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 . This Office action is based on the communications filed April 1, 2026. Claims 1 – 20 are currently pending and considered below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 29, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed April 1, 2026 in regards to claims 1 – 20 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 4, 8, 9, 11 – 14, and 18 of copending Application No. 18/745,486 in view of Ellis et al. (US 2005/0020223 A1), hereinafter Ellis, and Kuipers et al. (US 2018/0101614 A1), hereinafter Kuipers have been fully considered but they are not persuasive. Applicant argues “the independent claims recite "identify one or more keywords associated with a portion of the first audio content based on the detected user listening activity", and "generate a recommendation for second audio content based on the identified one or more keywords associated with the first audio content". The claims of the U.S. Patent Application No. 18/745,486 do not recite "identify one or more keywords associated with the portion of the first audio content based on the detected user listening activity" or "generate a recommendation for second audio content based on the identified one or more keywords associated with the first audio content" as recited in the present application's independent claims,” pages 6 – 7 of the remarks. However, the argued features pertaining to identifying one or more keywords were previously rendered obvious in view of Ellis. Since Applicant has not presented arguments in view of Ellis, the rejection is maintained below in the now revised, as necessitated by amendment, provisional nonstatutory double patenting rejection.
Applicant’s arguments with respect to claim(s) 1 and 12 under 35 U.S.C 102(a)(1) and 35 U.S.C 102(a)(2) as being anticipated by Ellis et al. (US 2005/0020223 A1), have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 – 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 4, 8, 9, 11 – 14, and 18 of copending Application No. 18/745,486 in view of Apokatanidis et al. (US 2018/0314959 A1), hereinafter Apokatanidis, Ellis et al. (US 2005/0020223 A1), hereinafter Ellis, and Kuipers et al. (US 2018/0101614 A1), hereinafter Kuipers . Although the claims at issue are not identical, they are not patentably distinct from each other because while obvious variations in wording are present claims 1 – 4, 8, 9, 11 – 14, and 18 of copending Application No. 18/745,486 are narrower in scope and therefore anticipate all of the claimed limitations of claims 1, 4 – 6, 8, 10, 12, 15 – 17, and 19 the instant application, respectively, except for those pertaining to “wherein the user listening activity includes mood or emotions determined from biometric information of the user while the user listens to the first audio content,” and “identify one or more keywords” in lieu of “identify program information” and “update the machine learning model” in lieu of “update the recommendation”, and. However, Apokatanidis discloses wherein the user listening activity includes mood or emotions determined from biometric information of the user while the user listens to the first audio content as presented in the rejection below. It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to incorporate the aforementioned features of Apokatanidis in the invention of copending Application No. 18/745,486 thereby meeting all of the required claim limitations. One being motivated to incorporate the mood or emotions determined from biometric information as taught by Apokatanidis since as explained by Apokatanidis, “Liking or disliking a song is an expression of human mind. Since mood is an emotional state of human mind, it has an immense role in deciding when a person likes or dislikes something. The primary objective of a music recommendation system is to predict songs that a listener would like and hence it would be beneficial for a recommendation system to consider the mood of the listener when recommending songs,” Apokatanidis [0003], with the further advantage of allowing for the ability to “take into account the changing music preferences of users from moment to moment as they engage in different activities or enter different environments,” Apokatanidis [0003]. Ellis discloses the aforementioned keywords as detailed in the rejection below. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the features of Ellis in the invention of copending Application No. 18/745,486 thereby meeting all of the required claim limitations. One being motivated to incorporate the keywords of Ellis since it allows for the benefit of “providing the most likely correct match,” Ellis [0188]. Further, Kuipers discloses the aforementioned machine learning as detailed in the rejection below. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the features of Kuipers in the invention of copending Application No. 18/745,486 thereby meeting all of the required claim limitations. One being motivated to incorporate the machine learning of Kuipers since it allows for the benefit of allowing for the advantage of “machine learning techniques to determine valuable keywords or phrases such as subject matter, names, events, and so forth. The keywords and/or phrases are used to create a search query that is used to search one or more social media platforms,” Kuipers [0015] - [0016] so that the invention “can curate social media content that is relevant (to the topics extracted from the news content and/or the personal preferences of the end user),” Kuipers [0018]. The additional features of claims 2, 3, 7, 9, 11, 13, 14, 18, and 20 are also rendered obvious for the reasons presented below.
