DETAILED ACTION
Applicant's response, filed 7 August 2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Claim Status
Claims 1-20 are currently pending and under exam herein.
Claims 1, 4, 8, 15, and 17-20 are currently amended.
Drawings
The Replacement Drawings for Figure 4 are accepted. As such, all Drawings submitted 7 August 2026 and 5 December 2022 are now accepted.
Specification
Note: All references to the Specification herein pertain to the PG publication: US20230201586.
The use of the terms NeuroLife®, CyberGlove®, Noitom Hi5®, Mediapipe®, which are trade names or a marks used in commerce, have been noted in this application. The terms should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term. Each term is present in the instant Specification but not all marked appropriately, (i.e., CyberGlove, Noitom Hi5, and Mediapipe do not include the appropriate symbol following the term).
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Objections
Claim 15 remains objected to because of the following informalities:
Claim 15 recites, “wherein further comprising determining one or more control signals for feedback or stimulation based on the determined difference”, wherein the recitation of “wherein further comprising” appears to be a typographical error. It is suggested that the claim recite instead, “ comprising determining one or more control signals for feedback or stimulation based on the determined difference” or the like. This is necessary as the claim is not a complete sentence otherwise. Appropriate correction is required.
Claim 17, as amended, recites, “further comprising teaching a new skill to a new user that was demonstrated by a reference training a development algorithm using EMG signals or position signals associated with a skill demonstrated by a reference user, and using the trained development algorithm to teach the skill to a new user”, wherein the claim appears to be missing the appropriate comma after “further comprising teaching a new skill to a new user that was demonstrated by a reference”. It is suggested that the claim recite, instead, “further comprising teaching a new skill to a new user that was demonstrated by a reference, training a development algorithm using EMG signals or position signals associated with a skill demonstrated by a reference user, and using the trained development algorithm to teach the skill to a new user”, so as to separate each action of “teaching…”, “training…”, and “using…”.
Appropriate correction is requested.
Claim Interpretation
Claims 1 and 18 have been amended and the final step of the claim herein recites: “determining a position of the EMG device based on input received from the EMG device using the decoding algorithm receiving new EMG signals from the EMG device and determining one or more joint positions of a user based on the new EMG signals using the trained decoding algorithm” and “to determine a position of the EMG device based on input received from the EMG device using the decoding algorithm the receiver further configured to receive new EMG signals from the EMG device”, respectively. Said steps are interpreted as the decoding algorithm as receiving new EMG signals from the EMG device. Alternatively, however, the claim is confusing with respect to this recitation. It is suggested that, while the above is the interpretation, for further clarity the claim be amended to recite, “determining a position of the EMG device based on input received from the EMG device using the decoding algorithm receiving new EMG signals at the decoding device from the EMG device and determining one or more joint positions of a user based on the new EMG signals using the trained decoding algorithm” or similar.
Claim Rejections - 35 USC § 112(b)-Indefiniteness
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Rejections set forth herein are necessitated by claim amendment.
Claims 1 and 18, as amended, recite, “creating a mapping between the first input and the second input the EMG signals and the one or more joint angles” and “create a mapping between the first input and the second input the EMG signals and the one or more joint angles”, respectively, wherein the claim is unclear with regard to the meaning of “creating a mapping…the EMG signals and the one or more joint angles” as the claim step fails to make sense grammatically and the Examiner cannot ascertain what is modified in the above. It is suggested that the claims be amended to recite, “creating a mapping between the first input and the second input, comprising the EMG signals and the one or more joint angles, respectively” and “create a mapping between the first input and the second input, comprising the EMG signals and the one or more joint angles, respectively”, or the like. Clarification is requested.
Claim 4, as amended, recite, “predicts joint angles of a user based on the mapping determining the one or more joint positions of the user comprises predicting one or more future joint positions of the user in real time”, wherein the claim is unclear with respect to “predicts joint angles…based on the mapping determining…comprises predicting”. The claim steps fails to make sense grammatically and the Examiner cannot ascertain what is modified in the above. It is suggested that the claim be amended to recite, “predicts joint angles of a user based on the mapping wherein determining the one or more joint positions of the user comprises predicting one or more future joint positions of the user in real time” or similar. Clarification is requested.
