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 .
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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/01/26 has been entered.
Response to Arguments
Applicant's arguments filed 05/01/26 have been fully considered but they are not persuasive.
In response to applicant’s arguments, it is noted that Everett explicitly discloses the scenario of creating (generating) the content scenes in real-time based upon the emotional input from the viewers (paragraph 85). Thus, while Everett describes branching and selecting different existing scenes matching the desired emotional state, the disclosure is not limited to such, as the desired scenes may instead be generated in real-time.
Thus, the claim limitations are met by Everett’s disclosure of comparing the viewer’s current emotional state using measured biometric data and the creation of new content with real-time adjusted elements to achieve the desired emotional state (paragraph 26-27, 85).
As “various elements of the show (e.g. audio volume, music tempo, background colors, etc.) can be adjusted in real time to increase the viewer's level of engagement”, these elements are the characteristic quality which is changed from a first value to a second value. The characteristic quality of the first scene (with biometric feedback being measured) is changed in real-time for the newly generated content to achieve the desired emotional state (paragraph 26-27).
This is further seen in the broad language used within the claims to define the “first and second characteristic qualities”. For example, Everett discloses one or more of presentation characteristic (changes to the presentation; paragraph 26-28, 37-38, 45-46), narrative attribute (paragraph 28), stylistic parameter (paragraph 26-28, 37-38, 45-46), complexity level, temporal characteristic (tempo; paragraph 26, 33), or tonal quality (changes to emotional “tone” of the content; paragraph 26-28, 37-38, 45-46).
As indicated within the rejections, however, Everett does not explicitly disclose wherein the generation is performed by artificial intelligence processing. Moore is thus relied upon for disclosing the specific use of artificial intelligence processing to generate content matching the viewer’s desired emotional state.
In response to applicant’s arguments, on pages 22-23 of applicant’s response, regarding the “specific causal sequence not present in the cited references”, it is again noted that Everett specifically discloses adjusting in real-time an element of the show to correct the measured emotional state of the viewer relative to the desired impact of the program (paragraph 26-27). Thus, any adjustment of the “various elements of the show (e.g. audio volume, music tempo, background colors, etc.) is a change to the next portion of the program relative to the current/previous portion.
As “various elements of the show (e.g. audio volume, music tempo, background colors, etc.) can be adjusted in real time to increase the viewer's level of engagement”, these elements are the characteristic quality which is changed from a first value to a second value. The characteristic quality of the first scene (with biometric feedback being measured) is changed in real-time for the newly generated content to achieve the desired emotional state (paragraph 26-27). Any adjustment/change to an attribute for a future scene is relative to the original attribute contained with the current content.
In response to applicant’s arguments, on pages 23-26 of applicant’s response, regarding Moore, it is noted that Moore is specifically relied upon for teaching the use of “artificial intelligence processing” to generate the content. It is the combination of Moore with Everett which meets the current claim limitations.
In response to applicant’s arguments, on page 26 of applicant’s response, that “Everett’s comparison is Evaluative, not Generative”, it is again noted that Everett explicitly discloses the scenario of creating (generating) the content scenes in real-time based upon the emotional input from the viewers (paragraph 85).
As “various elements of the show (e.g. audio volume, music tempo, background colors, etc.) can be adjusted in real time to increase the viewer's level of engagement”, these elements are the characteristic quality which is changed from a first value to a second value. The characteristic quality of the first scene (with biometric feedback being measured) is changed in real-time for the newly generated content to achieve the desired emotional state (paragraph 26-27).
Any generation of new content to adjust an element of the show, such as audio volume, music tempo, background colors, is adjusting that element relative to the original attribute level to provide a new value and achieve the desired emotional state (paragraph 26-27). Therefore, applicant’s arguments are not convincing.
In response to applicant’s arguments, on pages 26-27 of applicant’s response, that “The Unit of Generation Distinguishes the Claims from the Cited Art”, it is again noted that Everett explicitly discloses the scenario of creating (generating) the content scenes in real-time based upon the emotional input from the viewers (paragraph 85). Thus, changes made based upon the biometric data to generate content (paragraph 26-27, 33-35, 85) clearly meet the claim limitations.
In response to applicant’s arguments, on pages 27-297 of applicant’s response, see above regarding how the combination of Everett and Moore meet the current claim limitations.
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.