This is a provisional nonstatutory double patenting rejection.
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.
Claim(s) 1, 3 – 8, 12, and 14 – 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ellis et al. (US 2005/0020223 A1), hereinafter Ellis, in view of Apokatanidis et al. (US 2018/0314959 A1), hereinafter Apokatanidis.
Claim 1: Ellis discloses a system for audio content and radio programming recommendations, the system comprising (see at least, “The system may make radio station recommendations,” Ellis Abstract):
one or more memories; and one or more processors in communication with the one or more memories, the one or more processors configured to (see at least, “FIG. 2 shows an illustrative block diagram of controller 145 of FIG. 1. This subsystem may include processor 210, which may, for example be a microprocessor or other type of processing unit. Controller 145 may include local memory 220. Memory 220 may be used for storing software 225, which may run in processor 210. Memory 220 may also be used to store data 227, which may also be used by processor 210. Controller 145 may also include additional control circuitry 230, which may be used, for example, for communication with other subsystems in ERS 10 (FIG. 1) (ERS), and to process data sent to and received from the other subsystems,” Ellis [0146], “FIG. 21 shows illustrative process 2100 for providing radio station recommendations to a user. All steps in this process are optional, and may be performed in any suitable order. For example, a user may be traveling in an unfamiliar region, and may be listening to the ERS in a personal or rental car. The user may wish to quickly and easily find stations that match her preferences,” Ellis [0219]):
determine one or more radio stations receivable by a user device (see at least, “In step 2120, the system may acquire information about available radio stations. This step is explained in more detail below, in conjunction with FIG. 23,” Ellis [0220]);
detect a user listening activity occurring while a user listens to a portion of a first audio content broadcast by a first radio station (see at least, “In step 2110 of process 2100, the system may acquire information about the user's preferences. This step is explained in more detail in FIG. 22,” Ellis [0220], “FIG. 22 shows more detail of step 2110 of FIG. 21, acquiring user information. All steps in this sub-process are optional, and may be performed in any suitable order. In step 2210, the user may be allowed to enter user information. For example, a screen such as screen 2000 of FIG. 20 may be used by the user to specify likes and dislikes. In step 2220, the system may determine user information automatically by monitoring the content the user listens to. For example, the system may add points to a specific song, artist, genre, or other type of item or grouping, based on the user listening to an entire item. The system may assign additional points if the user rewinds or skips back to the start of an item to hear it in its entirety or to hear it again. The system may subtract points if the user skips over an item,” Ellis [0221], “The user may specify a like or dislike, or a level of like or dislike, for a specific group of content. The system may also monitor the content listened to by the user, and automatically determine the groups liked and disliked by the user. In step 1830, the system may automatically recognize an item in the group when it has been broadcast. For example, the system may recognize a specific item as described above, using an audio signature, unique identifier, information broadcast with the item in the radio signal, program schedule, or other means. The system may then determine the groups in which the recognized item is a member. Alternatively, the system may directly recognize the group itself, for example based on a published schedule or on information broadcast with the radio signal,” Ellis [0212], “The system may also keep track of user radio preferences, and may have the ability to recognize specific content items, such as songs, that the user may like. In step 1350, the system may monitor favorite stations (e.g., preset stations and recently tuned stations) for content of interest, i.e., content that matches the user's preferences. This may include step 1352 in which the user is allowed to specify content preferences. For example, a user may specify specific songs, artists, radio shows, types of content (e.g., traffic or weather reports), categories of music, or other content, and specify a level of like or dislike for that content. For example, the user may press LIKE button 638 or DISLIKE button 640 while a song is playing to indicate a preference for or against the song. In an embodiment with a personal computer, the system may present a screen such as screen 2000 of FIG. 20, discussed below, to enable the user to specify content preferences. User preferences may be loaded using communications device 155. The system may also determine user preferences automatically by monitoring what content the user listens to,” Ellis [0185]);
