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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Specification
The incorporation of essential material in the specification by reference to an unpublished U.S. application, foreign application or patent, or to a publication is improper. Applicant is required to amend the disclosure to include the material incorporated by reference, if the material is relied upon to overcome any objection, rejection, or other requirement imposed by the Office. The amendment must be accompanied by a statement executed by the applicant, or a practitioner representing the applicant, stating that the material being inserted is the material previously incorporated by reference and that the amendment contains no new matter. 37 CFR 1.57(g).
The disclosure is objected to because of the following informalities:
The specification recites multiple abbreviations. The first instance should be accompanied by the fully written term. Non-exhaustive examples include “EMR”, “TSH”, “NC”, “DM”, and “nDM”.
The disclosure uses different formats for citations throughout the specification. Uniformity is recommended. It is noted that if Applicant amends the specification to remove the list of references, that the citations will need to be further amended to clearly identify which references they refer to.
Appropriate correction is required.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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:
“the drawing component feature is determined by a drawing component analysis module, the drawing component analysis module being configured by computer-readable instructions to: i) identify, for each instance in the time and position log, an entry position and entry time for a given stroke and an exit position and an exit time for the given stroke and ii) determine a measure from the entry position, entry time, exit position, and exit time for the given stroke” in claims 6 and 19.
“the drawing component analysis module is configured to identify a number of extra strokes” in claims 9 and 20.
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 § 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.
Claims 6-10, 19, 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.
Claim limitations “the drawing component feature is determined by a drawing component analysis module, the drawing component analysis module being configured by computer-readable instructions to: i) identify, for each instance in the time and position log, an entry position and entry time for a given stroke and an exit position and an exit time for the given stroke and ii) determine a measure from the entry position, entry time, exit position, and exit time for the given stroke” in claims 6 and 19 and “the drawing component analysis module is configured to identify a number of extra strokes” in claims 9 and 20 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. As this is interpreted to be a computer-implemented 35 USC 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 USC 112(b). See MPEP 2181(II)(B). In particular, the disclosure merely recites that the function is performed in results-based language without providing a description of the steps, calculations, or formulas for performing the claimed functionality See, for example, at least para. 10, 13, 41, 42, 50-52, 54-61, 74, and 80 of the specification. For instance, para. 50 recites that the “drawing component analysis module 132 may further analyze characteristics of each stroke, the timing and/or sequence of strokes, or other mathematical operation or statistical analysis based on the strokes made by the user 110 in answering questions of the cognitive exam 108” but is silent regarding any description of the algorithms necessary to perform such analysis. Similarly, para. 55 and Table 1 recite drawing assessment features are calculated and may be employed as features in a machine learning analysis performed by one or more machine learning models, but are silent regarding how these features are determined themselves. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Dependent claims 7-10 and 20 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Regarding claim 10, it is unclear how “the measure further includes handwriting analysis”. In particular, “the measure” is part of the drawing component feature determined by a drawing component analysis module as identified in parent claim 6. However, independent claim 1 distinguishes a writing component feature as separate from a drawing component feature. Thus, it is unclear how handwriting analysis is an element of a drawing component feature when one of ordinary skill in the art would understand it to be an element of the writing component feature. Therefore, one of ordinary skill in the art would not be apprised of the metes and bounds of the patent protection sought.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1-4, 11, 16, and 18, the disclosure fails to provide sufficient written description for “determining, by the one or more processors, based on the one or more calculated values for the at least one of the timing component feature, the writing component feature, and the drawing component feature, an estimated value for a presence of a cognitive disease or a score for cognitive level function” in claims 1, 16, and 18, “wherein the estimated value for a presence of a cognitive condition or a score for the cognitive level function is determined using one or more trained ML models” in claim 2, “wherein the one or more trained ML models includes one or more logistic regression-associated models, one or more support vector machines, one or more neural networks, and/or one or more gradient boost-associated models” in claim 3, “wherein the estimated value for a presence of a cognitive condition or a score for the cognitive level function is determined using one or more trained AI models” in claim 4, and “determining, by the one or more processors utilizing a portion of the third data set, one or more calculated second values for a cognitive impairment feature, wherein the one or more calculated second values for the cognitive