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 .
Response to Amendment
This Office Action has been issued in response to Applicant’s Communication of amended application S/N 18/829,867 filed on July 13, 2026. Claims 1, 3 to 7, 9, and 11 to 23 are currently pending with the application.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Further, the specification is objected to as failing to provide proper antecedent basis for the claimed subject matter (See 37 CFR 1.75(d)(1) and MPEP § 608.01(o)). Correction of the following is required: claim 1 and 20 recite the limitation “data of the user associated with stable personal characteristics of the user”. The specification lacks antecedent basis for the claim terminology, and more specifically, for the term “stable personal characteristics”.
Examiner’s Note
Examiner respectfully points out that claim 6 recites the contingent limitations “in response to the final score of the target multimedia item being less than or equal to a first score threshold, reducing…” and “in response to the final score of the target multimedia item being greater than or equal to a second score threshold”, in lines 4 and 7, respectively, and that claim 7 recites the contingent limitations “in response to the final score of the target multimedia item being greater than a first score threshold and less than a second score threshold, keeping…” in line 1 at page 3. The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the conditions precedent are not met (See MPEP 2111.04(II)). Therefore, since claims 6 and 7 are method claims, the contingent limitations recited in claims 6 and 7 are not required.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 recites the limitation “and the at least one first multimedia item is different from…” in line 17, which appears to contain a typographical error, and that it should read “and wherein the at least one first multimedia item is different from…”.
Appropriate correction is required.
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, 3 to 7, 9, and 11 to 20 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 “data of the user associated with stable personal characteristics of the user”. This limitation is not clear, and more specifically, it is not clear what the element “stable personal characteristics” is, therefore rendering the claim indefinite. Same rationale applies to claim 20, and to claims 3 to 7, 9, and 11 to 19, since they inherit the same deficiencies by virtue of their dependency.
For purposes of examination, Examiner will interpret the limitation as “personal data of the user”, such as demographic data, or other personal traits, i.e., age, gender, geographic area being located, occupation, personality type, living habits, multimedia usage period, circadian rhythms, a belonging user group, social network information, exercise patterns, the presence of sleep disorders, the type of sleep disorders or the like, in line with paragraph [0061] of the specification.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3 to 7, 9, and 11 to 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 20, and 21 recite selecting a target multimedia item, adjusting recommendation parameters for recommending the item, and detecting a change.
The limitation of selecting a target multimedia item, which specifically recites “selecting a target multimedia item for playback from a plurality of candidate multimedia items based on the first user data, the second user data, and scenario data corresponding to a current multimedia usage scenario”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by at least one processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by at least one processor” language, “selecting”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, identifying an item of multimedia from a list of items, based on data associated with a user and current usage data, which could be identifying a song from a list that match user and usage specified conditions.
Continuing with the analysis, the limitation of adjusting recommendation parameters for recommending the item, which specifically recites “adjusting a recommendation parameter of at least one first multimedia item in the plurality of candidate multimedia items based at least in part on real-time physiological measurement data collected by the at least one physiological sensor of the wearable device during playback of the target multimedia item, wherein the adjusted recommendation parameter is used for subsequently selecting the at least one first multimedia item in the current multimedia usage scenario, and the at least one first multimedia item is different from the target multimedia item”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by the least one processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by the least one processor” language, “adjusting”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, reading data from a sheet of paper, that was collected by sensors, modifying a recommendation parameter of a different song based on the data, and determining a different song based on the modified parameter.
The limitation of detecting a change (new claim 21), which specifically recites “detecting a change in the physiological measurement data of the user during playback of the target multimedia item”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by the at least one physiological sensor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by the at least one physiological sensor” language, “detecting”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, identifying that a value of data is different from a previous value, which could be read from a sheet of paper. If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements – “obtaining first user data associated with a user wearing a wearable device, wherein the first user data of the user comprises physiological measurement data captured by at least one physiological sensor of the wearable device”, “obtaining second user data of the user, wherein the second user data comprises at least one of attribute data of the user associated with stable personal characteristics of the user”, “continuously collecting, by at least one physiological sensor of the wearable device worn by a user, physiological measurement data of the user comprising at least one of heart rate, heart rate variability, blood oxygen level, or body temperature” (claim 21), at least one processor, and a memory (claim 20). The “obtaining” and “collecting” limitations amount to data-gathering steps which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)). The at least one processor and memory in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activity identified above, which include the data gathering steps, is recognized by the courts as well-understood, routine, and conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). The claims are not patent eligible.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites the additional limitations of “obtaining user interaction data corresponding to the target multimedia item; and updating the recommendation parameter of the at least one first multimedia item in the plurality of candidate multimedia items based on the user interaction data”. The updating limitation can be performed in the human mind with the aid of pen and paper, and therefore is further elaborating on the abstract idea. The obtaining limitation amounts to data gathering steps, which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)), and recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d) (II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). The claim does not amount to significantly more than the abstract idea. Same rationale applies to claims 4, 5, 11 to 18, 22, and 23.
