DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/25/2025 has been entered.
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 .
Claim Objections
Claim 17 and 19 are objected to because of the following informalities:
Claim 17 recites ‘the suggested action’; however, it should recite - - a suggested action - -.
Claim 19 recites ‘the set of empathic outputs’; however, it should recite - - a set of empathic outputs - -.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 17-22 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.
Regarding claim 17, claim 17 recites “identifying a list of suggested actions with the digital content experience and based on the one or more behavioral outputs returned by the synthetic brain model; presenting, via the brain computer interface, the list of suggested actions to the user for selection; receiving, via the brain computer interface, user input selecting one of the suggested actions; and executing the suggested action selected via the user input to modify the digital content experience”
The specification discloses
[0096] In variations, the computing subsystem can additionally or alternatively apply outputs of synthetic brain model to perform actions associated with modulation or generation of digital content. In particular, the computing subsystem can apply desired or undesired response patterns (e.g., in terms of negative responses, in terms of emotional arc aspects, etc.) associated with outputs of the synthetic brain models to perform Boolean operations on content (e.g., in relation to cutting portions of digital content, in relation to adding portions of digital content, in relation to affecting play rates of digital content, in relation to affecting speeds of digital content, in relation to adjusting intensities of portions of digital content, in relation to generating repeats of content with or without modification, etc.). Boolean operations can be applied to content of any format (e.g., video, audio, games, text, haptic, etc.) being evaluated. Furthermore, Boolean operations can be automatically applied, or can alternatively be applied with generation of instructions for another entity or computing subsystem to apply (e.g., in a semi-autonomous or manual manner)
[0099] In another example, outputs indicating that voice feature characteristics of a virtual assistant are annoying can be used to modulate the voice features (e.g., in relation to intonation, in relation to language trees, in relation to speed of speech, etc.) to produce a less annoying virtual assistant. Additionally or alternatively, in another example, outputs indicating that timing of assistance from a virtual assistant contributing to reduced engagement (e.g., in relation to responses of users) can be used to adjust timing of assistance such that it produces higher engagement Generative actions can additionally or alternatively be applied to content of various formats in another manner. In another example, the computing subsystem can apply outputs of synthetic brain models used to process a novel, in order to generate suggestions for storyline feature modulation (e.g., in relation to fantastical elements, in relation to dramatic elements, in relation to character development, in relation to other aspects), in order to improve engagement.
While the specification does disclose suggestions and that another entity can apply operations, the specification does not disclose identifying a list of suggested actions with the digital content experience and based on the one or more behavioral outputs returned by the synthetic brain model, presenting, via the brain computer interface, the list of suggested actions to the user for selection, receiving, via the brain computer interface, user input selecting one of the suggested actions, and executing the suggested action selected via the user input to modify the digital content experience.
Claims 18-22 are also rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as being dependent on parent claims failing to comply with the written description requirement. Limitations in claims 18-22 regarding a list of suggested action are likewise rejected.
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 1-7,10-14 and 17-22 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.
Regarding claim 1, claim 1 recites “a population of users subdivided into a plurality of demographic categories, wherein neural signal data for one or more users of each demographic category is used to identify features specific to users of the demographic category”. It is unclear how “a plurality of demographic categories”, “each demographic category”, and “the demographic category” are intended to relate. For the purposes of examination, this limitation is interpreted as: a population of users subdivided into a plurality of demographic categories, wherein neural signal data for one or more users of each demographic category of the plurality of demographic categories is used to identify respective features specific to respective users of a respective demographic category
Regarding claims 10 and 17, claims 10 and 17 contain substantially similar limitations to those found in claim 1. Consequently, claims 10 and 17 are rejected for the same reasons.
Regarding claims 2-7, 11-14 and 18-22, claims 2-7, 11-14 and 18-22 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for depending on an indefinite parent claim.
