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 information disclosure statement filed 01 May 2025 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the box identifying that a full translation into the English language is checked for each foreign patent document and the first non-patent literature document, but only one of the foreign patent documents is translated into the English language. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. An English language translation of the Abstract and identification of a US Patent or US Patent Application Publication are not considered an English translation meriting the checking of the English language translation box. While footnote 5 identifies that “Applicant is to place a check mark here if English language translation is attached”, this is with respect to the document itself. The Abstract is considered a concise explanation of relevance of the document which is distinctly different from a translation. See at least MPEP 609.04(a) Content Requirements for an Information Disclosure Statement, section (II) Legible Copies, which recites
“37 CFR 1.98(a)(3)(ii) states that if a written English language translation of a non-English language document, or portion thereof, is within the possession, custody or control of, or is readily available to any individual designated in 37 CFR 1.56(c), a copy of the translation shall accompany the statement. Translations are not required to be filed unless they have been reduced to writing and are actually translations of what is contained in the non-English language information. If no translation is submitted, the examiner will consider the information in view of the concise explanation and insofar as it is understood on its face, e.g., drawings, chemical formulas, English language abstracts, in the same manner that non-English language information in Office search files is considered by examiners in conducting searches.”
See also MPEP 609.04(a)(III) Concise Explanation of Relevance for Non-English Language Information which also at least more explicitly recites that “[e]ach information disclosure statement must further include a concise explanation of the relevance, as it is presently understood by the individual designated in 37 CFR 1.56(c) most knowledgeable about the content of the information listed that is not in the English language. The concise explanation may be either separate from the specification or part of the specification. If the concise explanation is part of the specification, the IDS listing should include the page(s) or line(s) numbers where the concise explanation is located in the specification” and that “[s]ubmission of an English language abstract of a reference, such as one generated by a foreign patent office, may fulfill the requirement for a concise explanation.” Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The use of the term “random forest”, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
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 17-31 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 claims 17-31, each of these claims recite the language “a/the program causes a/the computer to execute processing of” followed by functions. This is grammatically incorrect and causes a lack of clarity regarding whether this is literally a computer executing processing (i.e., “interpreting and manipulating”) the functions in some analysis or is meant to mean that the program causes the computer to execute the functions. Thus, one of ordinary skill in the art would not be apprised of the metes and bounds of the patent protection sought. For the purposes of compact prosecution, the meaning of this grammatically incorrect language is construed as the latter – the program causes the computer to execute the functions. Dependent claims 18-30 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Claims 21, 23, and 25 each recite the limitation "the score of the pareidolia test" at the end of each claim. There is insufficient antecedent basis for this limitation in each of these claims. The limitation “computing a score of a pareidolia test” is recited in claim 18. However, claims 21, 23, and 25 are dependent from claim 17. Dependent claims 22 and 24 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Claim 30 recites the limitation "specifying a possibility of onset of the neuropsychiatric disorder based on changes in the risk score related to the neuropsychiatric disorder" at the end of the claim. There is insufficient antecedent basis for this limitation in the claim. In particular, there is no antecedent basis for “changes in the risk score”.