Claims 1-6, 8-14, 17-18, 20-25, 28-33, 36-37, 39-48, 51-59, 62-65, 70-73, 77 are rejected under 35 U.S.C. 103 as being unpatentable over Everett et al. (Everett) (US 2018/0376187) (of record) in view of Moore et al. (Moore) (US 2023/0282243) (of record).
As to claim 1, while Everett discloses a method for generating and performing content responsive to biometric data, the steps comprising:
receiving a content request from a user comprising a content characteristic preference (user menu selection to specify preferences and elements of show; paragraph 24-25, 29-30, 38, 42);
generating, by processing, a first content stream segment responsive to a content characteristic preference, the first content stream segment having a first characteristic quality (Fig. 7, paragraph 29-31, 42-44, 85);
outputting the first content stream segment via a playback device (paragraph 32-33, 35-36, 45);
receiving the biometric data regarding a user perceiving the output of the first segment on the playback device from at least one user monitor device (paragraph 33, 35, 45-46);
identifying a characteristic modification to the first characteristic quality responsive to the biometric data (required changes to content, such as increased volume, action, to increase viewer engagement; paragraph 33, 37, 46);
generating, by the processing, a second content stream segment, the second content segment comprising newly generated audio-visual content that is actively generated in real-time (paragraph 85) responsive to the biometric data and based at least in part on a deviation computed in real time between the biometric data and an intended physiological response to align the biometric data with the intended physiological response (“The viewer's level of engagement with the show can be estimated by comparing the emotional data with an expected emotional response for a given portion of the show’; paragraph 26, 33, 85), the second content stream segment having a second characteristic quality, the second characteristic quality being the result of applying the characteristic modification to the first characteristic quality of the first content stream segment (paragraph 26-27, 33, 35-37, 46), the second content stream segment being newly generated and not pre-chosen or pre-made (paragraph 85); and
outputting the second content stream segment on the playback device such that the playback of the first content stream segment and the second content stream segment by the playback device are perceived by the user as a continuous content stream (dynamic changes to provide a single video program that engages the viewer; Fig. 4-5; paragraph 26-27, 33, 35-37, 46-47),
wherein the first and second characteristic qualities are one or more of presentation characteristic (changes to the presentation; paragraph 26-28, 37-38, 45-46), narrative attribute (paragraph 28), stylistic parameter (paragraph 26-28, 37-38, 45-46), complexity level, temporal characteristic (tempo; paragraph 26, 33), or tonal quality (changes to emotional “tone” of the content; paragraph 26-28, 37-38, 45-46),
they fail to specifically disclose the first and second content stream segments are generated by artificial intelligence processing.
In an analogous art, Moore discloses a system for automatically preparing personalized video presentations (Fig. 1; paragraph 21-23) which will generate content segments utilizing a generative artificial intelligence model (Fig. 3, 110; paragraph 23), the content segments being newly generated and not pre-chosen (“The DSR may alternatively generate such a raw video presentation using a generative artificial intelligence engine 16 which may be defined by an artificial intelligence / machine learning model for generating video clips in response to prompts” paragraph 23, 38) and create future content generated based upon user biometric feedback (paragraph 29, 36, 38), by modifying characteristic the video content (paragraph 36) so as to create the ideal video using computer-generated imagery and eliminate the need for a database of millions of unique clips (paragraph 38).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Everett’s system to include the first and second content stream segments are generated by artificial intelligence processing, the second content stream segment being newly generated and not pre-chosen, as taught in combination with Moore, for the typical benefit of creating the ideal video using computer-generated imagery and eliminating the need for a database of millions of unique clips (paragraph 38).