Claim 1 further recites “one or more empathic outputs associated with predicted user emotional responses to digital content experiences”, “a set of one or more empathic outputs and one or more behavioral outputs associated with predicted user responses to an unevaluated digital content experience”, “the set of one or more empathic outputs diverge” and “the one or more empathic outputs diverge”. The relationship between these elements is unclear. It is unclear whether “a set of one or more empathic outputs and one or more behavioral outputs” refers to a combined set comprising both one or more empathic outputs and one or more behavioral outputs. It is unclear whether this limitation is instead intended to refer to two distinct limitations, a set of one or more empathic outputs and a separate group of one or more behavioral outputs. If the set is intended to be a combined set of both empathic and behavioral outputs, it is unclear how “the set of one or more empathic outputs” is intended to relate to the combined set.
It is further unclear as to which previous limitation “the one or more empathic outputs” is intended to refer. For the purposes of examination, these limitations are interpreted as: one or more empathic outputs associated with predicted user emotional responses to digital content experiences, a set of empathic and behavioral outputs associated with predicted user responses to an unevaluated digital content experience, the set of empathic and behavioral outputs, and the set of empathic and behavioral outputs.
Regarding claim 14, claim 14 recites “the one or more behavioral outputs”. It is unclear whether this limitation is intended to refer back to “one or more behavioral outputs associated with anticipated user input as part of user interaction with the digital content experiences” or “one or more behavioral outputs associated with predicted user responses to an unevaluated digital content experience” in parent claim 1. For the purposes of examination, this limitation is interpreted as: a set of one or more behavioral outputs
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 1-5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Frank et al. (US 20140108842 A1, published 04/17/2014), hereinafter Frank, in view of Anthony et al. (US 20190228439 A1, published 07/25/2019), hereinafter Anthony, in further view of Chappell et al. (US 20200296480 A1, published 09/17/2020), hereinafter Chappell. Examiner Note: Disclosure in Anthony used for rejection is supported in provisional application 62/619,699 filed on 01/19/2018.
Regarding claim 1, Frank teaches the claim comprising:
A method for synthetic brain refinement and implementation, the method comprising (Frank Figs. 1-27; [0099], the system 250 includes a measurement Emotional Response Predictor (measurement ERP) configured to predict the user's emotional response from data comprising measurements of affective response of the user 114 taken with the device 252; [0102], a training sample that includes values derived from measurements of a user, along with the corresponding emotional response, is generated from the tag 292a and measurements received by the model trainer 290 (e.g., the measurements 252a). The training sample is provided to an algorithm that trains the emotional response model 291 that may be used for predicting emotional response from measurements of affective response. For example, algorithm may train a model utilized by a predictor such as a neural network, Naive Bayes classifier, and/or decision trees):
providing, via a display of a brain computer interface coupled to a user, a digital content experience to the user; receiving a neural signal dataset from the brain computer interface coupled to the user, as the user interacts with the digital content experience, processing the neural signal dataset and a set of features of the digital content experience with a set of classification operations (Frank Figs. 1-27; [0038], Phrases like "an affective response of a user to content", or "a user's affective response to content", or "a user's affective response to being exposed to content" refer to the physiological and/or behavioral manifestations of an entity's emotional response to the content due to consuming the content; [0039], content refers to information (e.g., data and/or media) which a user may consume, such as communications (e.g., conversations, messages), video clips, movies, music, augmented reality objects, virtual reality objects, and/or computers games; [0064], a first mode of operation in which the user's brainwaves are measured extensively (e.g., by measuring multiple bands of frequencies) may be selected. For example, measuring the user with the EEG may help determine to more precisely how the user felt towards elements in the content; [0088], a device, such as the device 252, is used to measure affective response of the user 114; the sensor may be a physiological sensor (e.g., a sensor that measures heart rate, galvanic skin response, and/or brainwave activity), and/or a sensor that measures the user's behavior (e.g., a camera, and/or a motion detector). Optionally, the device may include additional components to the sensor, such as a memory, a processor, a battery, a transmitter, and/or a receiver. Optionally, the device may be coupled to a user. Herein a phrase like "a device coupled to a user" refers to a device that is attached to the user (e.g., on clothing, a bracelet, headgear), in contact with the user's body (e.g., a sticker on a user's skin), and/or implanted in the user's body (e.g., an implanted heart-rate sensor or implanted EEG electrodes); [0095], a head-mounted device may include both an EEG sensor; [0101], a tag may include a value indicative of an expected emotional response to a segment of content to which the user 114 was exposed essentially during a duration indicated by the tag. Thus, the model trainer 290 may be used to create training samples that include measurement values along with the corresponding emotional response a user is likely to experience. The training samples can then be utilized by a machine learning-based algorithm that trains an emotional response model 291);