impairment feature are used with the one or more calculated values for the drawing component feature to determine the estimated value for the presence of a cognitive disease or the score for cognitive level function” in claim 11 to show one of ordinary skill in the art that Applicant had possession of the claimed invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). The specification, at best, merely recites that this function is performed without providing sufficient, if any, description of the steps, calculations, or formulas necessary to perform the claimed functionality. See, for example, at least para. 40-42, 55, 61-65, 75, 80, and 82 of the specification which merely recite the generic use of artificial intelligence or machine learning models while para. 66-71 and 112-115 merely provide definitions for several machine learning models but are silent regarding how they are applied in the claimed invention. Dependent claims 2-15, 17, 19, and 20 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Regarding claims 6, 10, 19, and 20, the disclosure fails to provide sufficient written description for “the drawing component feature is determined by a drawing component analysis module, the drawing component analysis module being configured by computer-readable instructions to: i) identify, for each instance in the time and position log, an entry position and entry time for a given stroke and an exit position and an exit time for the given stroke and ii) determine a measure from the entry position, entry time, exit position, and exit time for the given stroke” in claims 6 and 19 and “the drawing component analysis module is configured to identify a number of extra strokes” in claims 9 and 20 to show one of ordinary skill in the art that Applicant had possession of the claimed invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. The written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function as identified in the rejection of the claim under 35 USC 112(b). Thus, just as this computer-implemented 35 USC 112(f) claim limitation is found to be indefinite under 35 USC 112(b) for failure to disclose sufficient corresponding structure in the specification that performs the entire claimed function, as identified above, it also lacks written description under 35 USC 112(a). See MPEP 2163.03(VI). Such a limitation lacks an adequate written description because an indefinite, unbounded limitation would cover all ways of performing a function and indicate that the inventor has not provided sufficient disclosure to show possession of the invention. See MPEP 2163.03(VI). Dependent claims 7-10 and 20 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Regarding claim 15, the disclosure fails to provide sufficient written description for “wherein the output… is used by a test evaluator, in part, to evaluate the user in a job interview, a job-related training, or a job-related assessment” to show that Applicant had possession of the claimed invention. While there is a presumption that an adequate written description of the claimed invention is present in the specification as filed. In re Wertheim, 541 F.2d 257, 262, 191 USPQ 90, 96 (CCPA 1976), a question as to whether a specification provides an adequate written description may arise in the context of an original claim. An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc). The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. "Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement." Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002). See MPEP 2163.03(V). In this instance, the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved. In particular, the disclosure merely recites the same language as the claim but is silent regarding any description of how the output is used “to evaluate the user in a job interview, a job-related training, or a job-related assessment”. See, for example, at least para. 18, 37, 63, 77, and 83 of the specification.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without including additional elements that are sufficient to amount to significantly more than the judicial exception itself.
Step 1
The instant claims are directed to a products and a method which fall under at least one of the four statutory categories (STEP 1: YES).
Step 2A, Prong 1
Independent claim 1 recites:
A method to assess cognitive impairment or cognitive function, the method comprising:
obtaining, by one or more processors, a first data set comprising a set of question scores for a set of cognitive questions performed by a user;
obtaining, by the one or more processors, a second data set comprising at least one of a timing component log, a writing component log, and a drawing component log acquired during completion of the at least one of the set of cognitive questions by the user;
determining, by the one or more processors utilizing at least a portion of the first data set and second data set, one or more calculated values for at least one of a timing component feature, a writing component feature, and a drawing component feature for each of the at least one of the set of cognitive questions, wherein the drawing component feature includes at least one of a number of strokes, a total length of strokes, an average length of strokes per stroke, an average speed of strokes per stroke, an average straightness per stroke, a geometric area assessment of the strokes, or a geometric perimeter assessment of the strokes;
determining, by the one or more processors, based on the one or more calculated values for the at least one of the timing component feature, the writing component feature, and the drawing component feature, an estimated value for a presence of a cognitive disease or a score for cognitive level function; and
outputting, via a report and/or display, (i) the estimated value for the presence of the cognitive disease, condition, or an indicator of either or (ii) the score for cognitive level function, wherein the output is made available to a healthcare provider, a test evaluator, or a user to assist in a diagnosis of a cognitive disease or condition or a quantification of cognitive function.
