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 .
Response to Preliminary Amendment
In the preliminary amendment filed on September 17, 2025, the following has occurred: claim(s) 1, 31 have been amended and claim(s) 11-30, 41-61 have been cancelled. Now, claim(s) 1-10, 31-40 are pending.
Claim Objections
Claim 1 objected to because of the following informalities: “the machine learning system” in p. 2, ll. 16, “based on physician feedback..” in p. 2, ll. 16. These appear to be typographical errors. Appropriate correction is required. “a machine learning system”, “based on physician feedback.”
Claim 2 objected to because of the following informalities: “2. The system of claim 1,” in p. 2, ll. 17. This appears to be a typographical error. Appropriate correction is required. “2. (Original) The system of claim 1,”
Claim 3 objected to because of the following informalities: “3. The system of claim 1,” in p. 2, ll. 20. This appears to be a typographical error. Appropriate correction is required. “3. (Original) The system of claim 1,”
Claim 4 objected to because of the following informalities: “4. The system of claim 1,” in p. 3, ll. 1. This appears to be a typographical error. Appropriate correction is required. “4. (Original) The system of claim 1,”
Claim 5 objected to because of the following informalities: “5. The system of claim 1,” in p. 3, ll. 6. This appears to be a typographical error. Appropriate correction is required. “5. (Original) The system of claim 1,”
Claim 6 objected to because of the following informalities: “6. The system of claim 1 wherein” in p. 3, ll. 9. This appears to be a typographical error. Appropriate correction is required. “6. (Original) The system of claim 1, wherein”
Claim 7 objected to because of the following informalities: “7. The system of claim 6,” in p. 3, ll. 12. This appears to be a typographical error. Appropriate correction is required. “7. (Original) The system of claim 6,”
Claim 8 objected to because of the following informalities: “8. The system of claim 7,” in p. 3, ll. 22. This appears to be a typographical error. Appropriate correction is required. “8. (Original) The system of claim 7,”
Claim 9 objected to because of the following informalities: “9. The system of claim 8,” in p. 4, ll. 1. This appears to be a typographical error. Appropriate correction is required. “9. (Original) The system of claim 8,”
Claim 10 objected to because of the following informalities: “10. The system of claim 9,” in p. 4, ll. 3. This appears to be a typographical error. Appropriate correction is required. “10. (Original) The system of claim 9,”
Claim 31 objected to because of the following informalities: “the machine learning system” in p. 4, ll. 19. This appears to be a typographical error. Appropriate correction is required. “a machine learning system”.
Claim 32 objected to because of the following informalities: “32. The method of claim 31,” in p. 4, ll. 20. This appears to be a typographical error. Appropriate correction is required. “32. (Original) The method of claim 31,”
Claim 33 objected to because of the following informalities: “33. The method of claim 31,” in p. 4, ll. 23. This appears to be a typographical error. Appropriate correction is required. “33. (Original) The method of claim 31,”
Claim 34 objected to because of the following informalities: “34. The method of claim 31,” in p. 5, ll. 5. This appears to be a typographical error. Appropriate correction is required. “34. (Original) The method of claim 31,”
Claim 35 objected to because of the following informalities: “35. The method of claim 31,” in p. 5, ll. 11. This appears to be a typographical error. Appropriate correction is required. “35. (Original) The method of claim 31,”
Claim 36 objected to because of the following informalities: “36. The method of claim 31 wherein” in p. 5, ll. 14. This appears to be a typographical error. Appropriate correction is required. “36. (Original) The method of claim 36, wherein”
Claim 37 objected to because of the following informalities: “37. The method of claim 36,” in p. 5, ll. 17. This appears to be a typographical error. Appropriate correction is required. “37. (Original) The method of claim 36,”
Claim 38 objected to because of the following informalities: “38. The method of claim 37,” in p. 6, ll. 4. This appears to be a typographical error. Appropriate correction is required. “38. (Original) The method of claim 37,”
Claim 39 objected to because of the following informalities: “39. The method of claim 38,” in p. 6, ll. 7. This appears to be a typographical error. Appropriate correction is required. “39. (Original) The method of claim 38,”
Claim 40 objected to because of the following informalities: “40. The method of claim 39,” in p. 6, ll. 10. This appears to be a typographical error. Appropriate correction is required. “40. (Original) The method of claim 39,”
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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:
“AI detector(s)/ asymmetry detector/ ataxia detector/ dysarthria detector” in Claim(s) 1, 5-7, and 31, 35-37 has been interpreted under 112(f) as a means plus function limitation because of the combination of a non-structural term “detector(s)” and functional language such as “process…”, “determine…” without reciting sufficient structure to achieve the function.