training a synthetic brain model with outputs of the set of classification operations and a response dataset characterizing actual responses of the user to the digital content experience, the synthetic brain model comprising architecture for returning one or more empathic outputs associated with predicted user emotional responses to digital content experiences and for returning one or more behavioral outputs associated with anticipated user input as part of user interaction with the digital content experiences (Frank Figs. 1-27; [0037-0038], "affect" and "affective response" refer to physiological and/or behavioral manifestation of an entity's emotional state; [0080], the method described in FIG. 2 includes an additional step of generating the first segment and the first tag by an interactive computer game having an element whose actions in the game are at least partially controlled by the user; [0083], a computer game may provide tags for certain portions of the game in which it is anticipated that users are likely to have noticeable affective responses; [0100], FIG. 4 illustrates one embodiment that includes a model trainer 290 for training a model for a measurement Emotional Response Predictor (measurement ERP). The model trainer 290 receives as input the measurements taken by a device that measures the user 114, such as the measurements 252a taken by the device 252. The received measurements are taken in temporal vicinity of the first duration indicated by the tag 292a. The measurements include affective response of the user 114 to the first segment of content corresponding to the tag 292a; [0101], a tag may include a value indicative of an expected emotional response to a segment of content to which the user 114 was exposed essentially during a duration indicated by the tag; [0102], a training sample that includes values derived from measurements of a user, along with the corresponding emotional response, is generated from the tag 292a and measurements received by the model trainer 290 (e.g., the measurements 252a). The training sample is provided to an algorithm that trains the emotional response model 291 that may be used for predicting emotional response from measurements of affective response. For example, algorithm may train a model utilized by a predictor such as a neural network, Naive Bayes classifier, and/or decision trees; [0116], a predetermined threshold is derived from a behavioral cue. In one example, the predetermined threshold may represent a minimum duration in which the user does not make substantial movements; [0272], the computer game 120 provides the content ERA 104 with context information regarding an event in the game that is related to the segments of content 102a and 102b; This information can provide context and assist the content ERA in determining the user's expected emotional response to the content; [0347], in the course of its analysis, the semantic analyzer 173 predicts how excited a user is likely to get from reading a certain post on a social network site. An indication that is generated by the semantic analyzer 173, indicates on a scale of 1-10 how excited a user is expected to be (10 being the extremely excited). The controller 175 receives the indication, and with respect to the indication, selects a measuring rate for an EEG head-mounted, battery operated, sensor that may be used to measure the affective response of the user 114 while reading content. In order to save power, the system may elect to measure the affective response of the user with the EEG sensor, while the user interacts with the social network site, if the emotional response is expected to reach a predefined threshold level, such as excitement of at least 3 on the scale of 1-10; [0440], A person's affective response may be expressed by behavioral cues, such as facial expressions, gestures, and/or other movements of the body. Behavioral measurements of a user may be obtained utilizing various types of sensors; [0471], Content the user consumes during interactions with a digital device can take many forms; [0493], an Emotional Response Predictor (ERP). A predictor of emotional response that receives a query sample that includes features that describe a segment of content may be referred to as a predictor of emotional response from content, a "content emotional response predictor", and/or a "content ERP"; [0495], a label returned by an ERP may represent an affective response, such as a value of a physiological signal (e.g., GSR, heart rate) and/or a behavioral cue (e.g., smile, frown, blush); [0517], the content ERA uses a predictor to predict a user's affective response to content, such as to what extent the user's heart-rate is expected to rise due to being exposed to the content, and/or whether the user expected to smile due to being exposed to the content);