Independent claim 16 recites:
A system comprising:
a processor; and
a memory having instructions stored thereon, wherein the instructions, when executed by a processor, cause the processor to:
obtain a first data set comprising a set of question scores for a set of cognitive questions performed by a user;
obtain a second data set comprising at least one of a timing component log, a writing component log, and a drawing component log acquired during completion of the at least one of the set of cognitive questions by the user;
determine, utilizing at least a portion of the first data set and second data set, one or more calculated values for at least one of a timing component feature, a writing component feature, and a drawing component feature for each of the at least one of the set of cognitive questions, wherein the drawing component feature includes at least one of a number of strokes, a total length of strokes, an average length of strokes per stroke, an average speed of strokes per stroke, an average straightness per stroke, a geometric area assessment of the strokes, or a geometric perimeter assessment of the strokes;
determine, based on the one or more calculated values for the at least one of the timing component feature, the writing component feature, and the drawing component feature, an estimated value for a presence of a cognitive disease or a score for cognitive level function; and
output, via a report and/or display, (i) the estimated value for the presence of the cognitive disease, condition, or an indicator of either or (ii) the score for cognitive level function, wherein the output is made available to a healthcare provider, a test evaluator, or a user to assist in a diagnosis of a cognitive disease or condition or a quantification of cognitive function.
Independent claim 18 recites:
A non-transitory computer-readable medium having instructions stored thereon, wherein the instructions, when executed by a processor, cause the processor to:
obtain a first data set comprising a set of question scores for a set of cognitive questions performed by a user;
obtain a second data set comprising at least one of a timing component log, a writing component log, and a drawing component log acquired during completion of the at least one of the set of cognitive questions by the user;
determine, utilizing at least a portion of the first data set and second data set, one or more calculated values for at least one of a timing component feature, a writing component feature, and a drawing component feature for each of the at least one of the set of cognitive questions, wherein the drawing component feature includes at least one of a number of strokes, a total length of strokes, an average length of strokes per stroke, an average speed of strokes per stroke, an average straightness per stroke, a geometric area assessment of the strokes, or a geometric perimeter assessment of the strokes;
determine, based on the one or more calculated values for the at least one of the timing component feature, the writing component feature, and the drawing component feature, an estimated value for a presence of a cognitive disease or a score for cognitive level function; and
output, via a report and/or display, (i) the estimated value for the presence of the cognitive disease, condition, or an indicator of either or (ii) the score for cognitive level function, wherein the output is made available to a healthcare provider, a test evaluator, or a user to assist in a diagnosis of a cognitive disease or condition or a quantification of cognitive function.
All of the foregoing underlined elements identified above, both individually and as a whole, amount to the abstract idea grouping of a certain method of organizing human activity because it is managing personal behavior or interactions between people (including social activities, teaching, and following rules or instructions) by collecting information, analyzing the information, and outputting the results of the collection and analysis. This also amounts to the abstract idea grouping of mental processes as the claims, under their broadest reasonable interpretation, cover performance of the limitations in the mind with the aid of pen and paper (including observation, evaluation, judgment, opinion) but for the recitation of generic computer components. See MPEP 2106.04(a)(2)(III)(C) - A Claim That Requires a Computer May Still Recite a Mental Process. Lastly, the determining steps amount to the abstract idea grouping of mathematical concepts because they recite mathematical calculations as defined in MPEP 2106.04(a)(2)(I) which recites that a “claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the ‘mathematical concepts’ grouping” because a “mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word ‘calculating’ in order to be considered a mathematical calculation. For example, a step of ‘determining’ a variable or number using mathematical methods or ‘performing’ a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation."
