DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement (IDS)
The IDS filed 09 January 2025 fails to comply with 37 CFR 1.98(b)(5), which requires each listed publication to be identified by publisher, author (if any), title, relevant pages of the publication, date, and place of publication. Non-patent literature citations 8, 12 and 17 each lack a date. Information referred to on the 09 January 2025 IDS that is struck through on the annotated copy thereof mailed herewith has not been considered, unless cited on the accompanying PTO-892.
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 ("BRI") 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 BRI 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(I), claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) 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), 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), 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), 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: "an input device" for capturing input from a user in independent claims 1 and 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), 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), Applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (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).
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, 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) 1-16 is/are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, 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.
Regarding claim 1, claim 8 and claims dependent thereon, the limitations "capturing input from a user via an input device connected to a force sensor resistor (FSR); collecting data from the FSR and one or more sensors in contact with the user; analyzing the collected data and the capture user input against the one or more reference ML models" of claim 1 and the comparable limitations of claim 8 are indefinite. It is unclear what "input" is being captured and how, if at all, it differs from collected FSR and sensor(s) data. The limitations of dependent claim 2 (and 9) further add to this lack of clarity. Claim 2 recites "the input is the user's handwriting" (and the limitations of claim 9 are comparable). However, Applicant discloses, for example, "The system 100 disclosed herein can provide real-time analysis of grip pressure and muscle movement during writing. Unlike known tablet-based techniques, which rely on handwriting captured on a tablet's surface, the disclosed system 100 can directly measure the pressure applied by a patient's hand while gripping the writing utensil 200 and can monitor muscle movements in the forearm via the electromyograph band 400. This real-time data may provide a more comprehensive and accurate understanding of the interaction between grip pressure, muscle coordination, and handwriting execution. This approach also offers insights into motor skill patterns that may be indicative of dysgraphia, enabling earlier and more precise diagnosis compared to methods that primarily focus on the visual, dynamic and kinematic aspects of handwriting" (¶ [0066]). In view of the above, Applicant discloses the data collected and/or input to the model comprises grip pressure (measured by the FSR) and muscle movement while the user is writing. However, Applicant does not appear to disclose any other input, particularly the handwriting itself, is "captured" while the user is writing and used by the model. To the contrary, Applicant distinguishes the disclosed system from prior art systems that rely on either analyzing online handwriting data captured by tablet devices or conducting analyses of offline images (e.g., ¶ [0070]). Further, the features used by the ML models disclosed by Applicant (Tables 1 and 2) include only FSR and EMG features (i.e., no features of the handwriting itself are disclosed as being used by and/or to train the model(s)). Accordingly, it is unclear to what "the captured user input" refers, as it is recited as separate/additional to the data collected from the FSR and sensor(s) in contact with the user.
Further, in view of the above-noted indefiniteness, the corresponding structure of the "input device" is further unclear. As discussed in the Claim Interpretation section above, the limitation "capturing input from a user via an input device connected to a force sensor resistor (FSR)" (i.e., input device for capturing input from a user) meets the three-prong analysis (see MPEP 2181(I)) and therefore invokes 35 U.S.C. 112(f). The only component referred to as an "input device" in the specification as filed is an input device associated with "apparatus 10," which, to the best of the examiner's understanding is/encompasses conventional computer input devices (e.g., mouse, keyboard, touchscreen, etc.). Applicant further discloses a "writing utensil" (200) that includes a force sensor(s), e.g., FSR (¶ [0037]) that captures/collects data while the user is writing with said utensil. However, because it is unclear to what the "captured input" refers, it is unclear to which of these "input devices," if any, the limitation refers.
For the purpose of this Office action, claims 1-2 and 8-9 will be further discussed with the understanding that the "input device" refers to a writing utensil having at least one FSR that is held and used by the user while (hand)writing; data is captured/collected by the FSR of the input device/writing utensil and a sensor(s) in contact with the user while the user is using/writing with the input device, and the data collected from the FSR and sensor(s) while the user is writing with the input device is analyzed against/by the reference ML model(s) to determine whether the user's input/handwriting is indicative of dysgraphia, consistent with the specification as filed.
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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
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(s) 1-3, 8-10, 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over "Handwriting Evaluation Using Deep Learning with SensoGrip" (Bublin) in view of US 2004/0199482 A1 (Wilson).
