DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Application filed on 1/10/2023. Claims 1-20 are pending in the case. Claims 1, 8, and 15 are independent claims.
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Liu et al. (US 2019/0324553 A1, hereinafter Liu) in view of Weston (US 2020/0276510 A1).
As to independent claim 1, Liu teaches a system for facilitating user interaction with interactive areas, the system comprising:
a memory encoding processor-executable routines (“MEMORY,” figure 10 part 1004); and
a processor (“PROCESSOR,” figure 10 part 1002) configured to access the memory and to execute the processor-executable routines, wherein the routines, when executed by the processor, cause the processor to:
identify a user of an interactive area based on identifying data (“The assistant system may enable the user to interact with it with multi-modal user input (such as voice, text, image, video, motion) in stateful and multi-turn conversations to get assistance. The assistant system may create and store a user profile comprising both personal and contextual information associated with the user. In particular embodiments, the assistant system may analyze the user input using natural-language understanding. The analysis may be based on the user profile for more personalized and context-aware understanding,” paragraph 0006 lines 2-12);
utilize a trained machine learning model personalized for the user in detecting an idiosyncratic task performed by the user interacting with the interactive area (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” paragraph 0007 lines 27-29) to activate a special effect associated with the interactive area based on interactive data obtained at the interactive area (“the assistant system may accurately recognize a user’s gesture and execute tasks corresponding to the recognized gesture,” paragraph 0010 lines 6-8), wherein the trained machine learning model personalized for the user is configured to recognize idiosyncrasies of the user (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” paragraph 0007 lines 27-29); and
instruct initiation of the special effect in response to detecting the idiosyncratic task (“the assistant system may accurately recognize a user’s gesture and execute tasks corresponding to the recognized gesture,” paragraph 0010 lines 6-8).
Liu does not appear to expressly teach a system comprising identifying data obtained at the interactive area.
Weston teaches a system comprising identifying data obtained at the interactive area (“At step 210, information about each user in a particular smart environment is determined. For example, the smart controller controls and receives feedback from one or more of each of the sensors illustrated with reference to the descriptions below, FIGS. 3-6, and, without limitation, other sensors that may be known by those of ordinary skill in the art. For example, the user input may be received by one or more of a scanning device, such as a camera, an RFID or other contactless scanner, a facial recognition or biometric recognition scanning device, manual user input, or age-detection software,” paragraph 0025 lines 9-19).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the identifying data of Liu to comprise the obtained at the interactive area of Weston. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely identifying data obtained at the interactive area (“At step 210, information about each user in a particular smart environment is determined. For example, the smart controller controls and receives feedback from one or more of each of the sensors illustrated with reference to the descriptions below, FIGS. 3-6, and, without limitation, other sensors that may be known by those of ordinary skill in the art. For example, the user input may be received by one or more of a scanning device, such as a camera, an RFID or other contactless scanner, a facial recognition or biometric recognition scanning device, manual user input, or age-detection software,” Weston paragraph 0025 lines 9-19). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 2, the rejection of claim 1 is incorporated. Liu/Weston further teaches a system wherein the routines, when executed by the processor, cause the processor to update the trained machine learning model personalized for the user utilizing the idiosyncrasies of the user recognized when performing the idiosyncratic task (“Repeated observation of coincidence speech intents and gestures, whether generated intentionally or sub-consciously by the first user, may result in more and more training data. Meanwhile, the personalized gesture-classification model may be gradually optimized as more and more training data are used. As a result, the personalize gesture-classification model may discover new gestures from a user and associate them with meanings (i.e., intents 520),” Liu paragraph 0068 lines 40-48).
As to dependent claim 3, the rejection of claim 2 is incorporated. Liu/Weston further teaches a system wherein the trained machine learning model personalized for the user is updated after each performance of the idiosyncratic task or a different idiosyncratic task at a different interactive area (“Repeated observation of coincidence speech intents and gestures, whether generated intentionally or sub-consciously by the first user, may result in more and more training data. Meanwhile, the personalized gesture-classification model may be gradually optimized as more and more training data are used. As a result, the personalize gesture-classification model may discover new gestures from a user and associate them with meanings (i.e., intents 520),” Liu paragraph 0068 lines 40-48).