Claim 6 is dependent on claim 4 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitations of “in response to the final score of the target multimedia item being less than or equal to a first score threshold, reducing at least one of the recommending weight or the recommending frequency of the at least one first multimedia item; or in response to the final score of the target multimedia item being greater than or equal to a second score threshold, increasing at least one of the recommending weight or the recommending frequency of the at least one first multimedia item”. As previously indicated in the Examiner’s note above, since claim 6 is a method claim, the limitations are not required. Nonetheless, and in the interest of compact prosecution, all the limitations have been considered as if the conditions precedent are positively recited. The limitations, however, can be performed in the human mind with the aid of pen and paper, by modifying a weight or frequency corresponding to the item in a sheet of paper, and therefore, the claim is further elaborating on the abstract idea. The claim does not amount to significantly more. Same rationale applies to claim 7.
Claim 9 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 9 recites the same abstract idea of claim 1. The claim recites the additional limitations of “the scenario data comprises at least one of environmental data of current environment, time data, or intention data”, which is tying the abstract idea to a field of use by further specifying the target data, and which is simply an attempt to limit the application of the abstract idea to a particular technological environment; merely indicating a field of use or technological environment in which to apply the judicial exception does not meaningfully limit the claim (See MPEP 2106.05(h)). Same rationale applies to claim 19.
Additionally, the claims do not include a requirement of anything other than conventional, generic computer technology for executing the abstract idea, and therefore, do not amount to significantly more than the abstract idea.
Claims 1, 3 to 7, 9, and 11 to 23 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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, 3, 9, and 11 to 16, and 18 to 23 are rejected under 35 U.S.C. 103 as being unpatentable over Cruz Huertas et al. (U.S. Publication No. 2019/0318008) hereinafter Cruz, and further in view of Aryan (U.S. Publication No. 2022/0067088).
As to claim 1:
Cruz discloses:
A method for multimedia recommendation, comprising:
obtaining, by at least one processor, first user data associated with a user wearing a
wearable device, wherein the first user data of the user comprises physiological measurement data captured by at least one physiological sensor of the wearable device [Paragraph 0027 teaches identifying the user’s mood, and cognitive status (from wearable sensor readings); Paragraph 0039 teaches receiving user data, including cognitive status, heartbeat rate, breathing rate, etc., from wearable biometric sensors associated with physiological recognition; Paragraph 0057 teaches receiving current user data from wearable biometric sensors];
obtaining, by the at least one processor, second user data of the user, wherein the second user data comprises of attribute data of the user associated with stable personal characteristics of the user [Paragraph 0003 teaches receiving a plurality of user data; Paragraph 0027 teaches identifying user’s current location; Paragraph 0030 teaches determining the user media preferences by location, activity, mood, etc., and user listening behavior; Paragraph 0067 teaches user’s personal data, e.g., age, gender; Paragraph 0069 teaches querying records of learned user preferences and behaviors];
selecting, by the at least one processor, a target multimedia item for playback from a plurality of candidate multimedia items based on the first user data, the second user data, and scenario data corresponding to a current multimedia usage scenario [Paragraph 0024 teaches selecting musical media based on identified current location, user activity data, user’s mood and cognitive status, environmental conditions, previously learned media preferences of the user, etc.; Paragraph 0025 teaches determine playlist for current location, activity, mood, cognitive status, context or environmental conditions, according to the user preferences; Paragraph 0027 identifying current environmental conditions, and current user’s activity; Paragraph 0028 teaches searching personal media databases and services for songs matching characteristics and retrieve the resulting media, where the personal media databases and services include the candidate multimedia items; Paragraph 0057 teaches receiving current user data, including current user’s activity; Paragraph 0069 teaches creating a dataset of the current user situation (e.g., including current user’s cognitive status, environmental conditions, activity data) and querying records of learned user preferences and behaviors, to be sent to a customized playlist selection module, to select media corresponding to the user’s current activity, mood, cognitive status, context, etc.; Paragraph 0072 teaches identifying and returning media titles suitable for the user’s current situation]; and
subsequently selecting at least one first multimedia item in the current multimedia usage scenario [Paragraph 0034 teaches monitoring the user’s status, user activity and cognitive status (i.e., user data) by utilizing biometric sensors to determine the user’s frustration level, heartbeat rate, respiratory breathing and temperature to propose media selections; Paragraph 0054 teaches recommending the determined particular songs to the user].