As to claim 20, while Everett discloses a device for generating and performing content responsive to biometric data comprising:
at least one processor (paragraph 17-18) configured to:
receive a content request from a user comprising a content characteristic preference (user menu selection to specify preferences and elements of show; paragraph 24-25, 29-30, 38, 42);
generate a first content stream segment responsive to a content characteristic preference, the first content stream segment having a first characteristic quality (Fig. 7, paragraph 29-31, 42-44);
output the first content stream segment via a playback device (paragraph 32-33, 35-36, 45);
receive biometric data regarding a user perceiving the output of the first segment on the playback device from at least one user monitor device (paragraph 33, 35, 45-46);
identify a characteristic modification to the first characteristic quality responsive to the biometric data (required changes to content, such as increased volume, action, to increase viewer engagement; paragraph 33, 37, 46);
generate a second content stream segment, the second content segment comprising newly generated audio-visual content that is actively generated in real-time (paragraph 85) responsive to the biometric data and based at least in part on a deviation computed in real time between the biometric data and an intended physiological response to align the biometric data with the intended physiological response (“The viewer's level of engagement with the show can be estimated by comparing the emotional data with an expected emotional response for a given portion of the show’; paragraph 26, 33), the second content stream segment having a second characteristic quality, the second characteristic quality being the result of applying the characteristic modification to the first characteristic quality of the first content stream segment (paragraph 26-27, 33, 35-37, 46) and being newly generated and not pre-chosen or pre-made (created in real time using characters whose every move and word is in response to emotional input from the viewer(s); paragraph 85); and
output the second content stream segment on the playback device such that the playback of the first content stream segment and the second content stream segment by the playback device are perceived by the user as a continuous content stream (dynamic changes to provide a single video program that engages the viewer; Fig. 4-5; paragraph 26-27, 33, 35-37, 46-47),
wherein the first and second characteristic qualities are one or more of presentation characteristic (changes to the presentation; paragraph 26-28, 37-38, 45-46), narrative attribute (paragraph 28), stylistic parameter (paragraph 26-28, 37-38, 45-46), complexity level, temporal characteristic (tempo; paragraph 26, 33), or tonal quality (changes to emotional “tone” of the content; paragraph 26-28, 37-38, 45-46).
they fail to specifically disclose the first and second content stream segments are generated by artificial intelligence processing.
In an analogous art, Moore discloses a system for automatically preparing personalized video presentations (Fig. 1; paragraph 21-23) which will generate content segments utilizing a generative artificial intelligence model (Fig. 3, 110; paragraph 23), the content segments being newly generated and not pre-chosen (“The DSR may alternatively generate such a raw video presentation using a generative artificial intelligence engine 16 which may be defined by an artificial intelligence / machine learning model for generating video clips in response to prompts” paragraph 23, 38) and create future content generated based upon user biometric feedback (paragraph 29, 36, 38), by modifying characteristic the video content (paragraph 36) so as to create the ideal video using computer-generated imagery and eliminate the need for a database of millions of unique clips (paragraph 38).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Everett’s system to include the first and second content stream segments are generated by artificial intelligence processing, the second content stream segment being newly generated and not pre-chosen, as taught in combination with Moore, for the typical benefit of creating the ideal video using computer-generated imagery and eliminating the need for a database of millions of unique clips (paragraph 38).
As to claim 39, while Everett discloses a method for generating and performing content responsive to biometric data, the steps comprising:
receiving a content request from a user comprising a content characteristic preference (user menu selection to specify preferences and elements of show; see Everett at paragraph 24-25, 29-30, 38, 42);
receiving an output parameter selection from a user selecting an output parameter from a listing of output parameters (see Everett at paragraph 27);
generating, by processing (paragraph 85), a first content stream segment responsive to a content characteristic preference and the output parameter selection, the first content stream segment having a first characteristic quality (see Everett at Fig. 7, paragraph 29-31, 42-44);
outputting the first content stream segment via a playback device (see Everett at paragraph 32-33, 35-36, 45);
receiving biometric data regarding a user perceiving the output of the first segment on the playback device from at least one user monitor device (see Everett at paragraph 33, 35, 45-46);
identifying a characteristic modification to the first characteristic quality responsive to the biometric data (required changes to content, such as increased volume, action, to increase viewer engagement; see Everett at paragraph 33, 37, 46);
generating, by the processing, a second content stream segment, the second content segment comprising newly generated audio-visual content that is actively generated in real-time (paragraph 85) responsive to the biometric data and based at least in part on a deviation computed in real time between the biometric data and an intended physiological response to align the biometric data with the intended physiological response (“The viewer's level of engagement with the show can be estimated by comparing the emotional data with an expected emotional response for a given portion of the show’; paragraph 26, 33, 85), the second content stream segment having a second characteristic quality, the second characteristic quality being the result of applying the characteristic modification to the first characteristic quality of the first content stream segment (paragraph 26-27, 33, 35-37, 46), the second content stream segment being newly generated and not pre-chosen or pre-made (paragraph 85); and
outputting the second content stream segment on the playback device such that the playback of the first content stream segment and the second content stream segment by the playback device are perceived by the user as a continuous content stream (dynamic changes to provide a single video program that engages the viewer; see Everett at Fig. 4-5; paragraph 26-27, 33, 35-37, 46-47);
outputting a sensory stimulation to the user that is associated with at least one of the first content stream segment and the second content stream segment (see Everett at paragraph 26, 46);
comprising compiling a user record associated with the user and based upon the content characteristic preference, the first content stream segment, the biometric data, the characteristic modification, the second content stream segment, the second characteristic quality, and the continuous content stream (viewer preference profile including preferences, history and feedback; see Everett at paragraph 22-25, 34, 42, 47, 96);
broadcasting the user record to a database of user records (see Everett at Fig. 3, 104; paragraph 22-23, 31); and
indexing the user record with a library of user records in the database of user records (see Everett at paragraph 22-23, 31, 33-34, 96),
wherein the first and second characteristic qualities are one or more of presentation characteristic (changes to the presentation; paragraph 26-28, 37-38, 45-46), narrative attribute (paragraph 28), stylistic parameter (paragraph 26-28, 37-38, 45-46), complexity level, temporal characteristic (tempo; paragraph 26, 33), or tonal quality (changes to emotional “tone” of the content; paragraph 26-28, 37-38, 45-46),
they fail to specifically disclose the first and second content stream segments are generated by artificial intelligence processing.