“AI scorer/ stroke scorer” in Claim(s) 1-4, 7-9, 31-34, 38 has been interpreted under 112(f) as a means plus function limitation because of the combination of a non-structural term “scorer” and functional language such as “to combine …”, “assigns…”, “determines…”, “calculates…” without reciting sufficient structure to achieve the function.
“speech-to-text unit” in Claim(s) 3 has been interpreted under 112(f) as a means plus function limitation because of the combination of a non-structural term “unit” and functional language such as “to convert the audio stream into text…”, without reciting sufficient structure to achieve the function.
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(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1-10, 31-40 is/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 pre -AIA the applicant regards as the invention.
Claim limitations, “AI detector(s) to respectively process one or both of the audio stream and the video stream...”, “asymmetry detector... determine a first stroke likelihood ... ataxia detector ... determine a second stroke likelihood... dysarthria detector ... determine a third stroke likelihood” in claims 1, 7, and 31, 37, “AI scorer to combine the at least two respective likelihoods of the health condition…”, “AI scorer assigns a separate weight…”, “determine the overall likelihood…” in claims 1-4, 31-34, “speech-to-text unit ... to convert the audio stream into text…”, in claim 3, “the stroke scorer automatically determines a stroke score…”, “the stroke scorer assigns a separate weight to each of the first, second, and third stroke likelihoods in calculating the stroke score” in claims 7-8, 37-38, 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.
The specification discloses a hardware structure of a generic PC/processor/central processing unit (CPU), laptop or tablet terminal which is not considered an adequate structure to perform the claimed functions. To perform the claimed functions, a computer comprising of hardware (processor, memory, etc., …) and software/algorithm to be programed to perform the functions may be considered an adequate structure yet there is no disclosure of any particular structure, either explicitly or inherently, to perform the functions, see (Applicant’s Specification in Paragraphs [0104]-[0106], [0115], [0118], [0169]). The use of the terms “detector”, “scorer”, “unit” in the claim language is not sufficient system structure for performing analysis or analyzing probability of developing health condition. As would be recognized by those of ordinary skill in the art, the terms “detector”, “scorer”, “unit” can be performed by any type of software and hardware combination which can be any generic computer. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which “detector”, “scorer”, “unit” structures perform(s) the claimed function.
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.
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.
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.
Claim(s) 1-10, 31-40 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1: Step 1
Claims 1-10 are drawn to a system, claims 31-40 are drawn to a method, and of which are within the four statutory categories (i.e., a machine and a process). Claims 1-10, 31-40 are further directed to an abstract idea on the grounds set out in detail below.
Claim 1: Step 2A Prong One
Claim 1 recite(s):
receive an audio stream and a video stream from an endpoint in proximity to a patient;
respectively process one or both of the audio stream and the video stream to automatically determine at least two respective likelihoods of the patient having a health condition;
combine the at least two respective likelihoods of the health condition to automatically determine a health condition score representing an overall likelihood of the patient having the health condition; and
a feedback process to update based on physician feedback
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. That is, other than reciting, “at least one communication interface to”, “at least two different artificial intelligence ("Al") detectors to”, “an Al scorer to”, “a machine learning system”, “using machine learning to” to perform these functions, nothing in the claim precludes the limitations from practically being performed by a person following instructions to make determinations of the health conditions based on received data. Further, claim 3 recites “a speech-to-text unit”, claim 4 recites “a medical monitoring device”, claims 5-6 recite “an asymmetry detector, an ataxia detector, and a dysarthria detector”, claim 7 recites “a stroke scorer”, claim 9 recites “a machine learning system”, and claim 10 recites “a deep learning neural network”. For example, the claim encompasses a person following instructions to receive an audio stream and a video stream, determine at least two respective likelihoods of the patient having a health condition from the data, determine a health condition score representing an overall likelihood of the patient having the health condition, and communicating the results to another person with the potential of receiving feedback. These steps could be accomplished by a person following instructions to make determinations by using obtained information, and therefore encompass Certain Methods of Organizing Human Activity.
