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 .
Prosecution History Summary
Claims 6 and 23 are cancelled.
Claims 1 and 27-28 are amended.
Claims 1-5, 7-22, and 24-29 are pending.
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.
Claims 1, 5, 7-14, 20, 22, and 24-29 are rejected under 35 U.S.C. 103 as being unpatentable over Ricciardi et al. (U.S. Publication No. 2019/0336065) in view of John et al. (U.S. Publication No. 2022/0176118).
As per claim 1, Ricciardi teaches a computer-implemented method for logging incontinence information, the method comprising:
-detecting, by one or more computing devices, a user input describing incontinence information (Ricciardi: para. 34; para. 37; Receive input regarding urinary episode.);
-updating, by the one or more computing devices and in response to detecting the user input, a private log based at least in part on the incontinence information (Ricciardi: para. 37);
-determining, by the one or more computing devices and after updating the private log, a performance metric based at least in part on comparing the private log with a current plan (Ricciardi: para. 53-54; para. 65; Correlating the user’s historical data to create a personalized curve and determine a confidence indicator determining user’s level of risk of an incident.); and
-providing, by the one or more computing devices, a progress notification based at least in part on the performance metric (Ricciardi: para. 65).
Ricciardi does not explicitly teach the following, however, John teaches:
-providing for display, by the one or more computing devices, data describing one or more learning plans each comprising one or more training exercises (John: para. 213-214; Providing a treatment regimen with a schedule.), wherein the one or more learning plans comprise a difficulty level, wherein the one or more training exercises comprise an incontinence treatment (John: para. 113; para. 221; Providing compliance by performing minimum number of defined behaviors (i.e. training exercises) within a selected interval defined by a user behavior criterion (i.e. difficulty level).), wherein each of the one or more training exercises comprise at least one of: a video, an audio, a game, and a text (John: para. 210; para. 213).;
-detecting, by the one or more computing devices, a user input describing a selection of at least one of the one or more learning plans (John: para. 213; Allowing establishment of a treatment regimen.), wherein the selection of at least one of the one or more learning plans comprises selecting, by the one or more computing devices, the at least one of the one or more learning plans based on the user input describing the incontinence information (John: para. 210; Patient symptoms and trend determines the therapeutic protocol using rules or lookup tables of the compliance module based on factors, such as patient input data.); and
-assigning, by the one or more computing devices, the at least one of the one or more learning plans as the current plan (John: para. 213; Establish treatment regimen for a user.); and
-administering, by a peripheral device of the one or more computing devices, the incontinence treatment (John: para. 113), the peripheral device (John: para. 101) comprising an electrical stimulation device (John: para. 94; para. 102; System is programed with a stimulation program for therapy.), for applying electrical signals to muscles, wherein intensity, pulse pattern, breaks between electrical pulses are customized based on the difficulty level of the selected one or more learning plans (John: para. 102; para. 51; Treatment protocols include stimulation protocols, which includes defining strength (i.e. intensity), pulsing parameter (i.e. pulse pattern), and timing (i.e. breaks between electrical pulses).).
One of ordinary skill in the art would have recognized that applying the known technique of John would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of John to the teachings of Ricciardi would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features into similar systems. Further, applying providing a treatment regimen and gathering compliance to the regimen to Ricciardi teaching management of incontinence disorder would have been recognized by those of ordinary skill in the art as resulting in an improved system that would provide a system that can provide an effective treatment to increase patient compliance and decrease treatment costs (John: para. 19).
As per claim 5, the method of claim 1 is as described. Ricciardi further teaches further comprising:
-generating, by the one or more computing devices, a set of one or more response options (Ricciardi: para. 34; para. 50; Prompt individual to submit information including response to questions.);
-providing for display, by the one or more computing devices, data describing one or more response options (Ricciardi: para. 50; Providing the user with drop down menu, text, etc. for responses.); and
-detecting, by the one or more computing devices, a user input directed to at least one of the one or more response options (Ricciardi: para. 50; Select a response based on the questions.);
-updating, by the one or more computing devices, the private log based at least in part on detecting the user input directed to the at least one of the one or more response options (Ricciardi: para. 50; Update the log in the database based on the response.).
As per claim 7, the method of claim 1 is as described. Ricciardi does not explicitly teach the following, however, John teaches further comprising:
-providing for display, by the one or more computing devices, data describing a task (John: para. 213);
-detecting, by the one or more computing devices, a user input (John: para. 213); and
-updating, by the one or more computing devices and in response to detecting the input, a progress associated with the task (John: para. 213; Track compliance with the treatment regimen.).
The motivation to combine the teachings is same as claim 1.