Claim 8, as amended, recites, “wherein the first input comprises an image of EMG activity and the decoding algorithm comprises a pre-trained computer vision model an image of the EMG signals is generated and decoded using a pre-trained computer vision model”, wherein the claim is unclear with respect to input comprises an image of EMG…and the decoding algorithm comprises a pre-trained computer vision model an image of the EMG is generated…”, as the step fails to make sense grammatically and the Examiner cannot ascertain what is modified in the above. It is suggested that the claim be amended to recite, “wherein the first input comprises an image of EMG activity and the decoding algorithm comprises a pre-trained computer vision model wherein the image of the EMG signals is generated and decoded using a pre-trained computer vision model” or the like. Clarification is requested.
Claim 19, as amended, recites, “with EMG activity and the second input is an input associated with physical movements of at least one joint the joint position capturing device comprises a camera configured to capture video of movement of the user, and wherein the processor is further configured to extract the one or more joint angles from frames of the video using a deep learning-based video processing algorithm”, wherein the claim is unclear with respect to “at least one joint the joint position…”, as the step fails to make sense grammatically and the Examiner cannot ascertain what is modified in the above. It is suggested that the claim be amended to read, “at least one joint, and wherein the joint position capturing device…”. Clarification is requested.
Claim 20, as amended, recites, “and predicts joint angles of a user based on the mapping the processor is further configured to predict one or more future joint positions of the user in real time”, wherein the claim is unclear with respect to “predicts joint angles of a user based on the mapping the processor …”, as the step fails to make sense grammatically and the Examiner cannot ascertain what is modified in the above. It is suggested that the claim be amended to read, “and predicts joint angles of a user based on the mapping and wherein the processor is further configured to predict one or more future joint positions of the user in real time”. Clarification is requested.
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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US2018/0333575 to Bouton (22 November 2018) in view of Jiang et al. (Med Biol Eng Comput (2013) Vol. 51:143-151). This is a new grounds of rejection necessitated by claim amendment herein.
The instant claims are directed to:
Independent Claim 1:
A method for training an electromyography (EMG) device, the method comprising:
obtaining a first input comprising EMG signals from the EMG device and a second input comprising position signals from a joint position capturing device;
extracting one or more joint angles from the position signals;
creating a mapping between the first input and the second input the EMG signals and the one or more joint angles;
training a decoding algorithm based on the mapping; and
determining a position of the EMG device based on input received from the EMG device using the decoding algorithm receiving new EMG signals from the EMG device and determining one or more joint positions of a user based on the new EMG signals using the trained decoding algorithm.
Independent Claim 18:
An electromyography (EMG) system comprising:
an EMG device;
a joint position capturing device; and
a controller communicatively coupled to the EMG device and the joint position capturing device, the controller comprising a receiver and a processor,
the receiver configured to receive a first input comprising EMG signals from the EMG device and a second input comprising position signals from the joint position capturing device;
the processor configured to extract one or more joint angles from the position signals;
the processor configured to create a mapping between the first input and the second input the EMG signals and the one or more joint angles;
the processor further configured to train a decoding algorithm based on the mapping;
the processor further configured to determine a position of the EMG device based on input received from the EMG device using the decoding algorithm the receiver further configured to receive new EMG signals from the EMG device; and the processor further configured to determine one or more joint positions of a user based on the new EMG signals using the trained decoding algorithm.