Claim 1: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining
elements amount to no more than general purpose computer components programmed to perform
the abstract idea, insignificant extra-solution activity, and generally linking the abstract idea to a
technical environment.
Claim 1, directly or indirectly, recites the following generic computer components configured to implement the abstract idea “at least one communication interface to”, “at least two different artificial intelligence ("AI") detectors to”, “an Al scorer to”, “a machine learning system”. As set forth in the MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “displays an indication of the health condition score to a physician” at a high degree of generality, amount no more than receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). As set forth in MPEP 2106.05(d)(II), computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claims recite “using machine learning to” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claim 1: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to integration of
the abstract idea into a practical application, the additional elements of using a computer
configured to perform above identified functions amounts to no more than mere instructions to
apply the exception using generic computer components. Mere instructions to apply an exception
using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at
223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea
into a patent-eligible invention.”)
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (See MPEP 2106.05(g)). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Additionally, generally linking the abstract idea to a particular technological environment
does not amount to significantly more than the abstract idea (See MPEP 2106.05(h) and Affinity
Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Dependent claims 2-10 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claim 2 describes assigning weights to the likelihoods. Similarly, claims 3, 7-10 further describe processing the audio stream and video stream. Similarly, claim 4 describes combing the diagnostic data with the respective likelihoods. Finally, claims 5-6 further describe the health condition as a stroke. Therefore, these claims recite limitations that fall into the Certain Methods of Organizing Human Activity grouping of abstract ideas.
Dependent claims recite additional subject matter which amount to limitations consisted with the additional elements in independent claim 1 (such as claims 3, 5-7, 9-10 recite additional limitations that amount to generic computer components).
Looking at the limitations as an ordered combination adds nothing that is not already
present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims are not patent eligible.
Claims 31-40 recite similar functions to claims 1-10, but in method form. Claim 33 lacks the generic computer component of “a speech-to-text unit”. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1, 4-7, 31, 34-37 are rejected under 35 U.S.C. 103 as being unpatentable over Eichler et al. (U.S. Patent Pre-Grant Publication No. 2022/0044821) in view of Rao et al. (U.S. Patent Pre-Grant Publication No. 2019/0110754).
As per independent claim 1, Eichler discloses a system for automated health condition scoring comprising:
at least one communication interface to receive an audio stream and a video stream from an endpoint in proximity to a patient (See Fig. 2, 9A-C, 10-12 and [0007], [0032], [0034]-[0035], [0037], [0040]: The system includes a client device enabled for communication with a remote computer, and at least one sensor configured to acquire at least one of image data, sound data, movement data, and tactile data, which the Examiner is interpreting the sensor to encompass an endpoint in proximity to a patient and interpreting image data and sound data to encompass an audio stream and a video stream);
at least two different artificial intelligence (“AI”) detectors to respectively process one or both of the audio stream and the video stream using machine learning to automatically determine at least two respective likelihoods of the patient having a health condition (See Fig. 2, 4-6 and [0040]-[0045], [0050]-[0051], [0057], [0060]: At least one sensor/sensors and a plurality of different machine learning classifiers for each subject-specific extracted potential stroke feature or a MLC for each modality type, which the Examiner is interpreting a plurality of different machine learning classifiers to encompass at least two different artificial intelligence (“AI”) detectors and interpreting determining a probability for a type of a stroke condition to encompass at least two respective likelihoods of the patient having a health condition);
an Al scorer to combine the at least two respective likelihoods of the health condition using machine learning to automatically determine a health condition score representing an overall likelihood of the patient having the health condition (See Fig. 14, 16, 19, Tables 1-12, and [0078]: A scoring tool for providing a score for the different categories of the video, sound and movement data of a patient and calculating a total score indicating the overall likelihood of the patient health condition, which the Examiner is interpreting a scoring tool to encompass automatically determine a health condition score representing an overall likelihood of the patient having the health condition);
a display interface that displays an indication of the health condition score to a physician (See Fig. 19 and [0069]-[0071]: The mobile management consoles are configured and operative to display the individual scores, as well as enable a physician to observe the scores, approve the scores, remark on individual scores, modify the scores (e.g., digitally fill, change, update the individual medical score rubrics, as well as receive automatic suggestions from the system for each one of the individual medical scale categories), which the Examiner is interpreting the mobile management consoles to encompass a display interface); and
a feedback process to update the machine learning system based on physician feedback (See [0058], [0069]-[0074]: The models may continuously update via defining parameters by using training data, which the Examiner is interpreting continuously update to encompass update the machine learning system based on physician feedback.)