As to dependent claim 4, the rejection of claim 2 is incorporated. Liu/Weston further teaches a system wherein the trained machine learning model personalized for the user is updated after a set number of performances of idiosyncratic tasks performed at any interactive area (“Repeated observation of coincidence speech intents and gestures, whether generated intentionally or sub-consciously by the first user, may result in more and more training data. Meanwhile, the personalized gesture-classification model may be gradually optimized as more and more training data are used. As a result, the personalize gesture-classification model may discover new gestures from a user and associate them with meanings (i.e., intents 520),” Liu paragraph 0068 lines 40-48, the “set number” being 1).
As to dependent claim 5, the rejection of claim 7 is incorporated. Liu/Weston further teaches a system wherein the idiosyncratic task comprises a voice command, movement of the user, or movement of a device manipulated by the user (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” Liu paragraph 0007 lines 27-29).
As to dependent claim 6, the rejection of claim 1 is incorporated. Liu/Weston further teaches a system wherein the routines, when executed by the processor, cause the processor to:
identify the user (“At step 210, information about each user in a particular smart environment is determined. For example, the smart controller controls and receives feedback from one or more of each of the sensors illustrated with reference to the descriptions below, FIGS. 3-6, and, without limitation, other sensors that may be known by those of ordinary skill in the art. For example, the user input may be received by one or more of a scanning device, such as a camera, an RFID or other contactless scanner, a facial recognition or biometric recognition scanning device, manual user input, or age-detection software,” Weston paragraph 0025 lines 9-19) at a different interactive area (“the modular architecture of the interactive attractions are illustrated with reference to FIG. 6, which shows a plurality of modular smart rooms (602, 604, 614, and 608, linked together with passageways 618, 612, 616, 606, and 610, that also contain interactive digital attractions to form a ‘ride’ or smart entertainment attraction,” Weston paragraph 0020 lines 1-7) based on the identifying data obtained (“At step 210, information about each user in a particular smart environment is determined. For example, the smart controller controls and receives feedback from one or more of each of the sensors illustrated with reference to the descriptions below, FIGS. 3-6, and, without limitation, other sensors that may be known by those of ordinary skill in the art. For example, the user input may be received by one or more of a scanning device, such as a camera, an RFID or other contactless scanner, a facial recognition or biometric recognition scanning device, manual user input, or age-detection software,” Weston paragraph 0025 lines 9-19) at the different interactive area (“the modular architecture of the interactive attractions are illustrated with reference to FIG. 6, which shows a plurality of modular smart rooms (602, 604, 614, and 608, linked together with passageways 618, 612, 616, 606, and 610, that also contain interactive digital attractions to form a ‘ride’ or smart entertainment attraction,” Weston paragraph 0020 lines 1-7);
utilize the trained machine learning model personalized for the user in detecting a different idiosyncratic task performed by the user (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” Liu paragraph 0007 lines 27-29) interacting with the different interactive area (“the modular architecture of the interactive attractions are illustrated with reference to FIG. 6, which shows a plurality of modular smart rooms (602, 604, 614, and 608, linked together with passageways 618, 612, 616, 606, and 610, that also contain interactive digital attractions to form a ‘ride’ or smart entertainment attraction,” Weston paragraph 0020 lines 1-7) to activate a particular special effect (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” Liu paragraph 0007 lines 27-29) associated with the different interactive area based on additional interactive data obtained at the different interactive area (“the modular architecture of the interactive attractions are illustrated with reference to FIG. 6, which shows a plurality of modular smart rooms (602, 604, 614, and 608, linked together with passageways 618, 612, 616, 606, and 610, that also contain interactive digital attractions to form a ‘ride’ or smart entertainment attraction,” Weston paragraph 0020 lines 1-7); and
instruct initiation of the particular special effect in response to detecting the different idiosyncratic task (“the assistant system may accurately recognize a user’s gesture and execute tasks corresponding to the recognized gesture,” Liu paragraph 0010 lines 6-8).