As per claim 8, the method of claim 7 is as described. Ricciardi does not explicitly teach the following, however, John teaches wherein the task comprises performing a Kegel (John: claim 14).
It would have been obvious to try, by one of ordinary skill in the art at the time of the invention was made, to specify treatments and incorporate it into the system of Ricciardi, since there are a finite number of identified, predictable potential solutions (i.e. types of treatments) to the recognized need (incontinence disorder) and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
As per claim 9, the method of claim 7 is as described. Ricciardi does not explicitly teach the following, however, John teaches wherein the task comprises a bladder holding event (John: claim 14).
It would have been obvious to try, by one of ordinary skill in the art at the time of the invention was made, to specify treatments and incorporate it into the system of Ricciardi, since there are a finite number of identified, predictable potential solutions (i.e. types of tasks) to the recognized need (incontinence disorder) and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
As per claim 10, the method of claim 7 is as described. Ricciardi does not explicitly teach the following, however, John teaches wherein detecting, by the one or more computing devices, the user input comprises detecting sensor data from at least one sensor (John: para. 181; para. 208).
It would have been obvious to one of ordinary skill in the art at the time of the invention to have gathering information using sensors as taught by John within the system of Ricciardi. It would have been obvious that a method of enhancing a particular class of devices (methods, or products) has been made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in other situations. One of ordinary skill in the art would have been capable of applying this known method of enhancement to a “base” device (method, or product) in the prior art and the results would have been predictable to one of ordinary skill in the art.
As per claim 11, the method of claim 1 is as described. Ricciardi further teaches further comprising:
-determining, by the one or more computing devices, a self-progress metric based at least in part on comparing the private log on a first day to the private log on a second day that occurs after the first day (Ricciardi: para. 53-54; para. 65); and
-providing, by the one or more computing devices, a notification that describes the self-progress metric (Ricciardi: para. 53-54; para. 65).
As per claim 12, the method of claim 11 is as described. Ricciardi does not explicitly teach the following, however, John teaches further comprising: updating, by the one or more computing devices and based at least in part on the self-progress metric at least one of the performance metric or a rewards account (John: para. 99; Providing compliance statistics and therapy progresses.).
One of ordinary skill in the art would have recognized that applying the known technique of John would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of John to the teachings of Ricciardi would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features into similar systems. Further, applying rewards based on progress for the treatment regimen to Ricciardi teaching management of incontinence disorder would have been recognized by those of ordinary skill in the art as resulting in an improved system that would provide a system that can provide an effective treatment to increase patient compliance and decrease treatment costs (John: para. 19).
As per claim 13, the method of claim 12 is as described. Ricciardi does not explicitly teach the following, however, John teaches further comprising:
-accessing, by the one or more computing devices, the rewards account to purchase an item (John: para. 134); and
-updating, by the one or more computing devices, the rewards account based on a cost associated with the item (John: para. 134).
The motivation to combine the teachings is same as claim 12.
As per claim 14, the method of claim 1 is as described. Ricciardi does not explicitly teach the following, however, John teaches further comprising: registering, by the one or more computing devices, a unique user id for a user (John: para. 171; Establishing a user account using an identification number.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to have personal account as taught by John within the system of Ricciardi. It would have been obvious that a method of enhancing a particular class of devices (methods, or products) has been made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in other situations. One of ordinary skill in the art would have been capable of applying this known method of enhancement to a “base” device (method, or product) in the prior art and the results would have been predictable to one of ordinary skill in the art.
As per claim 20, the method of claim 1 is as described. Ricciardi further teaches further comprising:
-providing for display, by the one or more computing devices and based at least in part on the private log at least one of a question or an answer field (Ricciardi: para. 34); and
-detecting, by the one or more computing devices, a user response directed to the answer field (Ricciardi: para. 34).
As per claim 22, the method of claim 1 is as described. Ricciardi does not explicitly teach the following, however, John teaches further comprising:
-providing, by the one or more computing devices, an instruction describing an incontinence exercise (John: para. 213); and
-detecting, by the one or more computing devices from a peripheral device, data describing performance of the incontinence exercise (John: para. 214).
The motivation to combine the teachings is same as claim 6.
As per claim 24, the method of claim 1 is as described. Ricciardi does not explicitly teach the following, however, John teaches further comprising:
-providing, by the one or more computing devices, an instruction describing an incontinence exercise (John: para. 218);
-detecting, by a camera of the one or more computing devices, data describing performance of the incontinence exercise (John: para. 214); and
-providing, by the one or more computing devices, feedback with respect to performance of the incontinence exercise (John: para. 214; Calculates measurement of compliance).