Regarding claims 1, 8 and 15, Bublin discloses and/or suggests a device for diagnosing dysgraphia, the device comprising: at least one processor; and at least one memory storing instructions, wherein the instructions, when executed by the at least one processor (throughout document, deep learning software and computer for executing said software, e.g., pg. 2, the SensoGrip system includes an Android device executing an app), cause the apparatus to perform a method, the method comprising:
identifying one or more reference machine learning (ML) models that that are associated with dysgraphia (pgs. 4-5, training a deep learning LSTM network for SEMS score prediction);
capturing input from a user via an input device (pgs. 2-4, SensoGrip pen) connected to a force sensor resistor (FSR) (pgs. 2-4, SensoGrip pen comprises an FSR 406 force-sensing resistor);
collecting data from the FSR and one or more sensors in contact with the user (pgs. 2-4, SensoGrip pen further comprises an IMU MEMS three-axis accelerometer and three-axis gyroscope, and different data streams provided by the sensors are captured by the SensoGrip pen and forwarded to the app via BLE with corresponding time stamps);
analyzing the collected data and the captured user input against the one or more reference ML models (pgs. 4-5, testing the trained a deep learning LSTM network for SEMS score prediction; pg. 11, using the model for more exact handwriting evaluation; etc.); and
determining based on inference results generated from the one or more reference ML models whether the user input is indicative of dysgraphia (pgs. 5-6, comparing the output SEMS score to a threshold above which dysgraphia was detected; pg. 11, pg. 11, using the model for dysgraphia detection).
Bublin does not disclose the method comprises updating the one or more reference ML models with the captured input and collected data.
Wilson discloses/suggests a method comprising, inter alia, determining based on inference results generated from one or more reference ML models whether data collected from a user is indicative of a patient state; and updating the one or more reference ML models with the collected data (Figs. 9-10), such that the one or more reference ML models are trained with the captured input and collected data (i.e., new training cases) along with additional captured input and additional collected data (i.e., training cases used to train constructed NN providing predictions at, e.g., Fig. 9, step 904).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Bublin with updating the one or more reference ML models with the captured input and collected data as disclosed/suggested by Wilson, such that the one or more reference ML models with the captured input and collected data along with additional captured input and additional collected data, in order to further add to the model(s) recognition capabilities (Wilson, Abstract), thereby providing more accurate/reliable dysgraphia determinations using the model(s).
Regarding claims 2 and 9, Bublin as modified discloses/suggests the input is the user's handwriting (pg. 4, children used the SensoGrip pen twice during data collection and were given the task of using the pen for at least 5 min to copy sentences from the assessment 'SEMS').
Regarding claims 3 and 10, Bublin as modified discloses/suggests the collected data from the FSR is user's grip pressure (pgs. 2-4, finger pressure was captured with the FSR).
Regarding claim 16, Bublin as modified discloses/suggests the training of the one or more reference ML models is based on at least one of a specific group to which the user belongs, and a condition associated with the user (pgs. 4-5, age and gender are input variables).
Claim(s) 4-5 and 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bublin in view of Wilson as applied to claim(s) 1 and 8 above, and further in view of "Muscle Activity during Handwriting on a Tablet: An Electromyographic Analysis of the Writing Process in Children and Adults" (Gerth).
Regarding claims 4-5 and 11-12, Bublin as modified discloses/suggests the limitations of claims 1 and 8, as discussed above, but does not disclose the one or more sensors comprise electronic sensors for capturing the user's physiological data.
Gerth discloses/suggests capturing input from a user via an input device (e.g., pgs. 4-6, recording handwriting process data based on stylus position, movement and/or pressure); and collecting data from sensors in contact with the user, the sensors comprising electronic sensors for capturing muscle activation data, i.e., sEMG sensors (e.g., pgs. 4-6, recording muscle activity of the writing arm and hand using an sEMG system). Gerth discloses there is a clear differential involvement of proximal and distal joints during handwriting tasks on a tablet for varying levels of handwriting automaticity, and indicates the disclosed combined analysis of the handwriting process allows directly linking graphomotor execution in the handwriting muscles and the actual performance of the pen. Gerth further at least suggests using this data/method to diagnose children with specific handwriting problems (pg. 15).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Bublin with the one or more sensors comprising electronic sensors for capturing the user's muscle activation data as disclosed/suggested by Gerth in order to provide additional, or more comprehensive, data indicative of handwriting performance and/or graphomotor execution for assessing whether the user input is indicative of dysgraphia and/or for training a model(s) for said assessment, to provide insight of the relationship between writing process measures, handwriting quality and muscle activity during handwriting tasks, etc. (Gerth, pg. 15).