As to dependent claim 7, the rejection of claim 1 is incorporated. Liu/Weston further teaches a system wherein the routines, when executed by the processor, cause the processor to train a general machine learning model based on one or more idiosyncratic tasks performed by the user at one or more interactive areas to recognize the idiosyncrasies of the user to generate the trained machine learning model personalized for the user, wherein the general machine learning model is configured to recognize tasks performed by users in general to active respective special effects (“the training of the personalized gesture-classification model may be based on a general gesture-classification model. The assistant system 140 may access, from the data store, a general gesture-classification model corresponding to a general user population. Accordingly, training the personalized gesture-classification model may be further based on the general gesture-classification model,” Liu paragraph 0066 lines 1-8) at different interactive areas based on obtained interactive data (“the modular architecture of the interactive attractions are illustrated with reference to FIG. 6, which shows a plurality of modular smart rooms (602, 604, 614, and 608, linked together with passageways 618, 612, 616, 606, and 610, that also contain interactive digital attractions to form a ‘ride’ or smart entertainment attraction,” Weston paragraph 0020 lines 1-7).
As to independent claim 8, Liu teaches a computer-implemented method for facilitating user interaction with interactive areas, the computer-implemented method comprising:
identifying a user of an interactive area based on identifying data (“The assistant system may enable the user to interact with it with multi-modal user input (such as voice, text, image, video, motion) in stateful and multi-turn conversations to get assistance. The assistant system may create and store a user profile comprising both personal and contextual information associated with the user. In particular embodiments, the assistant system may analyze the user input using natural-language understanding. The analysis may be based on the user profile for more personalized and context-aware understanding,” paragraph 0006 lines 2-12);
obtaining a trained machine learning model personalized for the user, wherein the trained machine learning model personalized for the user is configured to recognize idiosyncrasies of the user (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” paragraph 0007 lines 27-29);
utilizing the trained machine learning model in detecting an idiosyncratic task performed by the user interacting with the interactive area (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” paragraph 0007 lines 27-29) to activate a special effect associated with the interactive area based on interactive data obtained at the interactive area (“the assistant system may accurately recognize a user’s gesture and execute tasks corresponding to the recognized gesture,” paragraph 0010 lines 6-8); and
initiating the special effect in response to detecting the idiosyncratic task (“the assistant system may accurately recognize a user’s gesture and execute tasks corresponding to the recognized gesture,” paragraph 0010 lines 6-8).
Liu does not appear to expressly teach a method comprising identifying data obtained at the interactive area.
Weston teaches a method comprising identifying data obtained at the interactive area (“At step 210, information about each user in a particular smart environment is determined. For example, the smart controller controls and receives feedback from one or more of each of the sensors illustrated with reference to the descriptions below, FIGS. 3-6, and, without limitation, other sensors that may be known by those of ordinary skill in the art. For example, the user input may be received by one or more of a scanning device, such as a camera, an RFID or other contactless scanner, a facial recognition or biometric recognition scanning device, manual user input, or age-detection software,” paragraph 0025 lines 9-19).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the identifying data of Liu to comprise the obtained at the interactive area of Weston. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely identifying data obtained at the interactive area (“At step 210, information about each user in a particular smart environment is determined. For example, the smart controller controls and receives feedback from one or more of each of the sensors illustrated with reference to the descriptions below, FIGS. 3-6, and, without limitation, other sensors that may be known by those of ordinary skill in the art. For example, the user input may be received by one or more of a scanning device, such as a camera, an RFID or other contactless scanner, a facial recognition or biometric recognition scanning device, manual user input, or age-detection software,” Weston paragraph 0025 lines 9-19). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 9, the rejection of claim 8 is incorporated. Liu/Weston further teaches a method comprising updating the trained machine learning model personalized for the user utilizing the idiosyncrasies of the user recognized when performing the idiosyncratic task (“Repeated observation of coincidence speech intents and gestures, whether generated intentionally or sub-consciously by the first user, may result in more and more training data. Meanwhile, the personalized gesture-classification model may be gradually optimized as more and more training data are used. As a result, the personalize gesture-classification model may discover new gestures from a user and associate them with meanings (i.e., intents 520),” Liu paragraph 0068 lines 40-48).
As to dependent claim 10, the rejection of claim 9 is incorporated. Liu/Weston further teaches a method wherein the trained machine learning model personalized for the user is updated after each performance of the idiosyncratic task or a different idiosyncratic task at a different interactive area (“Repeated observation of coincidence speech intents and gestures, whether generated intentionally or sub-consciously by the first user, may result in more and more training data. Meanwhile, the personalized gesture-classification model may be gradually optimized as more and more training data are used. As a result, the personalize gesture-classification model may discover new gestures from a user and associate them with meanings (i.e., intents 520),” Liu paragraph 0068 lines 40-48).