identify one or more keywords associated with the portion of the first audio content based on the detected user listening activity (see at least, “To create a signature for a song, the system may take a number of signature/mask pairs at various offsets from the start of the song. One of the signature words may be considered the "keyword," and may be selected based on providing the most likely correct match. The system may store the offset of the keyword from the start of the audio segment. Additional signature/mask pairs may be stored, along with their offsets relative to the keyword. Keywords and other signature words may be selected based on run length (how long the signature word is constant), how quickly the signature values change at the end of the run, number of mask bits set, similarity to other signatures, avoiding the start and end of the segment, and other factors,” Ellis [0188], “To recognize incoming audio, the system may compare signature words from the incoming digitized audio against the keyword for all segments of interest. When a keyword match is found, the system may then compare the other signature words from the song of interest with the signature words in memory corresponding to the incoming audio signal, at the appropriate signature offsets from the matching keyword. If an acceptable level of matching of the complete signature is found, then a match is reported,” Ellis [0189]);
generate a recommendation for second audio content based on the identified one or more keywords associated with the first audio content (see at least, “In step 2130, the system may select one or more recommended stations from the set of available stations, based on the user preferences. This may be done, for example, correlating the play list from each station to the list of songs, artists, and genres selected as likes and dislikes by the user, and choosing the closest match or matches. For example, a score may be created for each station by adding a value for each match to a like and subtracting a value for each match to a dislike, with each value weighted by the level of like or dislike specified by the user. A higher value may be added for a music genre match, a lower value for an artist match, and a still lower value may be added for a song match,” Ellis [0220], “After the system recognizes an item of interest, the user may be notified in step 1360. The notification may include the name or other information about the content, or it may just indicate that something of interest has been found. For example, an audio notification may be sent to audio output 130. Alternatively or in addition, a message may be displayed on display device 150. The notification may also indicate that the user may quickly switch to the content of interest, for example by pressing a button, as in step 1362,” Ellis [0192]); and
output the generated recommendation to the user device (see at least, “In step 2140, the system may present one or more of the recommended radio stations to the user. This step is explained in more detail below, in conjunction with FIG. 24,” Ellis [0220], “After the system recognizes an item of interest, it may automatically switch the audio output to the station that broadcast the content in step 1370,” Ellis [0193]).
Ellis does not disclose wherein the user listening activity includes mood or emotions determined from biometric information of the user while the user listens to the first audio content. However, Apokatanidis discloses a similar music selection system and method for recommending music (see at least, “The present invention relates to a system for recommending music tracks to a group of users, and specifically to a system for analyzing user mood and ambient environment of a group of users, and recommending music tracks based on the analysis,” Apokatanidis [0001]) and further discloses to detect a user listening activity occurring while a user listens to a portion of a first audio content broadcast by a first radio station , wherein the user listening activity includes mood or emotions determined from biometric information of the user while the user listens to the first audio content (see at least, “Alternatively, and/or additionally, various biometric sensors may be placed around the user(s) in a geographical location, such as for example, one or more sensors may be located, for example, in or on the dashboard, visor, rearview mirror, window, radio and/or at a user's feet in a vehicle,” Apokatanidis [0031], “In various embodiments, a music database 103 is a collection of music tracks accessible by the player device 120. In an embodiment, the music database 103 may be in communication with, and may receive music tracks from various music databases (or content sources), such as, without limitation, local storage (e.g., various user devices), music streaming engines (e.g., PANDORA, SPOTIFY, etc.), radio stations, or the like,” Apokatanidis [0036], “Alternatively, the interpretation module 302 may determine that a listener has a negative opinion of a music track being played when an analysis of the biometric data corresponding to the listener indicates that the listener has a sad and/or disgusted expression, and the learned model maps sadness to a negative emotion, opinion, and/or mood. As another example, the interpretation module 302 may determine that a listener has a highly positive opinion of a music track being played when it analyzes the listeners' speech and/or conversation patterns and notes that the listeners are speaking excitedly, expressing a liking for the music track, or the like,” Apokatanidis [0051]); Apokatanidis also discloses to identify one or more keywords associated with the first audio content based on the detected user listening activity (see at least, “A positive, negative and/or neutral inference may be applied to sample conversations