Cruz does not appear to expressly disclose adjusting, by the at least one processor, a recommendation parameter of at least one first multimedia item in the plurality of candidate multimedia items based at least in part on real-time physiological measurement data collected by the at least one physiological sensor of the wearable device during playback of the target multimedia item, wherein the adjusted recommendation parameter is used for subsequently selecting the at least one first multimedia item in the current multimedia usage scenario, and the at least one first multimedia item is different from the target multimedia item.
Aryan discloses:
adjusting, by the at least one processor, a recommendation parameter of at least one first multimedia item in the plurality of candidate multimedia items based at least in part on real-time physiological measurement data collected by the at least one physiological sensor of the wearable device during playback of the target multimedia item [Paragraph 0011 teaches recommending, and populating a playback queue of media items to be played to a user according to preferences, current context, etc., where the current context includes factors as user’s location, user’s activity, user’s physiological conditions (e.g., heart-rate, body temperature, blood pressure), amongst others; Paragraph 0043 teaches recommending playback queue based on real time contextual information of the user; Paragraph 0062 teaches determining a combination of media items in a specific order; Paragraph 0064 teaches sensing or collecting the contextual features associated with the user, and computing probability distributions for the media files, and determining ranking of the media files based on the probability distributions], wherein the adjusted recommendation parameter is used for subsequently selecting the at least one first multimedia item in the current multimedia usage scenario [Paragraph 0019 teaches recommending the media items to be added or inserted into the queue; Paragraph 0062 teaches determining a combination of media items in a specific order, that are in turn used to determine the upcoming media items, or a subset of the media items that are to be queued at the play next queue component; Paragraph 0064 teaches recommending one or more media files from the ranking of the plurality of media files to the user for playback; Paragraph 0122 teaches probability distributions are used to create a ranking of the media files, and further recommend the songs to the user based on the ranking], and the at least one first multimedia item is different from the target multimedia item [Paragraph 0061 teaches collected data includes a media item that is being currently played, identified by the now playing queue component; Paragraph 0062 teaches determining a combination of media items in a specific order to be played next, therefore, the at least one first multimedia item is different from the target multimedia item].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Cruz, by adjusting, by the at least one processor, a recommendation parameter of at least one first multimedia item in the plurality of candidate multimedia items based at least in part on real-time physiological measurement data collected by the at least one physiological sensor of the wearable device during playback of the target multimedia item, wherein the adjusted recommendation parameter is used for subsequently selecting the at least one first multimedia item in the current multimedia usage scenario, and the at least one first multimedia item is different from the target multimedia item, as taught by Aryan [Paragraph 0011, 0019, 0043, 0061, 0062, 0064], because both applications are directed to generating recommendations to users based on detected data, including detected physiological data associated with the user; adjusting recommendation parameters based on real-time physiological data of the user to further determine item recommendations provides improved accuracy and personalization in the recommendations (See Aryan Para [0081]).
Same rationale applies to claim 20, since it recites the same limitations as claim 1.
As to claim 3:
The combination of Cruz and Aryan discloses:
obtaining user interaction data corresponding to the target multimedia item [Cruz - Paragraph 0075 teaches receiving user feedback of the retrieved media playlist, where the user can click on a “Reject” button; Aryan – Paragraph 0046 teaches sensing modules can operate to collect user’s interactions]; and
updating the recommendation parameter of the at least one first multimedia item in the plurality of candidate multimedia items based on the user interaction data [Cruz - Paragraph 0075 teaches based on the received user’s feedback, continue to search through the records for a better suited media playlist for the user’s current situation, in other words, updating the records so the previously selected items are not provided to the user, but to search for new items; Aryan – Paragraph 0055 teaches the combination and ordering of media items in a group may be determined in real-time as the user interacts with the playback queue].
