARACTERISTICS,” and describes that “providing treatment
and care for patients having illness requiring ongoing treatment is a major
issue in modern medicine” (Spec. ¶ 33). “Many times this ongoing
treatment and care is a shared responsibility between the medical workers,
e.g., doctors, nurses, etc. and the patient,” with the patient performing
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certain actions on his or her own to provide self-treatment for the illness, and
with the medical workers providing monitoring and periodic checks of the
patient’s progress to ensure that the patient is adhering to the treatment
needed to control and/or improve the patient’s condition (id.). The
Specification, thus, describes that a number of mechanisms have been
developed for assisting the patient and medical workers in handling their
shared responsibilities, including mechanisms for generating patient care
plans based on the patient’s medical condition (id. ¶ 34).3
The Specification further describes that in order to manage a patient’s
health, it is important to periodically administer health assessments to
establish where the patient is on the health continuum (Spec.¶ 220). The
health assessment may take different forms depending on its particular goals
(id. ¶ 221). For example, many health assessments are performed by
administering a questionnaire to the patient and receiving and processing the
patient’s responses to determine the patient’s current status with regard to a
particular medical condition; in other cases, the health assessment may
require that the patient be physically examined or that laboratory tests be
performed to obtain information regarding the patient’s current health
condition (id.). The information gathered from the health assessment may
be used to drive updates to the patient information in the patient registry,
e.g., by updating personal information about the patient, adding entries (new
medical codes) to the patient’s electronic medical records to reflect a

3 The Specification discloses that “[a] ‘mechanism,’ as the term is used
herein, may be an implementation of the functions or aspects of the
illustrative embodiments in the form of an apparatus, a procedure, or a
computer program product” (Spec. ¶ 29).
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diagnosed medical condition as a result of the health assessment, etc. —
updates that may trigger the creation of a new treatment or patient care plan
or the modification of an existing plan (id. ¶ 222).
The Specification describes that in an illustrative embodiment, the
health assessments are represented as data structures or electronic documents
representing the questions of a questionnaire and the corresponding answers
provided by the patient (Spec. ¶ 255). Although the assessment responses
are recorded in the patient registry, the Specification explains that the
meaning of the results of the assessment may not be readily apparent to the
automated systems that use this information in performing their respective
operations, e.g., creating a treatment or patient care plan or adjusting or
modifying an existing treatment or patient care plan (id. ¶ 251). For
example, without understanding what a question was asking, the answer,
e.g., “no,” is not of much use to the automated system; and, even if the
automated system is able to determine what the question is asking, the
automated system does not know how the answer relates to other patient
information (id. ¶¶ 251–252). In other words, according to the
Specification, the effect of the answer to a question is dependent on a
number of factors, including what the question is asking, how the patient
interprets the question, and the truthfulness of the patient (id. ¶ 252). As
such, “[a] full picture of the patient’s medical condition can only be obtained
with regard to an assessment by evaluating the responses of the assessment
in the context of the questions being asked and other patient information
obtained for the patient” (id.).