between one or more users in the geographical location, and the mood and/or or opinion of the users may be inferred from the conversations (and/or words used). As used herein, the term "conversation" is defined as a verbal or textual exchange of information between two or more parties. For example, a speech analysis system may analyze the textual content, speed, volume, timing, etc., of speech input and text, which can be used as feedback for the interpretation module for determining opinion and/or mood of the user(s ). All the conversations may be analyzed to determine an overall mood of the users in a geographical location. Alternatively, and/or additionally, a subset of conversations found to be related to the current music track and/or play list may be analyzed to determine the user(s) opinion regarding the current music track. For example, utterance of certain keywords such as "terrific", "cool", "enjoy", etc. in relation to the music being played may be interpreted as a positive opinion,” Apokatanidis [0060]); generate a recommendation for a second audio content based on the identified one or more keywords associated with the first audio content; and output the generated recommendation to the user device (see at least, “Referring back to FIG. 3, a recommendation module 303 may receive, from the interpretation module, information relating to the opinion of one or more users in a geographical location regarding a music track being played, current mood of the one or more users in the geographical location, and current scene at the geographical location, and analyze the received information to provide a recommendation for the next music track to be played by the player device and/or whether or not the current music track should be removed from a playlist. The recommendation module may also take into account the available music tracks (including their metadata and classification) as well as the user profiles of the one or more users to make the recommendation. In some embodiments, the recommendation module 303 may use the learned models generated by the training module 301 for correlating a value or range of values calculated based on one or more sensed environmental factors, biometric factors, and user profiles to a value or a range of values for metadata attributed to the available music tracks. In other embodiments, the recommendation module 303 may use the learned models generated by the training module 301 for mapping information relating to the opinion of one or more users in a geographical location regarding a music track being played, current mood of the one or more users in the geographical location, current scene at the geographical location, and/or a value or range of values calculated based on a combination thereof, to values or ranges of values for metadata attributed to the available music tracks. For example, the generated learned models may comprise one or more rules for including (or excluding) content items from a subset of content items from which the recommendation module selects items for output on the player device,” Apokatanidis [0063]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to augment the like/dislike feature of Ellis by taking into consideration the aforementioned mood or emotions determined from biometric information as taught by Apokatanidis since as explained by Apokatanidis, “Liking or disliking a song is an expression of human mind. Since mood is an emotional state of human mind, it has an immense role in deciding when a person likes or dislikes something. The primary objective of a music recommendation system is to predict songs that a listener would like and hence it would be beneficial for a recommendation system to consider the mood of the listener when recommending songs,” Apokatanidis [0003], with the further advantage of allowing for the ability to “take into account the changing music preferences of users from moment to moment as they engage in different activities or enter different environments,” Apokatanidis [0003].
Claim 3: Ellis and Apokatanidis disclose the system of claim 2, wherein the second audio content comprises a podcast, talk show, news program, or visual content (see at least, “As another example, on a news and talk station, the presence of a short period of music may be considered a cue,” Ellis “[0329], “For example, it may identify start and end times of specific individual songs, shows, or news items,” Ellis [0398]).
Claim 4: Ellis and Apokatanidis disclose the system of claim 1, wherein the user listening activity includes tuning the user device to a first broadcast channel for the first radio station or remaining on the first broadcast channel for a threshold time period (see at least, “FIG. 54 shows a flowchart of process 5405 for changing to a station that is not currently being monitored, i.e., no tuner is tuned to the user-selected station. The steps in this flowchart are each optional and may be performed in any suitable order. This process may be executed when the user requests a tune to a new station, e.g., by pressing a tune key, by turning a tuning knob, by speaking a tune command, or any other suitable method,” Ellis [0372], “In step 2220, the system may determine user information automatically by monitoring the content the user listens to. For example, the system may add points to a specific song, artist, genre, or other type of item or grouping, based on the user listening to an entire item,” Ellis [0221]).
