DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The following is a final office-action in response to the communication received on 07/23/2025. In this communication, besides the previously presented claims, new claims—claims 21-24—have been added.
Election by Original Presentation
3. Newly submitted claims 19 and 24 are directed to an invention that is independent or distinct from the invention originally claimed. For instance, claim 19 is directed to treating a subject for a neurological disorder, and adapting the course of treatment; whereas, claim 24 is directed to a clinical trial where the effect of treatment on the subject is assessed.
Accordingly, each of the above is distinct or independent from assessing cognitive impairment (or speech motor impairment) in a subject.
Since Applicant has already received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claims 19 and 24 are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
Accordingly, per this current office-action, claims 1-10, 12-18 and 21-23 are considered.
Claim Rejections - 35 USC § 101
4. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More)
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 1-10, 12-18 and 21-23 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1)
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
(Step 2A) [Wingdings font/0xE0] Prong-One:
The claim(s) recite a judicial exception, namely an abstract idea, as shown below:
— Considering each of claims 1 and 15, as representative claims, the claims recite abstract idea, namely certain methods of organizing human activity, mental processes and/or mathematical concepts.
(a) the following limitations recite certain methods of organizing human activity and/or mental processes:
assesses cognitive impairment in a subject: receive [speech] from a word-reading test from the subject, wherein the word-reading test comprises a sequence of words, each word drawn from a set of n words and wherein the subject is prompted to speak the sequence of words for the word-reading test; identify a plurality of segments of the [speech] that correspond to single words or syllables; determine a number of correctly read words in the [speech]; and determine a cognitive measure indicative of a level of cognitive impairment in the subject based at least in part on the number of correctly read words, etc.
(b) the following limitations recite mathematical concepts (e.g., mathematical calculations):
compute one or more Mel-frequency cepstral coefficients (MFCCs) for segments [corresponding to single words or syllables] to obtain a plurality of vectors of values, each vector being associated with a segment; clustering the plurality of vector of values into n clusters, wherein each cluster has n possible labels corresponding to each of the n words; for each of the n! permutations of labels, predicting a sequence of words in the [speech] using the labels associated with the clustered vectors of values, and perform a sequence alignment between the predicted sequence of words and the sequence of words used in the word reading test; and select the labels that result in the best alignment and counting the number of matches in the alignment, wherein the number of matches corresponds to the number of correctly read words, etc.
Thus, the limitations identified above recite an abstract idea since the limitations correspond to certain methods of organizing human activity, mental processes and/or mathematical concepts, which are part of the enumerated groups of abstract ideas established according to the current eligibility standard (see MPEP 2106.04(a)).
I. The limitations identified above in part (a) correspond to at least managing personal behavior and/or an evaluation/judgment process, wherein a subject’s cognitive impairment is assessed while the subject is reading aloud a sequence of words from a word-reading test; and wherein, based on identifying a plurality of speech segments that represent words/syllables from the subject’s speech, the number of correctly read words is determined from; and thereby, a cognitive measure, which indicates the level of cognitive impairment in the subject, is determined based on the number of correctly read words, etc.
II. Similarly, the limitations identified above in part (b) correspond to mathematical calculation, wherein the number of correctly read words is determined by: computing coeffects—namely MFCCs—for the single words/syllables in the speech segment in order to obtain a plurality of vector values; clustering or grouping the above vector values into n clusters, each having n possible labels corresponding to each of the n words; estimating/predicting—for each of the n! permutations of the labels—a sequence of words in the speech using the labels associated with the clustered vectors of values; aligning the predicted sequence of words with the sequence of words used in the word reading test; selecting the labels that result in the best alignment and counting the number of matches in the alignment, so that the number of matches represents the number of correctly read words, etc.
(Step 2A) [Wingdings font/0xE0] Prong-Two
The claims recite additional elements, wherein a computer/processor is utilized to facilitate the recited steps/functions regarding: collecting audio or speech data from a subject (e.g., “receiving a voice recording from a word-reading test from the subject, wherein the word-reading test comprises a sequence of words, each word drawn from a set of n words and wherein the subject is prompted to speak the sequence of words for the word-reading test”); and generating one or more results based on analyzing, using one or more algorithms, the data collected above (e.g., “identifying a plurality of segments of the voice recording that correspond to single words or syllables; and determining a number of correctly read words in the voice recording by: computing one or more Mel-frequency cepstral coefficients (MFCCs) for the segments to obtain a plurality of vectors of values, each vector being associated with a segment; clustering the plurality of vector of values into n clusters, wherein each cluster has n possible labels corresponding to each of the n words; for each of the n! permutations of labels, predicting a sequence of words in the voice recording using the labels associated with the clustered vectors of values, and performing a sequence alignment between the predicted sequence of words and the sequence of words used in the word reading test”; “selecting the labels that result in the best alignment and counting the number of matches in the alignment, wherein the number of matches corresponds to the number of correctly read words in the voice recording”; and “determining a cognitive measure indicative of a level of cognitive impairment in the subject based at least in part on the number of correctly read words in the voice recording”), etc.