The dependent claims amount to merely further defining the judicial exception.
Therefore, the claims recite a judicial exception. (STEP 2A, PRONG 1: YES).
Step 2A, Prong 2
This judicial exception is not integrated into a practical application because the independent and dependent claims do not include additional elements that are sufficient to integrate the exception into a practical application under the considerations set forth in MPEP 2106.04(d). The elements of the claims above that are not underlined constitute additional elements.
The following additional elements, both individually and as a whole, merely generally link the judicial exception to a particular technological environment or field of use: one or more processors (claim 1), a display (claims 1, 16, and 18), a drawing component analysis module (claims 6 and 19), a system comprising a processor and a memory (claim 16), a non-transitory computer-readable medium (claim 18), and a processor (claim 18). This is evidenced by the drawings and the nature in which any additional element is described in the claims and the specification. See, for example, Fig. 1A-1C which illustrates the elements as a collection of black boxes and stock icons in a conventional arrangement, and at least para. 19-21 and 88-96 of the specification which identify that the disclosed elements are merely schematically illustrated based on their function and do not represent specific hardware or software or combinations thereof. This also evidences that the computer components are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed as well as that the claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system. Similarly, in the event that “one or more trained ML models” and “one or more trained AI models” are considered additional elements, the mere use of “one or more trained ML models” or “one or more trained AI models”, and artificial intelligence as a whole, does not improve computer functionality as it merely invokes the use of a computer or other machinery in its ordinary capacity to process information. This is further evidenced by the specification which explicitly recites that the “term ‘artificial intelligence’ can include any technique that enables one or more computing devices or comping systems (i.e., a machine) to mimic human intelligence” and that “the machine learning model can be any supervised learning model, semi-supervised learning model, or unsupervised learning model.” See para. 64 and 66 of the specification. Therefore, the claims merely define the abstract idea identified above, and are focused on the abstract idea rather than an improvement to the computer functionality or another technology. The claims do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition. In particular, the claims are silent regarding any specific treatment or prophylaxis for any specific disease or medical condition. Accordingly, based on all of the considered factors, these additional elements do not integrate the abstract idea into a practical application. Therefore, the claims are directed to the judicial exception. (STEP 2A, PRONG 2: NO).
Step 2B
The independent and dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under the considerations set forth in MPEP 2106.05. As identified in Step 2A, Prong 2, above, the claimed system and the process it performs does not require the use of a particular machine, nor does it result in the transformation of an article. The claims do not involve an improvement in a computer or other technology. Although claims recite computer components associated with performing at least some of the recited functions, these elements are recited at a high level of generality in a conventional arrangement for performing their basic computer functions (i.e., collecting, processing, and outputting data). This is at least evidenced by the manner in which this is disclosed that indicates that Applicant believes the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 USC 112(a) as identified in Step 2A, Prong 2, above. This also evidences that the computer components are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. In particular, the claims are wholly focused on mathematically estimating cognitive impairment or cognitive function of an individual. Thus, the focus of the claimed invention is on the analysis of the collected data, which is itself at best merely an improvement within the abstract idea. See pg. 2-3 in SAP America Inc. v. lnvestpic, LLC (890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018) which proffered “[w]e may assume that the techniques claimed are groundbreaking, innovative, or even brilliant, but that is not enough for eligibility. Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. The claims here are ineligible because their innovation is an innovation in ineligible subject matter. Their subject is nothing but a series of mathematical calculations based on selected information and the presentation of the results of those calculations.” Furthermore, the steps are merely recited to be performed by, or using, the elements while the specification makes clear that the computerized system itself is ancillary to the claimed invention as identified above. See, for example, para. 19-21 and 88-96 which, at best, merely recite in results-based language that they are used. This further evidences that the claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system. Thus, none of the additional elements offer a meaningful limitation beyond generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Therefore, viewed as a whole, these additional claim elements do not provide any meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea of itself (STEP 2B: NO).