In an analogous art, Moore discloses a system for automatically preparing personalized video presentations (Fig. 1; paragraph 21-23) which will generate content segments utilizing a generative artificial intelligence model (Fig. 3, 110; paragraph 23), the content segments being newly generated and not pre-chosen (“The DSR may alternatively generate such a raw video presentation using a generative artificial intelligence engine 16 which may be defined by an artificial intelligence / machine learning model for generating video clips in response to prompts” paragraph 23, 38) and create future content generated based upon user biometric feedback (paragraph 29, 36, 38), by modifying characteristic the video content (paragraph 36) so as to create the ideal video using computer-generated imagery and eliminate the need for a database of millions of unique clips (paragraph 38).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Everett’s system to include the first and second content stream segments are generated by artificial intelligence processing, the second content stream segment being newly generated and not pre-chosen, as taught in combination with Moore, for the typical benefit of creating the ideal video using computer-generated imagery and eliminating the need for a database of millions of unique clips (paragraph 38).
As to claim 2, 21, Everett and Moore disclose receiving an output parameter selection from a user selecting an output parameter from a listing of output parameters (see Everett at paragraph 27).
As to claim 3, 22, 40, Everett, Moore and Jang disclose wherein the output parameter listing comprises at least one of volume (see Everett at paragraph 33), brightness, speed (see Everett at paragraph 33), data privacy, network connectivity, age restrictions, religious preferences, cultural sensitivity, strobe lighting effects, heart or stress preferences, time limit (see Everett at paragraph 37), language preferences, content rating, personal health conditions (see Everett at paragraph 27), color blindness considerations, hearing impairment considerations, accessibility settings, cognitive load preferences, content duration, or user interaction requirements.
As to claim 4, 23, Everett and Moore disclose compiling a user record associated with the user and based upon the content characteristic preference, the first content stream segment, the biometric data, the characteristic modification, the second content stream segment, the second characteristic quality, and the continuous content stream (viewer preference profile including preferences, history and feedback; see Everett at paragraph 22-25, 34, 42, 47, 96).
As to claim 5, 24, Everett and Moore disclose broadcasting the user record to a database of user records (see Everett at Fig. 3, 104; paragraph 22-23, 31); and indexing the user record with a library of user records in the database of user records (see Everett at paragraph 22-23, 31, 33-34, 96).
As to claim 6, 25, 41, Everett and Moore disclose receiving the user record associated with the user from the library of user records in the database of user records (see Everett at paragraph 22-23, 31, 33-34).
As to claim 8, 42, Everett and Moore disclose outputting a sensory stimulation signal that is associated with at least one of the first content stream segment and the second content stream segment (see Everett at paragraph 26, 46 and Moore at paragraph 36), wherein the sensory stimulation includes at least one of a sensation of touch, movement, vision (see Everett at paragraph 26, 46), hearing (see Everett at paragraph 26, 46), balance, proprioception, and pain by the user.