Accordingly, the additional elements fail to integrate the abstract idea into a patent-eligible practical application since the additional elements are utilized merely as a tool to facilitate the abstract idea.
Thus, when each claim is considered as a whole, the additional elements fail to integrate the abstract idea into a practical application since they fail to impose meaningful limits on practicing the abstract idea. For instance, when each of the claims is considered as a whole, none of the claims provides a technological improvement over the relevant existing technology.
The observations above confirm that the claims are indeed directed to an abstract idea.
(Step 2B)
Accordingly, when the claim(s) is considered as a whole (i.e. considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions. Accordingly, none of the current claims recites an element—or a combination of elements—directed to an inventive concept.
It is also worth to note that the utilization of the conventional computer technology to facilitate the process of assessing a subject’s speech; such as, evaluating the subject’s speech based on collecting and analyzing the words/phrases that the subject is uttering, including executing one or more algorithms to extract and/or determine one or more attributes from the subject’s voice/speech (e.g., the number of words that the subject has uttered or properly pronounced), etc., is already directed to a well-understood, routine or conventional activity in the art (e.g., see US 2007/0055514; US 5,583,961; US 5,305,422, etc.).
The above observation confirms that the current claimed invention fails to amount to “significantly more” than an abstract idea.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-10, 12-14, 16-18 and 21-23).
Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea.
Thus, none of the current claims, when considered as whole, is implementing an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims implements an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology).
► Applicant’s arguments directed to section §101 have been fully considered (the response filed on 07/23/2025). However, the arguments are not persuasive at least for the following reasons:
Firstly, while referring to the publication (WO 2021/213935), Applicant asserts that “the claimed method for assessing cognitive impairment comprises analyzing voice recordings from patient word-reading tests. The method is model-free (so no ‘ground truth’ model training data is required, and computational resource requirements are also reduced), is language-independent, and is also applicable to subjects with speech impairments (since the identification of correctly read words for each subject is based on segmentation and clustering of voice recordings made by the subject themself). A cognitive measure (or "score") is determined based on automated, accurate assessment of the number of correct words read even in the presence of any subject-specific speech motor impairment or pronunciation, thereby allowing the method to distinguish between cognitive impairment and speech motor impairment in the subject” (emphasis added).
However, except for Applicant’s subjective assumptions, there appears to be no evidence regarding the alleged reduction in computational resources. In particular, the absence of a pre-trained model does not necessarily imply that the claimed (and the disclosed) method/system saves computational resources. In fact, the claimed (and the disclosed) method/system appears to require significant computational resources since it is attempting to identify, based on analyzing voice data captured from a subject, one or more words that the subject is uttering when reading a sequence of words. For instance, it performs the process of: (a) capturing voice data of a subject, as the subject is reading from a word-reading test comprising a sequence of words drawn from a set of n words, (b) identifying—from the captured voice data above—segments of the voice that correspond to words/syllables, (c) generating a plurality of vectors of values by computing one or more MFCCs for each of the segments identified above, so that each vector is associated with a segment, (d) clustering the plurality of vectors into n clusters, wherein n represents the number of different words, (e) predicting, for each of the n! possible permutation of the n labels above, a sequence of words in the captured voice data, (f) performing a sequence alignment between each of the predicted sequence of words above and the sequence of words used in the word reading test, (g) selecting the labels with the highest alignment score as the true label for the cluster, (h) determining the number of correctly read words based on the number of matches in the alignment above, etc.
Thus, besides relying on existing technology, it is evident that the claimed (and the disclosed) method/system appears to spend significant computational resources. Consequently, Applicant’s arguments are not persuasive.