refining the synthetic brain model with an aggregate dataset comprising neural signal data from a population of users (Frank Figs. 1-27; [0102], a training sample that includes values derived from measurements of a user, along with the corresponding emotional response, is generated from the tag 292a and measurements received by the model trainer 290 (e.g., the measurements 252a); [0494], a model used by an ERP (e.g., a content ERP and/or a measurement ERP), is primarily trained on data collected from one or more different users that are not the user 114; for instance, at least 50% of the training data used to train the model does not involve the user 114; a prediction of emotional response made utilizing such a model may be considered a prediction of the emotional response of a representative user. It is to be noted that the representative user may in fact not correspond to an actual single user, but rather correspond to an "average" of a plurality of users. Additionally, under the assumption that the user 114 has emotional responses that are somewhat similar to other users' emotional responses, the prediction of emotional response for the representative user may be used in order to determine the likely emotional response of the user 114; [0508], training data used to create a content ERP is collected from one or more users. Optionally, a sample used as training data is derived from a segment of content to which a user is exposed; the sample's corresponding label may be generated from measurements of the user's affective response to the segment of content, e.g., by providing the measurements to a measurement ERP. Optionally, at least a portion of the training data is collected from the user 114. Additionally or alternatively, at least a portion of the training data is collected from a set of users that does not include the user 114; [0514], in order to predict the user's response to a previously unobserved segment of content, a content ERP may rely on the responses that other users, with a similar response profiles to the user, had to the unobserved segment);
returning a set of one or more empathic outputs and one or more behavioral outputs associated with predicted user responses to an unevaluated digital content experience, upon processing the unevaluated digital content experience with the synthetic brain model (Frank Figs. 1-27; [0493], an Emotional Response Predictor (ERP). A predictor of emotional response that receives a query sample that includes features that describe a segment of content may be referred to as a predictor of emotional response from content, a "content emotional response predictor", and/or a "content ERP"; [0495], a label returned by an ERP may represent an affective response, such as a value of a physiological signal (e.g., GSR, heart rate) and/or a behavioral cue (e.g., smile, frown, blush); [0496], a label returned by an ERP may be a value representing a type of emotional response and/or derived from an emotional response. For example, the label my indicate a level of interest and/or whether the response can be classified as positive or negative (e.g., "like" or "dislike"); [0497-0498], emotions are represented using discrete categories. For example, the categories may include three emotional states: negatively excited, positively excited, and neutral. In another example, the categories include emotions such as happiness, surprise, anger, fear, disgust, and sadness; [0499], emotions are represented using a multidimensional representation, which typically characterizes the emotion in terms of a small number of dimensions; potency/control (refers to the individual's sense of power or control over the eliciting event), expectation (the degree of anticipating or being taken unaware), and intensity (how far a person is away from a state of pure, cool rationality). The various dimensions used to represent emotions are often correlated. For example, the values of arousal and valence are often correlated, with very few emotional displays being recorded with high arousal and neutral valence; [0505], a content ERP is used to predict an emotional response of a user from a query sample that includes feature values derived from a segment of content. Optionally, the segment of content is preprocessed and/or undergoes feature extraction prior to being received by the content ERP. Optionally, the prediction of emotional response to the segment of content made by the content ERP is a prediction of the emotional response of the user 114 to the segment of content; [0514], in order to predict the user's response to a previously unobserved segment of content, a content ERP may rely on the responses that other users, with a similar response profiles to the user, had to the unobserved segment; [0517], the content ERA uses a predictor to predict a user's affective response to content, such as to what extent the user's heart-rate is expected to rise due to being exposed to the content, and/or whether the user expected to smile due to being exposed to the content; [0116], a predetermined threshold is derived from a behavioral cue. In one example, the predetermined threshold may represent a minimum duration in which the user does not make substantial movements; [0132], the computer game 294, which is illustrated in FIG. 5, generates the tags 292a and 292b received by the first interface 284. Measurements taken with the device 282 during the first duration, indicated by the tag 292a, may then be utilized by the interactive computer game in order to determine emotional response of the user 114 (e.g., by providing the measurements to a measurement ERP). The predicted emotional response may be used in order to determine if elements in the game need to be changed in order to improve the user experience; [0235], the content ERA 104 utilizes a prediction of emotional response to a segment of content, and