The Specification describes that, in accordance with the claimed
invention, a cognitive assessment evaluation system (which may be part of a
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system for generating patient care plans based on the patient’s medical
condition) utilizes natural language processing (“NLP”) to determine the
meaning, veracity, and accuracy of an answer to a question in a patient
health assessment and generate a corresponding data structure/annotation for
patient information (id. ¶ 258). Thus, as described in the Specification, the
evaluation system receives, as input, a health assessment document,
comprising one or more questions about the patient and associated answers,
from a patient registry (id. ¶ 259) and extracts features, (e.g., titles, section
heads, specific textual content, metadata) from the health assessment
document to determine the domain of the health assessment, i.e., its subject
matter and/or purpose (id. ¶ 260). The extracted features are then converted
to a natural language statement indicative of the correlation of the question
with the answer provided in the health assessment document to thereby
identify a patient characteristic (id. ¶ 261). For example, if the question in
the health assessment document is “are you a smoker?” and the answer is
“no,” the system generates a statement that “the patient is not a smoker” or
“the patient is a nonsmoker” (id.); the patient characteristic statement and/or
the extracted features are next evaluated in the context of other patient
information in the patient registry (e.g., clinical values of patient
characteristics, such as blood pressure readings, results of previous health
assessments, medical records indicating medical procedures, lifestyle
information indicating activities and conditions of the patient, or the like) to
determine the relative veracity and accuracy of the answer, i.e., to determine
the degree to which the patient characteristic is corroborated (id. ¶¶ 255,
262–264). More particularly, one or more search queries are generated
based on the patient characteristic statement, i.e., the natural language
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statement, and are used in searching the information in the patient registry
(id. ¶ 262). Based on the level of corroboration or non-corroboration, a
confidence value (e.g., 0 to 100, where 0 represents no confidence that the
natural language statement is correct and 100 represents full confidence that
the statement is correct) is generated and associated with the
question/answer pair (id. ¶¶ 256, 265). Thus, in the example, where the
patient characteristic statement is “patient is not a smoker,” if an evaluation
of other patient information (e.g., the patient’s previous history of smoking
and recent purchase of a smoking inhibitor) indicates that this statement may
not be true, i.e., that the patient may, in fact, be a smoker, the confidence
value assigned to the characteristic/condition may be a value, e.g., 0.4 or 0.8,
on a sliding scale of 0.0(0) to 1.0 (100), depending on the relative level of
corroboration or non-corroboration (id. ¶ 265). In some illustrative
embodiments, the confidence value, if sufficiently low, also may be basis for
modifying, i.e., inverting, the patient characteristic statement and increasing
the confidence value (id. ¶ 266).
The resulting data structure, including the patient characteristics and
their confidence, is added to the patient information in the patient registry,
and is used, along with other patient information from the patient registry, in
evaluating the patient’s health condition, or a trend in the health of the
patient, in order to generate a new patient care plan, modify an existing plan,
or perform other patient care operations (id. ¶¶ 256, 268).
Consistent with this disclosure, claim 1 recites a method, in a data
processing system comprising a processor and a memory, for determining
values to associate with medical conditions of a patient, comprising:
(1) receiving a patient assessment comprising a natural language question
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and a corresponding answer about the patient, provided in response to the
question, i.e.,
receiving, by the data processing system, a patient
assessment data structure comprising a natural language question
and a corresponding answer, about a patient, provided in
response to the question, wherein the patient assessment data
structure stores data corresponding to a plurality of natural
language questions and corresponding answers of a
questionnaire administered to the patient or another person about
the patient
(step (a)); (2) performing cognitive natural language processing on the
patient assessment to extract features from the natural language question and
corresponding answer, i.e.,
performing, by a cognitive assessment question answering
evaluation system of the data processing system, cognitive
natural language processing on the patient assessment to extract
features from the natural language question and corresponding
answer, wherein the cognitive assessment question answering
evaluation system comprises a plurality of stages of logic for
evaluating the natural language question and the corresponding
answer to determine a meaning, veracity, and accuracy of the
answer to the natural language question in the patient assessment
(step (b)); (3) evaluating the extracted features within the context of the
patient’s pre-existing medical record, i.e.,
evaluating, by query generation logic of the cognitive
assessment question answering evaluation system of the data
processing system, the extracted features within a context of a
preexisting electronic medical record of the patient at least by
generating one or more queries based on the extracted features
and applying the one or more queries to a corpus of electronic
documents specific to the patient
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(step (c)); and (4) determining a confidence value for a medical condition of
the patient based on the results of the evaluation, and storing the value in the
patient’s electronic medical record, i.e.,
determining, by patient data structure/annotation
generator logic of the cognitive assessment question answering
evaluation system of the data processing system, a value for a
medical condition of the patient based on results of evaluating
the extracted features within the context of the pre-existing
electronic medical record of the patient; and
storing, by patient data structure/annotations logic of the
cognitive assessment question answering evaluation system of
the data processing system, the determined value for the medical
condition in the electronic medical record for the patient, wherein
evaluating the extracted features comprises converting the
extracted features from the question and the corresponding
answer into a natural language statement and generating the one
or more queries based on the natural language statement, and
wherein determining the value for the medical condition
comprises associating with the natural language statement a
confidence value that the natural language statement is correct
(steps (d) and (e)).