As to claim 9, 28, 43, Everett and Moore disclose wherein biometric data includes attributes of the user; and wherein the attributes of the user comprises at least one of the user's blood pressure (see Everett at paragraph 20), heart rate (see Everett at paragraph 20), heart rate variability (HRV), body temperature (see Everett at paragraph 20), skin temperature, flushing of skin, electrodermal activity (EDA), perspiration, breathing pattern (see Everett at paragraph 33), body movement, eye movement, facial expression (see Everett at paragraph 18, 20), audible sound (see Everett at paragraph 77), and brain activity, oxygen saturation, hydration level, blood glucose level, hormone levels, sleep patterns, gait analysis, pupil dilation, vocal characteristics, muscle tension, pain response, smell or pheromone detection, or genetic markers.
As to claim 10, 29, 44, Everett and Moore disclose wherein the brain activity of the user includes the electrical activity of the brain, which may define gamma waves, beta waves, alpha waves, theta waves, delta waves event related potentials, magnetoencephalography signals, or near infrared spectroscopy signals (the “brain activity” is an alternative limitation within claim 9 and not required).
As to claim 11, 30, 45, Everett and Moore disclose wherein the at least one user monitor device comprises at least one of a heart monitor (see Everett at paragraph 18, 20), temperature monitor (see Everett at paragraph 18, 20), a brain monitor, breathing monitor (see Everett at paragraph 33), a camera (see Everett at paragraph 18, 20), a microphone (see Everett at paragraph 77), a motion monitor, and eye tracker, facial expression recognition system, sound analysis device, brain activity monitor including devices for electroencephalography, magnetoencephalography, or near-infrared spectroscopy, oxygen saturation monitor, hydration monitor, blood glucose monitor, hormone level monitor, sleep monitor, gait analysis device, pupil dilation monitor, voice analysis device, muscle tension monitor, pain response monitor, smell or pheromone detection device, or genetic marker analysis tool.
As to claim 31, Everett and Moore disclose wherein the listing of content types includes at least one of Al-human collaborative content generation (AI with user feedback; see Moore at paragraph 30-36), animal welfare improvement content, children's content, communication content, creativity, problem- solving content, cultural, historical appreciation content, custom or miscellaneous content (custom content to invoke a particular emotional response; see Everett at paragraph 30, 38), educational content, entertainment content, environmental, ecological literacy content, environmental interaction content, financial, economic literacy content, health, wellness content, inspirational, motivational content, leadership, management content, meditation content, microbial welfare improvement content, news, current events content, nutritional content, parenting, offspring care content, personal development content, pet content, physical activity, exercise content, plant welfare improvement content, professional development content, relationship content, relaxation content, scientific, technological literacy content, sensory stimulation content, sexual content, skill building, mastery content, social, behavioral content, survival, safety content, therapeutic content.
As to claim 12, 46, Everett, Moore and Jang disclose wherein the content characteristic preference comprises a selection from a list of content types, the listing of content types includes at least one of entertainment content, relaxation content, educational content, meditation content, health content, therapeutic content, relationship content, sexual content, and custom (custom content to invoke a particular emotional response; see Everett at paragraph 30, 38) or miscellaneous content.
As to claim 13, 32, 47, Everett and Moore disclose wherein the playback device comprises at least one of a visual display device (display device presenting television program; see Everett at paragraph 19, 50, 76), an audio generating device (see Everett at paragraph 26, 50, 77), a tactile or haptic stimulation device, an olfactory stimulation device, a gustatory stimulation device, a thermoregulatory stimulation device, a vestibular stimulation device, an electromagnetic stimulation device, a pain stimulation device, a kinesthetic stimulation device, a light stimulation device, an ultrasonic stimulation device, a pressure stimulation device, or a vibrational stimulation device.
As to claim 14, 33, 48, Everett and Moore disclose wherein the content request is received in the form of a selection of a content type from a list of content types, the selection defining the content characteristic preference (see Everett at paragraph 30, 38).
As to claims 18, 52, Everett, Moore and Jang disclose receiving a weighting value for each of the first user and the second user (see Everett at paragraph 41);
wherein identifying the characteristic modification to the first characteristic quality is responsive to the first biometric data set weighted by the weighting value of the first user and the second biometric data set weighted by the weighting value of the second user (see Everett at paragraph 41).
As to claim 53, Everett, Moore and Jang disclose identifying a weighting value for each of the first user and the second user (see Everett at paragraph 41);
wherein identifying the characteristic modification to the first characteristic quality is responsive to the first biometric data set weighted by the weighting value of the first user and the second biometric data set weighted by the weighting value of the second user (see Everett at paragraph 41).
