DETAILED ACTION
This office action is responsive to the Amendments/Request for reconsideration filed on 02/06/2026 after Non-Final filed 11/17/2025. The application contains claims 1-25, all examined and rejected.
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
It is acknowledged that claims 1, 4, 6, 7, 9, 12, 15, 17, 20 and 22 were amended. Claims 25 was newly added.
The objections to claim(s) 4, 12 and 20 are withdrawn in view of the amendments.
The rejections of claim(s) 4, 7, 8, 12, 15, 16, 20, 23 and 24 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite, is withdrawn in view of the amendments.
Response to Arguments
Applicant’s arguments with respect to the amended claim(s) 1, 9 and 17 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.
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, 2, 4, 5, 9, 10, 12, 13, 17, 18, 20, 21 and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pastor et al. (US 20190294690 A1) in view of Heitz et al. (US 20120290621 A1) and Bi. et al. “ATransformer-based Embedding Model for Personalized Product Search”; arXiv:2005.08936v1 [cs.IR] 18 May 2020.
Regarding claim 1, Pastor et al. (US 20190294690 A1) discloses:
a method for performing searches, the method comprising, by a server computing device: generating a query vector based at least in part on a query received from a client computing device, wherein the client computing device is associated with a user account, and the user account is associated with a user account vector, at least by (paragraph [0150] where the taste vector associated with the user identifier is the recited user account is associated with a user account vector, paragraph [0153] further describes receiving “suggestion characteristic data 232 of the playlist character suggestions 230 that have been selected by the user” and paragraph [0154] describes representing the suggestion characteristic data as the seed vector (e.g. query vector)
combining the query vector and the user account vector to establish a combined vector, at least by (paragraph [0155] “suggestion characteristic data 232 includes a user identifier or a playlist identifier, the seed vector can be a taste vector that represents the taste or preference of a user identified by the user identifier or the playlist identifier.” As such, the seed vector incorporates the query vector and user account vector)
generating an output vector based at least in part on the combined vector, at least by (paragraph [0154] the seed vector is the generated output vector based on the combined vector)
obtaining, based at least in part on the query, a plurality of item vectors, wherein each item vector of the plurality of item vectors corresponds to a respective item, at least by (paragraph [0156] “vectors for other media content items… corresponding vectors include vectors that represent different types of data elements”)
comparing the output vector to the plurality of item vectors to generate respective similarity scores, at least by (paragraph [0156-0158] “compare the seed vector with other corresponding vectors… determines vectors that are similar to the seed vector… similarity or comparison measurement can be used to compare two vectors”; where the similarity or comparison measurement is the generated respective similarity scores)
at least by (paragraph [0159] “determines media content items based on the similar vectors”
and causing the client computing device to display, in accordance with the at least by (paragraph [0159-0160] “Once one or more similar vectors are identified at the operation 438, at least some of the media content items associated with the similar vectors can be identified and determined as recommended media content items for the current playlist…update the current playlist with at least one of the recommended media content items.”)
As shown above, Pastor fails to specifically describes: (a) wherein the combined vector includes a portion of the query vector and a portion of the user account vector; … wherein the output vector is generated by processing the combined vector using a transformer-based large language model (LLM);
As shown above, Pastor fails to specifically describe: (b) ordering the plurality of item vectors, and displaying ordered items corresponding to the ordering of the item vectors
However, Bi teaches the above limitation (a) at least by (Sec. 3 TRANSFORMER-BASEDEMBEDDING MODEL (TEM), where, (Sec. 3.2) eq. (2) q defines the query embedding vector, (3.4) i sub (u) describes to user historical purchases embedding vectors as user profile vectors and further describes “feed the sequence (q,Iu) as the input to al-layer transformer encoder” and further describes “output vector of query q at the l-th layer,”
However, Heitz teaches the above limitation (b) at least by (paragraph [0062-0063] “Using the resulting vectors (embeddings) in the embedding space, for a given input, it is possible to rank possible outputs of interest such that the highest-ranked outputs are the best semantic match for the given input. For example, the following ranking functions, f.sub.i(x), identify, for a given seed track, a number of songs that are similar to the seed track, and a number of artists who are similar to the artist who performed the seed track… ranked list of desired results (e.g., a ranked list of audio tracks that have auditory features that are similar to an auditory feature of a seed track”)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Pastor with Bi to provide the ability to search for personalized content using transformer based personalization (Bi, Sec. 3.4) and to provide a ranked list of audio tracks based on similarity calculations provided by Heitz to improve the user’s ability to access to more relevant content by “improv[ing] the sequencing of the playlist”, (Heitz, 0046).
As per claim 2, claim 1 is incorporated and Pastor further describes:
wherein the query comprises text content, image content, audio content, video content, or some combination thereof, at least by (paragraph [0090-0091, 0105] describes text content as “user can describe the playlist with a playlist title”, “words or phrases”; paragraph [0106] describes audio content as “media content attribute description 240 can include item description information and acoustic attribute information… identifies one or more media content item” therefore describes some combination of text and audio content, that are included into the suggestion characteristic data that forms the seed vector).