provides in a corresponding indication it produces one or more values related to the prediction; [0250], an indication generated by the content ERA 104, such as the first indication 106a and/or the second indication 106b, may include a value that is indicative of a predicted interest level of a user in a segment of content. The controller 108 may select, according to the predicted interest level, parameters that define a mode of operation of the device 112; [0252], in the course of its analysis, the content ERA 104 predicts how frightened a user is likely to get from viewing a segment of content that is a video clip; if the predicted value is too low, it is not likely that the clip is going to scare the user at all, so the system chooses not to waste power on confirming that; [0292], if an indication indicates the predicted interest level is high, a second processing level may be selected in which the processor performs advanced compute-intensive procedures such as object, face, and/or gesture recognition in order to identify things such as the objects, people, and/or emotions expressed in the recorded video; [0527], the content ERA may be used to determine details regarding the content, such as the type of content, and whether the user experience can be enhanced with the type of content being consumed by the user)
identifying that the set of one or more empathic outputs diverge from a target empathic state to be achieved by the unevaluated digital content experience; in response to identifying that the one or more empathic outputs diverge from the target empathic state, modifying the unevaluated digital content experience with one or more features; and providing, via the display on the brain computer interface, the modified unevaluated digital content experience (Frank Figs. 1-27; [0103], if the user's affective response indicates that the user is losing interest and is not excited by playing at a certain level of difficulty, the game can increase the difficulty and add surprising effects and/or plot twists in order to make the game more exciting for the user; [0116], a predetermined threshold is derived from a behavioral cue. In one example, the predetermined threshold may represent a minimum duration in which the user does not make substantial movements; [0132], The predicted emotional response may be used in order to determine if elements in the game need to be changed in order to improve the user experience; [0241], the value related to the prediction of emotional response to a segment of content may represent a type and/or extent of an emotional response to the segment of content. For example, the predicted emotional response needs to be of a certain type and/or be at a certain extent, in order for the predetermined threshold to be reached; [0242], a predetermined threshold may relate to an emotional response of happiness; thus, if a user is not predicted to be happy from exposure to the first segment 102a, the indication 106a may indicate that the first predetermined threshold is not reached. However, if the user is predicted to feel happy due to being exposed to the second segment 102b, the indication 106b may indicate that the second predetermined threshold is reached; [0243], a predetermined threshold may relate to the extent of expected expression of excitement. The expected excitement to a segment of content may be a predicted value on a scale of 1 to 10; a predetermined threshold, in this example, may be set to a level of 5 on the scale of 1 to 10; [0252], in the course of its analysis, the content ERA 104 predicts how frightened a user is likely to get from viewing a segment of content that is a video clip; if the predicted value is too low, it is not likely that the clip is going to scare the user at all, so the system chooses not to waste power on confirming that; [0329], if the evaluation of the semantic analyzer 173 determines that a predefined threshold related to an emotional response is not reached, this fact may be communicated to a module that generated the segment of content. The module that generated the segment of content may modify the segment of content so that the predefined threshold may be reached in a future evaluation performed by the semantic analyzer on the modified segment of content; [0527], modifying elements in the content such as rendering a personalized video; the content ERA may be used to determine details regarding the content, such as the type of content, and whether the user experience can be enhanced with the type of content being consumed by the user)
However, Frank fails to expressly disclose a population of users subdivided into a plurality of demographic categories, wherein neural signal data for one or more users of each demographic category is used to identify features specific to users of the demographic category. In the same field of endeavor, Anthony teaches:
a population of users subdivided into a plurality of demographic categories, wherein neural signal data for one or more users of each demographic category is used to identify features specific to users of the demographic category (Anthony 1-13; [0020], The collected response data are representative of a user's conscious and/or non-conscious (or sub-conscious) responses to aspects or variables included in the presented digital content; [0021], EEG data from an EEG sensor may be utilized as a measure of cognitive-affective processing in absence of behavioral responses; [0022], the collected response data, actual user engagement in response to the presented digital content, and user profile information, such as age, demographics, location, etc., may be provided as inputs to the machine learning system; the machine learning system may be trained to produce models indicating different variables (e.g., text size, color, sounds, objects, etc.) that will produce a desired response by users of a particular user type; [0040], the controlled group 202-5 may be selected to be men living in Texas between the age of 30-45 that are married and earn income between $40,000 USD and $83,000 USD. As will be appreciated, any number and/or type of characteristics may be used to determine a user type for a group of users; [0041], FIG. 3 is a more detailed view of a controlled group participant 303 or user of a controlled group 302 within a controlled environment viewing an advertisement 315 and response data being collected by various sensors 304; Sensors may include, but are not limited to, an EEG sensor 304-1; [0044], at point 403 the sensor data 406-1, 406-2, 406-3, 406-4 indicate a change or peak in activity by the user at that point in time of the advertisement and it may be determined from that combination of sensor data that the user has presented a response of interest to the advertisement; [0046], FIG. 5B illustrates yet another example visualization 504 of user responses, such as neutral, happy, anger, feature, other, as determined from sensor data collected in a controlled environment as the user receives a presentation of an advertisement; [0059], a user type may include users having similar user profiles and that produce similar responses to segments or forms of digital content presented under the same or similar conditions; after the machine learning system 655 has been trained, and the models developed, the machine learning system may be provided with a user profile, current conditions, application information, digital content placement position information, and a device profile, and the machine learning system will generate a model and corresponding engagement prediction that is to be used to produce or select digital content, such as an advertisement, to present to the user; [0062-0063], each model may indicate one or more conditions 704 to form a user type and condition pair, and an engagement prediction 706 for the user type condition pair when digital content; Included in each model is an indication of the user type (e.g., demographics, age range, device types, etc.) corresponding to the model and representative of the response profiles utilized to develop the model 700; [0064], The models also include predicted engagement values 706 for each combination of user type 702 and condition 704. The predicted engagement indicates the probability of a user engagement (e.g., click-through, purchase, interaction, etc.) in response to a digital content presented to a user similar to the determined user type with the other conditions being present; [0085], response data from the user is aggregated with response data from other users viewing the same and/or different digital content, as in 1010; [0089], Based on the user type and the conditions, a model generated by the machine learning system, discussed above, is selected that is most likely to produce a desired response and engagement; [0090], Based on the determined model, digital content is generated or selected that includes variables indicated in the model that, when presented to the user, will produce the desired response and increase the likelihood of engagement with the digital content, 1108. Finally, the generated or selected digital content is presented to the user, as in 1110; see also [0067-0072])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated a population of users subdivided into a plurality of demographic categories, wherein neural signal data for one or more users of each demographic category is used to identify features specific to users of the demographic category as suggested in Anthony into Frank. Doing so would be desirable because with the continued increase in mobile device usage and the availability to digital content, advertising is shifting from generic print advertising to user specific and targeted digital advertising. However, this shift has resulted in advertisers having more difficulty developing targeted advertisements for the wide variety of consumers and their preferences. Likewise, consumers have become more inundated with advertisements making it even more difficult for advertisements to stand out and be engaging to the consumers (see Anthony [0002]). The produced models may be used to refine or revise other existing digital content to improve user responses to digital content and/or to generate new digital content that will produce a desired user response and corresponding likelihood of engagement (see Anthony [0023]). The ad format builder tool 192 provides a technological improvement to advertisers allowing them to provide content items for advertisement campaigns so that advertisements can be dynamically generated according to a model specific to that user and the existing conditions such that the advertisement will produce specific system 1 and system 2 responses from that user, thereby increasing the likelihood of engagement by the user (see Anthony [0037]). Additionally, the system of Anthony would improve the system of Frank by clarifying that any number and type of demographics (see Anthony [0040]) can be collected for training and prediction (see Frank [0519]). Additionally, the system of Anthony would improve the system of Frank by providing more types of representative users to better enable modeling of desired characteristics (see Frank [0494]), thereby increasing the usefulness of the system.