The motivation to combine the teachings is same as claim 6.
As per claim 25, the method of claim 1 is as described. Ricciardi does not explicitly teach the following, however, John teaches further comprising: inputting, by the one or more computing devices, one or more input parameters into an exercise customization model that is configured to receive the input parameters, and in response to receiving the input parameters, output customized exercise parameters (John: para. 213).
The motivation to combine the teachings is same as claim 6.
As per claim 26, the method of claim 25 is as described. Ricciardi does not explicitly teach the following, however, John teaches wherein the customized exercise parameters comprise at least one of a frequency of sets, a frequency of exercise sessions, a frequency of exercise sessions, a number of repetitions per set, a rest time between sets, or a hold time for a given exercise (John: para. 213).
The motivation to combine the teachings is same as claim 8.
Claims 27-28 recite substantially similar limitations as those already addressed in claim 1, and, as such, are rejected for similar reasons as given above.
As per claim 29, the method of claim 1 is as described. Ricciardi further teaches wherein detecting the user input describing incontinence information comprises at least one of detecting a user touch input with respect to touch sensitive display, detecting audio, or detecting sensor data from one or more sensors (Ricciardi: para. 20).
Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Ricciardi et al. (U.S. Publication No. 2019/0336065) in view of John et al. (U.S. Publication No. 2022/0176118) and further in view of Nims et al. (U.S. Publication No. 2009/0300513).
As per claim 2, the method of claim 1 is as described. Ricciardi and John do not explicitly teach the following, however, Nims teaches further comprising:
-displaying, by the one or more computing devices, a user interface comprising an avatar interface element (Nims: para. 34; para. 60; Create an avatar based on user’s preferences.);
-detecting, by the one or more computing devices, a user input directed to the avatar interface element, wherein the user input comprises a selection of at least one of the one or more learning plans (Nims: para. 63; Selecting personal goas achieved by the user.); and
-in response to receiving the user interaction directed to the avatar interface element, one or more of creating a personal avatar and modifying the personal avatar, wherein modifying the personal avatar comprises modifying the avatar in response to selecting the at least one of the one or more learning plans (Nims: para. 63; Based on selection of achieved goal, the avatar is modified.).
One of ordinary skill in the art would have recognized that applying the known technique of Nims would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Nims to the teachings of Ricciardi and John would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features into similar systems. Further, applying avatar customization for learning plans to Ricciardi and John teaching help with managing incontinence would have been recognized by those of ordinary skill in the art as resulting in an improved system that would provide a system that can help a user represent their identity and provide support regarding specific issues related to the user and its effect (Nims: para. 2-3).
As per claim 3, the method of claim 1 is as described. Ricciardi and John do not explicitly teach the following, however, Nims teaches further comprising:
-displaying, by the one or more computing devices, a user interface comprising a community element (Nims: para. 35; para. 44; A user can participate in a social networking system.);
-detecting, by the one or more computing devices, a user input directed to the community element (Nims: para. 35; para. 44; Provide a message or comment.);
-in response to receiving the user input directed to the community element, providing for display, by the one or more computing devices, data describing a public board comprising one or more forum interface elements for posting content to the public board (Nims: para. 44; Provide commends and review other people’s comments.).
The motivation to combine the teachings is same as claim 2.
As per claim 4, the method of claim 3 is as described. Ricciardi and John do not explicitly teach the following, however, Nims teaches further comprising: providing, by the one or more computing devices, a reminder in response to detecting the user input directed to the community element, detecting the user input directed to an avatar interface element, or both (Nims: para. 34-35).
The motivation to combine the teachings is same as claim 2.
Claims 15-19 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Ricciardi et al. (U.S. Publication No. 2019/0336065) in view of John et al. (U.S. Publication No. 2022/0176118) and further in view of Wei et al. (U.S. Patent No. 10,856,792).
As per claim 15, the method of claim 1 is as described. Ricciardi and John do not explicitly teach the following, however, Wei teaches further comprising:
-generating, by the one or more computing devices, a global dataset by aggregating information included in respective private logs for a plurality of users (Wei: col. 12, 65 to col. 13, 28); and
-training, by the one or more computing devices, a global machine-learned model with an input comprising at least a portion of the global dataset, wherein the global machine-learned model is configured to receive an input comprising at least a portion of the private log associated with a unique user, and in response to receiving the input comprising the at least the portion of the private log associated with the unique user, provide an output that describes a suggested user action for the unique user (Wei: col. 12, 65 to col. 13, 6).