Claim 5: Ellis and Apokatanidis disclose the system of claim 1, wherein the one or more processors are further configured to retrieve metadata of the first audio content from a database and transmit the metadata to the user device, wherein the metadata includes information related to a subject, host, or date of broadcast (see at least, “In check 4750, the thread may check to see if there is related data available for this station. For example, signal-processing unit 4304 (FIG. 43) associated with this station may be polled to see if new data is available. The related data may be, for example, Radio Data Service (RDS) or Radio Broadcast Data System (RBDS) data. This data may include, for example, program type (e.g., rock, jazz, news, etc.), call sign, emergency alerts, station database information (e.g., information about this and other stations available in this area), song information such as artist and song title, supplemental information about advertisements (e.g., phone numbers, store hours, location, etc.), current time, traffic information, etc. Some of the data may not be related to the audio, such as the current time (which may be used to update a clock in the ERS). However, most of the data is likely to be related to the received audio content,” Ellis [0331], “In order to jump to the start or end of an item of content, or to jump to a specific type of content, the ERS has to find the start and end of the content and/or the type of content. The ERS may use different techniques to find the desired content type and/or location, in step 5650. In one example, the system may use related data that may be broadcast with the radio signal or may have previously been stored when the signal was broadcast, in sub-step 5651. For example, the system may store RDS or RBDS information that is received with the audio signal. The RDS or RBDS data may change at certain content boundaries. Certain types of content, such as traffic reports, may be tagged as well. If the listener requests a jump to a traffic report, the ERS may search all stored related data for traffic report tags. The system may then jump to the start of the most recently broadcast traffic report (assuming that the most recent traffic report is of the most interest). An additional request to jump to a traffic report may then jump to the start of the next most recent traffic report, or to a new traffic report if one has been broadcast since the most recent jump,” Ellis [0396], “In sub-step 5653, the ERS may use schedule information to identify specific content items and their start and end times. The schedule may be downloaded, for example, over a communications device, such as communications device 4340 of FIG. 43. The schedule may indicate specific items, or it may indicate types of content. The schedule may be to any suitable level of granularity. For example, it may identify start and end times of specific individual songs, shows, or news items,” Ellis [0398]).
Claim 6: Ellis and Apokatanidis disclose the system of claim 1, wherein the one or more processors are further configured to: determine preference data or historical data for the user based on one or more previous listening activities for the user; and generate the recommendation based on the determined user’s preference data or historical data (see at least, “Other aspects of this invention relate to portability and configurability. This ERS may be used at home, at the office, in the shower, on the go, in the car, on the boat, or in any other environment. It may be used in multiple environments. And user preferences and profiles may follow the listener in any of these environments, from radio to radio,” Ellis [0024], “Listener preferences and profiles may follow the listener wherever he goes. Listener actions may be reported to a radio rating service 4222, to collect information on what radio content and features are most popular,” Ellis [0139], “In step 2230, the system may download user information. For example, the user may have entered preferences in a web site, and the system may access that web site using communications device 155 (FIG. 1) to obtain that information. The system may also download user information from another ERS that the user may have previously used. The system may alternatively load the user information from a portable memory device 440 (FIG. 4), such as a flash memory card, a smart card, a PDA, or the like, in step 2240. The user may have loaded the information into the portable memory device 440 from another ERS. Portable memory device 440 may be used to hold a user profile, and may be loaded into multiple ERSs as they are used by the user, so that the user preferences may be available in each location,” Ellis [0221]).