As to claim 36, Everett and Moore disclose wherein the biometric data is a first biometric data set related to a first user perceiving the first content stream segment, the method further comprising receiving a second biometric data set regarding a second user perceiving the output of the first segment on the playback device (see Everett at Fig. 6, paragraph 19-20, 39-41);
wherein identifying the characteristic modification to the first characteristic quality is responsive to the first biometric data set and the second biometric data set (combined biometric data to generated composite profile; see Everett at Fig. 6, paragraph 19-20, 39-41); and
wherein generating the second content stream segment is responsive to each of the content characteristic preference, the first biometric data set, and the second biometric data set (see Everett at Fig. 6, paragraph 19-20, 39-41).
As to claim 37, Everett and Moore disclose receiving a weighting value for each of the first user and the second user (see Everett at paragraph 41);
wherein identifying the characteristic modification to the first characteristic quality is responsive to the first biometric data set weighted by the weighting value of the first user and the second biometric data set weighted by the weighting value of the second user (see Everett at paragraph 41).
As to claim 62, Everett and Moore disclose wherein generating the second content stream segment occurs during playback of the first content stream segment (see Everett at paragraph 26-27, 33, 35-37, 46-47, 85 and Moore at paragraph 23-25, 29, 38).
As to claim 63, Everett and Moore disclose wherein the second content stream segment did not exist prior to receiving the biometric data (see Everett at paragraph 26-27, 33, 35-37, 46-47 and Moore at paragraph 23-25, 29, 38).
As to claim 64, Everett and Moore disclose determining whether the biometric data indicates that a biometric state of the user satisfies an intended physiological response associated with the content characteristic preference (see Everett at paragraph 26, 33 and Moore at paragraph 28-29, 36, 38); and
in response to determining that the biometric state satisfies the intended physiological response, generating the second content stream segment to maintain the first characteristic quality (see Everett at paragraph 26, 33 and Moore at paragraph 28-29, 36, 38).
As to claim 65, Everett and Moore disclose determining whether the biometric data indicates that a biometric state of the user satisfies an intended physiological response associated with the content characteristic preference (see Everett at paragraph 26, 33 and Moore at paragraph 28-29, 36, 38); and
in response to determining that the biometric state does not satisfy the intended physiological response, identifying the characteristic modification and generating the second content stream segment to implement the characteristic modification (see Everett at paragraph 26, 33 and Moore at paragraph 28-29, 36, 38).
As to claim 17, 51, Everett, Moore and Jang disclose wherein the biometric data is a first biometric data set related to a first user perceiving the first content stream segment, the method further comprising receiving a second biometric data set regarding a second user perceiving the output of the first segment on the playback device (see Everett at Fig. 6, paragraph 19-20, 39-41);
wherein identifying the characteristic modification to the first characteristic quality is responsive to the first biometric data set and the second biometric data set (combined biometric data to generated composite profile; see Everett at Fig. 6, paragraph 19-20, 39-41); and
wherein generating the second content stream segment is responsive to each of the content characteristic preference, the first biometric data set, and the second biometric data set (see Everett at Fig. 6, paragraph 19-20, 39-41).
As to claims 56, 70, Everett, Moore and Jang disclose wherein generating the second content stream segment occurs during playback of the first content stream segment (see Everett at paragraph 26-27, 33, 35-37, 46-47 and Moore at paragraph 23-25, 29, 38).
As to claims 57, 71, Everett, Moore and Jang disclose wherein the second content stream segment did not exist prior to receiving the biometric data (see Everett at paragraph 26-27, 33, 35-37, 46-47 and Moore at paragraph 23-25, 29, 38).
As to claims 58, 72, Everett, Moore and Jang disclose determining whether the biometric data indicates that a biometric state of the user satisfies an intended physiological response associated with the content characteristic preference (see Everett at paragraph 26, 33 and Moore at paragraph 28-29, 36, 38); and
in response to determining that the biometric state satisfies the intended physiological response, generating the second content stream segment to maintain the first characteristic quality (see Everett at paragraph 26, 33 and Moore at paragraph 28-29, 36, 38).
As to claims 59, 73, Everett, Moore and Jang disclose determining whether the biometric data indicates that a biometric state of the user satisfies an intended physiological response associated with the content characteristic preference (see Everett at paragraph 26, 33 and Moore at paragraph 28-29, 36, 38); and
in response to determining that the biometric state does not satisfy the intended physiological response, identifying the characteristic modification and generating the second content stream segment to implement the characteristic modification (see Everett at paragraph 26, 33 and Moore at paragraph 28-29, 36, 38).