One of ordinary skill in the art would have recognized that applying the known technique of Wei would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Wei to the teachings of Ricciardi and John would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features into similar systems. Further, applying machine learning models to predict outcomes to Ricciardi and John teaching management of incontinence disorder would have been recognized by those of ordinary skill in the art as resulting in an improved system that would provide a system that can provide an early detection method to prevent incontinence event (Wei: col. 1, 45-61).
As per claim 16, the method of claim 15 is as described. Ricciardi and John do not explicitly teach the following, however, Wei teaches further comprising: providing for display, by the one or more computing devices and based at least in part on the output of the global machine-learned model, a prompt to assign one plan as the current plan (Wei: col. 13, 37-43).
The motivation to combine the teachings is same as claim 15.
As per claim 17, the method of claim 1 is as described. Ricciardi and John do not explicitly teach the following, however, Wei teaches further comprising:
-generating, by the one or more computing devices, a personal dataset by aggregating information included in a private log associated with a user (Wei: col. 12, 65 to col. 13, 28); and
-training, by the one or more computing devices, a personal machine-learned model with an input describing at least a portion of the private log associated with the user, wherein the personal machine-learned model is configured to receive an input describing at least a portion of the private log associated with the user, and in response to receiving the input describing the at least the portion of the private log associated with user, provide an output that describes a suggested user action (Wei: col. 12, 65 to col. 13, 6).
The motivation to combine the teachings is same as claim 15.
As per claim 18, the method of claim 1 is as described. Ricciardi and John do not explicitly teach the following, however, Wei teaches further comprising:
-generating, by the one or more computing devices, a global dataset by aggregating information included in respective private logs for a plurality of users (Wei: col. 12, 65 to col. 13, 28); and
-training, by the one or more computing devices, a global machine-learned model with an input comprising the global dataset, wherein the global machine-learned model is configured to receive an input comprising at least a portion of the private log associated with a unique user, and in response to receiving the input comprising the at least the portion of the private log associated with the unique user, provide an output that describes a suggested user action for the unique user (Wei: col. 12, 65 to col. 13, 6).
The motivation to combine the teachings is same as claim 15.
As per claim 19, the method of claim 1 is as described. Ricciardi and John do not explicitly teach the following, however, Wei teaches further comprising:
-training, by the one or more computing devices, a global machine-learned model with an input comprising at least a portion of a global dataset that comprises aggregated incontinence information for a plurality of users (Wei: col. 12, 65 to col. 13, 6); and
-after training the global machine-learned model with the input comprising the at least the portion of the global dataset that comprises aggregated incontinence information for a plurality of users, further training, by the one or more computing devices, the global machine-learned model with incontinence information unique to a respective user to generate a personal machine-learned model for the respective user (Wei: col. 16, 37-43).
The motivation to combine the teachings is same as claim 15.
As per claim 21, the method of claim 20 is as described. Ricciardi and John do not explicitly teach the following, however, Wei teaches further comprising:
-labeling, by the one or more computing devices, a machine-learning training dataset based at least in part on the user response (Wei: col. 12, 65 to col. 13, 6).
The motivation to combine the teachings is same as claim 15.
Response to Arguments
Applicant's arguments filed for claims 1-22 and 24-29 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant argues that Applicant was unable to locate support in U.S. Provisional 62/375,898 for paragraphs 94, 113, 213, and 214 of John. Examiner disagrees.
Applicant argues that ‘898 does not provide support for providing a user input selecting at least one of the one or more learning plans and control of compliance module. Examiner disagrees. ‘898 page 41-42 teaches a menu screen for setting usage and compliance parameters for the treatment plan, therefore providing a user input selecting one or more learning plans.
Applicant argues that provisional ‘898 does not provide support for cited para. 210 of John, which is for limitation of “wherein the selection of at least one or the one or more learning plans comprises, selecting, by the one or more computing devices, the at least one of the one or more learning plans based on the user input describing incontinence information.” Examiner disagrees. Provisional ‘898 pg. 29 provides user input regarding sensed information or patient’s input to determine treatment protocol, therefore teaching the limitation.
Applicant argues that ‘898 application fails to describe displaying a learning plan including training exercises where the training exercises comprise video, audio, games or text. Examiner disagrees. Provision ‘898 pg. 45 states that the bladder treatment kit provided includes instructions in written, audio, or mixed technology.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
JP 2022524080A – Teaches a system for managing urinary incontinence.
CN 117731290A – Teaches a system and method for recording urinary incontinence event.
WO 2022156614A1 – Teaches a Kegel training method and system.
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEETAL R. PAULSON whose telephone number is (571)270-1368. The examiner can normally be reached M-F 8am-5pm.
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/SHEETAL R PAULSON/Primary Examiner, Art Unit 3686