Same rationale applies to claim 23, since it recites similar limitations as those in claim 3.
As to claim 9:
Cruz discloses:
the scenario data comprises at least one of environmental data of current environment, time data, or intention data [Paragraph 0028 teaches data includes context, i.e., data and time, environmental conditions, current activity, etc.].
As to claim 11:
Cruz discloses:
obtaining target user data of the user based on the first user data, the second user data and the scenario data [Paragraph 0028 teaches obtaining the user’s mood, cognitive status, context, environmental conditions, location, and user’s activity];
obtaining multimedia characteristic data of the plurality of candidate multimedia items [Paragraph 0025 teaches assessing the characteristics of one or more songs; Paragraph 0028 teaches obtaining characteristics and types of media, to find songs matching the characteristics]; and
selecting the target multimedia item from the plurality of candidate multimedia items based on the target user data and the multimedia characteristic data of the plurality of candidate multimedia items [Paragraph 0025 teaches determine playlist for current location, activity, mood, cognitive status, context or environmental conditions, according to the user preferences, based on the characteristics of the songs; Paragraph 0028 teaches searching and retrieving songs matching the characteristics, and related to the current location, activity, mood, cognitive status, context and environmental conditions].
As to claim 12:
The combination of Cruz and Aryan discloses:
the multimedia characteristic data comprises acoustic features [Aryan – Paragraph 0069 teaches factors of an audio file include beats per minute, tempo, etc.; Paragraph 0080 teaches audio features include a tempo, a rhythm, a loudness, a pitch, etc.];
determining recommendation probabilities of the plurality of candidate multimedia items based on the target user data and the acoustic features of the plurality of candidate multimedia items [Cruz - Paragraph 0023 teaches provide media files for those songs that are more probable to be played based on the context, where the context includes location, user activity, weather, date/time, cognitive state, and the songs characteristics, in other words, where probabilities are determined in order to provide the songs that are most probable to be played; Aryan – Paragraph 0081 teaches computing probability distributions over the media files]; and
determining the target multimedia item from the plurality of candidate multimedia items based on the recommendation probabilities of the plurality of candidate multimedia items [Cruz - Paragraph 0023 teaches provide media files for those songs that are more probable to be played based on the context; Aryan – Paragraph 0091 teaches computing the probability of the ideal song, which is to be recommended to the user].
As to claim 13:
Cruz discloses:
obtaining target user data of the user based on the first user data and the attribute data of the user [Paragraph 0067 teaches determining current user data associated with user’s anatomical functions or systems, including elevated high blood pressure, respiratory breathing pace, heartbeat rate, etc., and based on biometric sensors, and thresholds that are determined based on the user’s personal data (e.g., age, gender)]; and
determining the target multimedia item from the plurality of candidate multimedia items based on the target user data and the scenario data [Paragraph 0069 teaches determining media corresponding to the current user’s context data, and data associated with the location, user’s activity, mood, context, and environmental conditions].
As to claim 14:
Cruz discloses:
wherein the second user data further comprises multimedia preference data of the user [Paragraph 0030 teaches determining user media preferences]; and
wherein determining the target multimedia item from the plurality of candidate multimedia items based on the first user data, the second user data, and the scenario data comprises: correcting the multimedia preference data of the user based on the first user data and the scenario data to obtain multimedia preference correction data of the user [Paragraph 0030 teaches updating the training into the records of learned user preferences and behaviors database, to grow the correlation, by determining location, activity (e.g., exercise, reading, picnic), mood, etc.; Paragraph 0053 teaches updating and storing the learned user listening behavior]; and
determining the target multimedia item from the plurality of candidate multimedia items based on the multimedia preference correction data [Paragraph 0025 teaches determine playlist for current location, activity, mood, cognitive status, context or environmental conditions, according to the user preferences, hence the updated user preferences].
As to claim 15:
Cruz discloses:
obtaining multimedia playback history data of the user [Paragraph 0053 teaches determining user queried songs, and additional user data and data on external conditions; Paragraph 0054 teaches determining music queried by the user, hence, playback history data]; and
obtaining the multimedia preference data of the user based on the multimedia playback history data [Paragraph 0053 teaches correlate the raw data to determine the media preferences of the user based on location, activity, mood, cognitive status, context and environmental conditions (i.e., the user listening behavior), where the correlated raw data, which associates the queried songs with corresponding user data and data on external conditions, may be categorized and sent to the records of learned user preferences and behaviors database to update and store the learned user listening behavior].