With respect to claims 1 and 18, Bouton discloses a method for training an electromyography (EMG) device/electromyography (EMG) system (systems, methods and devices for rehabilitation of patients with motor impairment, Abstract, [0005]-[0015], EMG and/or other sensing can be used by the neural sleeve to amplify weak muscle signals or to provide strength or assistance in desired movements neural sleeve is used by itself to provide rehabilitative sensing and stimulation. When the electrodes are not stimulating, the neural sleeve can be used to measure a baseline EMG signal or a baseline range of motion, [0088]-[0091]), the method/system comprising:
an EMG device (neural sleeve also contains sensors for monitoring movement of the patient (position, orientation, acceleration, etc.), which can be used to track the patient's progress (e.g. their increase in strength or range of motion), [0079]-[0086]);
a joint position capturing device (body state observer - predicts joint position and velocity, the stimulation current can be adjusted accordingly to maintain a force or torque desired by a user, [0052]-[0054]); and
a controller communicatively coupled to the EMG device and the joint position capturing device, the controller comprising a receiver and a processor (Computer or processing unit 154 is connected to receive neural signals from the electrodes, and to output control signals to the transcutaneous neurostimulation sleeve 152, and further programmed to determine a volitional intent of the subject based on the received neural signals and to generate the output control signals to implement that volitional intent, [0031]-[0036], neural sleeve, [0079]-[0086]);
obtaining/receive a first input from the EMG device and a second input from a joint position capturing device (features are extracted from the neural signal - contain useful information for determining the desired movement - nonlinear or linear transform may be use to map the features to N-dimensional space - body state observer predicts joint position and velocity, the stimulation current can be adjusted accordingly to maintain a force or torque desired by a user, [0047]-[0054], decoders are trained to recognize both discrete type motions and rhythmic type motions - piecewise-linear interpolation is used to construct a mapping for decoder output to stimulation amplitudes for each motion [0064]-[0067], neural sleeve also contains sensors for monitoring movement of the patient (position, orientation, acceleration, etc.), which can be used to track the patient's progress (e.g. their increase in strength or range of motion), [0079]-[0086], [0088]-[0091]);
creating a mapping between the first input and the second input (body state observer itself receives input from body and/or neural sensors 414 [0063]; in decoder training, suitable machine learning techniques may be employed. For example, support vector machine techniques may be used. During training, cues (e.g. visual, aural, etc.) may be presented to the user while neural data is collected. This data is collected for various positions over many different contexts in which the user might be thinking about a certain motion, to provide better training data. For each point in time that a feature is calculated, the corresponding cue may be recorded in the cue vector. After training, a decoder may be built using the feature matrix and cue vector [0064]; three stimulation levels corresponding to a low, medium, and high evoked force response are provided to a user. A piecewise-linear interpolation is used to construct a mapping for decoder output to stimulation amplitudes for each motion. During real-time decoding, this mapping allows for smooth physical transitions from one movement to the next, controlled by the modulation of intracortical signals [0066]—the instant Specification with respect to “mapping” indicates mapping of EMG signals and position information in the context of a decoding algorithm and methodologies such as feature extraction using canonical correlation or matrix realignment and as such the instant reference meets said limitation (see e.g., [0049] of instant Specification).
training a decoding algorithm based on the mapping (decoder training - user may close his hand with his arm either moving or not moving while the decoders continuously search for patterns. The decoder 406 receives inputs from both the lag minimization filters 404 and the training visual cues 410. In addition, during decoder training, the decoder 406 also receives an input from a body state observer 412, which is shown by dashed line 420 - machine learning techniques, [0061]-[0067]); and determining a position of the EMG device based on input received from the EMG device using the decoding algorithm (during decoder training, a training feedback type selector 408 operates in conjunction with training visual cues 410. The visual cue of operation/component 410 may be provided in the form of a large graphical body part 510 (shown in FIG. 6). In this way, the user may attempt or imagine a movement that the user is being shown, for example, by large graphical body part 510, and then the user will produce neural signals that are useful in training a decoder 406 to identify the movement being shown, [0061]-[0067]).
While the prior art to Bouton et al. is focused on receiving electrical signals from electrodes positioned on or in the patient's head and determining an intent to move a specific body part of the patient from the electrical signals, the art specifically is directed to receiving signal input from an EMG device, such as a sleeve, and further including position information signals, as described above. Bouton et al. do not specifically disclose “extracting one or more joint angles from the position signals” such that “mapping” between the EMG signal and the joint angle occurs, as now claimed. However the prior art to Jiang et al. discloses specific use of joint position input (Figure 1 and p. 144, col. 1-2) with EMG input (p. 145, col. 1) for extraction of joint angle (p. 145, col. 2 to p. 146) for mapping using neural networking associations between EMG and the kinematic signals.
As such, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention wo have incorporated the use of both joint position data and EMG to extract joint angel information for the purpose of improved operation of joint assistive device. One would have been motivated to do so because the prior art to Bouton et al. specifically discloses that the body state observer can also accept data from body movement sensors as input. Such sensors may provide information on the position, velocity, acceleration, contraction, etc. of a body part and thus receiving signals not only from EMG device but also other sensors [at least at 0052], which can reflect position, would be obvious in light of the teachings of Jiang et al. The inclusion of a body state observer that actually performs the decoding, is taught by the art to Bouton et al. as disclosed above.