As to dependent claim 11, the rejection of claim 9 is incorporated. Liu/Weston further teaches a method wherein the trained machine learning model personalized for the user is updated after a set number of performances of idiosyncratic tasks performed at any interactive area (“Repeated observation of coincidence speech intents and gestures, whether generated intentionally or sub-consciously by the first user, may result in more and more training data. Meanwhile, the personalized gesture-classification model may be gradually optimized as more and more training data are used. As a result, the personalize gesture-classification model may discover new gestures from a user and associate them with meanings (i.e., intents 520),” Liu paragraph 0068 lines 40-48, the “set number” being 1).
As to dependent claim 12, the rejection of claim 8 is incorporated. Liu/Weston further teaches a method wherein the idiosyncratic task comprises a voice command, movement of the user, or movement of a device manipulated by the user (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” Liu paragraph 0007 lines 27-29).
As to dependent claim 13, the rejection of claim 8 is incorporated. Liu/Weston further teaches a method wherein the trained machine learning model personalized for the user is configured to be utilized for performance of different types of idiosyncratic tasks by the user (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” Liu paragraph 0007 lines 27-29) at different interactive areas (“the modular architecture of the interactive attractions are illustrated with reference to FIG. 6, which shows a plurality of modular smart rooms (602, 604, 614, and 608, linked together with passageways 618, 612, 616, 606, and 610, that also contain interactive digital attractions to form a ‘ride’ or smart entertainment attraction,” Weston paragraph 0020 lines 1-7).
As to dependent claim 14, the rejection of claim 8 is incorporated. Liu/Weston further teaches a method comprising obtaining a general machine learning model, wherein the general machine learning model is configured to recognize tasks performed by users in general to activate respective special effects at different interactive areas, and training the general machine learning model based on the interactive data obtained of one or more idiosyncratic tasks performed by the user at one or more interactive areas to recognize the idiosyncrasies of the user to generate the trained machine learning model personalized for the user (“the training of the personalized gesture-classification model may be based on a general gesture-classification model. The assistant system 140 may access, from the data store, a general gesture-classification model corresponding to a general user population. Accordingly, training the personalized gesture-classification model may be further based on the general gesture-classification model,” Liu paragraph 0066 lines 1-8).
As to independent claim 15, Liu teaches a non-transitory computer-readable medium (“MEMORY,” figure 10 part 1004), the computer-readable medium comprising processor-executable code that when executed by a processor, causes the processor to:
identify a user of an interactive area based on identifying data (“The assistant system may enable the user to interact with it with multi-modal user input (such as voice, text, image, video, motion) in stateful and multi-turn conversations to get assistance. The assistant system may create and store a user profile comprising both personal and contextual information associated with the user. In particular embodiments, the assistant system may analyze the user input using natural-language understanding. The analysis may be based on the user profile for more personalized and context-aware understanding,” paragraph 0006 lines 2-12);
utilize a trained machine learning model personalized for the user in detecting an idiosyncratic task performed by the user interacting with the interactive area (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” paragraph 0007 lines 27-29) to activate a special effect associated with the interactive area based on interactive data obtained at the interactive area (“the assistant system may accurately recognize a user’s gesture and execute tasks corresponding to the recognized gesture,” paragraph 0010 lines 6-8), wherein the trained machine learning model personalized for the user is configured to recognize idiosyncrasies of the user (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” paragraph 0007 lines 27-29); and
instruct initiation of the special effect in response to detecting the idiosyncratic task (“the assistant system may accurately recognize a user’s gesture and execute tasks corresponding to the recognized gesture,” paragraph 0010 lines 6-8).
Liu does not appear to expressly teach a medium comprising identifying data obtained at the interactive area.