Claim 7: Ellis and Apokatanidis disclose the system of claim 1, wherein generating the recommendation comprises generating a recommendation for the second audio content based on information related to a host associated with the first audio content (see at least, “Any other suitable type of search may be selected. This may include, for example, news, music, jazz, weather, baseball, talk, NPR, Howard Stern, etc.,” Ellis [0430], “FIG. 25 shows illustrative data structure 2500 for storing user preferences. It may include field 2510 for storing information about music formats the user likes. It may include field 2520 for storing information about music formats the user does not like. It may include field 2530 for storing information about talk formats the user likes. It may include field 2540 for storing information about talk formats the user does not like. It may include field 2550 for storing information about performing artists the user likes. It may include field 2560 for storing information about performing artists the user does not like,” Ellis [0230]).
Claim 8: Ellis and Apokatanidis disclose the system of claim 1, wherein the one or more processors are further configured to receive a request from the user for a customized recommendation using the user device (see at least, “In step 6315, the user may be allowed to initiate a search for a specific item of content. This could be, for example, a specific song. If desired, the user may be allowed to search for multiple items of content, in which case the ERS may search for any content that matches any of the requested items,” Ellis [0431], “In step 6330 the ERS may use signature matching to search for a requested item or list of items. In step 6335 the ERS may use previously downloaded schedule information to identify requested content or types of content,” Ellis [0433], “FIG. 73 shows a flowchart of illustrative process 7305 to automatically set the favorite stations in the ERS. This process may be executed in response to a user command to "set all",” Ellis [0497], “In loop 7315, the ERS processes all available radio stations. In step 7320, the ERS searches through all available radio stations. One or more of the tuners in the ERS is used to scan through the stations that may be received in the current location. This may be done in the background while the user listens to other content, or it may be done in the foreground so that the user may hear each station in turn. It may be done using a single tuner, or using multiple tuners to find desirable stations more quickly. In step 7325, each of the stations is monitored for a period of time. This may be a predetermined period of time, until suitable related data is received, until a certain number of signature matches are detected, or any other suitable criteria or combination of criteria. In step 7330, some of the monitored stations may be set as favorites. This may be based on the criteria such as matching between related data and the user's preferences, matching of item signatures for liked content, matching of signatures for disliked content, and any other suitable criteria,” Ellis [0499]).
Claims 12 and 14 – 19 are directed to a method for audio content and radio programming recommendations, the method substantially similar in scope to that performed by the system of claims 1 and 3 – 8, respectively and therefore are rejected for the same reasons.
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.
Claim(s) 2, 9, 11, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ellis and Apokatanidis in view of Kuipers et al. (US 2018/0101614 A1), hereinafter Kuipers.
Claim 2: Ellis and Apokatanidis disclose the system of claim 1, but do not disclose wherein the one or more processors are further configured to identify one or more uniform resource locators (URLs) to access the second audio content. Ellis does suggest, “This system may tune in one or more radio sources, such as broadcast radio, satellite radio, Internet radio, shortwave radio, and radio scanner,” Ellis [0025], “The user may also be allowed to scan across multiple radio sources (e.g., both Internet and radio frequency broadcasts),” Ellis [0274], “Alternatively, it may include information on the source of the content (e.g., an Internet Protocol (IP) address of the computer from which it was received.),” Ellis [0302]. Kuipers discloses a similar processing system where “Machine learning-based data aggregation using social media content is described herein. An example method includes receiving news content from a plurality of online sources, parsing the news content to determine keywords or phrases related to topics of interest, creating a search query from the keywords or phrases, searching one or more social networks for social media content that matches the keywords or phrases in the search query, processing the social media content by at least one of filtering and ranking, and providing the processed social media content to an individual,” Kuipers Abstract. Kuipers further discloses wherein the one or more processors are further configured to identify one or more t uniform resource locators (URLs) to access the second audio content (see at least, “a podcast or radio program can be processed to extract important keywords or phrases,” Kuipers [0031], “articles are fetched from the Internet using an OEmbed module that is a component part of the processing system 102. The OEmbed module ensures that complete article contents are fetched from a corresponding website. The OEmbed module uses JavaScript selectors that specify where the content is located on the website. The output of the OEmbed module is a document which contains a title, a description, the article content and the canonical URL. These fields are used for extracting features from an article or other document,” Kuipers [0026]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the aforementioned features of Kuipers in the invention of Ellis and Apokatanidis thereby allowing for the advantage of finding matches to “Services that support queries for items that contain a URL,” Kuipers [0039] such as audio content on the Internet as suggested by Ellis.