While Eichler teaches a system that uses machine learning classifiers, Eichler may not explicitly teach artificial intelligence scoring and detecting.
Rao teaches a system for at least two different artificial intelligence (“AI”) detectors (See [0031], [0081], [0085]: The Examiner is interpreting the machine learning algorithms to encompass at least two different artificial intelligence (“AI”) detectors); and
an AI scorer (See [0032], [0074], [0081], [0096]: The purposes of the machine learning system is to take as input the temporal or static data recorded from the sensors and produce as output a probability score for each of a collection of diagnoses, which the Examiner is interpreting the trained machine learning system that produces an output of a diagnosis probability score to encompass an AI scorer.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Eichler to include artificial intelligence scoring and detecting as taught by Rao. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Eichler with Rao with the motivation of accurately diagnose a specific neurological disorder without the need of prior training in diagnosing neurological disorder conditions (See Background of the Invention of Rao in [0027]).
Claim(s) 31 mirrors claim 1 only within a different statutory category, and is rejected for the same reason as claim 1.
As per claim 4, Eichler/Rao discloses the method of claim 1 as described above. Eicher further teaches wherein the at least one communication interface receives diagnostic data from a medical monitoring device in proximity to the patient (See Table 11 and [0007], [0035], [0040], [0052]: A client device comprising a user and communication module and sensors/sensory acquire image data, sound data, and movement data where sensory testing is performed in proximity of the user limbs, which the Examiner is interpreting image data, sound data, and movement data to encompass diagnostic data ([0052])), and wherein the Al scorer is configured to combine the diagnostic data with the at least two respective likelihoods of the health condition using machine learning to automatically determine the overall likelihood of the patient having the health condition (See Fig. 14, 16, 19 and [0071]: A scoring tool is used to provide a score for the different categories of the video, sound and movement data of a patient and calculating a total score indicating the overall likelihood of the patient health condition.)
Claim(s) 34 mirrors claim 4 only within a different statutory category, and is rejected for the same reason as claim 4.
As per claim 5, Eichler/Rao discloses the method of claim 1 as described above. Eicher further teaches wherein the health condition is a stroke, and wherein the at least two different Al detectors are selected from a group consisting of an asymmetry detector, an ataxia detector, and a dysarthria detector (See Fig. 2, 11, Table 5, 8 and 10, and [0040]-[0045]: Attaining a current estimation of a likelihood of a stroke condition (which can serve as a time - stamped baseline profile) is facilitated by acquiring clinical measurement data via the acquisition units), which the Examiner is interpreting the clinical measurement data via the
acquisition units to encompass a group consisting of an asymmetry detector, an ataxia detector, and a dysarthria detector as Eichler describes acquiring facial landmarks and face asymmetry, sound sensor detects and acquires speech pathology or voice segment, and movement sensor detects and acquires limb ataxia or weakness (Tables 5, 8, and 10).)
Claim(s) 35 mirrors claim 5 only within a different statutory category, and is rejected for the same reason as claim 5.
As per claim 6, Eichler/Rao discloses the method of claim 1 as described above. Eicher further teaches wherein the health condition is a stroke, and wherein the at least two different Al detectors comprise three Al detectors including an asymmetry detector, an ataxia detector, and a dysarthria detector (See Fig. 2, 11, Table 5, 8 and 10, and [0040]-[0045]: Attaining a current estimation of a likelihood of a stroke condition (which can serve as a time - stamped baseline profile) is facilitated by acquiring clinical measurement data via the acquisition units), which the Examiner is interpreting the clinical measurement data via the
acquisition units to encompass a group consisting of an asymmetry detector, an ataxia detector, and a dysarthria detector as Eichler describes acquiring facial landmarks and face asymmetry, sound sensor detects and acquires speech pathology or voice segment, and movement sensor detects and acquires limb ataxia or weakness (Tables 5, 8, and 10).)