As to claim 16:
Cruz discloses:
obtaining a multimedia preference correction parameter based on the first user data and the scenario data [Paragraph 0030 teaches determining user media preferences, and updating the training into the records of learned user preferences and behaviors database, to grow the correlation, by determining location, activity (e.g., exercise, reading, picnic), mood, etc.]; and obtaining the multimedia preference correction data of the user based on the multimedia preference data and the multimedia preference correction parameter [Paragraph 0030 teaches determining user media preferences, and updating the training into the records of learned user preferences and behaviors database, to grow the correlation, by determining location, activity (e.g., exercise, reading, picnic), mood, etc.; Paragraph 0053 teaches updating and storing the learned user listening behavior].
As to claim 18:
Cruz discloses:
selecting the target multimedia item from the plurality of candidate multimedia items based on the multimedia preference correction data and the multimedia playback history data of the user [Paragraph 0030 teaches Behavior Capture may then update the training into the records of learned user preferences and behaviors database, where the behavior capture determines the media that the user was listening in a location, during an activity, gestures, and corresponding cognitive status; Paragraph 0054 teaches music queried and the raw data on the user and external conditions are stored in the records of learned user preferences and behaviors database and utilized to update the user listening behavior, which will be used in a next time, to recommend the particular songs based on the updated preference data and the listening behavior or playback history].
As to claim 19:
Cruz discloses:
the plurality of candidate multimedia items comprise a plurality of audio candidates [Paragraph 0026 teaches selecting songs that are more in line with the user’s taste and preferences; Paragraph 0028 teaches searching personal media databases and services for songs matching characteristics and retrieve the resulting media, therefore, items comprise audio candidates or songs], and the current multimedia usage scenario comprises at least one of stress relief and relaxation, sleep aid, exercise or concentration [Paragraph 0030 teaches user’s activity includes exercise, reading, picnic, etc.].
As to claim 21:
Cruz discloses:
A method for multimedia recommendation using a wearable device, comprising:
continuously collecting, by at least one physiological sensor of the wearable device worn by a user, physiological measurement data of the user comprising at least one of heart rate, heart rate variability, blood oxygen level, or body temperature [Paragraph 0027 teaches identifying the user’s mood, and cognitive status (from wearable sensor readings); Paragraph 0034 teaches monitor the user’s status, user activity and cognitive status (i.e., user data) by utilizing biometric sensors to determine the user’s frustration level, heartbeat rate, respiratory breathing and temperature to propose media selections to alter the user’s status or cognitive status thereby returning the user’s status to a healthy or safe level for the user, in other words, continuously collecting physiological data of the user; Paragraph 0039 teaches receiving user data, including cognitive status, heartbeat rate, breathing rate, etc., from wearable biometric sensors associated with physiological recognition; Paragraph 0057 teaches receiving current user data from wearable biometric sensors];
selecting, by the at least one processor, a target multimedia item for playback from a plurality of candidate multimedia items based in part on the physiological measurement data and scenario data corresponding to a current multimedia usage scenario [Paragraph 0024 teaches selecting musical media based on identified current location, user activity data, user’s mood and cognitive status, environmental conditions, previously learned media preferences of the user, etc.; Paragraph 0025 teaches determine playlist for current location, activity, mood, cognitive status, context or environmental conditions, according to the user preferences; Paragraph 0027 identifying current environmental conditions, and current user’s activity; Paragraph 0028 teaches searching personal media databases and services for songs matching characteristics and retrieve the resulting media, where the personal media databases and services include the candidate multimedia items; Paragraph 0057 teaches receiving current user data, including current user’s activity; Paragraph 0069 teaches creating a dataset of the current user situation (e.g., including current user’s cognitive status, environmental conditions, activity data) and querying records of learned user preferences and behaviors, to be sent to a customized playlist selection module, to select media corresponding to the user’s current activity, mood, cognitive status, context, etc.; Paragraph 0072 teaches identifying and returning media titles suitable for the user’s current situation];
detecting, by the at least one physiological sensor, a change in the physiological measurement data of the user during playback of the target multimedia item [Paragraph 0034 teaches monitoring the user’s status by utilizing biometric sensors to determine the user’s frustration level, heartbeat rate, respiratory breathing and temperature and to propose media selections to alter the user’s status or cognitive status; Paragraph 0052 teaches monitoring the changes to the user data while the user is playing the queried media selections; Paragraph 0070 teaches creating a dataset based on the user’s reaction to a particular media selection, based on a change in the anatomical functions or systems associated with the user]; and
adjusting, by the at least one processor, a recommendation of at least one candidate multimedia item other than the target multimedia item in response to the detected change in the physiological measurement data [Paragraph 0034 teaches monitoring the user’s status, user activity and cognitive status (i.e., user data) by utilizing biometric sensors to determine the user’s frustration level, heartbeat rate, respiratory breathing and temperature to propose media selections; Paragraph 0070 teaches creating a dataset based on the user’s reaction to a particular media selection, based on a change in the anatomical functions or systems associated with the user; Paragraph 0079 teaches automatically precluding playing a media selection when the user’s status returns to a normal range, and resuming with playing a media selection based on current raw data associated with the user and the current external conditions, therefore, adjusting a recommendation of a media item in response to the detected change in the physiological measurement data].