Claim 9: Ellis and Apokatanidis disclose the system of claim 1, but do not disclose wherein the one or more keywords associated with the first audio content is identified using a machine learning model. However, Kuipers discloses a similar processing system where “Machine learning-based data aggregation using social media content is described herein. An example method includes receiving news content from a plurality of online sources, parsing the news content to determine keywords or phrases related to topics of interest, creating a search query from the keywords or phrases, searching one or more social networks for social media content that matches the keywords or phrases in the search query, processing the social media content by at least one of filtering and ranking, and providing the processed social media content to an individual,” Kuipers Abstract. Kuipers further discloses wherein the one or more keywords associated with first audio content is identified using a machine learning model (see at least, “a podcast or radio program can be processed to extract important keywords or phrases,” Kuipers [0031], “In some embodiments, the text fields are processed by separate Named Entity Recognition (NER) systems that extract named entities from the text. Different systems are used to recognize named entities, pattern-based, Natural Language Processing (NLP)-based and Machine Learning-based–just to name a few,” Kuipers [0034]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the aforementioned features of Kuipers in the invention of Ellis and Apokatanidis thereby allowing for the advantage of “machine learning techniques to determine valuable keywords or phrases such as subject matter, names, events, and so forth. The keywords and/or phrases are used to create a search query that is used to search one or more social media platforms,” Kuipers [0015] - [0016] so that the invention “can curate social media content that is relevant (to the topics extracted from the news content and/or the personal preferences of the end user),” Kuipers [0018].
Claim 11: Ellis and Apokatanidis disclose the system of claim 1, wherein the one or more keywords associated with the first audio content broadcast by the first radio station are identified based on keyword identifications derived from post-processing a previously recorded broadcast of the first audio content (see at least, “Even if recorded content is not saved as a preset, the system may allow the user to replay the recorded content at a later time in step 1040. For example, the system may present a list of previously recorded radio content items. If the user selects an item from the list and presses PLAY button 634, the system may begin playing the content item as if it were currently being broadcast. Controls such as pause, skip-back, skip-forward, etc., may be allowed while the content item is being played. Upon reaching the end of the recorded content item, the system may stop, may automatically begin playing the content item from the beginning, may return to the most recent radio station, or another appropriate action,” Ellis [0173], “In step 1014, the user may be allowed to indicate any point during a radio item of interest, for example by pressing RECORD button 642. The system may automatically determine the start and end of the content of interest using audio cues. Audio cues may be algorithmically determined points in the audio content, based on, for example, silence in the audio, changed frequency content of the audio, changed power content of the audio, and changed rhythmic content of the audio, combined with the length of the audio segment. Refer to discussion of FIG. 15 below for more details on audio cues. Cues may have already occurred and may be extracted from radio data stored in memory 120. Cues may also occur at some time after the user indicates the item to be recorded. The system may also use a combination of steps 1012 and 1014, allowing the user to specify one end point of the content to be recorded and determining the other automatically,” Ellis [0176], “To create a signature for a song, the system may take a number of signature/mask pairs at various offsets from the start of the song. One of the signature words may be considered the "keyword," and may be selected based on providing the most likely correct match,” Ellis [0188]) but does not disclose semantic keyword identifications. However, Kuipers discloses a similar processing system where “Machine learning-based data aggregation using social media content is described herein. An example method includes receiving news content from a plurality of online sources, parsing the news content to determine keywords or phrases related to topics of interest, creating a search query from the keywords or phrases, searching one or more social networks for social media content that matches the keywords or phrases in the search query, processing the social media content by at least one of filtering and ranking, and providing the processed social media content to an individual,” Kuipers Abstract. Kuipers further discloses semantic keyword identifications (see at least, “a podcast or radio program can be processed to extract important keywords or phrases,” Kuipers [0031], “While examples disclosed herein contemplate the processing of text from written/electronic textual content such as HTML pages or PDF content, the processing system 102 can also be utilized to process audio news content as well as any other news content from which natural language processing techniques can be used to extract content. That is, the processing system 102 can utilize both speech-to-text as well as text-to-speech functionality,” Kuipers [0022], “According to some embodiments the processing system evaluates the news content to extract keywords and/or phrases. The processing system 102 can also refine