However, Frank in view of Anthony fails to expressly disclose modifying the unevaluated digital content experience with one or more features of the digital content experience. In the same field of endeavor, Chappell teaches:
modifying the unevaluated digital content experience with one or more features of the digital content experience (Chappell figs. 1-19; [0037], methods for controlling output of digital cinematic content responsive to sensor data indicating a user's emotional state; [0040], users are always viewers of cinematic content from which a system node collects real-time emotional response data for use in controlling cinematic output; [0065], A cinematic content control node may be configured to change the characteristics or behaviors of characters, objects, or environments appearing in cinematic content (collectively, “supportive content”), with or without altering the narrative. A supportive content selection operation 530 selects characteristics and behaviors of audio-video elements based on based on emotional indicators, predictions of emotional response, and a targeted emotional arc for the user or cohort. Supportive content selection may predict responses to changes and weigh emotional inputs with user inputs, using techniques that parallel branch selection. For example, a first user's past responses may indicate an association between the color red and happiness, while a second user's responses indicate an association between green and happiness. For scenes intended to be happy, the supportive content selection operation may cause more red objects to be displayed for the first user, and more green objects for the second user. More complex supportive content selection may include character interactions)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated modifying the unevaluated digital content experience with one or more features of the digital content experience as suggested in Chappell into Frank in view of Anthony. Doing so would be desirable because it is now possible to gather more real-time and batch data about consumers of content than ever before (see Chappell [0003]). Present cinematic offerings do not take advantage of available technology to deliver more interesting and compelling content for diverse viewers within a single content package (see Chappell [0004]). It would be desirable, therefore, to develop new methods and other new technologies for production and control of cinematic content, that overcome these and other limitations of the prior art and deliver more compelling entertainment experiences for the audiences of tomorrow (see Chappell [0005]). Additionally, the system of Chappell would improve the system of Frank by enabling the system to consider past user responses to content when determining how to modify content (see Chappell [0065]), thereby better personalizing the modifications to the user and increasing the likelihood that the user will react positively to the modifications.
Regarding claim 2, Frank in view of Anthony in further view of Chappell teaches all the limitations of claim 1, further comprising:
wherein the digital content experience comprises one or more of: an audio listening experience, a video watching experience, an image viewing experience, a text reading experience, a shopping experience, and a video gameplay experience provided by way of a digital content file (Frank Figs. 1-27; [0038], Phrases like "an affective response of a user to content", or "a user's affective response to content", or "a user's affective response to being exposed to content" refer to the physiological and/or behavioral manifestations of an entity's emotional response to the content due to consuming the content; [0039], content refers to information (e.g., data and/or media) which a user may consume, such as communications (e.g., conversations, messages), video clips, movies, music, augmented reality objects, virtual reality objects, and/or computers games; [0101-0102], a tag may include a value indicative of an expected emotional response to a segment of content to which the user 114 was exposed essentially during a duration indicated by the tag)
Regarding claim 3, Frank in view of Anthony in further view of Chappell teaches all the limitations of claim 1, further comprising:
wherein the set of classification operations comprises a first subset of operations applied to externally derived features comprising the set of features of the digital content experience and environmentally-derived signals, and a second subset of operations applied to the neural signal dataset and biometric features (Frank Figs. 1-27; Fra [0064], a first mode of operation in which the user's brainwaves are measured extensively (e.g., by measuring multiple bands of frequencies) may be selected. For example, measuring the user with the EEG may help determine to more precisely how the user felt towards elements in the content (see also [0095]); [0088], a device, such as the device 252, is used to measure affective response of the user 114; the sensor may be a physiological sensor (e.g., a sensor that measures heart rate, galvanic skin response, and/or brainwave activity); [0100-0101], a tag may include a value indicative of an expected emotional response to a segment of content to which the user 114 was exposed essentially during a duration indicated by the tag. Thus, the model trainer 290 may be used to create training samples that include measurement values along with the corresponding emotional response a user is likely to experience. The training samples can then be utilized by a machine learning-based algorithm that trains an emotional response model 291; [0102], a training sample that includes values derived from measurements of a user, along with the corresponding emotional response, is generated from the tag 292a and measurements received by the model trainer 290 (e.g., the measurements 252a). The training sample is provided to an algorithm that trains the emotional response model 291 that may be used for predicting emotional response from measurements of affective response; [0491], the more data available for training a model, and the more the training samples are similar to the samples on which the predictor will be used (also referred to as test samples); [0506], feature