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 17-32 are 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 17-19, 21, 23-25, 31, and 32, the disclosure fails to provide sufficient written description for “computing a risk score related to a neuropsychiatric disorder based on the information related to the response of the subject” in claims 17, 31, and 32, “computing the risk score related to the neuropsychiatric disorder based on the information related to the response of the subject including the score of the pareidolia test” in claim 18, “computing the risk score related to the neuropsychiatric disorder based on the information related to the response of the subject including the score of the pareidolia test and the eye gaze tracking score” in claim 19, “computing an utterance tracking score based on the information related to the voice feature; and computing the risk score related to the neuropsychiatric disorder based on the information related to the response of the subject including the score of the pareidolia test and the utterance tracking score” in claim 21, “acquiring the utterance tracking score of the subject by inputting, to a learning model trained to output an utterance tracking score in response to input of information related to a voice feature, the acquired information related to the voice feature” in claim 22, “computing a health profile score based on the acquired answer; and computing the risk score related to the neuropsychiatric disorder based on the information related to the response of the subject including the score of the pareidolia test and the health profile score” in claim 23, “computing a health profile score based on the acquired answer; and computing the risk score related to the neuropsychiatric disorder based on the information related to the reaction of the subject including the score of the pareidolia test, the utterance tracking score, and the health profile score” in claim 24, “computing a memory test score based on the received words; and computing the risk score related to the neuropsychiatric disorder based on the information related to the reaction of the subject including the score of the pareidolia test and the memory test score” in claim 25, “specifying any one of a plurality of neuropsychiatric disorders subjected to determination based on the risk score related to the neuropsychiatric disorder” in claim 28, and “specifying a possibility of onset of the neuropsychiatric disorder based on changes in the risk score related to the neuropsychiatric disorder” in claim 30 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). In particular, the disclosure recites similar language as the claims, but is silent regarding any meaningful disclosure of analyzing information related to determining any of the claimed scores in order to determine a risk score related to a neuropsychiatric disorder. See, for example, at least para. 37-39, 45, 47, 48, 51, 59, 86, 87, and 111. For instance, the disclosure fails to provide any meaningful description for analyzing information related to a voice feature in order to compute an utterance tracking score nor compute the risk related to the neuropsychiatric disorder based on any information related to the utterance tracking score. See, for example, Fig. 6B which is identified by the specification to illustrate the utterance tracking score computation model M4 (see at least para. 38 of the specification). However, utterance tracking score computation model M4 is not illustrated in Fig. 6B. It is merely a black box in the drawing. Para. 38 of the specification also merely recites that the “utterance tracking score computation model M4 has a configuration similar to that of the eye gaze tracking score computation model M3, and is configured using algorithms such as SVM, random forest, CNN, RNN, LSTM, and Transformer. The utterance tracking score computation model M4 may be configured using other learning algorithms, or may be configured by combining a plurality of learning algorithms.” Yet, the disclosure is silent how computation model M4 is configured using such algorithms or any other “learning algorithms”, let alone any identification what constitutes a “Transformer” in the context of the claimed invention. This is exemplary for each of the other scores (score of a pareidolia test, eye gaze tracking score, health profile score, memory test score) used to compute a risk score related to a neuropsychiatric disorder. See at least para. 31 regarding the eye gaze tracking score, para. 46 regarding the disease risk score, para. 86 regarding the health profile score, and para. 111 regarding the memory test score. Also just like the utterance tracking score computation model, the eye gaze tracking score computation model, and disease risk score computation model are each shown as a black box. Then, the specification recites in results-based language that the subject is then determined to be at risk for one or several neuropsychiatric disorders of a plurality of neuropsychiatric disorders without any description of the analysis or combining, weighting, etc. of the test scores to make such a determination of risk. In summary, the disclosure merely recites that scores are computed by inputting information into a number of black boxes that represent an unbounded list of algorithms which output scores without any meaningful description or limiting of the algorithms used to perform such calculations. Dependent claims 18-30 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
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 17-32 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 claims are directed to a method and products which fall under the four statutory categories (STEP 1: YES).
Step 2A, Prong 1
Independent claim 17 recites:
A non-transitory computer-readable storage medium storing a program causing a computer to execute processing of:
outputting a test image used in a pareidolia test;
acquiring information related to a response of a subject to the test image; and
computing a risk score related to a neuropsychiatric disorder based on the information related to the response of the subject.
Independent claim 31 recites:
An information processing method in which a computer executes processing of:
outputting a test image used in a pareidolia test;
acquiring information related to a response of a subject to the test image; and
computing a risk score related to a neuropsychiatric disorder based on the information related to the response of the subject.
Independent claim 32:
An information processing device including a controller, wherein the controller is configured to:
output a test image used in a pareidolia test to a display unit;
acquire information related to a response of a subject to the test image; and
compute a risk score related to a neuropsychiatric disorder based on the information related to the response of the subject.
All of the foregoing underlined elements 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 it, and outputting the results of the collection and analysis typically performed by a clinician, researcher, etc. Similarly, the acquiring, computing, detecting, generating, specifying, outputting, and storing steps amount 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 computing and generating 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." This is further evidenced in the specification which recites that the “present disclosure provides for an automated analysis algorithm of a subject’s psychophysiological responses to a test protocol”. See para. 108 of the specification.
