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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 24 May 2026 has been entered.
Status of the Claims
Claims 1-7 and 21-36 are currently pending in the present application, with claims 1 and 32 being independent.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 13 August 2024, 30 March 2025, 02 January 2026, and 24 May 2026 has been considered by the examiner.
Response to Arguments
Applicant’s arguments, see page 9, filed 19 January 2023, with respect to the objection to the claims, along with accompanying amendments received on the same date, have been fully considered and are partially persuasive. While app has been spelled out in claim 1, it has not been spelled out in claim 32. Claim 1 should recite “presenting to the one or more users of the app
Applicant’s arguments, see page 9-13, filed 19 January 2023, with respect to the 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejection of claims 2, 3-5, 22, 23, 28, 31 and 33, along with accompanying amendments received on the same date, have been fully considered and are partially persuasive. The 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejection of claims 2, 3-5, 22, 23, 28, 31 and 33 has been maintained.
It is noted that many of applicant’s arguments appear to be directed towards a written description rejection – illustrating where support can be found (e.g., Applicant contends that claim 2 wording is supported by the present disclosure, see page 9), rather than clarifying the claim limitation in light of the corresponding disclosure.
It is also noted that applicant argues limitations that are not within the claims (e.g., Applicant refers to “relax accuracy requirements” when using the term “tolerance threshold” indicating that the “tolerance” is a range of permittable accuracy, and the “threshold” is the “requirements” imposed by the system to insure assessment within the allowable range, see page 9 of applicant’s remarks). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
With respect to claims 2 and 33, given the plain and ordinary meaning of the words when interpreted in light of the corresponding disclosure, the scope of the claimed limitation is unclear. As an initial matter, the antecedent basis for the initial part of claim 2 (wherein the updated information pertains to one or more of the following” has been removed from claim 1, so it is unclear as to how current claim 2 fits in with claim 1. Furthermore, while applicant argues the scope of each of “adjusting tolerance thresholds”, “adapting UX feature presentations”, and “adapting social sharing recommendations” is clear, the examiner respectfully disagrees. As currently claimed, claim 2 requires updated information to pertain to adjusting tolerance thresholds, claim 1 recites by playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback of the user. Even if applicant means relax accuracy requirements when using the term tolerance threshold, it remains unclear, as claimed how to how updated information pertains to the generally recited adjusting tolerance levels. With respect to the information pertaining to adapting UX feature presentations, it remains unclear as to what constitutes UX feature presentations and how they are currently adapted as claimed. While applicant points to different examples, as claimed, it remains unclear as to what UX feature presentations are adapted and how that pertains to the updated information. As an example, tying the UX feature presentations to the application itself, such that the UX feature presentations of the application are adjusted based on the determined one or more states would be one potential way to address this portion of the rejection. With respect to “adapting social sharing recommendations”, it remains unclear as to how the updated information pertains to social sharing recommendations and how the social sharing recommendations are adapted/updated based on the emotional state of the user. Applicant’s remarks appear to be directed to the emotional state portion and not the adapting portion. The examiner respectfully requests the applicant clarify the scope of the claimed limitations and to how those limitations fit within currently amended claims 1 and 32. The 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejection of claims 2 and 33 has been maintained
With respect to claim 3 and claims depending thereon, given the plain and ordinary meaning of the words when interpreted in light of the corresponding disclosure, the scope of the claimed limitation is unclear. For instance, it is not immediately clear as to how the probability of continued utilization of the application is determined. The originally filed disclosure appears to only use probability in paragraph 133, which recites “Data received by the system relating to emotion-related parameter information of one or more app-users and/or audience members may be analyzed for determining the probability of continued app utilization by the one or more users and/or audience members....the system may be configured to associate an emotional state determined to be experienced by an app user with a score...the system may be determine a weighted overall score for a plurality of emotional states collectively experienced by an app user. Based on the determined score, the system may determine the probability of continued app utilization by the one or more users and/or audience members, and/or update features and/or exercises and/or musical pieces presented to the app-users”. It would seem from the disclosure that the probability is determined from a weighted overall score for a plurality of emotional states collectively experienced by a user of the application, but even with that taken into consideration the scope is unclear because it would be unclear as to how the emotional state ties to a given score. The examiner respectfully requests the applicant clarify the scope of the claimed limitation. The 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejection of claim 3 and claims depending thereon has been maintained
With respect to claim 22, given the plain and ordinary meaning of the words themselves when interpreted on their own and/or in light of the corresponding disclosure, it is not immediately clear, as currently drafted, as to how the system is configured to determine which sequence of musical notations evokes which emotion. Support for the claimed subject matter can be found in paragraph 105. Aside from reciting the claim language, paragraph 105 appears to indicate the determination is performed with machine learning model that is trained for associating emotion information with musical notations, user profile information, time data information, and/or location information. However, taking paragraph 105 into consideration, along with the remaining disclosure and applicant’s arguments, it remains unclear as to the scope of determining which sequence of musical notations evokes which emotion. Are different visual representations of musical notes shown to the user and then the user selects their emotion? Is sensor data used to determine an emotional state of a user in response to seeing the musical notation? The examiner respectfully requests the applicant clarify the scope of the claimed limitation. The 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejection of claim 22 and claims depending thereon has been maintained
With respect to claim 23, given the plain and ordinary meaning of the words themselves when interpreted on their own and/or in light of the corresponding disclosure, it is not immediately clear, as currently drafted, as to how the system is configured to determine which sequence of musical notations evokes which emotion with respect to one of the categories presented. Support for the claimed subject matter can be found in paragraph 105. Aside from reciting the claim language, paragraph 105 appears to indicate the determination is performed with machine learning model that is trained for associating emotion information with musical notations, user profile information, time data information, and/or location information. However, taking paragraph 105 into consideration, along with the remaining disclosure and applicant’s remarks, it remains unclear as to the scope of determining which sequence of musical notations evokes which emotion. Are different visual representations of musical notes shown to the user and then the user selects their emotion? Is sensor data used to determine an emotional state of a user in response to seeing the musical notation? Furthermore, how is the algorithm able to determine which emotion is invoked by a specific sequence of musical notes displayed to an entire audience? The examiner respectfully requests the applicant clarify the scope of the claimed limitation. The examiner respectfully requests the applicant clarify the scope of the claimed limitation. The 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejection of claim 23 and claims depending thereon has been maintained
With respect to claim 28, given the plain and ordinary meaning of the words themselves when interpreted on their own and/or in light of the corresponding disclosure, it is not immediately clear, as currently drafted, as to how the system is configured to determine an interaction parameter of collaboratively performing app-users and how that is used to determine an emotional state of the collaboratively performing app users. What is collaboratively performing? Is that two users playing together? How is the emotional state of the collaboratively performing app users determined? Support for the claimed limitation can be found in paragraph 114. It appears from paragraph 114 that collaboratively performing is playing a musical piece together (e.g., jamming). However, it remains unclear as to how the interaction parameter value is used to determine emotional information associated with the collaboratively performing app users. The examiner respectfully requests the applicant clarify the scope of the claimed limitation. The 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejection of claim 28 and claims depending thereon has been maintained
With respect to claim 31, given the plain and ordinary meaning of the words themselves when interpreted on their own and/or in light of the corresponding disclosure, it is not immediately clear, as currently drafted, as to how the tolerance thresholds are adapted to provide less negative feedback to the user either alone or in combination with making the musical information less challenging to perform. What are the tolerance thresholds as claimed and how is the user being frustrated lead to adjusting negative feedback? The use of less negative feedback implies that negative feedback is being provided. The examiner respectfully requests the applicant clarify the scope of the claimed limitation. The 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejection of claim 31 and claims depending thereon has been maintained
Applicant’s arguments, see page 13-15, filed 19 January 2023, with respect to the prior art rejection, along with accompanying amendments received on the same date, have been fully considered and are not persuasive. Applicant argues that there is not motivation to combine the references (see page 14). In addition, applicant argues that Shkedy and Si do not teach or suggest, alone or in combination, at least, determining whether to relax playing accuracy requirements. The examiner respectfully disagrees.