Thus, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-14 and 16-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pascual-Leone et al. (US 2023/0255564 which has an effective filing date of 25 September 2020, hereinafter referred to as PL).
Regarding claims 1, 16, and 18, PL teaches a method to assess cognitive impairment or function (claim 1), a system comprising: a processor; and a memory having instructions stored thereon (claim 16), and a non-transitory computer-readable medium having instructions stored thereon (claim 18) (PL, Title, Systems and Methods for Machine-learning-Assisted Cognitive Evaluation and Treatment), wherein the instructions, when executed by a processor, cause the processor to:
obtain a first data set comprising a set of question scores for a set of cognitive questions performed by a user (PL, Fig. 11, Assessment Battery; para. 34, “neuropsychological test scores”);
obtain a second data set comprising at least one of a timing component log, a writing component log, and a drawing component log acquired during completion of the at least one of the set of cognitive questions by the user (PL, Fig. 3, First-Order Metrics; Fig. 11, Patient Multimodal Data);
determine, utilizing at least a portion of the first data set and second data set, one or more calculated values for at least one of a timing component feature, a writing component feature, and a drawing component feature for each of the at least one of the set of cognitive questions (PL, para. 46, “multimodal data may be input to the learning system (e.g., a neural network) used to determine a latent representation of a patient's health data for use in another learning system to determine a biomarker (e.g., a cognitive score) and/or health condition of the patient (e.g., cognitive disease).”), wherein the drawing component feature includes at least one of a number of strokes, a total length of strokes, an average length of strokes per stroke, an average speed of strokes per stroke, an average straightness per stroke, a geometric area assessment of the strokes, or a geometric perimeter assessment of the strokes (PL, Fig. 3, First-Order Metrics; para. 40, “The behavior signal elicited from the tasks can be measured to collect modalities and first-order features. For example, a clock drawing task may allow the system to measure elements such as: drawing efficiency, correct component placement, drawing position, distribution of latencies, total ink used, drawing velocities, and oscillatory motion.” Para. 47, “recording positional data of user interactions such as inputs (i.e., time stamped X-axis and Y-axis coordinates on a touch screen)”; para. 48, “Examples of temporal data inputs include but are not limited to: time-stamped X-axis, Y-axis coordinates captured during a health assessment where the patient is asked to draw on a mobile device (an array of coordinates over time itself may be treated as a time series)”; para. 49, “with each sample (taken 240 times per second) we capture the X-coordinate, Y-coordinate, Azimuth, Altitude, and Force.”);
determine, based on the one or more calculated values for the at least one of the timing component feature, the writing component feature, and the drawing component feature, an estimated value for a presence of a cognitive disease or a score for cognitive level function (PL, para. 46, “multimodal data may be input to the learning system (e.g., a neural network) used to determine a latent representation of a patient's health data for use in another learning system to determine a biomarker (e.g., a cognitive score) and/or health condition of the patient (e.g., cognitive disease).”); and
output, via a report and/or display, (i) the estimated value for the presence of the cognitive disease, condition, or an indicator of either or (ii) the score for cognitive level function, wherein the output is made available to a healthcare provider, a test evaluator, or a user to assist in a diagnosis of a cognitive disease or condition or a quantification of cognitive function (PL, Fig. 11, Patient Biomarkers, Patient Predicted Health Conditions [Wingdings font/0xE0] Recommendation Engine [Wingdings font/0xE0] Intervention Recommendation [Wingdings font/0xE0] Clinician; para. 39, “following test analysis, a report may be automatically generated and may provide for immediate availability for review by, for example, clinical staff, administrators, or the patients themselves. In various embodiments, the results and recommendations generated by the analysis of the tasks may then be used clinically for a more accurate assessment of cognitive function and brain health.”).
Regarding claim 2, PL teaches the method of claim 1, wherein the estimated value for a presence of a cognitive condition or a score for the cognitive level function is determined using one or more trained ML models (PL, Fig. 11, Machine Learning Models Ensemble [Wingdings font/0xE0] Patient Biomarkers, Patient Predicted Health Conditions; para. 46, “multimodal data may be input to the learning system (e.g., a neural network) used to determine a latent representation of a patient's health data for use in another learning system to determine a biomarker (e.g., a cognitive score) and/or health condition of the patient (e.g., cognitive disease).”).