The dependent claims, except for claims 10-13 and 22-25 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 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 merely generally link the judicial exception to a particular technological environment or field of use: a non-transitory computer-readable storage medium storing a program (claim 17), a computer (claims 17 and 31), and an information processing device including a controller (claim 32). Although the claims recite the elements identified above, these elements are recited at a high level of generality in a conventional arrangement for performing their basic computer functions (i.e., receiving, processing, outputting data). This is evidenced by at least Fig. 1 and 2 which illustrate the components as non-descript black boxes or stock icons while Fig. 3A-23B illustrate that the claimed invention is focused on the judicial exception itself. Further evidence is provided by the specification. See also, for example, at least para. 11-21, 51, 52, 100, 109, and 134 of the specification which identify that any suitable combination of hardware, software, or firmware may be used to implement the judicial exception. Thus, the judicial exception is not implemented with, or used in, a particular machine or manufacture. Additionally, the claims do not recite any limitations that improve the functionality of the computer system as the computer system and its elements are merely recited to be used in the performance of the steps. For instance, the claimed steps do not integrate a judicial exception into a practical application because they are merely using a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f) since the claims merely recite the use of a computer in its ordinary capacity to perform these tasks. See MPEP 2106.04(d). This also evidences that the claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system as the claimed steps are wholly focused on the judicial exception itself while the computerized elements are merely recited to be used in their performance. In the event that “a learning model trained to output an eye gaze tracking score”, “a learning model trained to output a facial image of a different race”, “a random field model”, and “a learning model trained to output an utterance tracking score” are considered additional elements, the mere use of such models 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. The claims are silent regarding any specific rules with specific characteristics that improve the functionality of the computer system. For instance, the specification identifies that the learning models and random field model are not specific, nor have specific characteristics. In fact, they are all generically recited as black boxes. See, for example, at least para. 22-28, 31, 35-38, and 46 of the specification. For instance, many of these paragraphs recite similar language – “[the computation model] is configured using algorithms such as SVM, random forest, CNN, RNN, LSTM, and Transformer. The [computation model] may be configured using other learning algorithms, or may be configured by combining a plurality of learning algorithms.” This further identifies that none of the hardware offer a meaningful limitation beyond, at best, generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. The claims do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition. Additionally, the additional elements do not apply or use a judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, that is implementation with a computer. For instance, the claims, and disclosure as a whole, merely recite the use of conventional or generic technology in a nascent but well-known environment, without any assertion that the invention reflects an inventive solution to any problem presented in the technology, itself. 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 the claims recite components (identified in Step 2A, Prong 2) for 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., receiving, processing, outputting data). This is at least evidenced by the manner in which this is disclosed that indicates that 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. Thus, the judicial exception is not implemented with, or used in, a particular machine or manufacture. Furthermore, this also evidences that the 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. Additionally, as identified in Step 2A, Prong 2, the mere inclusion of generically recited computerized elements conventionally configured to perform their conventional functions merely indicates a field of use or technological environment in which to apply a judicial exception. This further evidences that the claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system. 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.” This further identifies that none of the hardware offer a meaningful limitation beyond, at best, generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Viewed as a whole, these additional claim elements do not provide meaningful limitation 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).
Therefore, 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 17-19 and 28-32 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Takeda et al. (US 2020/0383626, hereinafter referred to as Takeda).
Regarding claims 17 and 31, Takeda teaches a non-transitory computer-readable storage medium storing a program causing a computer to execute processing of (claim 17) and an information processing method in which a computer executes processing of (claim 31):
outputting a test image used in a pareidolia test (Takeda, Fig. 10, Start displaying of fourth video S41; para. 103, “a fourth video which is a video for diagnosis based on fourth video data 304 is presented to the subject. In this case, the fourth video may be a still image including both the inducing image and a plurality of non-inducing images which do not induce visual hallucination of a human face.” This describes a noise pareidolia test coinciding with the pareidolia test described in the instant disclosure.);
acquiring information related to a response of a subject to the test image (Takeda, Fig. 4, Fourth characteristic data 314; Fig. 10, Start or continue capturing of images S42, Start or continue detection of viewpoints S43); and
computing a risk score related to a neuropsychiatric disorder based on the information related to the response of the subject (Takeda, Fig. 10, Create fourth distribution map S44, Determine whether fourth characteristic (viewpoints are gathered in inducing image) is included S45, Included? Yes S46, Diagnose that there is possibility of dementia with Lewy bodies S47).