With respect to claim 2, Bouton discloses a method wherein the first input is an input associated with physical movements of at least one joint of a user, an input associated with muscle activities of the user, or an input associated with EMG activity (system can determine both the user's movement intention before the physical movement begins, and also during movement as well. Again, it should be noted that the neural signals from the user are for movements that may be imagined or attempted, as well as actually performed, [0051], neural sleeve also contains sensors for monitoring movement of the patient (position, orientation, acceleration, etc.), which can be used to track the patient's progress (e.g. their increase in strength or range of motion), [0079]-[0086]).
With respect to claim 3, Bouton discloses a method wherein the EMG device is an EMG sleeve (neuromuscular stimulation sleeve, Abstract, neural sleeve can be used to measure a baseline EMG signal or a baseline range of motion, [0088]-[0091]).
With respect to claims 4 and 20, Bouton discloses a method/EMG system wherein the decoding algorithm receives EMG inputs from a user, generates a mapping between the received EMG inputs and joint angles, and predicts joint angles of a user based on the mapping (inputs from body movement sensors 110 may also be taken into account in the body state observer 108, which is used to predict future movements that need to be made to obtain the imagined movement, [0034]-[0036]).
With respect to claim 5, Bouton discloses a method wherein the decoding algorithm is trained on a reference user and a new user's data is mapped to the reference user's inputs to calibrate the new user (intent to move and/or the focus level are generally calculated after the patient goes through a calibration period of about five seconds to about 20 seconds to determine a baseline by which changes are measured, [0005]-[0015], [0042], [0066]).
With respect to claim 6, Bouton discloses a method wherein training the decoding algorithm uses training data for a previous EMG device or data for the same device at one or more different points of time (Each decoder has previously been "trained" to associate certain features with a particular movement training dataset used to build the decoder may only maintain a certain amount of history, so that the decoder may adapt to changes in the neural signals over time, [0047]-[0054]).
With respect to claim 7, Bouton discloses a method wherein the joint position capturing device is one of a camera or sensor device (body state observer can also accept data from body movement sensors 110 as input. Such sensors may provide information on the position, velocity, acceleration, contraction, etc. of a body part, [0047]-[0054], camera, [0077]-[0078]).
With respect to claim 8, Bouton discloses a method wherein the first input comprises an image of EMG activity and the decoding algorithm comprises a pre-trained computer vision model (decoder is typically built by solving for weights matrix 'w` and bias `.GAMMA.` using suitable machine learning techniques to perform classification (e.g. discrete output) or regression (e.g. continuous output). Examples of methods that can be used to build the decoder include: Linear Discriminant Analysis, Least Squares Method, and Decision Trees such as Random Forest Type, [0060]-[0066]).
With respect to claim 9, Bouton discloses a method wherein the second input is an input associated with physical movements of at least one joint or an input associated with muscle activities (system can determine both the user's movement intention before the physical movement begins, and also during movement as well. Again, it should be noted that the neural signals from the user are for movements that may be imagined or attempted, as well as actually performed, [0051], neural sleeve also contains sensors for monitoring movement of the patient (position, orientation, acceleration, etc.), which can be used to track the patient's progress (e.g. their increase in strength or range of motion), [0079]-[0086]).
With respect to claim 10, Bouton discloses a method wherein the decoding algorithm is refined by learning a mapping on a related task that does not require data labels (training dataset used to build the decoder may only maintain a certain amount of history, so that the decoder may adapt to changes in the neural signals over time, [0050]-[0056]).
With respect to claim 11, Bouton discloses a method wherein the first input, the second input, or both the first input and the second input is artificially expanded to include randomly generated examples (decoder is trained to identify two motions that a user may perform, motion A and motion B. Then, the user is asked to perform a random series of the motions A and B, and the accuracy of the decoder is determined, [0071]-[0075]).
With respect to claim 12, Bouton discloses a method wherein a dead-band filter is used to smooth outputs of the decoding algorithm (decoder is trained to identify two motions that a user may perform, motion A and motion B. Then, the user is asked to perform a random series of the motions A and B, and the accuracy of the decoder is determined, [0064]-[0066]).
With respect to claim 13, Bouton discloses a method wherein decoded movements are used as pseudo-labels which are used to update the decoding algorithm (training dataset used to build the decoder may only maintain a certain amount of history, so that the decoder may adapt to changes in the neural signals over time, [0050]-[0056], decoder is trained to identify two motions that a user may perform, motion A and motion B. Then, the user is asked to perform a random series of the motions A and B, and the accuracy of the decoder is determined, [0071]-[0075]).