In addition, Applicant appears to fail to articulate how the claimed method is considered to identify “correctly read words for each subject is based on segmentation and clustering of voice recordings made by the subject themself” (emphasis added). For instance, segmenting (e.g., dividing) and clustering (e.g. grouping) the voice recording of a subject does not necessarily indicate whether the subject is “correctly reading” a word. In particular, a subject experiencing a cognitive/speech impairment lacks the ability to correctly read words (e.g., a cognitive impaired subject typically cannot correctly utter/pronounce words). Thus, Applicant does not appear to provide any feasible scenario regarding how the current method/system determines, based merely on the process of segmenting and clustering the voice recording of the cognitive impaired subject, the words that the subject has read correctly. Consequently, Applicant’s arguments are not persuasive.
Secondly, while attempting summarize claims 1 and 2 of Example 48 of the USPTO guidance (see pages 10 and 11 of the argument), Applicant asserts, “[a]lthough claim 1 of the instant application recites elements . . . that recite mathematical operations, Claim 1 also recites . . . ‘receiving, by a processor, a voice recording from a word-reading test from the subject, wherein the word-reading test comprises a sequence of words, each word drawn from a set of n words and wherein the subject is prompted to speak the sequence of words for the word-reading test;’ . . . ‘determining a cognitive measure indicative of a level of cognitive impairment in the subject based at least in part on the number of correctly read words in the voice recording.’ . . . which, in analogy to the analysis provided in Example 48 of the USPTO's subject matter eligibility guidance, comprise additional elements that, when the claim is taken as a whole, indicate that the claim is directed to a practical application under Step 2A, Prong Two of the subject matter edibility analysis” (emphasis added).
However, unlike the exemplary eligible claim, namely Claim 2 of Example 48, Applicant is relying on part of the claimed limitations, which recite the use of the existing computer technology to facilitate the abstract idea. Particularly, while admitting part of the limitations that recite mathematical concepts, Applicant is relying on part of the limitations that recite the process of: (i) capturing, via a microphone, the subject’s speech as the subject is reading a set of words (“receiving, by a processor, a voice recording from a word-reading test from the subject . . . a sequence of words, each word drawn from a set of n words and wherein the subject is prompted to speak the sequence of words for the word-reading test”), and (ii) determining, based on the number of correctly read words, the subject’s level of cognitive impairment (“determining a cognitive measure indicative of a level of cognitive impairment in the subject based at least in part on the number of correctly read words in the voice recording”).
Thus, it is evident from the observation above that Applicant is relying on the abstract idea in order to substantiate the alleged eligibility of the current claims. In particular, a human—such as a therapist—can perform the above process mentally and/or using a pen and paper. For instance, once providing the subject with a set of words that the subject is required to read, the therapist evaluates mentally and/or using a pen and paper, the number of words that the subject is correctly reading (e.g., the therapist takes notes—using a pen and paper—regarding the words that the subject is correctly reading/pronouncing; including the total number of words that the subject has read correctly, etc.).
Of course, once the therapist has obtained the information above, the therapist estimates the subject’s level of cognitive impairment based on the total number of words that the subject has read/pronounced correctly, etc.
The above indeed confirms that Applicant is emphasizing part of the abstract idea in an attempt to substantiate the alleged eligibility of the current claims. In contrast, regarding Claim 2 of Example 48, a human cannot mentally (or using a pen and paper) synthesize speech waveforms from a set of numbers. Particularly, as already pointed out in the USPTO guidance (page 23 of the guidance), “[s]ynthesizing speech waveforms from a cluster of numbers is not a process that can be practically performed in the human mind” (emphasis added). Of course, besides the above process of synthesizing speech waveforms from a cluster of numbers, Claim 2 of Example 48 above also generates—while excluding the speech signal from the target source—a mixed speech signal by combining/stitching the speech waveforms that correspond to the different sources. It is readily evident that such process of generating a mixed speech signal also cannot be practically preformed in the human mind (and/or using a pen and paper).
The observation above demonstrates that Applicant’s attempt to substantiate the alleged eligibility of the current claims, while relying on Claim 2 of Example 48, is not persuasive. Unlike the case of Claim 2 of Example 48, Applicant is emphasizing the limitations of current claim 1, which a human—e.g., a therapist—can perform mentally and/or using a pen and paper (e.g., see the discussion above). Consequently, Applicant’s arguments are not persuasive.
Applicant further asserts, “[a]s noted above, the claimed method provides improvements over existing computer technology, i.e., over existing computer technology for assessing cognitive impairment in a patient, in terms of, for example, accuracy, simplicity, and the ability to distinguish between cognitive impairment and speech motor impairment in the subject . . . the improved accuracy (e.g., in determining a number of correctly read words) and simplicity of the claimed method (e.g., no supervised learning AI-based speech recognition models are required, computational resource requirements are thus reduced, and the method can be used to assess cognitive impairment in subjects that speak different languages, have different accents and/or have subject-specific a priori unknown pronunciations anomalies associated with speech motor impairment)” (emphasis added). Applicant has also listed part of the claimed limitations that recite the abstract idea in order to substantiate the above assertion (see page 13 of the argument).