Regarding claim 32, Takeda teaches an information processing device including a controller, wherein the controller is configured to:
output a test image used in a pareidolia test to a display unit (Takeda, Fig. 10, Start displaying of fourth video S41; para. 103, “a fourth video which is a video for diagnosis based on fourth video data 304 is presented to the subject. In this case, the fourth video may be a still image including both the inducing image and a plurality of non-inducing images which do not induce visual hallucination of a human face.” This describes a noise pareidolia test coinciding with the pareidolia test described in the instant disclosure.);
acquire information related to a response of a subject to the test image (Takeda, Fig. 4, Fourth characteristic data 314; Fig. 10, Start or continue capturing of images S42, Start or continue detection of viewpoints S43); and
compute a risk score related to a neuropsychiatric disorder based on the information related to the response of the subject (Takeda, Fig. 10, Create fourth distribution map S44, Determine whether fourth characteristic (viewpoints are gathered in inducing image) is included S45, Included? Yes S46, Diagnose that there is possibility of dementia with Lewy bodies S47).
Regarding claim 18, Takeda teaches the non-transitory computer-readable storage medium according to claim 17, wherein the program causes the computer to execute processing of:
acquiring a visual recognition result of the subject with respect to the test image (Takeda, Fig. 10, Create fourth distribution map S44, Determine whether fourth characteristic (viewpoints are gathered in inducing image) is included S45);
computing a score of a pareidolia test based on the visual recognition result of the subject (Takeda, Fig. 10, Create fourth distribution map S44, Determine whether fourth characteristic (viewpoints are gathered in inducing image) is included S45, Included? Yes S46); and
computing the risk score related to the neuropsychiatric disorder based on the information related to the response of the subject including the score of the pareidolia test (Takeda, Fig. 10, Diagnose that there is possibility of dementia with Lewy bodies S47).
Regarding claim 19, Takeda teaches the non-transitory computer-readable storage medium according to claim 18, wherein the program causes the computer to execute processing of:
detecting an eye gaze of the subject with respect to the test image (Takeda, Fig. 10, Start or continue detection of viewpoints S43);
generating an eye gaze map indicating a fixation point and a saccade of the subject based on the eye gaze (Takeda, Fig. 10, Create fourth distribution map S44; para. 95, “Viewpoint data 322 is time-series data indicating the positions and time points of the viewpoints detected by detection unit 37, and is, for example, sets of coordinate data (x, y, t) including the time points already described.”);
computing an eye gaze tracking score based on the eye gaze map (Takeda, Fig. 10, Create fourth distribution map S44, Determine whether fourth characteristic (viewpoints are gathered in inducing image) is included S45, Included? Yes S46, Diagnose that there is possibility of dementia with Lewy bodies S47); and
computing the risk score related to the neuropsychiatric disorder based on the information related to the response of the subject including the score of the pareidolia test and the eye gaze tracking score (Takeda, Fig. 10, Create fourth distribution map S44, Determine whether fourth characteristic (viewpoints are gathered in inducing image) is included S45, Included? Yes S46, Diagnose that there is possibility of dementia with Lewy bodies S47).
Regarding claim 28, Takeda teaches the non-transitory computer-readable storage medium according to claim 17, wherein the program causes the computer to execute processing of specifying any one of a plurality of neuropsychiatric disorders subjected to determination based on the risk score related to the neuropsychiatric disorder (Takeda, para. 25, “disease discrimination method”; para. 92, “diagnosing whether there is cognitive impairment or the degree of cognitive impairment, or… discriminating a case from the other cases in cognitive impairment.” The term “cases” in Takeda is used in reference to neuropsychiatric disorders.).
Regarding claim 29, Takeda teaches the non-transitory computer-readable storage medium according to claim 28, wherein the program causes the computer to execute processing of outputting a determination result including the computed risk score related to the neuropsychiatric disorder or the specified neuropsychiatric disorder (Takeda, para. 25, “disease discrimination method”; para. 92, “diagnosing whether there is cognitive impairment or the degree of cognitive impairment, or… discriminating a case from the other cases in cognitive impairment.” The term “cases” in Takeda is used in reference to neuropsychiatric disorders.).