the extracted key words or phrases by removing or ignoring textual content that is not indicative of topics for information that is relevant to the subject matter of the news article. For example, parts of speech such as indefinite articles and less relevant information are determined and ignored. That is, the processing system 102 can examine the actual content for words that are repeated frequently, phrases that indicate important subject matter, and the like,” Kuipers [0031], “In some embodiments, the text fields are processed by separate Named Entity Recognition (NER) systems that extract named entities from the text. Different systems are used to recognize named entities, pattern-based, Natural Language Processing (NLP)-based and Machine Learning-based–just to name a few,” Kuipers [0034]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the aforementioned features of Kuipers in the invention of Ellis and Apokatanidis thereby allowing for the advantage of “machine learning techniques to determine valuable keywords or phrases such as subject matter, names, events, and so forth. The keywords and/or phrases are used to create a search query that is used to search one or more social media platforms,” Kuipers [0015] - [0016] so that the invention “can curate social media content that is relevant (to the topics extracted from the news content and/or the personal preferences of the end user),” Kuipers [0018].
Claim 13 is substantially similar in scope to claim 2 and therefore is rejected for the same reasons.
Claim 20 is substantially similar in scope to claim 9 and therefore is rejected for the same reasons.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ellis, Apokatanidis, and Kuipers in view of Faenger et al. (US 2011/0035031 A1), hereinafter Faenger.
Claim 10: Ellis, Apokatanidis, and Kuipers discloses the system of claim 9, but do not disclose wherein the one or more processors are further configured to receive feedback on the generated recommendation and update the machine learning model based on the received feedback using the user device. However, Faenger discloses a similar personalized entertainment system wherein the one or more processors are further configured to receive feedback on the generated recommendation and update the machine learning model based on the received feedback using the user device (see at least, “A third approach uses ratings and requires active feedback from the user by letting him specify how much he likes or dislikes a specific song. Based on the feedback, and in conjunction with additional information such as the song characteristics, it is possible to determine if there are similar songs that the user might like,” Faenger [0008], “The state of the art music recommendation approaches require a base knowledge about the user and the music he likes. Otherwise, an entertainment system is unable to offer help to the user in choosing the right music. This learning and feedback period is a critical point for all known personalized entertainment systems. A system is unable to give the user useful recommendations right from the very first moment of usage because the system needs time to learn about the music preferences of the user. This process usually requires tracking of listening behaviors over a period of time, active user feedback or a combination of both,” Faenger [0010], “The entertainment system may also use music classification technologies such as described in "Aggregate Features and AdaBoost for Music Classification" by Bergstra, Machine Learning, Vol. 65, No. 2-3, pp. 473-484, 2006, and "A model-based approach to constructing music similarity functions" by West, EURASIP Journal on Advances in Signal Process, 2007, each of which are hereby incorporated herein by reference in their entireties. The entertainment system may also use music classification technologies to generate a set of characteristics for each song or other item,” Faenger [0032]). It would have been obvious to one of ordinary skill in the at before the effective filing date of the claimed invention to incorporate the aforementioned feedback features of Faenger in the invention of Ellis, Apokatanidis, and Kuipers thereby allowing for the advantage of using “such user-provided feedback information to adapt or modify the previously-learned user's musical preference profile accordingly,” Faenger [0043], while taking “into account the musical preferences of the user without the user having to manually train the entertainment system about his specific preferences (radio stations, preferred music, etc.). That is, the system automatically, or at least substantially automatically, learns the musical preferences of the user,” Faenger [0053].
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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/JOSEPH SAUNDERS JR/Primary Examiner, Art Unit 2692
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