Shkedy teaches the lesson, such as the music is changed based on the emotional of the user, see for instance, paragraphs 26-30. A lesson is selected from a selection module that defines the lesson to be taught, where...the lessons to be taught can be a sequence of lessons and tasks to...learn how to play a musical instrument, see paragraph 49. Next, the selected lesson is presented to the student ...(and)...the student response module receives a response to the lesson, see for instance, paragraph 49. Concurrently, the students biometric response module 362 will capture the student's biometric data S312A... such as their heart rate or facial expressions or both...a response evaluation module 375 evaluates the student response S315 to determine if the lesson was completed correctly....a biometric evaluation module 370 will use the biometric data to determine the student's emotional state S315A, see for instance, paragraph 49. Then, a diagnostic module 380 combines the output of the response evaluation module 375 with the output of the biometric evaluation module 370 to optimize the parameters S320 for section of the next lesson to be presented to the student, see for instance, paragraph 49. Parameters can be optimized for selection of the next lesson to be presented to the student, based on their responses and/or emotional state, see for instance, paragraphs 49 and 58.
Shkedy further teaches using biometrics to determine lessons to present to a student, such as: teaching special needs students who struggle to attend to a task and need motivating lessons in order to be engaged; teaching students a dynamic curriculum that evolves based on their emotional state; teaching a student how to play the piano while changing the type of music they use based on their level of excitement as measured by the biometric systems; ...; and using machine learning to present the next lesson that uses performance and biometric data as inputs, see for instance, paragraphs 25-30. Shkedy, also teaches optimizing parameters for selection of the next lesson to be presented to the student, based on their responses and/or emotional state, see for instance, paragraphs 49 and 58.
Si teaches that a user can be presented with a musical piece to be played and the musical piece can be adjusted based on the needs of the user, see for instance, paragraph 31-34, 44, and 51. Visual instruction can be projected on respective keys, see for instance, paragraph 34. The skill learner may be able to communicate with the AI based skill learning system to adjust the learning parameters, e.g., adjust the speed of the playing at different stages of learning...For example, if a skill learning is initially unfamiliar with the piece of music, the speed of tutoring by the AI based skill learning may be much slower than the actual speed of the player’s performance, see for instance, paragraph 35. In some situations (e.g., piano playing skill learning), any oral instructions may be invoked only when certain conditions are met, e.g., the tutoring speed is set below a certain threshold (otherwise there may not be possible to playback the oral instruction), see for instance, paragraph 37. Other needs of the skill learner may be detected by analyzing performance of the skill learning, see for instance, paragraph 51. For instance, a skill learner may repeatedly exhibit difficulty in, e.g., playing a drum with a particular pattern in a certain rhythm, in this case, the AI based skill learning system may adaptively adjust the tutoring process by adding specific sessions targeting at a particularly sub skills determined based on each individual skill learners, see for instance, paragraph 51. If the skill learner’s playing speed is consistently lagging behind the expected speed, the adaptive tutoring plan generator may adjust the required speed of the hand movements to slow down until the skill learning becomes familiar with the piece.
That is Shkedy in combination with Si would teach based on the determined one or more emotional states, determining whether to relax the accuracy requirements (e.g., required speed of hand movements) by altering a tolerance threshold (e.g., adjusting the required speed) with respect to feedback of the user (e.g., user struggling).
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, both Shkedy and Si are in the same art of teaching students, they both use examples of playing the piano, and adjust/optimize information in order to facilitate student learning. One of ordinary skill in the art at the time of the effective filing date of the invention would have been motivated to combine Shkedy and Si to improve the user experience, enhance functionality and the learning experience and effectiveness (e.g., by adaptively adjusting the information being presented to the user, reducing user frustration, etc, see Si, paragraph 51).
Claim Objections
Claim(s) 1, 22, 23, 28, and 32 is/are objected to because of the following informalities:
App should be spelled application (at least in the first instance), since app is an abbreviation for application and abbreviations should be spelled out before being used in shorthand form. Claim 32 uses app before spelling out application.
Claim 1 should recite in part “presenting to the one or more users of the app
Claims 22, 23, and 28 appear as though they should recite “further configured to...”
Claim 7 should recite “non-verbal interactions between the one or more users of the app .
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7 and 21-36 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “based on the determined one or more emotional states, determining whether playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback of the user”. The corresponding disclosure in paragraph 136 sets forth “the system may increase or decrease tolerance threshold (also relax accuracy requirements) with respect to mistimed playing, out-of-pitch signing, and/or the like, to decrease user frustration and/or increase happiness”. Paragraph 137 goes on to recite “For example, based on determined emotion information, the system may employ alternate tolerance thresholds with respect to displaying negative feedback to the user. For example, if an identified emotional state includes “frustration” or “unhappiness”, the system may increase its tolerance towards mistimed playing, partial chord playing, and/or the like, and thus alter the feedback presented in any of the following ways: decrease ongoing indications of errors to the user while playing, increase indications of success (such as quick celebratory animations indicating success in a subsection of the piece); increase the overall score awarded to the user at the end of the play session; and/or other similar feedback measures. The system may also choose the alter or otherwise avoid displaying holistic feedback presented to the user, such as verbal feedback relating to the overall performance, accumulated quantitative scores, and/or the like”. That is, while the originally filed disclosure teaches relaxing playing accuracy requirements (e.g., decrease ongoing indications of errors to the user while playing) and adjusting the tolerance based on determined one or more emotional states, it does not appear to set forth how a determination is made as to whether playing accuracy requirements are to be relaxed by altering a tolerance threshold with respect to feedback of the user and based on the determined one or more emotional states. In addition, it is not clear that applicant has support for an algorithm that would accomplish said claimed function of based on the determined one or more emotional states, determining whether playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback of the user. The examiner respectfully requests the applicant clarify as to where support can be found for the claimed amendment.
Claim 32 recites substantially similar limitations as to that of claim 1 and is accordingly also rejected using substantially similar rationale as to that set forth with respect to claim 1.
Claims depending thereon do not cure the noted deficiency and are accordingly, also rejected using substantially similar rationale as to that set forth for claims 1 and 32.
Claim 34 recites that the feedback pertains to positive feedback, or negative feedback, or both. The feedback to which the claim references is recited in the last limitation of claim 1, which recites “based on the determined one or more emotional states, determining whether playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback to the user”. The corresponding disclosure, appears to use the words “positive” and “negative” once in the disclosure, with positive being recited in paragraph 131 (“In some examples, additional or alternative experiences resulting from app usage (such as positive social interactions or feedback experienced while using the app....” and “negative” in paragraph 137 (based on determined emotion information, the system may employ alternate tolerance thresholds with respect to displaying negative feedback to the user). The disclosure does use the broader term feedback throughout the specification and it would have been obvious to one of ordinary skill in the art that feedback can be positive, negative, neutral, etc. However, the corresponding disclosure, does not appear to support the feedback, as claimed, to pertain to both positive feedback and negative feedback (the both part of the recited claim). The examiner respectfully requests the applicant clarify as to where support can be found for the claimed amendment.
Claims 35 recites the tolerance threshold determines whether negative playing feedback, or positive motivational information is to be presented to the user or both. The tolerance threshold to which the claim references is recited in the last limitation of claim 1, which recites “based on the determined one or more emotional states, determining whether playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback to the user”. The corresponding disclosure, appears to use the words “positive” and “negative” once in the disclosure, with positive being recited in paragraph 131 (“In some examples, additional or alternative experiences resulting from app usage (such as positive social interactions or feedback experienced while using the app....” and “negative” in paragraph 137 (based on determined emotion information, the system may employ alternate tolerance thresholds with respect to displaying negative feedback to the user). The disclosure does support both the recited “negative playing feedback” (see for instance, paragraph 137) and “positive motivational information”, (see for instance, paragraphs 135 and 127). However, the corresponding disclosure, does not appear to support that the tolerance threshold “determines whether” negative playing feedback or positive motivational feedback is to be presented to the user, “or both”. That is, the corresponding disclosure does not appear to support the determination step or presenting both negative playing feedback and positive motivational information, as claimed. The examiner respectfully requests the applicant clarify as to where support can be found for the claimed amendment.
Claim 36 recites the feedback pertains to negative playing feedback, or the presentation of motivational feedback to the user, or both. The feedback to which the claim references is recited in the last limitation of claim 1, which recites “based on the determined one or more emotional states, determining whether playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback to the user”. The corresponding disclosure, appears to use the word “negative” once in the disclosure in paragraph 137 (based on determined emotion information, the system may employ alternate tolerance thresholds with respect to displaying negative feedback to the user). The disclosure does use the broader term feedback throughout the specification and would support feedback pertaining to “negative playing feedback” (e.g., see for instance, paragraph 137) and the “presentation of motivational feedback to the user” (e.g., indications of success, increasing overall score, etc, see paragraph 137). However, the corresponding disclosure, does not appear to support a) the feedback pertaining to the presentation of motivational feedback or both the feedback pertaining to negative playing feedback and the presentation of motivational feedback to the user. The examiner respectfully requests the applicant clarify as to where support can be found for the claimed amendment.