However, once again except for the subjective assertions above, Applicant does not identify the alleged technological improvement (if any) that the claimed (or the disclosed) method/system is supposedly providing. Note also that the absence of a machine-learning—and/or an artificial intelligence (AI)—algorithm(s) does not necessarily signify an improvement in accuracy. If anything, basic commonsense dictates that systems that do not implement AI technology are typically less accurate than those that implement AI technology. Moreover, depending on the type of objective to be achieved, different systems normally implement different types of algorithms; and furthermore, some systems are required to process a small amount of data, whereas other systems are required to process a large amount of data. Thus, excluding the use of a pre-trained machine-learning algorithm(s), and/or limiting the size of data that the system is required to process, etc., does not necessarily signify a technological improvement. Consequently, Applicant’s alleged reduction in computational resources does not necessarily demonstrate a technological improvement over the relevant existing technology.
Moreover, given the claimed (and disclosed) functions that Applicant’s method/system is performing (see the discussion presented above), the claimed (and disclosed) method/system does expend significant computational resources. Thus, Applicant’s conclusory assertions, “the improvements in accuracy and simplicity of the claimed method arise in part from the fact that the plurality of voice recording segments are processed to compute Mel-frequency cepstral coefficients (MFCCs) . . . The claimed method thus automatically takes into account the speech characteristics of the individual subject without requiring any extensive training of a speech recognition model using large amounts of ground truth data . . . The presently claimed methods inherently accurately account for the speech characteristics for any and all subjects without requiring such computationally expensive training), provides for a more accurate count of correctly read words, and enables the method to distinguish between cognitive impairment and speech motor impairment in the subject” (emphasis added), are not persuasive. This is because Applicant is once again attempting to substantiate the alleged technological improvement while emphasizing the existing technology (e.g., the process of: capturing voice data of a subject, segmenting the voice data into a plurality of segments, computing MFCCs for the segments to obtain a plurality of vectors, clustering the plurality of vectors, etc.). In fact, part of the discussion presented in the USPTO guidance, namely the part that relates to Example 48, already demonstrates that such processing of voice data is part of the existing technology.
Similarly, simply asserting the absence of “extensive training of a speech recognition model using large amounts of ground truth data” does not demonstrate a technological improvement. For instance, due to some hardware/software limitations, a given system may not have the capability to handle “extensive training of a speech recognition model using large amounts of ground truth data”; and therefore, such system may be arranged to perform less-complex tasks. However, this does not necessarily mean a technological improvement. Particularly, Applicant’s emphasis regrading one or more procedures (e.g., training a model, etc.), which the claimed (the disclosed) method (or system) is not allegedly performing does not necessarily signify a technological improvement. Consequently, Applicant’s arguments are not persuasive.
Of course, the same is true regarding Applicant’s alleged “more accurate count of correctly read words” and/or the alleged capability “to distinguish between cognitive impairment and speech motor impairment in the subject”. Particularly, except for the conclusory assertions, Applicant does not demonstrate whether the claimed (and/or the disclosed) method is implementing an element—or a combination of elements—that provides a technological improvement over the relevant existing technology. Moreover, the process of accurately determining the number of words that the subject has read correctly during a reading test, including determining whether the subject is suffering from a cognitive impairment or a speech motor impairment, etc., can indeed be performed by a human—such as a therapist. For instance, the therapist may determine the subject’s level of cognitive impairment based on the number of words that the subject has read correctly. Similarly, the therapist may determine the subject’s level of speech motor impairment based on the subject’s pace/speed of reading, etc. Thus, Applicant is once again relying on part of the abstract idea in an attempt to substantiate the alleged technological improvement. Consequently, Applicant’s arguments are not persuasive.
Thus, at least for the reasons above, the Office concludes that none of the current claims—when considered as a whole—implement an inventive concept that amount to “significantly more” than abstract idea.
Prior Art.
5. Considering each of claims 1 and 15 as a whole (including each of the dependent claims), the prior art does not teach or suggest the current claims (regarding the state of the prior art, see the office-action dated 08/15/2024).
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this final office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filled within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUK A GEBREMICHAEL whose telephone number is (571) 270-3079. The examiner can normally be reached on 7:00AM-3:00PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DAVID LEWIS can be reached on (571) 272-7673. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715