Cruz does not appear to expressly disclose adjusting a recommendation parameter of at least one candidate multimedia item, wherein the adjusted recommendation parameter is used for subsequent selection of the at least one candidate multimedia item for playback.
Aryan discloses:
adjusting a recommendation parameter of at least one candidate multimedia item in response to the detected change, wherein the adjusted recommendation parameter is used for subsequent selection of the at least one candidate multimedia item for playback [Paragraph 0011 teaches recommending, and populating a playback queue of media items to be played to a user according to preferences, current context, etc., where the current context includes factors as user’s location, user’s activity, user’s physiological conditions (e.g., heart-rate, body temperature, blood pressure), amongst others; Paragraph 0019 teaches recommending the media items to be added or inserted into the queue; Paragraph 0043 teaches recommending playback queue based on real time contextual information of the user; Paragraph 0061 teaches collected data includes a media item that is being currently played, identified by the now playing queue component; Paragraph 0062 teaches determining a combination of media items in a specific order, that are in turn used to determine the upcoming media items, or a subset of the media items that are to be queued at the play next queue component; Paragraph 0064 teaches sensing or collecting the contextual features associated with the user, and computing probability distributions for the media files, and determining ranking of the media files based on the probability distributions, and further recommending one or more media files from the ranking of the plurality of media files to the user for playback; Paragraph 0068 teaches updating current recommendations of media items upon detection of user interaction, where affinity measures associated with particular files may be based on a continuous scoring method in accordance with the user’s interaction with the media files; Paragraph 0122 teaches probability distributions are used to create a ranking of the media files, and further recommend the songs to the user based on the ranking].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Cruz, by adjusting a recommendation parameter of at least one candidate multimedia item, wherein the adjusted recommendation parameter is used for subsequent selection of the at least one candidate multimedia item for playback, as taught by Aryan [Paragraph 0011, 0019, 0043, 0061, 0062, 0064], because both applications are directed to generating recommendations to users based on detected data, including detected physiological data associated with the user; adjusting recommendation parameters based on real-time physiological data of the user to further determine item recommendations provides improved accuracy and personalization in the recommendations (See Aryan Para [0081]).
As to claim 22:
Cruz as modified by Aryan discloses:
adjusting the recommendation parameter comprises determining at least one of a direction or a magnitude of adjustment of the recommendation parameter of the at least one candidate multimedia item other than the target multimedia item based on the detected change in the physiological measurement data [Paragraph 0084 teaches defining a community vector for each song; Paragraph 0085 teaches a greater magnitude indicates that the node n has a greater affiliation with the community C.sub.i, where affiliation can be viewed as a metric that represents the degree to which an individual shares a characteristic or environment with a community, where the characteristic or environment is selected as an indicator that reflects a propensity of the individual to share a preference or interest with the community; Paragraph 0090 teaches determining prediction Y, which is a vector indicating the probability of the ideal node which is to be recommended to the user].
Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over Cruz Huertas et al. (U.S. Publication No. 2019/0318008) hereinafter Cruz, in view of Aryan (U.S. Publication No. 2022/0067088), and further in view of Kimmerling (U.S. Publication No. 2018/0158084).