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) 1-7 and 21-36 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “based on the determined one or more emotional states, determining whether playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback of the user”. The corresponding disclosure in paragraph 136 sets forth “the system may increase or decrease tolerance threshold (also relax accuracy requirements) with respect to mistimed playing, out-of-pitch signing, and/or the like, to decrease user frustration and/or increase happiness”. Paragraph 137 goes on to recite “For example, based on determined emotion information, the system may employ alternate tolerance thresholds with respect to displaying negative feedback to the user. For example, if an identified emotional state includes “frustration” or “unhappiness”, the system may increase its tolerance towards mistimed playing, partial chord playing, and/or the like, and thus alter the feedback presented in any of the following ways: decrease ongoing indications of errors to the user while playing, increase indications of success (such as quick celebratory animations indicating success in a subsection of the piece); increase the overall score awarded to the user at the end of the play session; and/or other similar feedback measures. The system may also choose the alter or otherwise avoid displaying holistic feedback presented to the user, such as verbal feedback relating to the overall performance, accumulated quantitative scores, and/or the like”. However, taking into the broadest reasonable interpretation of the claim limitation in light of the corresponding disclosure, the scope of the claim remains unclear. How does altering a tolerance threshold with respect to feedback of the user determine whether playing accuracy requirements are to be relaxed or not based on the determined one or more emotional states? In addition, is the feedback from/about the user or is it as recited to the user and if it is to the user, what does feedback to the user mean? The examiner respectfully requests the applicant clarify the scope of the claimed limitations.
Claim 32 recites substantially similar limitations as to that of claim 1 and is accordingly also rejected using substantially similar rationale as to that set forth with respect to claim 1.
Claims depending thereon do not cure the noted deficiency and are accordingly, also rejected using substantially similar rationale as to that set forth for claims 1 and 32.
Claim 2 recites “wherein the updated information pertains to one or more the following: lesson assignment; changing a level of difficulty of a same musical exercise; adjusting tolerance thresholds; adapting UX feature presentations; adapting social sharing recommendations; or any combination of the above” Given the plain and ordinary meaning of the words themselves when interpreted on their own and/or in light of the corresponding disclosure, it is not immediately clear as to what constitutes “adjusting tolerance thresholds” (tolerance of what?), “adapting UX feature presentations” – are the UX feature presentations in the application? What constitutes a feature presentation?; and “adapting social sharing recommendations” – how are the social sharing recommendations adapted/updated based on the emotional state of the user? In addition, the updated information lacks antecedent basis with respect to claim 1 and it is not immediately clear as to how claim 2 fits in with the subject matter of claim 1. The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
Claim 33 recites substantially similar limitations as to that of claim 2 and is accordingly also rejected using substantially similar rationale as to that set forth with respect to claim 2.
Claim 3 recites “configured to determine, based on the one or more emotional states, the probability of continued app utilization.” The originally filed disclosure appears to only use probability in paragraph 133, which recites “Data received by the system relating to emotion-related parameter information of one or more app-users and/or audience members may be analyzed for determining the probability of continued app utilization by the one or more users and/or audience members....the system may be configured to associate an emotional state determined to be experienced by an app user with a score...the system may be determine a weighted overall score for a plurality of emotional states collectively experienced by an app user. Based on the determined score, the system may determine the probability of continued app utilization by the one or more users and/or audience members, and/or update features and/or exercises and/or musical pieces presented to the app-users”. Given the plain and ordinary meaning of the words themselves when interpreted on their own and/or in light of the corresponding disclosure, it is not immediately clear as to how the probability of continued utilization of the application is determined? It would seem from the disclosure that the probability is determined from a weighted overall score for a plurality of emotional states collectively experienced by a user of the application, but even with that taken into consideration the scope is unclear because it would be unclear as to how the emotional state ties to a given score. The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
Claims depending thereon do not cure the noted deficiency and are accordingly, also rejected using substantially similar rationale as to that set forth for claim 3.
With respect to claim 22, given the plain and ordinary meaning of the words themselves when interpreted on their own and/or in light of the corresponding disclosure, it is not immediately clear, as currently drafted, as to how the system is configured to determine which sequence of musical notations evokes which emotion. Support for the claimed subject matter can be found in paragraph 105. Aside from reciting the claim language, paragraph 105 appears to indicate the determination is performed with machine learning model that is trained for associating emotion information with musical notations, user profile information, time data information, and/or location information. However, even taking paragraph 105 into consideration, along with the remaining disclosure, it remains unclear as to the scope of determining which sequence of musical notations evokes which emotion. Are different visual representations of musical notes shown to the user and then the user selects their emotion? Is sensor data used to determine an emotional state of a user in response to seeing the musical notation? The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
With respect to claim 23, given the plain and ordinary meaning of the words themselves when interpreted on their own and/or in light of the corresponding disclosure, it is not immediately clear, as currently drafted, as to how the system is configured to determine which sequence of musical notations evokes which emotion with respect to one of the categories presented. Support for the claimed subject matter can be found in paragraph 105. Aside from reciting the claim language, paragraph 105 appears to indicate the determination is performed with machine learning model that is trained for associating emotion information with musical notations, user profile information, time data information, and/or location information. However, even taking paragraph 105 into consideration, along with the remaining disclosure, it remains unclear as to the scope of determining which sequence of musical notations evokes which emotion. Are different visual representations of musical notes shown to the user and then the user selects their emotion? Is sensor data used to determine an emotional state of a user in response to seeing the musical notation? Furthermore, how is the algorithm able to determine which emotion is invoked by a specific sequence of musical notes displayed to an entire audience? The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
With respect to claim 28, given the plain and ordinary meaning of the words themselves when interpreted on their own and/or in light of the corresponding disclosure, it is not immediately clear, as currently drafted, as to how the system is configured to determine an interaction parameter of collaboratively performing app-users and how that is used to determine an emotional state of the collaboratively performing app users. What is collaboratively performing? Is that two users playing together? How is the emotional state of the collaboratively performing app users determined? Support for the claimed limitation can be found in paragraph 114. It appears from paragraph 114 that collaboratively performing is playing a musical piece together (e.g., jamming). However, it remains unclear as to how the interaction parameter value is used to determine emotional information associated with the collaboratively performing app users. The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
With respect to claim 31, given the plain and ordinary meaning of the words themselves when interpreted on their own and/or in light of the corresponding disclosure, it is not immediately clear, as currently drafted, as to how the tolerance thresholds are adapted to provide less negative feedback to the user either alone or in combination with making the musical information less challenging to perform. What are the tolerance thresholds and how is the user being frustrated lead to adjusting negative feedback? The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
Claim 34 recites that the feedback pertains to positive feedback, or negative feedback, or both. The feedback to which the claim references is recited in the last limitation of claim 1, which recites “based on the determined one or more emotional states, determining whether playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback to the user”. The corresponding disclosure, appears to use the words “positive” and “negative” once in the disclosure, with positive being recited in paragraph 131 (“In some examples, additional or alternative experiences resulting from app usage (such as positive social interactions or feedback experienced while using the app....” and “negative” in paragraph 137 (based on determined emotion information, the system may employ alternate tolerance thresholds with respect to displaying negative feedback to the user). The disclosure does use the broader term feedback throughout the specification and it would have been obvious to one of ordinary skill in the art that feedback can be positive, negative, neutral, etc. However, given the plain and ordinary meaning of the words themselves when interpreted in light of the corresponding disclosure, it is not immediately clear as to how the feedback, as claimed, can pertain to both positive feedback and negative feedback (the both part of the recited claim). The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
Claims 35 recites the tolerance threshold determines whether negative playing feedback, or positive motivational information is to be presented to the user or both. The tolerance threshold to which the claim references is recited in the last limitation of claim 1, which recites “based on the determined one or more emotional states, determining whether playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback to the user”. The corresponding disclosure, appears to use the words “positive” and “negative” once in the disclosure, with positive being recited in paragraph 131 (“In some examples, additional or alternative experiences resulting from app usage (such as positive social interactions or feedback experienced while using the app....” and “negative” in paragraph 137 (based on determined emotion information, the system may employ alternate tolerance thresholds with respect to displaying negative feedback to the user). The scope of both the recited “negative playing feedback” (see for instance, paragraph 137) and “positive motivational information”, (see for instance, paragraphs 135 and 127) would be clear on their own. However, it is not immediately clear as to how the tolerance threshold “determines whether” negative playing feedback or positive motivational feedback is to be presented to the user, “or both”. The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
Claim 36 recites the feedback pertains to negative playing feedback, or the presentation of motivational feedback to the user, or both. The feedback to which the claim references is recited in the last limitation of claim 1, which recites “based on the determined one or more emotional states, determining whether playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback to the user”. The corresponding disclosure, appears to use the word “negative” once in the disclosure in paragraph 137 (based on determined emotion information, the system may employ alternate tolerance thresholds with respect to displaying negative feedback to the user). The disclosure does use the broader term feedback throughout the specification and the scope of “negative playing feedback” (e.g., see for instance, paragraph 137) and the “presentation of motivational feedback to the user” (e.g., indications of success, increasing overall score, etc, see paragraph 137) on their own would be clear. Given the plain and ordinary meaning of the words themselves when interpreted on their own and/or in light of the corresponding disclosure, it is not immediately clear as to how the feedback pertains to motivational feedback to the user, to both negative playing feedback and the presentation of motivational feedback to the user, and how the feedback recited in claim 36 relates previously recited feedback in claim 1. The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
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.