As to claims 74, Everett, Moore and Jang disclose wherein the first characteristic quality and the second characteristic quality each comprise at least one semantic narrative attribute selected from the group consisting of: pacing (see Everett at paragraph 25-28, 33), plot tension (see Everett at paragraph 25-28, 33), camera motion profile, color palette (see Everett at paragraph 25-28, 33), and soundtrack motif (see Everett at paragraph 25-28, 33).
Claims 54, 55, 69, 77 are rejected under 35 U.S.C. 103 as being unpatentable over Everett and Moore and further in view of Jang et al. (Jang) (US 2014/0237495) (of record).
As to claim 54, 55, 69, 77, while Everett and Moore disclose outputting a sensory stimulation signal that is associated with at least one of the first content stream segment and the second content stream segment (see Everett at paragraph 26, 46 and Moore at paragraph 36), they fail to specifically disclose the sensory stimulation received by at least one sensory stimulation device, wherein the at least one sensory stimulation device is configured to take a sensory stimulation action based on the sensory stimulation signal received, and wherein the sensory stimulation action includes causing a sensation of at least one of temperature, smell, and taste.
In an analogous art, Jang discloses a system (Fig. 1; paragraph 49-57) for providing additional sensory stimulation feedback to viewers during output of a content segment (paragraph 56-57, 96-125) by outputting a signal to at least one sensory stimulation device is configured to take a sensory stimulation action based on the sensory stimulation signal received, and wherein the sensory stimulation action includes causing a sensation of at least one of temperature, smell, and taste (sensory feedback including temperature, taste and smell; paragraph 13, 108, 111-112, 118-123) so as to increase user immersion and amusement through the stimulation of different user senses (paragraph 57).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Everett and Moore’s system to include the sensory stimulation received by at least one sensory stimulation device, wherein the at least one sensory stimulation device is configured to take a sensory stimulation action based on the sensory stimulation signal received, and wherein the sensory stimulation action includes causing a sensation of at least one of temperature, smell, and taste, as taught in combination with Jang, for the typical benefit of increasing user immersion and amusement through the stimulation of different user senses (paragraph 57).
Claims 15-16, 34-35, 49-50 are rejected under 35 U.S.C. 103 as being unpatentable over Everett and Moore and further in view of Orr et al. (Orr) (US 2017/0358302) (of record).
As to claims 15, 34, 49, while Everett and Moore disclose receiving a content request from a user comprising a content characteristic preference (user menu selection to specify preferences and elements of show; see Everett at paragraph 24-25, 29-30, 38, 42 and Moore at paragraph 23) and wherein the audio system may be used for voice recognition (see Everett at paragraph 77),
wherein the generative artificial intelligence model uses the content characteristic preference to synthesize the first content stream segment (see Moore at paragraph 23-25, 38),
they fail to specifically disclose wherein the content request is received in the form of a natural language request from the user, parsing the natural language request, analyzing the parsed natural language request to determine a user intent and identifying the content characteristic preference from the user intent.
In an analogous art, Orr discloses a system which will receive a content request in the form of a natural language request from a user (Fig. 1, paragraph 29-30), parse the natural language request (paragraph 208, 241-242), analyze the parsed natural language request to determine a user intent and identify a content characteristic preference from the user intent (paragraph 241-257) so as to allow a user to request personalized content in a hands-free manner through a spoken request (paragraph 3-5, 25).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Everett’s system to include wherein the content request is received in the form of a natural language request from the user, parsing the natural language request, analyzing the parsed natural language request to determine a user intent and identifying the content characteristic preference from the user intent, as taught in combination with Orr, for the typical benefit of providing a more user friendly system allowing a user to request personalized content in a hands-free manner.
As to claims 16, 35, 50, Everett, Moore, and Orr disclose wherein identifying the content characteristic preference from the user intent comprises matching the user intent with a content type from a list of content types that is most closely related to the user intent (see Everett at paragraph 30, 38 and Orr at paragraph 241-257) and wherein the matched content type is used by the generative artificial intelligence model to synthesize the first content stream segment (see Moore at paragraph 23-25, 38).
Conclusion
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/James R Sheleheda/ Primary Examiner, Art Unit 2424
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