Claim(s) 36 mirrors claim 6 only within a different statutory category, and is rejected for the same reason as claim 6.
As per claim 7, Eichler/Rao discloses the method of claims 1 and 6 as described above. Eicher further teaches wherein:
the Al scorer comprises a stroke scorer (See Fig. 14, 16, 19, Tables 1-12, [0078]: The disclosed technique is configured and operative to calculate the total severity score in a “decision-making” mode);
the asymmetry detector processes the video stream to automatically determine a first stroke likelihood based on a measurement of facial droop (See Fig. 2, 14, Table 5 and [0040], [0054]: Client device includes a plurality of acquisition units: image sensors, movement sensors, and tactile sensors, which the Examiner is interpreting the image sensors and the comparison to baseline datasets to encompass determine a first stroke likelihood based on a measurement of facial droop);
the ataxia detector processes the video stream to automatically determine a second stroke likelihood based on a measurement of limb weakness (See Fig. 2, 14, Table 8 and [0040], [0051], [0054]: Client device includes a plurality of acquisition units: image sensors, sound sensors, movement sensors, and tactile sensors, which the Examiner is interpreting the tactile and movement sensors to encompass the ataxia detector and the determination of limb ataxia from the analysis of the extracted potential stroke features to encompass determine a second stroke likelihood based on a measurement of limb weakness (Table 8));
the dysarthria detector processes the audio stream to automatically determine a third stroke likelihood based on a measurement of slurred speech (See Fig. 2, 14, Table 10 and [0040], [0054]: Client device includes a plurality of acquisition units: image sensors, movement sensors, and tactile sensors, which the Examiner is interpreting the sound sensors to encompass the dysarthria detector and interpreting the determination of speech in Table 10 to encompass automatically determine a third stroke likelihood based on a measurement of slurred speech); and
the stroke scorer automatically determines a stroke score for the patient based on a combination of the first, second, and third stroke likelihoods (See Fig. 14, 19, Tables 1-12, and [0078]: The total score can define the stroke severity after quantifying each category of the NIHSS.)
Claim(s) 37 mirrors claim 7 only within a different statutory category, and is rejected for the same reason as claim 7.
Claims 2-3, 8-10, 32-33, 38-40 are rejected under 35 U.S.C. 103 as being unpatentable over Eichler et al. (U.S. Patent Pre-Grant Publication No. 2022/0044821) in view of Rao et al. (U.S. Patent Pre-Grant Publication No. 2019/0110754) in further view of Kostic et al. (U.S. Patent Pre-Grant Publication No. 2017/0007167).
As per claim 2, Eichler/Rao discloses the method of claim 1 as described above. Eicher/Rao may not explicitly teach wherein the Al scorer assigns a separate weight to each of the at least two respective likelihoods of the health condition in determining the health condition score.
Kostic teaches a method wherein the Al scorer assigns a separate weight to each of the at least two respective likelihoods of the health condition in determining the health condition score (See [0100]: The controller is programmed to assign a numerical score to the differences for each characteristic and multiply that score by a weighting factor, which the Examiner is interpreting the process to encompass the claimed portion when combined with Eichler’s teachings of a score for the different categories of the video, sound and movement data ([0078]).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Eichler/Rao to include the Al scorer assigns a separate weight to each of the at least two respective likelihoods of the health condition in determining the health condition score as taught by Kostic. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Eichler/Rao with Kostic with the motivation of ensuring prompt seeking for medical attention (See Summary of Kostic in [0004]).
Claim(s) 32 mirrors claim 2 only within a different statutory category, and is rejected for the same reason as claim 2.
As per claim 3, Eichler/Rao discloses the method of claim 1 as described above. Eicher/Rao may not explicitly teach further comprising:
a speech-to-text unit to convert the audio stream into text that is combined by the Al scorer with the at least two respective likelihoods of the health condition using machine learning to automatically determine the overall likelihood of the patient having the health condition.