Claim(s) 1, 2, 6, 24, 26, 29, and 32-36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shkedy et al. (US PG Publication 2019/0362138) in view of Si (US PG Publication 2021/0104169).
Regarding claim 1, Shkedy teaches a system for determining an emotional state of one or more users of an instrument teaching and/or singing teaching application (app) (see for instance, abstract, 28, and 49), the system comprising: a processor (see for instance, paragraphs 39, 42, and 52 and figs. 2 and 4); and a memory storing instructions executable by the processor to result in the execution (see for instance, paragraphs 37, 39, 43, 44, and 52) of the following:
presenting to the one or more users of the app user with information about a musical piece to be played and/or sung (The application can present a lesson to a user to learn how to play a musical instrument, see for instance, paragraphs 28, 49, and 50. The type of music can be changed, see for instance, paragraph 28);
receiving data relating to one of the following of the one or more users of the app: user physiological parameter, behavioral parameter, or both (Biometric data, such as a user’s heart rate and facial expressions, is received, see for instance, paragraph 49 and fig. 4);
processing the received data to determine one or more emotional states of the one or more users of the app (Concurrently, a biometric evaluation module will use the biometric data to determine the student’s emotional state, see for instance, paragraph 49 and figs. 3 and 4); and
based on the determined one or more emotional states, determining whether playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback to the user (The lesson, such as the music is changed based on the emotional of the user, see for instance, paragraphs 26-30. A lesson is selected from a selection module that defines the lesson to be taught, where...the lessons to be taught can be a sequence of lessons and tasks to...learn how to play a musical instrument, see paragraph 49. Next, the selected lesson is presented to the student ...(and)...the student response module receives a response to the lesson, see for instance, paragraph 49. Concurrently, the students biometric response module 362 will capture the student's biometric data S312A... such as their heart rate or facial expressions or both...a response evaluation module 375 evaluates the student response S315 to determine if the lesson was completed correctly....a biometric evaluation module 370 will use the biometric data to determine the student's emotional state S315A, see for instance, paragraph 49. Then, a diagnostic module 380 combines the output of the response evaluation module 375 with the output of the biometric evaluation module 370 to optimize the parameters S320 for section of the next lesson to be presented to the student, see for instance, paragraph 49. Parameters can be optimized for selection of the next lesson to be presented to the student, based on their responses and/or emotional state, see for instance, paragraphs 49 and 58).
Shkedy does not appear to explicitly state that the type of music is a musical piece to be played. In addition, Shkedy teaches using biometrics to determine lessons to present to a student, such as: teaching special needs students who struggle to attend to a task and need motivating lessons in order to be engaged; teaching students a dynamic curriculum that evolves based on their emotional state; teaching a student how to play the piano while changing the type of music they use based on their level of excitement as measured by the biometric systems; ...; and using machine learning to present the next lesson that uses performance and biometric data as inputs, see for instance, paragraphs 25-30. Shkedy, also teaches optimizing parameters for selection of the next lesson to be presented to the student, based on their responses and/or emotional state, see for instance, paragraphs 49 and 58. While Shkedy teaches taking into account feedback of the user and emotional responses to optimize parameters for determining the next lesson, Shkedy, does not appear to teach based on the determined one or more emotional states, determining whether playing accuracy requirements are to be relaxed or not by altering a tolerance threshold with respect to feedback to the user
In the same art of learning, Si teaches that a user can be presented with a musical piece to be played and the musical piece can be adjusted based on the needs of the user, see for instance, paragraph 31-34, 44, and 51. Visual instruction can be projected on respective keys, see for instance, paragraph 34. The skill learner may be able to communicate with the AI based skill learning system to adjust the learning parameters, e.g., adjust the speed of the playing at different stages of learning...For example, if a skill learning is initially unfamiliar with the piece of music, the speed of tutoring by the AI based skill learning may be much slower than the actual speed of the player’s performance, see for instance, paragraph 35. In some situations (e.g., piano playing skill learning), any oral instructions may be invoked only when certain conditions are met, e.g., the tutoring speed is set below a certain threshold (otherwise there may not be possible to playback the oral instruction), see for instance, paragraph 37. Other needs of the skill learner may be detected by analyzing performance of the skill learning, see for instance, paragraph 51. For instance, a skill learner may repeatedly exhibit difficulty in, e.g., playing a drum with a particular pattern in a certain rhythm, in this case, the AI based skill learning system may adaptively adjust the tutoring process by adding specific sessions targeting at a particularly sub skills determined based on each individual skill learners, see for instance, paragraph 51. If the skill learner’s playing speed is consistently lagging behind the expected speed, the adaptive tutoring plan generator may adjust the required speed of the hand movements to slow down until the skill learning becomes familiar with the piece.
It would have been obvious to one of ordinary skill in the art having the teachings of Shkedy and Si in front of them before the effective filing date of the claimed invention to incorporate skill based learning as taught by Si into Shkedy’s adaptive learning, as presenting information relating to a musical piece to a user of an application, such as described by Si was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Shkedy.
The modification of Shkedy with Si would have explicitly allowed the type of music to be a information about a musical piece and for information, such as hand position to be displayed to the user. In addition, the combination teaches determining whether to relax the accuracy requirements by altering a tolerance threshold with respect to feedback of the user.
The motivation for combining Shkedy with Si would have been to improve the user experience, enhance functionality and the learning experience and effectiveness, see for instance, Si, paragraph 51.
Regarding claim 2, Shkedy in view of Si teach the system of claim 1 and further teach wherein the updated information pertains to one or more the following: lesson assignment; changing a level of difficulty of a same musical exercise; adjusting tolerance thresholds; adapting UX feature presentations; adapting social sharing recommendations; adapting motivational information; providing holistic feedback; or any combination of the above (see for instance, Shkedy, paragraphs 6, and 26-30 and Si, paragraph 51). The motivation to combine Shkedy and Si is the same as that which was set forth in claim 1.
Regarding claim 6, Shkedy in view of Si teach the system of claim 1 and further teach wherein monitoring the at least one behavioral parameter includes monitoring: facial expressions, gestures, posture, motion and/or movement of the one or more users of the app (Biometric data, such as a user’s heart rate and facial expressions, is received, see for instance, Shkedy, paragraph 49 and fig. 4). The motivation to combine Shkedy and Si is the same as that which was set forth in claim 1.
Regarding claim 24, Shkedy in view of Si teach the system of claim 1 and further teach wherein one of the following is associated with the determined one or more emotional states: time data, location information, setting information, user profile, or any combination of the aforesaid (Biometric data, such as a user’s heart rate and facial expressions, is received, see for instance, Shkedy, paragraph 49 and fig. 4. One or more devices physically or operable connected to collect biometric data from the study, such as a camera for facial recognition, a heart rate monitor, electro-muscular measurement, measurement of galvanic skin resistance or a blood pressure monitor, see for instance, Shkedy, paragraph 48). The motivation to combine Shkedy and Si is the same as that which was set forth in claim 1.