Weston teaches a medium comprising identifying data obtained at the interactive area (“At step 210, information about each user in a particular smart environment is determined. For example, the smart controller controls and receives feedback from one or more of each of the sensors illustrated with reference to the descriptions below, FIGS. 3-6, and, without limitation, other sensors that may be known by those of ordinary skill in the art. For example, the user input may be received by one or more of a scanning device, such as a camera, an RFID or other contactless scanner, a facial recognition or biometric recognition scanning device, manual user input, or age-detection software,” paragraph 0025 lines 9-19).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the identifying data of Liu to comprise the obtained at the interactive area of Weston. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely identifying data obtained at the interactive area (“At step 210, information about each user in a particular smart environment is determined. For example, the smart controller controls and receives feedback from one or more of each of the sensors illustrated with reference to the descriptions below, FIGS. 3-6, and, without limitation, other sensors that may be known by those of ordinary skill in the art. For example, the user input may be received by one or more of a scanning device, such as a camera, an RFID or other contactless scanner, a facial recognition or biometric recognition scanning device, manual user input, or age-detection software,” Weston paragraph 0025 lines 9-19). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 16, the rejection of claim 15 is incorporated. Liu/Weston further teaches a medium wherein the code, when executed by the processor, causes the processor to update the trained machine learning model personalized for the user utilizing the idiosyncrasies of the user recognized when performing the idiosyncratic task (“Repeated observation of coincidence speech intents and gestures, whether generated intentionally or sub-consciously by the first user, may result in more and more training data. Meanwhile, the personalized gesture-classification model may be gradually optimized as more and more training data are used. As a result, the personalize gesture-classification model may discover new gestures from a user and associate them with meanings (i.e., intents 520),” Liu paragraph 0068 lines 40-48).
As to dependent claim 17, the rejection of claim 16 is incorporated. Liu/Weston further teaches a medium wherein the trained machine learning model personalized for the user is updated after each performance of the idiosyncratic task or a different idiosyncratic task at a different interactive area (“Repeated observation of coincidence speech intents and gestures, whether generated intentionally or sub-consciously by the first user, may result in more and more training data. Meanwhile, the personalized gesture-classification model may be gradually optimized as more and more training data are used. As a result, the personalize gesture-classification model may discover new gestures from a user and associate them with meanings (i.e., intents 520),” Liu paragraph 0068 lines 40-48), or the trained machine learning model personalized for the user is updated after a set number of performances of idiosyncratic tasks performed at any interactive area (“Repeated observation of coincidence speech intents and gestures, whether generated intentionally or sub-consciously by the first user, may result in more and more training data. Meanwhile, the personalized gesture-classification model may be gradually optimized as more and more training data are used. As a result, the personalize gesture-classification model may discover new gestures from a user and associate them with meanings (i.e., intents 520),” Liu paragraph 0068 lines 40-48, the “set number” being 1).
As to dependent claim 18, the rejection of claim 17 is incorporated. Liu/Weston further teaches a medium wherein the idiosyncratic task comprises a voice command, movement of the user, or movement of a device manipulated by the user (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” Liu paragraph 0007 lines 27-29).
As to dependent claim 19, the rejection of claim 15 is incorporated. Liu/Weston further teaches a medium wherein the trained machine learning model personalized for the user is configured to be utilized for the performance of different idiosyncratic tasks by the user (“Once the personalized gesture-classification model is trained, the assistant system may use it to determine a user’s intents corresponding to his/her own gestures in the future,” Liu paragraph 0007 lines 27-29) at different interactive areas (“the modular architecture of the interactive attractions are illustrated with reference to FIG. 6, which shows a plurality of modular smart rooms (602, 604, 614, and 608, linked together with passageways 618, 612, 616, 606, and 610, that also contain interactive digital attractions to form a ‘ride’ or smart entertainment attraction,” Weston paragraph 0020 lines 1-7).
As to dependent claim 20, the rejection of claim 15 is incorporated. Liu/Weston further teaches a medium wherein the code, when executed by the processor, causes the processor to train a general machine learning model based on one or more idiosyncratic tasks performed by the user at one or more interactive areas to recognize the idiosyncrasies of the user to generate the trained machine learning model personalized for the user, wherein the general machine learning model is configured to recognize tasks performed by users in general to activate respective special effects at different interactive areas (“the training of the personalized gesture-classification model may be based on a general gesture-classification model. The assistant system 140 may access, from the data store, a general gesture-classification model corresponding to a general user population. Accordingly, training the personalized gesture-classification model may be further based on the general gesture-classification model,” Liu paragraph 0066 lines 1-8).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
US 20230128658 A1 disclosing detecting an idiosyncratic task performed by the user interacting with the interactive area to activate a special effect associated with the interactive area based on interactive data obtained at the interactive area
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ryan Barrett whose telephone number is 571 270 3311. The examiner can normally be reached 9:00am to 5:30pm.
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/Ryan Barrett/
Primary Examiner, Art Unit 2148