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.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Moore et al. (Moore) (US 2023/02822443) is now relied upon to disclose the newly amended limitations.
It is noted that the newly amended language includes numerous limitations which do not appear to be supported by applicant’s specification as originally filed. New rejections for these limitations under 35 U.S.C. 112(a) are presented below.
While applicant’s arguments point to portions of the specification as supporting these limitations, these citations are insufficient to support some of the claim language, as indicated within the rejections.
The specification as originally filed does not include any specific recitation of “generative AI models”, content not “made from preexisting content assets”, “world models”, “physics engines”, or “auxiliary actuators”.
Further, applicant’s arguments appear to reference paragraphs which do not exist in applicant’s specification. While applicant’s originally filed specification (and the corresponding PGPUB) include 78 paragraphs in total, applicant’s arguments point to additional paragraphs 83, 89, 94, and 102.
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 1-6, 8-18, 20-25, 27-37, 39-77 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 “a generative artificial intelligence model” which is not supported by applicant’s specification as originally filed. While the specification generally recites “artificial intelligence (AI) processing” (paragraph 35), “a generative artificial intelligence model” is a specific term in the art which is not sufficiently disclosed by applicant’s specification. There is no specific disclosure of how the content segments are generated beyond the general description of not being “pre-chosen or pre-made” and no specific disclosure of generating the segments beyond via general “AI processing”.
Claim 1 recites “the second content stream segment…being newly generated and not pre-chosen, not pre-made, and not assembled from pre-existing content assets” which is not supported by applicant’s specification as originally filed. While the specification recites “such that the content stream segments are not pre-chosen or pre-made” (paragraph 35), there is no specific disclosure of how the content segments are generated and nothing indicating that the content is “not assembled from pre-existing content assets”. As specification makes no specific reference to how the content is generated, there is no support for describing whether the content is or is not generated by being “assembled from pre-existing content assets”.
Claim 20 recites “a generative artificial intelligence model” which is not supported by applicant’s specification as originally filed. While the specification generally recites “artificial intelligence (AI) processing” (paragraph 35), “a generative artificial intelligence model” is a specific term in the art which is not sufficiently disclosed by applicant’s specification. There is no specific disclosure of how the content segments are generated beyond the general description of not being “pre-chosen or pre-made” and no specific disclosure of generating the segments beyond via general “AI processing”.
Claim 20 recites “the second content stream segment…being newly generated and not pre-chosen, not pre-made, and not assembled from pre-existing content assets” which is not supported by applicant’s specification as originally filed. While the specification recites “such that the content stream segments are not pre-chosen or pre-made” (paragraph 35), there is no specific disclosure of how the content segments are generated and nothing indicating that the content is “not assembled from pre-existing content assets”. As specification makes no specific reference to how the content is generated, there is no support for describing whether the content is or is not generated by being “assembled from pre-existing content assets”.
Claim 39 recites “a generative artificial intelligence model” which is not supported by applicant’s specification as originally filed. While the specification generally recites “artificial intelligence (AI) processing” (paragraph 35), “a generative artificial intelligence model” is a specific term in the art which is not sufficiently disclosed by applicant’s specification. There is no specific disclosure of how the content segments are generated beyond the general description of not being “pre-chosen or pre-made” and no specific disclosure of generating the segments beyond via general “AI processing”.
Claim 39 recites “the second content stream segment…being newly generated and not pre-chosen, not pre-made, and not assembled from pre-existing content assets” which is not supported by applicant’s specification as originally filed. While the specification recites “such that the content stream segments are not pre-chosen or pre-made” (paragraph 35), there is no specific disclosure of how the content segments are generated and nothing indicating that the content is “not assembled from pre-existing content assets”. As specification makes no specific reference to how the content is generated, there is no support for describing whether the content is or is not generated by being “assembled from pre-existing content assets”.
Claim 60, 68, 76 recites “a loudspeaker array spatialization controller” which is not supported by applicant’s specification as originally filed. While the specification generally recites “an audio generating device and “a speaker” (paragraph 40), there is no specific disclosure of “a loudspeaker array spatialization controller” as recited within the claims.
Claim 60, 68, 76 recite “wherein outputting the sensory stimulation comprises controlling one or more auxiliary actuators” which is not supported by applicant’s specification as originally filed. While the specification generally recites different “sensory stimulation devices” (see paragraph 38-41), there is no specific disclosure of “auxiliary actuators” and no indication what would constitute an “actuator” in this context or what would render it as “auxiliary”.
Claim 61, 67, 75 recite “wherein the generative artificial intelligence model comprises a world model or physics engine” which is not supported by applicant’s specification as originally filed. While the specification generally recites “artificial intelligence (AI) processing” (paragraph 35), there is no specific disclosure of what form of “artificial intelligence” processing would be performed, or the specific usage of “a world model” or a “physics engine”.