Regarding claim 26, Shkedy in view of Si teach the system of claim 1 and further teach comprising one or more sensors configured to provide sensor outputs relating to monitored physiological and/or behavioral parameters of the at least one user, and/or of the at least one audience member (Biometric data, such as a user’s heart rate and facial expressions, is received, see for instance, Shkedy, paragraph 49 and fig. 4. Biometric device is one or more devices physically or operable connected to collect biometric data from the study, such as a camera for facial recognition, a heart rate monitor, electro-muscular measurement, measurement of galvanic skin resistance or a blood pressure monitor, see for instance, Shkedy, paragraph 48. There are many non-limiting biometric devices that can be used in the present invention, such as an Apple Watch, Fitbit, or custom biometric device, see for instance, Shkedy, paragraph 48). The motivation to combine Shkedy and Si is the same as that which was set forth in claim 1.
Regarding claim 29, Shkedy in view of Si teach the system of claim 1 and further teach further configured to determining an emotional state based on nonverbal parameters (Biometric data, such as a user’s heart rate and facial expressions, is received, see for instance, Shkedy, paragraph 49 and fig. 4. Biometric device is one or more devices physically or operable connected to collect biometric data from the study, such as a camera for facial recognition, a heart rate monitor, electro-muscular measurement, measurement of galvanic skin resistance or a blood pressure monitor, see for instance, Shkedy, paragraph 48. There are many non-limiting biometric devices that can be used in the present invention, such as an Apple Watch, Fitbit, or custom biometric device, see for instance, Shkedy, paragraph 48). The motivation to combine Shkedy and Si is the same as that which was set forth in claim 1.
Regarding claim 32, claim 32 is the method claim of the system claim 1 and is accordingly rejected using substantially similar rationale as to that set forth with respect to claim 1.
Regarding claim 33, claim 33 is the method claim of the system claim 2 and is accordingly rejected using substantially similar rationale as to that set forth with respect to claim 2.
Regarding claim 34, Shkedy in view of Si teach the system of claim 1 and further teach wherein the feedback pertains to positive feedback, or negative feedback, or both (The lesson, such as the music is changed based on the emotional of the user, see for instance, Shkedy, paragraphs 26-30. A lesson is selected from a selection module that defines the lesson to be taught, where...the lessons to be taught can be a sequence of lessons and tasks to...learn how to play a musical instrument, see paragraph 49. Next, the selected lesson is presented to the student ...(and)...the student response module receives a response to the lesson, see for instance, Shkedy, paragraph 49. Concurrently, the students biometric response module 362 will capture the student's biometric data S312A... such as their heart rate or facial expressions or both...a response evaluation module 375 evaluates the student response S315 to determine if the lesson was completed correctly....a biometric evaluation module 370 will use the biometric data to determine the student's emotional state S315A, see for instance, Shkedy, paragraph 49. Then, a diagnostic module 380 combines the output of the response evaluation module 375 with the output of the biometric evaluation module 370 to optimize the parameters S320 for section of the next lesson to be presented to the student, see for instance, Shkedy, paragraph 49. Parameters can be optimized for selection of the next lesson to be presented to the student, based on their responses and/or emotional state, see for instance, Shkedy, paragraphs 49 and 58. Visual instruction can be projected on respective keys, see for instance, Si, paragraph 34. The skill learner may be able to communicate with the AI based skill learning system to adjust the learning parameters, e.g., adjust the speed of the playing at different stages of learning...For example, if a skill learning is initially unfamiliar with the piece of music, the speed of tutoring by the AI based skill learning may be much slower than the actual speed of the player’s performance, see for instance, Si, paragraph 35. In some situations (e.g., piano playing skill learning), any oral instructions may be invoked only when certain conditions are met, e.g., the tutoring speed is set below a certain threshold (otherwise there may not be possible to playback the oral instruction), see for instance, Si, paragraph 37. Other needs of the skill learner may be detected by analyzing performance of the skill learning, see for instance, Si, paragraph 51. For instance, a skill learner may repeatedly exhibit difficulty in, e.g., playing a drum with a particular pattern in a certain rhythm, in this case, the AI based skill learning system may adaptively adjust the tutoring process by adding specific sessions targeting at a particularly sub skills determined based on each individual skill learners, see for instance, Si, paragraph 51. If the skill learner’s playing speed is consistently lagging behind the expected speed, the adaptive tutoring plan generator may adjust the required speed of the hand movements to slow down until the skill learning becomes familiar with the piece, see for instance, Si, paragraph 65).The motivation to combine Shkedy and Si is the same as that which was set forth in claim 1.
Regarding claim 35, Shkedy in view of Si teach the system of claim 1 and further teach wherein the tolerance threshold determines whether negative playing feedback, or positive motivational information is to be presented to the user, or both (The lesson, such as the music is changed based on the emotional of the user, see for instance, Shkedy, paragraphs 26-30. A lesson is selected from a selection module that defines the lesson to be taught, where...the lessons to be taught can be a sequence of lessons and tasks to...learn how to play a musical instrument, see paragraph 49. Next, the selected lesson is presented to the student ...(and)...the student response module receives a response to the lesson, see for instance, Shkedy, paragraph 49. Concurrently, the students biometric response module 362 will capture the student's biometric data S312A... such as their heart rate or facial expressions or both...a response evaluation module 375 evaluates the student response S315 to determine if the lesson was completed correctly....a biometric evaluation module 370 will use the biometric data to determine the student's emotional state S315A, see for instance, Shkedy, paragraph 49. Then, a diagnostic module 380 combines the output of the response evaluation module 375 with the output of the biometric evaluation module 370 to optimize the parameters S320 for section of the next lesson to be presented to the student, see for instance, Shkedy, paragraph 49. Parameters can be optimized for selection of the next lesson to be presented to the student, based on their responses and/or emotional state, see for instance, Shkedy, paragraphs 49 and 58. Visual instruction can be projected on respective keys, see for instance, Si, paragraph 34. The skill learner may be able to communicate with the AI based skill learning system to adjust the learning parameters, e.g., adjust the speed of the playing at different stages of learning...For example, if a skill learning is initially unfamiliar with the piece of music, the speed of tutoring by the AI based skill learning may be much slower than the actual speed of the player’s performance, see for instance, Si, paragraph 35. In some situations (e.g., piano playing skill learning), any oral instructions may be invoked only when certain conditions are met, e.g., the tutoring speed is set below a certain threshold (otherwise there may not be possible to playback the oral instruction), see for instance, Si, paragraph 37. Other needs of the skill learner may be detected by analyzing performance of the skill learning, see for instance, Si, paragraph 51. For instance, a skill learner may repeatedly exhibit difficulty in, e.g., playing a drum with a particular pattern in a certain rhythm, in this case, the AI based skill learning system may adaptively adjust the tutoring process by adding specific sessions targeting at a particularly sub skills determined based on each individual skill learners, see for instance, Si, paragraph 51. If the skill learner’s playing speed is consistently lagging behind the expected speed, the adaptive tutoring plan generator may adjust the required speed of the hand movements to slow down until the skill learning becomes familiar with the piece, see for instance, Si, paragraph 65). The motivation to combine Shkedy and Si is the same as that which was set forth in claim 1.
Regarding claim 36, Shkedy in view of Si teach the system of claim 1 and further teach wherein the feedback pertains to negative playing feedback, or the presentation of motivational feedback, or both (The lesson, such as the music is changed based on the emotional of the user, see for instance, Shkedy, paragraphs 26-30. A lesson is selected from a selection module that defines the lesson to be taught, where...the lessons to be taught can be a sequence of lessons and tasks to...learn how to play a musical instrument, see paragraph 49. Next, the selected lesson is presented to the student ...(and)...the student response module receives a response to the lesson, see for instance, Shkedy, paragraph 49. Concurrently, the students biometric response module 362 will capture the student's biometric data S312A... such as their heart rate or facial expressions or both...a response evaluation module 375 evaluates the student response S315 to determine if the lesson was completed correctly....a biometric evaluation module 370 will use the biometric data to determine the student's emotional state S315A, see for instance, Shkedy, paragraph 49. Then, a diagnostic module 380 combines the output of the response evaluation module 375 with the output of the biometric evaluation module 370 to optimize the parameters S320 for section of the next lesson to be presented to the student, see for instance, Shkedy, paragraph 49. Parameters can be optimized for selection of the next lesson to be presented to the student, based on their responses and/or emotional state, see for instance, Shkedy, paragraphs 49 and 58. Visual instruction can be projected on respective keys, see for instance, Si, paragraph 34. The skill learner may be able to communicate with the AI based skill learning system to adjust the learning parameters, e.g., adjust the speed of the playing at different stages of learning...For example, if a skill learning is initially unfamiliar with the piece of music, the speed of tutoring by the AI based skill learning may be much slower than the actual speed of the player’s performance, see for instance, Si, paragraph 35. In some situations (e.g., piano playing skill learning), any oral instructions may be invoked only when certain conditions are met, e.g., the tutoring speed is set below a certain threshold (otherwise there may not be possible to playback the oral instruction), see for instance, Si, paragraph 37. Other needs of the skill learner may be detected by analyzing performance of the skill learning, see for instance, Si, paragraph 51. For instance, a skill learner may repeatedly exhibit difficulty in, e.g., playing a drum with a particular pattern in a certain rhythm, in this case, the AI based skill learning system may adaptively adjust the tutoring process by adding specific sessions targeting at a particularly sub skills determined based on each individual skill learners, see for instance, Si, paragraph 51. If the skill learner’s playing speed is consistently lagging behind the expected speed, the adaptive tutoring plan generator may adjust the required speed of the hand movements to slow down until the skill learning becomes familiar with the piece, see for instance, Si, paragraph 65). The motivation to combine Shkedy and Si is the same as that which was set forth in claim 1.