values are used to represent at least some aspects of a segment of content; [0507], There are many feature extraction methods mentioned in the literature that can be utilized to create features for audio-, image-, and/or video-containing content; [0508], a sample used as training data is derived from a segment of content to which a user is exposed; [0510-0511], in many cases, a prediction of a user's emotional response to content may depend on the context and situation in which the content is consumed. For example, for content such as an action movie, a user's emotional response might be different when viewing a movie with friends compared to when viewing alone (e.g., the user might be more animated and expressive with his emotional response when viewing with company); [0512], in order to capture information regarding context and/or situation in which a user consumes the content, in some embodiments, samples that may be provided to an ERP include feature values describing the context in which the content is consumed and/or the user's situation. For example, these feature values may describe aspects related to the user's location, device on which the content is consumed, people in the user's vicinity, tasks or activities the user performed or needs to perform (e.g., work remaining to do), and/or the user's or other peoples emotional state as determined, for example, from analyzing communications of a log of the activities of the user and/or other people related to the user. In another example, the feature values describing context and/or situation may include physiological measurements and/or baseline values (e.g., current and/or typical heart rate) of the user and/or other people; [0535], semantic analysis of a segment of content utilizes a lexicon that associates words and/or phrases with their core emotions)
Regarding claim 4, Frank in view of Anthony in further view of Chappell teaches all the limitations of claim 1, further comprising:
wherein features neural signal data are derived from at least one of: event-related potentials, spatiotemporal aspects, spectrum aspects, and distance features across feature matrices (Frank Figs. 1-27; [0064], a first mode of operation in which the user's brainwaves are measured extensively (e.g., by measuring multiple bands of frequencies) may be selected. For example, measuring the user with the EEG may help determine to more precisely how the user felt towards elements in the content; [0088], a device, such as the device 252, is used to measure affective response of the user 114; the sensor may be a physiological sensor (e.g., a sensor that measures heart rate, galvanic skin response, and/or brainwave activity), and/or a sensor that measures the user's behavior (e.g., a camera, and/or a motion detector). Optionally, the device may include additional components to the sensor, such as a memory, a processor, a battery, a transmitter, and/or a receiver. Optionally, the device may be coupled to a user. Herein a phrase like "a device coupled to a user" refers to a device that is attached to the user (e.g., on clothing, a bracelet, headgear), in contact with the user's body (e.g., a sticker on a user's skin), and/or implanted in the user's body (e.g., an implanted heart-rate sensor or implanted EEG electrodes); [0095], a head-mounted device may include both an EEG sensor; [0101], a tag may include a value indicative of an expected emotional response to a segment of content to which the user 114 was exposed essentially during a duration indicated by the tag. Thus, the model trainer 290 may be used to create training samples that include measurement values along with the corresponding emotional response a user is likely to experience. The training samples can then be utilized by a machine learning-based algorithm that trains an emotional response model 291; [0166], the image capturing device is directed at a location in which the user is expected to be in order to look at a displayed content; [0468], values representing the environment are used to predict the value, such as the location; [0512], these feature values may describe aspects related to the user's location, device on which the content is consumed, people in the user's vicinity, tasks or activities the user performed or needs to perform; the feature values describing context and/or situation may include physiological measurements)
Regarding claim 5, Frank in view of Anthony in further view of Chappell teaches all the limitations of claim 1, further comprising:
wherein the set of empathic and behavioral outputs comprises empathic outputs characterizing one or more of: boredom, joy, flow, anger, stress, sadness, and relaxation experienced by a target audience of the unevaluated digital content experience (Frank Figs. 1-27; [0494], a model used by an ERP (e.g., a content ERP and/or a measurement ERP), is primarily trained on data collected from one or more different users that are not the user 114; for instance, at least 50% of the training data used to train the model does not involve the user 114; a prediction of emotional response made utilizing such a model may be considered a prediction of the emotional response of a representative user. It is to be noted that the representative user may in fact not correspond to an actual single user, but rather correspond to an "average" of a plurality of users; [0495], a label returned by an ERP may represent an affective response, such as a value of a physiological signal (e.g., GSR, heart rate) and/or a behavioral cue (e.g., smile, frown, blush); [0496], a label returned by an ERP may be a value representing a type of emotional response and/or derived from an emotional response. For example, the label my indicate a level of interest and/or whether the response can be classified as positive or negative (e.g., "like" or "dislike"); [0497-0498], emotions are represented using discrete categories. For example, the categories may include three emotional states: negatively excited, positively excited, and neutral. In another example, the categories include emotions such as happiness, surprise, anger, fear, disgust, and sadness; [0499], emotions are represented using a multidimensional representation, which typically characterizes the emotion in terms of a small number of dimens