With respect to claim 14, Bouton discloses a method further comprising determining one or more target joint angles based on a skill level of a user and determining a difference between the one or more target joint angles and one or more intended joint angles (decoder training, a user may close his hand with his arm either moving or not moving while the decoders continuously search for patterns. The decoder 406 receives inputs from both the lag minimization filters 404 and the training visual cues 410 user is provided with both visual feedback (from the large hand 520) as well as electrical simulation feedback (from the electrodes). The data collected is used to "train" the decoder for the particular movement. This is repeated for multiple different potential movements, [0060]-[0068]).
With respect to claim 15, Bouton discloses a method wherein further comprising determining one or more control signals for feedback or stimulation based on the determined difference (sensors could provide information on the position of the elbow relative to the shoulder and the wrist, and how these body parts are moving relative to each other - Based on the output of the body state observer, the electrical stimulation pattern that is to be sent to the neurostimulation sleeve is determined by an encoding algorithm that generates the appropriate spatiotemporal patterns to evoke appropriate muscle contractions. The present disclosure permits high definition stimulation. In high definition stimulation, the simulation pattern to the electrodes is "continuously" updated, [0051]-[0056]).
With respect to claim 16, Bouton discloses a method wherein the stimulation is neuromuscular electrical stimulation (Based on the electrical signals, neuromuscular stimulation is delivered to the patient. The stimulation may be delivered through a neuromuscular stimulation sleeve, Abstract).
With respect to claim 17, Bouton discloses a method further comprising teaching a new skill to a new user that was demonstrated by a reference (electrical stimulation applied to specific muscles or nerve may help the patient effectuate an intended movement. As another example, electrical stimulation applied to a patient's brain appears to increase neuroplasticity in a patient's brain. Thus the patient is able to more easily learn/relearn specific movements or to compensate for the injury, [0031]-[0036]).
With respect to claim 19, Bouton discloses a EMG system wherein the first input is an input associated with EMG activity and the second input is an input associated with physical movements of at least one joint (system can determine both the user's movement intention before the physical movement begins, and also during movement as well. Again, it should be noted that the neural signals from the user are for movements that may be imagined or attempted, as well as actually performed, [0051], neural sleeve also contains sensors for monitoring movement of the patient (position, orientation, acceleration, etc.), which can be used to track the patient's progress (e.g. their increase in strength or range of motion), [0079]-[0086]).
Response to Applicant’s Arguments
With respect to claims as amended, Applicant’s remarks have been considered with respect to Bouton et al., however, are not persuasive in view of the rejection under 35 USC 103, as necessitated by claim amendment.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this 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 filed 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Inquiries
Papers related to this application may be submitted to Technical Center 1600 by facsimile transmission. Papers should be faxed to Technical Center 1600 via the PTO Fax Center. The faxing of such papers must conform to the notices published in the Official Gazette, 1096 OG 30 (November 15, 1988), 1156 OG 61 (November 16, 1993), and 1157 OG 94 (December 28, 1993) (See 37 CFR § 1.6(d)). The Central Fax Center Number is (571) 273-8300.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lori A. Clow, whose telephone number is (571) 272-0715. The examiner can normally be reached on Monday-Thursday from 11:00AM to 9:00PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached on (571) 272-9047.
Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to (571) 272-0547.
Patent applicants with problems or questions regarding electronic images that can be viewed in the Patent Application Information Retrieval system (PAIR) can now contact the USPTO’s Patent Electronic Business Center (Patent EBC) for assistance. Representatives are available to answer your questions daily from 6 am to midnight (EST). The toll free number is (866) 217-9197. When calling please have your application serial or patent number, the type of document you are having an image problem with, the number of pages and the specific nature of the problem. The Patent Electronic Business Center will notify applicants of the resolution of the problem within 5-7 business days. Applicants can also check PAIR to confirm that the problem has been corrected. The USPTO’s Patent Electronic Business Center is a complete service center supporting all patent business on the Internet. The USPTO’s PAIR system provides Internet-based access to patent application status and history information. It also enables applicants to view the scanned images of their own application file folder(s) as well as general patent information available to the public.
/Lori A. Clow/Primary Examiner, Art Unit 1687