Claim(s) 3-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shkedy et al. (US PG Publication 2019/0362138) in view of Si (US PG Publication 2021/0104169), as applied to claim 1 above, in further view of Podgorny et al. (US PG Publication 2021/0209633).
Regarding claim 3, Shkedy in view of Si teach the system of claim 1, but mention probability and thus do not teach configured to determine, based on the one or more emotional states, the probability of continued app utilization.
In the same art of applications, Podgorny teaches that a prediction component includes a propensity model, user reaction model and analysis component, see for instance, paragraph 57 and fig. 3. The prediction component receives the array of sentiment (or emotion states) for each interactive screen (e.g., from speech component), user activity indicators and /or user metadata (such as user age, gender, occupation, how long the user has been using the application, etc), see for instance, paragraphs 57 and 58. User metadata may be determined from a profile of the user stored in a database hosted by the interactive computing service, see for instance, paragraph 59. The user profile may be a unified user profile maintained by the interactive computing service for different applications hosted by the interactive computing service, see for instance, paragraph 59. In response to receiving the user’s emotional states, the analysis component can evaluate the received information with propensity model to determine a user abandonment score that indicates probability of the user to abandon the interactive computing service, see for instance, paragraph 60. The propensity model may have been trained over historical user data, including historical user sentiment data and historical user actions (e.g., whether the users adopted the application, discontinued use of the application, etc), see for instance, paragraph 60. Once the online service determines that the probability of the user to abandon the online service exceeds a threshold, the online service may take proactive steps to reduce the likelihood of the user abandoning the service, see for instance, paragraph 26. An uplift model uses the probability score of the user to abandon in addition to one or user attributes to determine the type of intervention (e.g., when to intervene and how to intervene) to apply to the user, see for instance, paragraphs 27, and 60-63.
It would have been obvious to one of ordinary skill in the art having the teachings of Shkedy, Si, and Podgorny in front of them before the effective filing date of the claimed invention to incorporate user abandonment and intervention as taught by Podgorny into Shkedy’s modified adaptive learning system, as determining the probability that a user will abandon/stop using an application and potential intervention strategies, such as described by Podgorny was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Shkedy and Si.
The modification of Shkedy and Si with Podgorny would have allowed determining, based on the one or more emotional states, the probability of continued app utilization. Note the probability of an event occurring is 1 - probability of the event not occurring (Probability of the complement to the event).
The motivation for combining Shkedy and Si with Podgorny would have been to improve the user experience, enhance functionality and increase user engagement, see for instance, Podgorny, paragraphs 28 and 62.
Regarding claim 4, Shkedy in view of Si in further view of Podgorny teach the system of claim 3, and further teach further configured to adapt, based on the determined probability of continued app utilization, information presented to the user (see for instance, Shkedy, paragraphs 26-30, 49, and 50, Si, paragraph 51, and Podgorny, paragraphs 27, and 60-63). The motivation to combine Shkedy, Si, and Podgorny is the same as set forth in claim 3.
Regarding claim 5, Shkedy in view of Si teach the system of claim 4 and further teach wherein the adapting of the information includes adapting one or more of the following: lesson assignment; changing a level of difficulty of a same musical exercise; adjusting tolerance thresholds; adapting UX feature presentations; adapting social sharing recommendations; or any combination of the above (see for instance, Shkedy, paragraphs 6, and 26-30; Si, paragraph 51; and Podgorny, paragraphs 27, and 60-63). The motivation to combine Shkedy, Si, and Podgorny is the same as set forth in claim 3.
Claim(s) 7, 21-23, 25, 27, 28, 30, and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shkedy et al. (US PG Publication 2019/0362138) in view of Si (US PG Publication 2021/0104169), as applied to claim 1 above, in further view of Sumant et al. (US PG Publication 2020/0206631).
Regarding claim 7, Shkedy in view of Si teach the system of claim 1, but do not appear to teach further configured to monitor at least one physiological and/or behavioral parameter of one or more audience members experiencing the performance of the one or more users of the app playing an instrument and/or singing a musical piece; and/or verbal and/or non-verbal interactions between the one or more users of the app and the at least one audience member.
In the same art of emotional response, Sumant teaches operations of the processes 300 and 400 may be based at least in part on sensory data obtained from multiple users, see for instance, paragraph 104. For example, in some cases, a user may play video game with other users in the room who may or may not be playing the video game with the user, see paragraph 104. Emotion capture and determination system may identify the sensory and/or biometric data that is associated with non-player users based at least in part on the sensory data, see for instance, paragraph 104.
It would have been obvious to one of ordinary skill in the art having the teachings of Shkedy, Si, and Sumant in front of them before the effective filing date of the claimed invention to incorporate sensory-based dynamic game-state configurations, as taught by Sumant into Shkedy’s modified adaptive learning system, as determining the biometric data for audience members, such as described by Sumant was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Shkedy and Si.
The modification of Shkedy and Si with Sumant would have allowed monitoring at least one physiological and/or behavioral parameter of one or more audience members experiencing the performance of the at least one app user playing an instrument and/or singing a musical piece; and/or verbal and/or non-verbal interactions between the at least one app users and the at least one audience member.
The motivation for combining Shkedy and Si with Sumant would have been to improve the user experience, enhance functionality, such as by improving positive feelings and reducing negative feelings, see for instance, Sumant, paragraphs 28 and 62.
Regarding claim 21, Shkedy in view of Si teach the system of claim 1, but do not appear to teach further configured to determine the intensity of one or more emotional states of the one or more users of the app.
In the same art of emotional response, Sumant teaches that the retention analysis system may compare the values associated with the emotional states to determine whether the predicted amount of happiness matches the desired amount of happiness, see for instance, paragraph 100.
It would have been obvious to one of ordinary skill in the art having the teachings of Shkedy, Si, and Sumant in front of them before the effective filing date of the claimed invention to incorporate sensory-based dynamic game-state configurations, as taught by Sumant into Shkedy’s modified adaptive learning system, as determining the amount associated with a particular emotional state, such as happiness, such as described by Sumant was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Shkedy and Si.
The modification of Shkedy and Si with Sumant would have allowed determining, the intensity of one or more emotional states of the one or more users of the app.
The motivation for combining Shkedy and Si with Sumant would have been to improve the user experience, enhance functionality, such as by improving positive feelings and reducing negative feelings, see for instance, Sumant, paragraphs 28 and 62.
Regarding claim 25, Shkedy in view of Si teach the system of claim 1, but do not appear to explicitly teach wherein the one or more emotional states comprise: happiness, satisfaction, joy, pride, amusement, nervousness, stress, anger, frustration, boredom, or any combination of the aforesaid.
In the same art of emotional response, Sumant teaches sensory data may include audio data (including speech and non-alphanumeric sounds, for example, grunts and moans), visual or image data (including still images and videos that may be used to determine facial expressions or posture), physiological data (e.g., heart rate breath rate, perspiration level), and any other data that may be used to determine or predict an emotional state of the user, see for instance, paragraph 77. Emotional states include happiness, sadness, frustration, disgust, scared, fear, boredom, nervousness, excitement, and the like, see for instance, paragraphs 64, 74, 80, 89, 100.