Regarding claim 30, Takeda teaches the non-transitory computer-readable storage medium according to claim 17, wherein the program causes the computer to execute processing of:
storing a risk score related to the neuropsychiatric disorder computed in time series (Takeda, para. 206, “storage unit 32 which stores case characteristic data 310 indicating characteristics of viewpoint distributions corresponding respectively to typical cases in cognitive impairment.”); and
specifying a possibility of onset of the neuropsychiatric disorder based on changes in the risk score related to the neuropsychiatric disorder (Takeda, at least para. 186-190 describe an iterative process wherein the risk score changes relative to discriminating the disease case.).
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 20 is rejected under 35 U.S.C. 103 as being unpatentable over Takeda et al. (US 2020/0383626, hereinafter referred to as Takeda) as applied to claim 19, in view of Frazier et al. (US 2020/0352499, hereinafter referred to as Frazier).
Regarding claim 20, Takeda teaches the non-transitory computer-readable storage medium according to claim 19, wherein the program causes the computer to execute processing of acquiring an eye gaze tracking score of the subject by inputting, to a learning model trained to output an eye gaze tracking score (Takeda, Fig. 10, Create fourth distribution map S44, Determine whether fourth characteristic (viewpoints are gathered in inducing image) is included S45, Included? Yes S46, Diagnose that there is possibility of dementia with Lewy bodies S47).
Takeda does not explicitly teach in response to input of information related to a fixation point and a saccade indicated by an eye gaze map, information related to the fixation point and the saccade of the subject indicated by the generated eye gaze map.
However, in a related art, Frazier teaches acquiring an eye gaze tracking score of the subject by inputting, to a learning model trained to output an eye gaze tracking score in response to input of information related to a fixation point and a saccade indicated by an eye gaze map, information related to the fixation point and the saccade of the subject indicated by the generated eye gaze map (Frazier, Fig. 5, Receive gaze tracking data responsive to the stimulus 504, Determine a deviation of the gaze tracking data from a standard response 506, Update a score and confidence value for at least one of a plurality of indices based on the deviation of the gaze tracking data from the standard response 508; para. 27, “As used herein, the term ‘gaze tracking data’ can include any data that can be retrieved or derived from information provided from a gaze tracking device. This can include parameters derived from the tracking the location of the patient's gaze, such a dwell time on a portion of a stimulus, a fixation time on a portion of the stimulus, a fixation count, a number of gaze visits, and a number of saccades of the patient, as well as parameters derived from one or more cameras associated with the gaze tracking device, including parameters derived from images and video of the patient's pupils.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for Takeda to use information related to the fixation point and the saccade of the subject as taught by Frazier because it allows for assessment of psychological conditions via eye tracking. See Frazier at least at para. 18, 19, and 29-31.
Claims 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over Takeda et al. (US 2020/0383626, hereinafter referred to as Takeda) as applied to claim 17, in view of Wall et al. (US 2021/0133509, hereinafter referred to as Wall).
Regarding claims 21, 22, and 24, Takeda teaches the non-transitory computer-readable storage medium according to claim 17.
Takeda does not explicitly teach:
(claim 21) wherein the program causes the computer to execute processing of:
acquiring spoken voice of the subject visually recognizing the test image;
acquiring information related to a voice feature from the spoken voice;
computing an utterance tracking score based on the information related to the voice feature; and
computing the risk score related to the neuropsychiatric disorder based on the information related to the response of the subject including the score of the pareidolia test and the utterance tracking score.
(claim 22) wherein the program causes the computer to execute processing of acquiring the utterance tracking score of the subject by inputting, to a learning model trained to output an utterance tracking score in response to input of information related to a voice feature, the acquired information related to the voice feature.
(claim 24) wherein the program causes the computer to execute processing of:
acquiring an answer to a health profile questionnaire;
computing a health profile score based on the acquired answer; and
computing the risk score related to the neuropsychiatric disorder based on the information related to the reaction of the subject including the score of the pareidolia test, the utterance tracking score, and the health profile score.