Regarding claim 3, PL teaches the method of claim 2, wherein the one or more trained ML models includes one or more logistic regression-associated models, one or more support vector machines (PL, para. 67, “support vector machines”), one or more neural networks (PL, fig. 7, “first artificial neural network… second artificial neural network”), and/or one or more gradient boost-associated models (PL, para. 43, “machine learning models such as linear regression, deep learning, random forests, and gradient boosters”).
Regarding claim 4, PL teaches the method of claim 1, wherein the estimated value for a presence of a cognitive condition or a score for the cognitive level function is determined using one or more trained AI models (PL, (PL, Fig. 11, Machine Learning Models Ensemble [Wingdings font/0xE0] Patient Biomarkers, Patient Predicted Health Conditions; para. 46, “multimodal data may be input to the learning system (e.g., a neural network) used to determine a latent representation of a patient's health data for use in another learning system to determine a biomarker (e.g., a cognitive score) and/or health condition of the patient (e.g., cognitive disease).” Para. 52, “In various embodiments, both raw data and constructed features are used as inputs to artificial intelligence algorithms.” The machine learning teachings in PL are considered as one or more trained AI models as the instant specification confirms that “machine learning” is a subset of AI. See at least para. 64 of the instant specification. Thus, machine learning models are a species that anticipates the AI genus. See MPEP 2131.02(I) A Species Will Anticipate a Claim to a Genus).
Regarding claim 5, PL teaches the method of claim 1, wherein the drawing component feature is determined from a time and position log of a user input to a pre-defined writing or drawing area during the completion of the at least one of the set of cognitive questions by the user (The prior art does not need to teach this limitation as the drawing component feature is claimed in the alternative in independent claim 1. Regardless, PL, para. 47, “recording positional data of user interactions such as inputs (i.e., time stamped X-axis and Y-axis coordinates on a touch screen)”).
Regarding claim 17, PL teaches the system of claim 16 further comprising:
a cognitive test server configured to present, and obtain answers for, the set of cognitive questions to the user, wherein the cognitive test server is configured to generate a time and position log for one or more actions of the user when answering the set of cognitive questions (PL, Fig. 15, Computer System/Server 12).
Regarding claims 6 and 19, PL teaches the method of claim 5 and the system of claim 17, wherein the drawing component feature is determined by a drawing component analysis module (The prior art does not need to teach this limitation as the drawing component feature is claimed in the alternative in independent claims 1 and 16. Regardless, PL, Fig. 3, Digital Clock Drawing Test with associated First-Order Metrics; para. 148, “each block in the flowchart or block diagrams may represent a module”), the drawing component analysis module being configured by computer-readable instructions to:
i) identify, for each instance in the time and position log, an entry position and entry time for a given stroke and an exit position and an exit time for the given stroke (PL, para. 47, “recording positional data of user interactions such as inputs (i.e., time stamped X-axis and Y-axis coordinates on a touch screen)”; para. 48, “Examples of temporal data inputs include but are not limited to: time-stamped X-axis, Y-axis coordinates captured during a health assessment where the patient is asked to draw on a mobile device (an array of coordinates over time itself may be treated as a time series)”) and
ii) determine a measure from the entry position, entry time, exit position, and exit time for the given stroke (PL, Fig. 3, First-Order Metrics; para. 47, “recording positional data of user interactions such as inputs (i.e., time stamped X-axis and Y-axis coordinates on a touch screen)”; para. 48, “Examples of temporal data inputs include but are not limited to: time-stamped X-axis, Y-axis coordinates captured during a health assessment where the patient is asked to draw on a mobile device (an array of coordinates over time itself may be treated as a time series)”; para. 49, “with each sample (taken 240 times per second) we capture the X-coordinate, Y-coordinate, Azimuth, Altitude, and Force.”).