Claim(s) 6 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bublin in view of Wilson and Gerth as applied to claim(s) 5 and 12 above; or alternatively, over Bublin in view of Wilson and Gerth as applied to claim(s) 5 and 12 above, and further in view of Gerth and "Handwriting Analysis in Children and Adolescents with Hemophilia: A Pilot Study" (Pasta).
Regarding claims 6 and 13, Bublin as modified discloses/suggests the limitations of claims 5 and 12, as discussed above, and further discloses the muscle activation data is captured from muscle movements of the user's wrist (Gerth, pg. 4), but does not disclose the muscle activation data is captured from muscle movements of the user's bicep. However, at the time the invention was effectively filed, it would have been an obvious matter of design choice to a person of ordinary skill in the art to modify the method of Bublin with the muscle activation data being captured from muscle movements of the user's bicep because Applicant has not disclosed that capturing bicep muscle movements provides an advantage, is used for a particular purpose, or solves a stated problem. Rather, Applicant discloses the sEMG band is configured to attach to, and monitor muscle movements in, the forearm, and provides no indication that capturing bicep muscle movement data specifically provides any benefit. As no evidence has been provided to the contrary, one of ordinary skill in the art would have expected Applicant's invention to perform equally well with capturing the muscle movements as taught/suggested by Gerth (or Bublin as modified thereby) because either arrangement permits provide additional, or more comprehensive, data indicative of handwriting performance and/or graphomotor execution for use in diagnosis of conditions, identifying relationships between handwriting quality and muscle activity during handwriting tasks, etc. (Gerth, pg. 15).
Alternatively/Additionally, Pasta discloses, "Isometric contraction of biceps was significantly higher in boys with instrumental dysgraphia. This indicates a starting contraction and the necessity to stretch. It is well-known that elbows develop flexion contraction in the course of hemarthropathy; this might be the first step of the functional impairment that precedes structural involvement, and it is likely that this dysfunction might influence the task of handwriting" (pg. 8). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Bublin with the muscle activation data being captured from muscle movements of the user's bicep as disclosed/suggested by Pasta in order to provide additional, or more comprehensive, data that may influence the task of handwriting for assessing whether the user input is indicative of dysgraphia and/or for training a model(s) for said assessment; to permit detection of early changes in muscle dysfunction; etc. (Pasta, pg. 8).
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bublin in view of Wilson as applied to claim(s) 1 and 8 above, and further in view of Gerth and US 2015/0080697 A1 (Gilmore).
Regarding claim 7 and 14, Bublin discloses/suggest the limitations of claim 1 and 8, as discussed above, but does not disclose the one or more sensors is a sEMG sensor band.
As discussed with respect to claims 4-6 and 11-13 above, Gerth discloses a system comprising one or more sensors for capturing muscle activation data including an sEMG sensor. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Bublin with the one or more sensors comprising an sEMG sensor(s) for capturing the user's muscle activation data as disclosed/suggested by Gerth in order to provide additional, or more comprehensive, data indicative of handwriting performance and/or graphomotor execution for assessing whether the user input is indicative of dysgraphia and/or for training a model(s) for said assessment, to provide insight of the relationship between writing process measures, handwriting quality and muscle activity during handwriting tasks, etc. (Gerth, pg. 15).
Bublin as modified does not disclose the sEMG sensor(s) is a sEMG sensor band. Gilmore discloses/suggests a system comprising at least one sEMG sensor band (Fig. 2d). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Bublin with the sEMG sensor(s) comprising an sEMG sensor(s) band as taught/suggested by Gilmore in order to secure and shield the sensor from mechanical and electrostatic disturbances when used under clothing (e.g., Gerth, pg. 5, shirt and/or fabric cuff) (Gilmore, ¶ [0006]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure: see attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Meredith Weare whose telephone number is 571-270-3957. The examiner can normally be reached Monday - Friday, 9 AM - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. Applicant is encouraged to use the USPTO Automated Interview Request at http://www.uspto.gov/interviewpractice to schedule an interview.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Tse Chen, can be reached on 571-272-3672. 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.
/Meredith Weare/Primary Examiner, Art Unit 3791
Prosecution Insights