However, in a related art, Wall teaches (claim 21) wherein the program causes the computer to execute processing of:
acquiring spoken voice of the subject visually recognizing the test image (Wall, para. 377, “The sounds and/or voices recorded in the video files may also be analyzed.” para. 378, “The subject may be presented with one or more stimuli (e.g., visual stimuli presented to the subject via a display), and the response of the subject to the stimuli may be used to evaluate the subject’s features.;
acquiring information related to a voice feature from the spoken voice (Wall, para. 436, “the active sources can include audio feed data source such as speech patterns, lexical/syntactic patterns (for example, size of vocabulary, correct/incorrect use of pronouns, correct/incorrect inflection and conjugation, use of grammatical structures such as active/passive voice etc., and sentence flow), higher order linguistic patterns (for example, coherence, comprehension, conversational engagement, and curiosity).”);
computing an utterance tracking score based on the information related to the voice feature (Wall, para. 470, “The analysis of the data to determine progress and current diagnosis can include automated analysis such as question scoring and voice-recognition for vocabulary and speech analysis.”).
(claim 22, depending from claim 21) wherein the program causes the computer to execute processing of acquiring the utterance tracking score of the subject by inputting, to a learning model trained to output an utterance tracking score in response to input of information related to a voice feature, the acquired information related to the voice feature (Wall, Fig. 7, Prediction module fits updated dataset to an assessment model to generate a predicted classification 720; para. 470, “The analysis of the data to determine progress and current diagnosis can include automated analysis such as question scoring and voice-recognition for vocabulary and speech analysis.”).
(claim 24, depending from claim 21) wherein the program causes the computer to execute processing of:
acquiring an answer to a health profile questionnaire (Wall, para. 458, “The data may also be collected passively, such as by monitoring online behavior of patients and caregivers, such as recording questions asked and topics investigated relating to a diagnosed developmental disorder.” Para. 487, “Data can comprise information collected through diagnostic tests, diagnostic questions, or questionnaires (2605).”);
computing a health profile score based on the acquired answer (Wall, Fig. 14, Model 1 Questionnaire based 1410 - Numerical Score Output).
It would have been obvious to a person having ordinary skill in the art to incorporate the voice feature and health profile analyses of Wall in the diagnostic functionality of Takeda to compute the risk score related to the neuropsychiatric disorder based on the information related to the response of the subject including the score of the pareidolia test, the utterance tracking score, and the health profile score because Wall provides for combining scores from multiple data sets for diagnosis of cognitive disorders, developmental disorders, mood disorders, behavioral disorders, and neurodegenerative diseases, all of which are considered neuropsychiatric disorders.
Regarding claim 23, Takeda teaches the non-transitory computer-readable storage medium according to claim 17.
Takeda does not explicitly teach wherein the program causes the computer to execute processing of:
acquiring an answer to a health profile questionnaire;
computing a health profile score based on the acquired answer; and
computing the risk score related to the neuropsychiatric disorder based on the information related to the reaction of the subject including the score of the pareidolia test and the health profile score.
However, in a related art, Wall teaches wherein the program causes the computer to execute processing of:
acquiring an answer to a health profile questionnaire (Wall, para. 458, “The data may also be collected passively, such as by monitoring online behavior of patients and caregivers, such as recording questions asked and topics investigated relating to a diagnosed developmental disorder.” Para. 487, “Data can comprise information collected through diagnostic tests, diagnostic questions, or questionnaires (2605).”);
computing a health profile score based on the acquired answer (Wall, Fig. 14, Model 1 Questionnaire based 1410 - Numerical Score Output).
It would have been obvious to a person having ordinary skill in the art to incorporate the health profile analysis of Wall in the diagnostic functionality of Takeda to compute the risk score related to the neuropsychiatric disorder based on the information related to the response of the subject including the score of the pareidolia test and the health profile score because Wall provides for combining scores from multiple data sets for diagnosis of cognitive disorders, developmental disorders, mood disorders, behavioral disorders, and neurodegenerative diseases, all of which are considered neuropsychiatric disorders.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Takeda et al. (US 2020/0383626, hereinafter referred to as Takeda) as applied to claim 17, in view of Pascual-Leone et al. (WO 2022/067189, hereinafter referred to as PL).
Regarding claim 25, Takeda teaches the non-transitory computer-readable storage medium according to claim 17.