We are persuaded that even if claim 1 recites an abstract idea, as the
Examiner determined, the Examiner has not sufficiently established that the
claim fails to improve an existing technology (i.e., automated systems for
generating patient care plans based on patient information in a patient
registry) and, therefore, in the context of the 2019 Revised Guidance, that
the claim fails to integrate the abstract idea into a practical application, or
that the claim otherwise fails to recite significantly more than the alleged
abstract idea.
The Specification discloses, as described above, that many health
assessments are performed by administering a questionnaire to the patient
and receiving and processing the patient’s responses to determine the
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patient’s current status with regard to a particular medical condition. And
the Specification details the shortcomings of automated systems (that use
this information to perform their operations), in terms of both their inability
to determine the meaning of the assessment results and their related inability
to determine, from the questions and answers in the patient assessment
alone, a complete picture of the patient’s medical condition (see, e.g., Spec.
¶¶ 251–252).
The Specification describes that the claimed invention addresses these
shortcomings by providing a mechanism for performing a cognitive natural
language analysis of the questions and answers provided in a patient
assessment data structure in the context of other patient information, e.g., the
patient’s pre-existing medical record and a corpus of electronic documents
specific to the patient, to determine the meaning, veracity, and accuracy of
the answers to the questions (id. ¶ 258). A full and accurate picture of the
patient’s medical condition is, thus, obtained that can be effectively used by
the automated system, e.g., to generate a new treatment care plan for the
patient, modify an existing plan, or perform other patient care operations.
The Examiner posits that the judicial exception is not integrated into a
practical application because the claims “merely use computer elements as
tools to perform abstract ideas and generally link the use of a judicial
exception to a particular technological environment” and that “[t]he use of a
computer or processor to merely automate or implement the abstract idea
cannot provide significantly more than the abstract idea itself” (Final Act. 3–
4). But, the Examiner has not addressed whether the functionality described
above, viewed in light of Appellant’s Specification, entails an improvement
in technology and/or a technical field. And, as such, the Examiner has not
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established that claim 1 fails to integrate the asserted abstract idea into a
practical application.
The Examiner also has not addressed whether the claim limitations,
when viewed in light of Appellant’s Specification, relate to a challenge
particular to automated systems for generating patient treatment plans, and
thus, recite significantly more than the alleged abstract idea.
Responding to Appellant’s argument that the claimed invention
addresses a problem rooted in technology, the Examiner opines, in the
Answer, “the claims, aside from the computer technology, may be
performed in the human mind by a health care provider” (Ans. 5–6; see also
id. at 5 (“[A] [d]octor[ ] may read patient assessment data, understand and
evaluate the necessary data to calculate a value for a medical condition, and
store the value either in his or her mind or on paper.”)). Yet, although the
claimed process and a person’s language recognition may result in similar
outcomes, the outcomes are achieved through different processes. One is the
way a human recognizes speech, and the other involves performing natural
language processing on a question and corresponding answer in a patient
assessment data structure to extract features (e.g., titles, section heads,
specific textual content, metadata) from the data structure, converting the
features to a natural language statement, and generating one or more queries
based on the natural language statement that are applied to a corpus of
electronic documents to thereby determine a value to be assigned to a
medical condition in the patient’s electronic medical record.
On this record, we are persuaded that the Examiner erred in rejecting
independent claim 1 under 35 U.S.C. § 101. Therefore, we do not sustain
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the Examiner’s rejection of claim 1. For the same reasons, we also do not
sustain the Examiner’s rejection of dependent claims 2–4 and 6–10.
Independent Claims 11 and 20 and Dependent Claims 12–14 and 16–19
Independent claims 11 and 20 include limitations substantially similar
to the limitations of claim 1. Therefore, we do not sustain the Examiner’s
rejection under 35 U.S.C. § 101 of independent claims 11 and 20, and
claims 12–14 and 16–19, which depend from claim 11, for the same reasons
set forth above with respect to claim 1.
CONCLUSION
In summary:
Claims
Rejected
35 U.S.C. § Reference(s)/Basis Affirmed Reversed
1–4, 6–14,
16–20
101 Eligibility 1–4, 6–14,
16–20

REVERSED