Claims 61, 67, 75 recite “wherein generating the second content stream segment comprises modifying a simulation parameter selected from the group consisting of: environmental physics, lighting dynamics, object behavior, and temporal flow” which is not supported by applicant’s specification as originally filed.
While the specification recites modifying the content stream, including playback speed and brightness (see paragraph 49) and the general use of artificial intelligence (paragraph 35), there is no specific disclosure of a “simulation parameter” or any sort of “simulation” processing, modification of “environmental physics” or “object behavior”. While the specification further discloses “the processor 202 may cause a content stream segment increase or reduce the degree of intensity of violence, gore, or fear-inducing content” (see paragraph 49), there is no description of how this a change would occur and nothing to indicate it would include a “simulation parameter” of “environmental physics” or “object behavior”.
Claim 66, 74 recite “plot tension”, “camera motion profile” and “soundtrack motif” which is not supported by applicant’s specification as originally filed.
While the specification recites content characteristics including “pacing of plot development”, and “background music” (see paragraph 37), there is no specific disclosure of a “plot tension”, “camera motion profile”, or “soundtrack motif”.
There does not appear to be any specific description regarding camera motion or a “camera motion profile”. While “plot” is described, there is no specific description of what constitutes “plot tension” and how it could be modified. While the specification generally discloses “background music”, there is no specific disclosure of a “soundtrack motif”.
Claim Rejections - 35 USC § 112
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.
Claims 1-6, 8-18, 54, 56-61, 76 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 the limitation "the sensory stimulation" in line 30. There is insufficient antecedent basis for this limitation in the claim. The prior recitation of this limitation, in line 28, was removed by applicant.
Claim 60 recites the limitation "outputting a sensory stimulation to the user" which renders the claim indefinite, as claim 1 already recites “the sensory stimulation”, it is unclear if this is meant to reference the same or a different sensory stimulation.
Claim 76 recites the limitation "outputting a sensory stimulation to the user" which renders the claim indefinite, as claim 39 already recites “outputting a sensory stimulation to the user”, it is unclear if this is meant to reference the same or a different sensory stimulation.
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 20-25, 27-33, 36-37, 62-68 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).
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 responsive to each of the content characteristic preference and the biometric data in real-time, 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 (paragraph 26-27, 33, 35-37, 46); 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), and
output a sensory stimulation that is associated with at least one of the first content stream segment and the second content stream segment (paragraph 26, 46), wherein the at least one sensory stimulation device is configured to take a sensory stimulation action based on the sensory stimulation signal received (paragraph 26, 46),
they fail to specifically disclose the first and second content stream segments are generated by a generative artificial intelligence model, the second content stream segment being newly generated and not pre-chosen, not pre-made, and not assembled from pre-existing content assets.
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, not pre-made, and not assembled from pre-existing content assets (“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 creating future generated based upon user biometric feedback (paragraph 29, 36, 38) 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 a generative artificial intelligence model, the second content stream segment being newly generated and not pre-chosen, not pre-made, and not assembled from pre-existing content assets, 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 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 22, Everett and Moore 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, and time limit (see Everett at paragraph 37).
As to claim 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 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) to have the user record indexed 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 25, 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 27, Everett and Moore disclose wherein the sensory stimulation includes at least one of a sensation of touch (see Jang at paragraph 109), movement, vision (see Everett at paragraph 26, 46 and Jang at paragraph 100), hearing (see Everett at paragraph 26, 46 and Jang at paragraph 109), balance, proprioception, and pain by the user.
As to claim 28, 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.
As to claim 29, Everett and Moore disclose wherein the brain activity of the user includes the electrical activity of the brain of the user defining gamma waves, beta waves, alpha waves, theta waves, delta waves (the “brain activity” is an alternative limitation within claim 9 and not required).
As to claim 30, 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 a perspiration monitor.
As to claim 31, Everett and Moore 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 32, 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).
As to claim 33, 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 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 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 66, Everett and Moore 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).
As to claim 68, Everett and Moore disclose 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); and
wherein outputting the sensory stimulation comprises controlling one or more auxiliary actuators selected from the group consisting of: a haptic actuator, an olfactory stimulation device, a gustatory stimulation device, a thermoregulatory stimulation device, an illumination device (video display device; see Everett at paragraph 26, 46), and a loudspeaker array spatialization controller (audio output; see Everett at paragraph 26, 46).
As to claim 67, Everett and Moore disclose wherein the generative artificial intelligence model comprises a world model or physics engine (see Moore at paragraph 23, 41), and wherein generating the second content stream segment comprises modifying a simulation parameter selected from the group consisting of: environmental physics, lighting dynamics (see Moore at paragraph 41), object behavior, and temporal flow (see Everett at paragraph 26, 33), based on a deviation between the biometric data and an intended physiological response computed in real time (see Everett at paragraph 26, 33 and Moore at paragraph 28-29, 36, 38).