It would have been obvious to one of ordinary skill in the art having the teachings of Shkedy, Si, and Sumant in front of them before the effective filing date of the claimed invention to incorporate sensory-based dynamic game-state configurations, as taught by Sumant into Shkedy’s modified adaptive learning system, as the emotional states including specific states, such as happiness, nervousness and boredom, such as described by Sumant was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Shkedy and Si.
The modification of Shkedy and Si with Sumant would have allowed the one or more emotional states to comprise: happiness, satisfaction, joy, pride, amusement, nervousness, stress, anger frustration, boredom, or any combination of the aforesaid
The motivation for combining Shkedy and Si with Sumant would have been to improve the user experience, enhance functionality, such as by improving positive feelings and reducing negative feelings, see for instance, Sumant, paragraphs 28 and 62.
Regarding claim 22, Shkedy in view of Si teach the system of claim 1, and further teach changing the type of music/lesson based on the emotional state of the user, such as adjusting the type of music based on excitement level or how well a user is able to play a piece of music, but do not appear to teach configured to determine which sequence of musical notations evokes which emotion.
In the same art of emotional response, Sumant teaches sensory data may include audio data (including speech and non-alphanumeric sounds, for example, grunts and moans), visual or image data (including still images and videos that may be used to determine facial expressions or posture), physiological data (e.g., heart rate breath rate, perspiration level), and any other data that may be used to determine or predict an emotional state of the user, see for instance, paragraph 77. Emotional states include happiness, sadness, frustration, disgust, scared, fear, boredom, nervousness, excitement, and the like, see for instance, paragraphs 64, 74, 80, 89, 100. The predicted overall emotional state may be used to modify the state of the video game, and how to modify the state of the video game (for example, to make the game more or less scary by adjusting light or music in the game), see for instance, paragraph 74. Some non-limiting changes to the video game may relate to difficulty, the amount of blood or gore, the music, the sound effects, the volume level of music or sound effects, the lighting, textures or shaders used, particular game events, levels made available to the user, an order or sequence of stages or levels,..., or any other factor related to the video game that can have an impact on the user’s emotional state when playing or interacting with the video game, see for instance, paragraph 92. In some embodiments, the predicted emotional state may be used as an index for to access the user data repository for determining a corresponding churn rate...one or more generally negative predicted emotional states, such as feelings of fear, boredom, or disgust, may be associated with a high churn rate or a low retention rate, see for instance, paragraph 89. The video game can be modified based on the predicted churn rate for the user, see for instance, paragraphs 91-94. The desired emotional state for the user playing the video game may be determined based in part on a churn rate associated with the predicted emotional state of the user, see for instance, paragraph 99. The game configuration system can determine settings associated with providing a scarier experience with playing a scary video game, such as by playing eerier music, see for instance, paragraph 102.
It would have been obvious to one of ordinary skill in the art having the teachings of Shkedy, Si, and Sumant in front of them before the effective filing date of the claimed invention to incorporate sensory-based dynamic game-state configurations, as taught by Sumant into Shkedy’s modified adaptive learning system, as the emotional states including specific states, such as happiness, nervousness and boredom and modifying the presentation of information based on the emotional state – such as adjusting music to make a scene scarier, such as described by Sumant was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Shkedy and Si.
The modification of Shkedy and Si with Sumant would have allowed determining which sequence of musical notations evokes which emotion and provide music to the user that increases their excitement associated with the music.
The motivation for combining Shkedy and Si with Sumant would have been to improve the user experience, enhance functionality, such as by improving positive feelings and reducing negative feelings, see for instance, Sumant, paragraphs 28 and 62.
Regarding claim 23, Shkedy in view of Si teach the system of claim 1, and further teach changing the type of music/lesson based on the emotional state of the user, such as adjusting the type of music based on excitement level or how well a user is able to play a piece of music, but do not appear to teach configured to determine which sequence of musical notations evokes which emotion with respect to one of the following: a certain user, a user profile. in one or more individual audience members, in an audience as a whole, or any combination of the aforesaid.
In the same art of emotional response, Sumant teaches sensory data may include audio data (including speech and non-alphanumeric sounds, for example, grunts and moans), visual or image data (including still images and videos that may be used to determine facial expressions or posture), physiological data (e.g., heart rate breath rate, perspiration level), and any other data that may be used to determine or predict an emotional state of the user, see for instance, paragraph 77. Emotional states include happiness, sadness, frustration, disgust, scared, fear, boredom, nervousness, excitement, and the like, see for instance, paragraphs 64, 74, 80, 89, 100. The predicted overall emotional state may be used to modify the state of the video game, and how to modify the state of the video game (for example, to make the game more or less scary by adjusting light or music in the game), see for instance, paragraph 74. Some non-limiting changes to the video game may relate to difficulty, the amount of blood or gore, the music, the sound effects, the volume level of music or sound effects, the lighting, textures or shaders used, particular game events, levels made available to the user, an order or sequence of stages or levels,..., or any other factor related to the video game that can have an impact on the user’s emotional state when playing or interacting with the video game, see for instance, paragraph 92. In some embodiments, the predicted emotional state may be used as an index for to access the user data repository for determining a corresponding churn rate...one or more generally negative predicted emotional states, such as feelings of fear, boredom, or disgust, may be associated with a high churn rate or a low retention rate, see for instance, paragraph 89. The video game can be modified based on the predicted churn rate for the user, see for instance, paragraphs 91-94. The desired emotional state for the user playing the video game may be determined based in part on a churn rate associated with the predicted emotional state of the user, see for instance, paragraph 99. The game configuration system can determine settings associated with providing a scarier experience with playing a scary video game, such as by playing eerier music, see for instance, paragraph 102.
It would have been obvious to one of ordinary skill in the art having the teachings of Shkedy, Si, and Sumant in front of them before the effective filing date of the claimed invention to incorporate sensory-based dynamic game-state configurations, as taught by Sumant into Shkedy’s modified adaptive learning system, as the emotional states including specific states, such as happiness, nervousness and boredom and modifying the presentation of information based on the emotional state – such as adjusting music to make a scene scarier, such as described by Sumant was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Shkedy and Si.
The modification of Shkedy and Si with Sumant would have allowed determining which sequence of musical notations evokes which emotion with respect to one of the following: a certain user, a user profile. in one or more individual audience members, in an audience as a whole, or any combination of the aforesaid and music can provided to the user that increases their excitement associated with the music.
The motivation for combining Shkedy and Si with Sumant would have been to improve the user experience, enhance functionality, such as by improving positive feelings and reducing negative feelings, see for instance, Sumant, paragraphs 28 and 62.
Regarding claim 27, Shkedy in view of Si teach the system of claim 1, but do not appear to teach further configured to determine an emotional state of the at least one user and/or one or more audience members based on voice output.
In the same art of emotional response, Sumant teaches operations of the processes 300 and 400 may be based at least in part on sensory data obtained from multiple users, see for instance, paragraph 104. For example, in some cases, a user may play video game with other users in the room who may or may not be playing the video game with the user, see paragraph 104. Emotion capture and determination system may identify the sensory and/or biometric data that is associated with non-player users based at least in part on the sensory data, see for instance, paragraph 104. Sensory data includes audio data, visual data, heart rate data, respiratory rate data, galvanic skin response data, etc, see for instance, paragraphs 5, 26, 35, and 44. Tone and/or sentiment of speech or audio made by the user, the tone and sentiment analysis system may determine a sentiment with respect to images and/or videos of the user captured by the sensory and biometric sensors, see for instance, paragraph 46. Sensory data may include audio data (including speech and non-alphanumeric sounds, for example, grunts and moans), visual or image data (including still images and videos that may be used to determine facial expressions or posture), physiological data (e.g., heart rate breath rate, perspiration level), and any other data that may be used to determine or predict an emotional state of the user, see for instance, paragraph 77.
It would have been obvious to one of ordinary skill in the art having the teachings of Shkedy, Si, and Sumant in front of them before the effective filing date of the claimed invention to incorporate sensory-based dynamic game-state configurations, as taught by Sumant into Shkedy’s modified adaptive learning system, as determining the emotional state of the user and/or audience members based on voice output, such as described by Sumant was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Shkedy and Si.
The modification of Shkedy and Si with Sumant would have allowed determining an emotional state of the at least one user and/or one or more audience members based on voice output.
The motivation for combining Shkedy and Si with Sumant would have been to improve the user experience, enhance functionality, such as by improving positive feelings and reducing negative feelings, see for instance, Sumant, paragraphs 28 and 62.