Takeda does not explicitly teach wherein the program causes the computer to execute processing of:
outputting a plurality of words used in a memory test;
receiving input of the words at a predetermined timing;
computing a memory test score based on the received words; and
computing the risk score related to the neuropsychiatric disorder based on the information related to the reaction of the subject including the score of the pareidolia test and the memory test score.
However, in a related art, PL teaches wherein the program causes the computer to execute processing of:
outputting a plurality of words used in a memory test (PL, para. 58, “a subject may be asked to listen to three words being spoken”);
receiving input of the words at a predetermined timing (PL, para. 59, “additional examples of first order features (using the above speech example) that are based on clinical subject matter expertise include but are not limited to: immediate recall; delayed recall”. Immediate recall and delayed recall each involve a predetermined timing.);
computing a memory test score based on the received words (PL, para. 89, “immediate and delayed recall scores”).
It would have been obvious to a person having ordinary skill in the art to incorporate the memory test of PL in the diagnostic functionality of Takeda to compute the risk score related to the neuropsychiatric disorder based on the information related to the reaction of the subject including the score of the pareidolia test and the memory test score because PL provides for combining scores from multiple data sets for diagnosis of cognitive disorders, developmental disorders, mood disorders, behavioral disorders, and neurodegenerative diseases, all of which are considered neuropsychiatric disorders.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Takeda et al. (US 2020/0383626, hereinafter referred to as Takeda) as applied to claim 17.
Regarding claim 26, Takeda teaches the non-transitory computer-readable storage medium according to claim 17, wherein the program causes the computer to execute processing of:
generating a noise pattern image (Takeda, para. 103, “a still image including both the inducing image and a plurality of non-inducing images which do not induce visual hallucination of a human face”);
generating the test image by synthesizing the generated noise pattern image and the generated facial image (Takeda, para. 103, “a still image including both the inducing image and a plurality of non-inducing images which do not induce visual hallucination of a human face”).
Takeda does not explicitly teach generating a facial image having an arbitrary eye gaze direction.
However these differences are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The method would be performed the same regardless of the eye gaze direction of the facial image. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention for Takeda to include generating a facial image having an arbitrary eye gaze direction because the eye gaze direction in a facial image is merely a design choice that does not functionally relate to the steps claimed and has not been disclosed to solve any stated problem or is for any particular purpose which does not patentably distinguish the claimed invention.
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Takeda et al. (US 2020/0383626, hereinafter referred to as Takeda) as applied to claim 26, in view of Shibano (US 4,866,785) and Zavesky et al. (US 11,347,387, hereinafter referred to as Zavesky).
Regarding claim 27, Takeda teaches the non-transitory computer-readable storage medium according to claim 26, wherein:
the noise pattern image includes a noise pattern inducing pareidolia (Takeda, Fig. 4, inducing image which induces visual hallucination of a human face).
Takeda does not explicitly teach the program causes the computer to execute processing of:
generating the noise pattern image from a seed image represented as a binary image using a random field model; and
inputting a binary facial image to a learning model trained to output a facial image of a different race from a race of a binary facial image in response to input of the facial image, thereby acquiring a facial image of a different race from a race of the input facial image.
However, the entirety of Shibano illustrates that generating the noise pattern image from a seed image represented as a binary image using a random field model is old and well-known.
Similarly, Zavesky teaches that inputting a binary facial image to a learning model trained to output a facial image of a different race from a race of a binary facial image in response to input of the facial image, thereby acquiring a facial image of a different race from a race of the input facial image is also old and well-known (Zavesky, para. 21, “traditional creative methods using generative adversarial networks (GAN) and style-transfer methods for creating derivative content from original content have no constraints (e.g., face swaps between gender, ethnicity)”).
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for Takeda to generate the noise pattern image from a seed image represented as a binary image using a random field model as well as input a binary facial image to a learning model trained to output a facial image of a different race from a race of a binary facial image in response to input of the facial image, thereby acquiring a facial image of a different race from a race of the input facial image since such image generating techniques were commonly used for such purposes before the effective filing date of the claimed invention and it is well within the general abilities of a person of ordinary skill to use a known technique on the basis of its suitability for the intended use as a matter of obvious design choice.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xu et al. (WO 2021/083069) also discloses that using a GAN to change the race of a facial image is old and well-known.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANIEL LANE/Examiner, Art Unit 3715
Prosecution Insights