Claims 1-6, 8-14, 17-18, 39-48, 51-52, 56-61, 69-77 are rejected under 35 U.S.C. 103 as being unpatentable over Everett in view of Moore and Jang et al. (Jang) (US 2014/0237495) (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 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);
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 a second content stream segment based at least in part on a deviation computed in real time between the biometric state and the intended physiological response to align the biometric state 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 (paragraph 26-27, 33, 35-37, 46); 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),
they fail to specifically disclose the first and second content stream segments are generated by a generative artificial intelligence model, the second content stream segment being newly generated and not pre-chosen, not pre-made, and not assembled from pre-existing content assets and wherein the sensory stimulation includes at least one of a sensation of temperature, smell, and taste.
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, not pre-made, and not assembled from pre-existing content assets (“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 creating future generated based upon user biometric feedback (paragraph 29, 36, 38) 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).
Additionally, 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) wherein the sensory stimulation includes at least one of a sensation 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’s system to include the first and second content stream segments are generated by a generative artificial intelligence model, the second content stream segment being newly generated and not pre-chosen, not pre-made, and not assembled from pre-existing content assets, 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).
Additionally, 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 wherein the sensory stimulation includes at least one of a sensation 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).
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 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 a second content stream segment based at least in part on a deviation computed in real time between the biometric state and the intended physiological response to align the biometric state 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 (paragraph 26-27, 33, 35-37, 46); 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),
they fail to specifically disclose the first and second content stream segments are generated by a generative artificial intelligence model, the second content stream segment being newly generated and not pre-chosen, not pre-made, and not assembled from pre-existing content assets and wherein the sensory stimulation includes at least one of a sensation of temperature, smell, and taste.
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, not pre-made, and not assembled from pre-existing content assets (“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 creating future generated based upon user biometric feedback (paragraph 29, 36, 38) 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).
Additionally, 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) wherein the sensory stimulation includes at least one of a sensation 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’s system to include the first and second content stream segments are generated by a generative artificial intelligence model, the second content stream segment being newly generated and not pre-chosen, not pre-made, and not assembled from pre-existing content assets, 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).
Additionally, 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 wherein the sensory stimulation includes at least one of a sensation 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).
As to claim 2, Everett, Moore and Jang 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, 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, and time limit (see Everett at paragraph 37).
As to claim 4, Everett, Moore and Jang 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, Everett, Moore and Jang 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, 41, Everett, Moore and Jang 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, Moore and Jang disclose wherein the sensory stimulation includes at least one of a sensation of touch (see Jang at paragraph 109), movement, vision (see Everett at paragraph 26, 46 and Jang at paragraph 100), hearing (see Everett at paragraph 26, 46 and Jang at paragraph 109), balance, proprioception, and pain by the user.
As to claim 9, 43, Everett, Moore and Jang 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.
As to claim 10, 44, Everett, Moore and Jang disclose wherein the brain activity of the user includes the electrical activity of the brain of the user defining gamma waves, beta waves, alpha waves, theta waves, delta waves (the “brain activity” is an alternative limitation within claim 9 and not required).
As to claim 11, 45, Everett, Moore and Jang 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 a perspiration monitor.
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, 47, Everett, Moore and Jang 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).
As to claim 14, 48, Everett, Moore and Jang 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 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 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 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).
As to claims 60, 76, Everett, Moore and Jang disclose 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); and
wherein outputting the sensory stimulation comprises controlling one or more auxiliary actuators selected from the group consisting of: a haptic actuator, an olfactory stimulation device, a gustatory stimulation device, a thermoregulatory stimulation device, an illumination device (video display device; see Everett at paragraph 26, 46), and a loudspeaker array spatialization controller (audio output; see Everett at paragraph 26, 46).
As to claims 61, 75, Everett, Moore and Jang disclose wherein the generative artificial intelligence model comprises a world model or physics engine (see Moore at paragraph 23, 41), and wherein generating the second content stream segment comprises modifying a simulation parameter selected from the group consisting of: environmental physics, lighting dynamics (see Moore at paragraph 41), object behavior, and temporal flow (see Everett at paragraph 26, 33), based on a deviation between the biometric data and an intended physiological response computed in real time (see Everett at paragraph 26, 33 and Moore at paragraph 28-29, 36, 38).
As to claims 77, Everett, Moore and Jang disclose 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, wherein the sensory stimulation includes at least one of a sensation of temperature, smell, and taste (sensory feedback including temperature, taste and smell; see Jang at paragraph 13, 108, 111-112, 118-123).
As to claim 69, 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, 49-50 are rejected under 35 U.S.C. 103 as being unpatentable over Everett, Moore and Jang and further in view of Orr et al. (Orr) (US 2017/0358302) (of record).
As to claims 15, 34, 49, while Everett, Moore and Jang 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, Jang 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).
Claims 34-35 are rejected under 35 U.S.C. 103 as being unpatentable over Everett and Moore and further in view of Orr.
As to claims 34, 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
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.
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/James R Sheleheda/ Primary Examiner, Art Unit 2424