As to claim 17:
Cruz discloses all the limitations as set forth in the rejections of claim 16 above, but does not appear to expressly disclose the multimedia preference data comprises a multimedia preference vector of length M, the multimedia preference correction parameter comprises a multimedia preference correction vector of length M; and wherein obtaining the multimedia preference correction data of the user based on the multimedia preference data and the multimedia preference correction parameter comprises: multiplying M elements of the multimedia preference vector with M elements of the multimedia preference correction vector to obtain the multimedia preference correction data.
Kimmerling discloses:
the multimedia preference data comprises a multimedia preference vector of length M, the multimedia preference correction parameter comprises a multimedia preference correction vector of length M; and wherein obtaining the multimedia preference correction data of the user based on the multimedia preference data and the multimedia preference correction parameter comprises: multiplying M elements of the multimedia preference vector with M elements of the multimedia preference correction vector to obtain the multimedia preference correction data [Paragraph 0050 teaches product of the individual's preference vector and the item's vector is indicative of how much the individual should like the item, with higher numbers indicating an item that should be more preferred; Paragraph 0134 teaches recalculate the user preference vector based on the additional data and then recalculate the scores and update the recommendation, where a multivariate regression may be run against the drivers expressed by the movies using the idiosyncratic adjustment to determine the new user preference vector].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Cruz, by incorporating wherein the recommendation parameter comprises at least one of a recommending weight or a recommending frequency, and the updated recommendation parameter is used for subsequently selecting a multimedia item to be recommended to the user in the current multimedia usage scenario, as taught by Kimmerling [Paragraph 0050, 0134], because both applications are directed to generating recommendations to users based on preference data; employing the use of vectors in the calculations of user preferences improves the selection of candidates, improving thereby, the user’s experience (See Kimmerling Para [0121]).
Response to Arguments
The following is in response to arguments filed on July 13, 2026. Arguments have been carefully and respectfully considered.
Claim Rejections - 35 USC § 101
Applicant’s arguments have been fully and respectfully considered, but are not persuasive.
In regards to claim 1, Applicant argues that “Collecting real-time physiological measurement data from the wearable device’s physiological sensor during playback, and using that data to adjust a recommendation parameter of a specific candidate item for subsequent selection within the same current multimedia usage scenario, is performed by the wearable device’s physiological sensor and processor. These limitations require real-time physical operation of the wearable device’s physiological sensor and processor before and during playback, and cannot be performed in the human mind”.
In response to the preceding argument, Examiner respectfully points out that the “collecting” limitation is an additional limitation, and amounts to data-gathering steps, which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)), and recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d) (II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). The adjusting limitation as presently presented covers performance of the limitation in the mind, but for the recitation of generic computer components, a processor. This limitation can be performed in the human mind with the aid of pen and paper, and therefore is part of the abstract idea.
In regards to claim 1, Applicant argues that “if an abstract idea were present, the claims integrate it into a practical application”, and further, that “The continuous, real-time data collection and the adjustment are functionally integrated into a practical application, not separable”.
In response to the preceding argument, Examiner respectfully points out that, as described above, and further in the 101 Rejections, data collection amounts to data-gathering steps and is considered insignificant extra-solution activity. Furthermore, as presently presented in the claims, the “adjustment” limitation can be performed in the human mind, with or without the aid of pen and paper, and therefore, is part of the abstract idea.
In regards to claim 1, Applicant argues that “the additional elements amount to significantly more”, and further, that “the processor uses [[the physiological data]] to automatically adjust a recommendation parameter of a specific candidate multimedia item”.
In response to the preceding argument, it is noted that the features upon which applicant relies (i.e., “automatically adjust a recommendation parameter of a specific candidate multimedia item”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Furthermore, Examiner respectfully submits that adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection (Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014)). That is, even if the claim would require to perform the adjustment “automatically by the processor”, as described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception (See, e.g., Versata Development Group v. SAP America, 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015) (explaining that in order for a machine to add significantly more, it must “play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly”). The claims are directed to an abstract idea without significantly more, under the “Mental Processes” group of abstract ideas. 101 Rejections are hereby sustained.
Claim Rejections - 35 USC § 103
Applicant’s arguments have been carefully and respectfully considered, but are moot in view of new grounds of rejections, as necessitated by the amendments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/RAQUEL PEREZ-ARROYO/Primary Examiner, Art Unit 2169