Regarding claim 7, PL teaches the method of claim 6, wherein the measure includes at least one of:
i) determining the number of strokes (The prior art does not need to teach this limitation because it is claimed in the alternative);
ii) determining the total length of the strokes by (a) determining a length for each of the strokes and (b) summing the determined lengths (PL, para. 53, “the exact length of the input layer can be optimized using grid search techniques for different model architectures.”);
iii) determine the average length of strokes per stroke by (a) determining a length for each stroke and (b) performing an average operation on the determined lengths (The prior art does not need to teach this limitation because it is claimed in the alternative);
iv) determining the average speed of strokes per stroke by (a) determining a velocity for each stroke using length and time measure for a given stroke and (b) performing an average operation on the determined lengths (PL, para. 40, “drawing velocities”);
v) the average straightness per stroke by determining a ratio of a distance between each endpoint of the stroke to a corresponding length of the stroke (PL, para. 40, “oscillatory motion”); and
vi) determining a size of a response comprising the strokes (PL, para. 40, “drawing efficiency, correct component placement,… distribution of latencies, total ink used” are all indicators of a size of a response comprising the strokes; para. 53, “the exact length of the input layer can be optimized using grid search techniques for different model architectures.”).
Regarding claim 8, PL teaches the method of claim 7, wherein the measure of the average straightness per stroke is further determined by:
segmenting a single stroke of a geometric shape at corners of the geometric shape to generate individual strokes for each side of the geometric shape (This does not need to be taught by the prior art because it is dependent on a limitation claimed in the alternative.).
Regarding claims 9 and 20, PL teaches the method of claim 6 and the system of claim 19, wherein the drawing component analysis module is configured to identify a number of extra strokes, wherein the extra strokes are not employed in the measure determination (This does not need to be taught by the prior art because it is dependent on a limitation claimed in the alternative.).
Regarding claim 10, PL teaches the method of claim 6, wherein the measure further includes handwriting analysis (This does not need to be taught by the prior art because it is dependent on a limitation claimed in the alternative.).
Regarding claim 11, PL teaches the method of claim 1, further comprising:
obtaining, by the one or more processors, a third data set comprising electronic health records of the user (PL, para. 97, “Raw data from electronic health records”; para. 104, “Additional data from electronic health records”); and
determining, by the one or more processors utilizing a portion of the third data set, one or more calculated second values for a cognitive impairment feature (PL, Fig. 3, Second-order measures; para. 92, “the machine learning models described herein may be used for anomaly detection in health data of a patient (e.g., an EHR).”),
wherein the one or more calculated second values for the cognitive impairment feature are used with the one or more calculated values for the drawing component feature to determine the estimated value for the presence of a cognitive disease or the score for cognitive level function (PL, para. 97, “Prediction of biomarker values from methods described in prior sections along with feature importance in determining the various predicted values; Raw data from electronic health records and multimodal assessments; first order measures calculated from multimodal assessment data; second order measures (latent variable representations); and/or Clinical settings. In various embodiments, the rules may apply logic which combines these input values into an output recommendation based on clinically established best practices.”).
Regarding claim 12, PL teaches the method of claim 11, wherein the first data and the second data are acquired through web services, and wherein the estimated value for the presence of the cognitive disease or cognitive level function are outputted through the web services to be displayed at a client device associated with the user (PL, para. 39, “task data captured by the device(s) can be securely transmitted to the system's servers where it can be decrypted, then analyzed using advanced analytics. In various embodiments, following test analysis, a report may be automatically generated and may provide for immediate availability for review by, for example, clinical staff, administrators, or the patients themselves.”).
Regarding claim 13, PL teaches the method of claim 12, wherein the output includes the estimated value for the presence or non-presence of the cognitive disease, condition, or an indicator of either, includes: a measure for normal cognition, mild cognitive impairment (MCI), or dementia (PL, para. 70, “estimate risk for cognitive impairment and dementia”; para. 92, “Evaluate the patient data model vector associated with the new subject using the one-class classification model to determine if the new subject is considered part of that class or not. If not, then the new subject is an outlier and deviates from normal neurological functioning.”).