Regarding claim 28, Shkedy in view of Si teach the system of claim 1 but do not teach configured to determine at least one interaction parameter value of collaboratively performing app-users; and
determining, based on the at least one interaction parameter value, an emotional state of the collaboratively performing app users.
In the same art of emotional response, Sumant teaches operations of the processes 300 and 400 may be based at least in part on sensory data obtained from multiple users, see for instance, paragraph 104. For example, in some cases, a user may play video game with other users in the room who may or may not be playing the video game with the user, see paragraph 104. The emotion capture and determination system may filter out sensory and/or biometric data associated with users who are not playing the video game, see for instance, paragraph 104. Emotion capture and determination system may identify the sensory and/or biometric data that is associated with non-player users based at least in part on the sensory data, see for instance, paragraph 104. Sensory data includes audio data, visual data, heart rate data, respiratory rate data, galvanic skin response data, etc, see for instance, paragraphs 5, 26, 35, and 44. Tone and/or sentiment of speech or audio made by the user, the tone and sentiment analysis system may determine a sentiment with respect to images and/or videos of the user captured by the sensory and biometric sensors, see for instance, paragraph 46. Sensory data may include audio data (including speech and non-alphanumeric sounds, for example, grunts and moans), visual or image data (including still images and videos that may be used to determine facial expressions or posture), physiological data (e.g., heart rate breath rate, perspiration level), and any other data that may be used to determine or predict an emotional state of the user, see for instance, paragraph 77. The processes 300 and/or 400 may determine the aggregate predicted emotional state for the users playing the video game and/or the individual emotional state of the players, see paragraph 105. The system may determine whether to modify the video game may be based on the emotional state of the users playing the game, see for instance, paragraph 105.
It would have been obvious to one of ordinary skill in the art having the teachings of Shkedy, Si, and Sumant in front of them before the effective filing date of the claimed invention to incorporate sensory-based dynamic game-state configurations, as taught by Sumant into Shkedy’s modified adaptive learning system, as determining the emotional state of the user and/or audience members based on voice output, such as described by Sumant was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Shkedy and Si.
The modification of Shkedy and Si with Sumant would have allowed determining at least one interaction parameter value of collaboratively performing app-users; and determining, based on the at least one interaction parameter value, an emotional state of the collaboratively performing app users.
The motivation for combining Shkedy and Si with Sumant would have been to improve the user experience, enhance functionality, such as by improving positive feelings and reducing negative feelings, see for instance, Sumant, paragraphs 28 and 62.
Regarding claim 30, Shkedy in view of Si teach the system of claim 1, and further teach changing the type of music/lesson based on the emotional state of the user, such as adjusting the type of music based on excitement level or how well a user is able to play a musical piece but do not appear to teach wherein if the determined emotional state is "boredom", the app user is presented with musical information that is comparatively more challenging to perform.
In the same art of emotional response, Sumant teaches sensory data may include audio data (including speech and non-alphanumeric sounds, for example, grunts and moans), visual or image data (including still images and videos that may be used to determine facial expressions or posture), physiological data (e.g., heart rate breath rate, perspiration level), and any other data that may be used to determine or predict an emotional state of the user, see for instance, paragraph 77. Emotional states include happiness, sadness, frustration, disgust, scared, fear, boredom, nervousness, excitement, and the like, see for instance, paragraphs 64, 74, 80, 89, 100. The predicted overall emotional state may be used to modify the state of the video game, and how to modify the state of the video game (for example, to make the game more or less scary by adjusting light or music in the game), see for instance, paragraph 74. Some non-limiting changes to the video game may relate to difficulty, the amount of blood or gore, the music, the sound effects, the volume level of music or sound effects, the lighting, textures or shaders used, particular game events, levels made available to the user, an order or sequence of stages or levels,..., or any other factor related to the video game that can have an impact on the user’s emotional state when playing or interacting with the video game, see for instance, paragraph 92. In some embodiments, the predicted emotional state may be used as an index for to access the user data repository for determining a corresponding churn rate...one or more generally negative predicted emotional states, such as feelings of fear, boredom, or disgust, may be associated with a high churn rate or a low retention rate, see for instance, paragraph 89. The video game can be modified based on the predicted churn rate for the user, see for instance, paragraphs 91-94. The desired emotional state for the user playing the video game may be determined based in part on a churn rate associated with the predicted emotional state of the user, see for instance, paragraph 99.
It would have been obvious to one of ordinary skill in the art having the teachings of Shkedy, Si, and Sumant in front of them before the effective filing date of the claimed invention to incorporate sensory-based dynamic game-state configurations, as taught by Sumant into Shkedy’s modified adaptive learning system, as the emotional states including specific states, such as happiness, nervousness and boredom and modifying the presentation of information based on the emotional state, such as described by Sumant was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Shkedy and Si.
The modification of Shkedy and Si with Sumant would have allowed the app user is presented with musical information that is comparatively more challenging to perform if the determined emotional state is "boredom".
The motivation for combining Shkedy and Si with Sumant would have been to improve the user experience, enhance functionality, such as by improving positive feelings and reducing negative feelings, see for instance, Sumant, paragraphs 28 and 62.
Regarding claim 31, Shkedy in view of Si teach the system of claim 1, and further teach changing the type of music/lesson based on the emotional state of the user, such as adjusting the type of music based on excitement level or how well a user is able to play a musical piece but do not appear to teach wherein if the determined emotional state is "frustration": the app user is presented with musical information that is comparatively less challenging to perform: adapt tolerance thresholds to provide less negative feedback to the user, or both.
In the same art of emotional response, Sumant teaches sensory data may include audio data (including speech and non-alphanumeric sounds, for example, grunts and moans), visual or image data (including still images and videos that may be used to determine facial expressions or posture), physiological data (e.g., heart rate breath rate, perspiration level), and any other data that may be used to determine or predict an emotional state of the user, see for instance, paragraph 77. Emotional states include happiness, sadness, frustration, disgust, scared, fear, boredom, nervousness, excitement, and the like, see for instance, paragraphs 64, 74, 80, 89, 100. The predicted overall emotional state may be used to modify the state of the video game, and how to modify the state of the video game (for example, to make the game more or less scary by adjusting light or music in the game), see for instance, paragraph 74. Some non-limiting changes to the video game may relate to difficulty, the amount of blood or gore, the music, the sound effects, the volume level of music or sound effects, the lighting, textures or shaders used, particular game events, levels made available to the user, an order or sequence of stages or levels,..., or any other factor related to the video game that can have an impact on the user’s emotional state when playing or interacting with the video game, see for instance, paragraph 92. In some embodiments, the predicted emotional state may be used as an index for to access the user data repository for determining a corresponding churn rate...one or more generally negative predicted emotional states, such as feelings of fear, boredom, or disgust, may be associated with a high churn rate or a low retention rate, see for instance, paragraph 89. The video game can be modified based on the predicted churn rate for the user, see for instance, paragraphs 91-94. The desired emotional state for the user playing the video game may be determined based in part on a churn rate associated with the predicted emotional state of the user, see for instance, paragraph 99.
It would have been obvious to one of ordinary skill in the art having the teachings of Shkedy, Si, and Sumant in front of them before the effective filing date of the claimed invention to incorporate sensory-based dynamic game-state configurations, as taught by Sumant into Shkedy’s modified adaptive learning system, as the emotional states including specific states, such as happiness, nervousness and boredom and modifying the presentation of information based on the emotional state, such as described by Sumant was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Shkedy and Si.
The modification of Shkedy and Si with Sumant would have the app user to be presented with musical information that is comparatively less challenging to perform allowed if the determined emotional state is "frustration".
The motivation for combining Shkedy and Si with Sumant would have been to improve the user experience, enhance functionality, such as by improving positive feelings and reducing negative feelings, see for instance, Sumant, paragraphs 28 and 62.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG Publication 2014/0260898 to Bales et al. teaches adjusting the level of difficulty of music being played, see for instance, paragraph 96.
US PG Publication 2023/0089269 to Sikangwan et al. teaches performance improvement with Damonn notation system.
US Patent 11,798,429 to Canberk et al. teaches tracking user performance while playing a musical instrument, see for instance, column 3, lines 1-11.
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 MICHAEL J COBB whose telephone number is (571)270-3875. The examiner can normally be reached Monday - Friday, 11am - 7pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alicia Harrington can be reached at 571-272-2330. 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.
/MICHAEL J COBB/ Primary Examiner, Art Unit 2615
Prosecution Insights