Kostic teaches a method further comprising:
a speech-to-text unit to convert the audio stream into text that is combined by the Al scorer with the at least two respective likelihoods of the health condition using machine learning to automatically determine the overall likelihood of the patient having the health condition (See [0085]-[0088]: The controller uses speech-to-text technology to recognize the words spoken by the user and store the sound samples in memory according to the spoken words and/or phrases, which the Examiner is interpreting the speech-to-text technology to encompass a speech-to-text unit, and interpreting the comparison of the samples with the baseline sound samples to encompass combined by the Al scorer with the at least two respective likelihoods of the health condition.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Eichler/Rao to include a speech-to-text unit to convert the audio stream into text that is combined by the Al scorer with the at least two respective likelihoods of the health condition using machine learning to automatically determine the overall likelihood of the patient having the health condition as taught by Kostic. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Eichler/Rao with Kostic with the motivation of ensuring prompt seeking for medical attention (See Summary of Kostic in [0004]).
Claim(s) 33 mirrors claim 3 only within a different statutory category, and is rejected for the same reason as claim 3.
As per claim 8, Eichler/Rao discloses the method of claims 1 and 6-7 as described above. Eicher/Rao may not explicitly teach wherein the stroke scorer assigns a separate weight to each of the first, second, and third stroke likelihoods in calculating the stroke score.
Kostic teaches a method wherein the stroke scorer assigns a separate weight to each of the first, second, and third stroke likelihoods in calculating the stroke score (See [0100]: The controller is programmed to assign a numerical score to the differences for each characteristic and multiply that score by a weighting factor, which the Examiner is interpreting the process to encompass the claimed portion when combined with Eichler’s teachings of a score for the different categories of the video, sound and movement score (Tables 5, 8, 10).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Eichler/Rao to include the stroke scorer assigns a separate weight to each of the first, second, and third stroke likelihoods in calculating the stroke score as taught by Kostic. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Eichler/Rao with Kostic with the motivation of ensuring prompt seeking for medical attention (See Summary of Kostic in [0004]).
Claim(s) 38 mirrors claim 8 only within a different statutory category, and is rejected for the same reason as claim 8.
As per claim 9, Eichler/Rao discloses the method of claims 1 and 6-7 and Eichler/Rao/Kostic discloses the method of claim 8 as described above. Eicher further teaches wherein the stroke scorer assigns each separate weight using a machine learning system (See [0078]: The disclosed technique is configured and operative to calculate the total severity score in a “decision-making” mode, which the Examiner is interpreting to encompass the claimed portion when combined with Kostic’s teachings.)
Claim(s) 39 mirrors claim 9 only within a different statutory category, and is rejected for the same reason as claim 9.
As per claim 9, Eichler/Rao discloses the method of claims 1 and 6-7 and Eichler/Rao/Kostic discloses the method of claims 8-9 as described above. Eichler further teaches wherein the machine learning system comprises a deep learning neural network (See [005[]-[0058]: Typical examples of MLCs include artificial neural networks (ANNs), decision trees, support vector machines (SVMs), Bayesian networks, k-nearest neighbor (KNN) classifiers, regression analysis (e.g., linear, logistic), etc.)
Claim(s) 40 mirrors claim 10 only within a different statutory category, and is rejected for the same reason as claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Keene et al. (U.S. Patent Pre-Grant Publication No. 2021/0313041), describes reminiscence therapy and media sharing platform, methods, and systems for a patient user which provide an immediate and First Mobile Application positive impact on emotional functioning inpatients with dementia, major neurocognitive disorders, social isolation, traumatic brain injury (TBI), and psychiatric conditions such as posttraumatic stress disorder, by reducing anxiety, depression, and overall emotional distress.
Kusens et al. (U.S. Patent Pre-Grant Publication No. 2017/0195637), describes a stroke detection system analyzes images of a person's face over time to detect asymmetric changes in the position of certain reference points that are consistent with sagging or drooping that may be symptomatic of a stroke or TIA. On detecting possible symptoms of a stroke or TIA, the system may alert caregivers or others, and log the event in a database.
Khalifa et al. (“Developing an Evidence-Based Framework for Grading and Assessment of Predictive Tools for Clinical Decision Support”), describes a comprehensive framework to Grade and Assess Predictive tools (GRASP), and provide clinicians with a standardised, evidence based system to support their search for and selection of effective tools.
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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683
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