Regarding claim 14, PL teaches the method of claim 13, wherein the output includes the estimated value for the presence or non-presence of the cognitive disease, condition, or an indicator of either and is used by the healthcare provider to assist in the diagnosis of an early onset of Alzheimer's, dementia, memory loss, or cognitive impairment (PL, para. 29, “Fig. 13 illustrates an exemplary model leveraging first and second order features to predict the onset of Alzheimer’s disease according to embodiments of the present disclosure.” Para. 34, “generate second-order features tied to specific brain health domains (e.g., memory, motor control, executive function), specific brain areas and networks (e.g., right or left hippocampal formation, right or left prefrontal cortex, right or left attentional network), and clinical diagnoses (e.g., Alzheimer's Disease, Parkinson's Disease).” Para. 36, “the second-order features may include… associated disease constructs (e.g., potential for Alzheimer's disease, Parkinson's disease, frontotemporal dementia)”; para. 70, “estimate risk for cognitive impairment and dementia”; para. 46, “determine a biomarker (e.g., a cognitive score) and/or health condition of the patient (e.g., cognitive disease).”).
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.
The factual inquiries 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.
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 15 is rejected under 35 U.S.C. 103 as being unpatentable over PL as applied to claim 14, in view of Severson et al. (US 2022/0044824, hereinafter referred to as Severson).
Regarding claim 15, PL teaches the method of claim 14, wherein the output includes the score for cognitive level function (PL, para. 46, “determine a biomarker (e.g., a cognitive score) and/or health condition of the patient (e.g., cognitive disease).”).
PL does not explicitly teach the output is used by a test evaluator, in part, to evaluate the user in a job interview, a job-related training, or a job-related assessment.
However, in a related art, Severson teaches the output is used by a test evaluator, in part, to evaluate the user in a job interview, a job-related training, or a job-related assessment (Severson, para. 167, “embodiments disclosed herein allow the military to assess cognitive function of military personnel at the beginning of military service, periodically during the service career, prior to deployment, during deployment, anytime after a mild or traumatic brain injury and/or on return from deployment.” Para. 173, “Embodiments described herein may allow employers and human resources departments to test an employee's cognitive functions prior to and during the course of employment. Accordingly, this application of the embodiments disclosed herein provides numerous benefits including employee self testing with automated scoring and reporting of an employees performance measures at the beginning of employment and over longitudinal measurements of performance and the potential to link cognitive assessments to additional digital employee testing instruments. Performance metrics of individuals can be used to create a baseline of multiple domains of cognitive function to determine areas of strength and weaknesses in an employee’s ability. These measures can be used to evaluate areas of service that, personnel can be assigned to or trained in that would be appropriate to enhance work performance.” Para. 178, “Uses to Assess Fatigue - This use of the systems and methods disclosed herein can be useful to assess the fatigue level of shift workers, drivers, residents in hospitals, surgeons before beginning an operation, athletes before being put into a game, etc.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use the output of PL by a test evaluator, in part, to evaluate the user in a job interview, a job-related training, or a job-related assessment as identified by Severson because “performance metrics of individuals can be used to create a baseline of multiple domains of cognitive function to determine areas of strength and weaknesses in an employee's ability [which] can be used to evaluate areas of service that, personnel can be assigned to or trained in that would be appropriate to enhance work performance.” See Severson at para. 173.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Alberts (US 2017/0296101) discloses computerized assessment of cognitive function including drawing data.
Stewart et al. (US 9,883,831) discloses cognitive testing on a touch screen device.
Davis et al. (US 2019/0076078 and US 2022/0054077) discloses quantifying cognitive function based on hand movements.
Penney and Davis (US 2020/0337627) discloses cognitive assessment using input position data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL LANE whose telephone number is (303)297-4311. The examiner can normally be reached Monday - Friday 8:00 - 4:30 MT.
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/DANIEL LANE/ Examiner, Art Unit 3715
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