As per claim 4, claim 1 is incorporated and Pastor further describes:
wherein the user account vector is generated based at least in part on: a first set item vectors that correspond to items marked as favorites under the user account, at least by (paragraph [0078] which describes description vectors that represent metadata representing “lists of favorite songs”)
a second set of item vectors that correspond to Decisions included in a library of items associated with the user account, at least by (paragraph [0150-0151] describes vectors that represent user preferences and tastes)
a third set of item vectors that correspond to items accessed by the user account within a first threshold period of time, paragraph [0150] describes a taste vector associated with user identifier that the represents the user’s “long term” tasted which describes a first threshold period of time.
and a fourth set of query vectors that correspond to queries provided in association with the user account within a second threshold period of time, at least by (paragraph [0150] “the taste vector can define the user's real-time listening taste.” where real-time describes a second threshold period of time)
As per claim 5, claim 1 is incorporated and Pastor further describes:
wherein combining the query vector and the user account vector to establish the combined vector comprises concatenating the query vector to the user account vector, or vice-versa, at least by (paragraph [0154-0155] which describe the seed vector as the query vector that includes characteristic data selected by the user as a request to create a new playlist, and a taste vector represents the taste or preference of a user identified by the user identifier, and that seed vector further incorporated the taste vector (e.g. combining/concatenating)
As per claim 25, claim 1 is incorporated and Pastor fails to describe:
wherein the output vector includes novel generative content as a result of processing the combined vector using the transformer-based LLM.
However, Bi teaches the above limitation (a) at least by (Sec. 3 TRANSFORMER-BASEDEMBEDDING MODEL (TEM), where, (Sec. 3.2) eq. (2) q defines the query embedding vector, (3.4) i sub (u) describes to user historical purchases embedding vectors as user profile vectors and further describes “feed the sequence (q,Iu) as the input to al-layer transformer encoder” and further describes “output vector of query q at the l-th layer,”)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Pastor with Bi to provide the ability to search for personalized content using transformer based personalization (Bi, Sec. 3.4).
Claim(s) 9, 10, 12 and 13 recite equivalent claim limitations as claim(s) 1, 2, 4 and 5 above, except that they set forth the claimed invention as a non-transitory computer readable storage medium; Claim(s) 17, 18, 20 and 21 recite equivalent claim limitations as claim(s) 1, 2, 4 and 5 above, except that they set forth the claimed invention as a server computing device; as such they are rejected for the same reasons as applied hereinabove.
Claim(s) 3, 6-8, 11, 14-16, 19 and 22-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pastor, Heitz and Bi further in view of Glesinger et al. (US 20240104305 A1).
As per claim 3, claim 1 is incorporated and Pastor further describes:
wherein the query vector is generated based at least in part on the query at least by (paragraph [0154] describes representing the suggestion characteristic data as the seed vector (e.g. query vector)
But Pastor and Heitz fails to specifically recite using a transformer-based large language model (LLM) to generate the vector
However, Glesinger teaches the above limitations at least by (paragraph [0168] “semantic chains are converted to numeric-based representations by a vector embedding process such as a process that applies trained neural networks (e.g., Large Language Models or LLMs))
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Pastor and Heitz with Glesinger to “search for subsets of the content that are most relevant to the one or more semantic chains” (e.g. combined vectors) through “application of statistical-based methods such as neural networks, including long short-term memory (LSTM) deep learning neural networks and/or associated variations of LSTM such as Gated Recurrent Units (GRUs), or transformer-based models”, (Glesinger, para. 0189); “improving the system's interpretations of content,” (Glesinger, para. 0199); and accuracy, “enabling increasingly complex and subtle inferences by the system,” (Glesinger, para. 0192).
As per claim 6, claim 1 is incorporated and Pastor further describes:
wherein, and the transformer-based LLM implements a set of fully connected layers and a set of input normalization layers,
But Pastor and Heitz fails to specifically recite
However, Glesinger teaches the above limitations at least by (paragraph [0168] “semantic chains are converted to numeric-based representations by a vector embedding process such as a process that applies trained neural networks (e.g., Large Language Models or LLMs)”; and paragraph [0189] “application of statistical-based methods such as neural networks, including long short-term memory (LSTM) deep learning neural networks and/or associated variations of LSTM such as Gated Recurrent Units (GRUs), or transformer-based models”)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Pastor and Heitz with Glesinger to “search for subsets of the content that are most relevant to the one or more semantic chains” (e.g. combined vectors) through “application of statistical-based methods such as neural networks, including long short-term memory (LSTM) deep learning neural networks and/or associated variations of LSTM such as Gated Recurrent Units (GRUs), or transformer-based models”, (Glesinger, para. 0189); “improving the system's interpretations of content,” (Glesinger, para. 0199); and accuracy, “enabling increasingly complex and subtle inferences by the system,” (Glesinger, para. 0192).
As per claim 7, claim 1 is incorporated and Pastor further describes:
wherein a given item vector of the plurality of item vectors is generated by: obtaining, at least by (paragraph [0077] “Acoustic metadata may also include spectral information such as melody, pitch, harmony, timbre, chroma, loudness, vocalness, or other possible features. Acoustic metadata may take the form of one or more vectors”)
obtaining, at least by (paragraph [0078] “Cultural metadata may be derived from expert opinion such as music reviews or classification of music into genres… Cultural metadata may take the form of one or more vectors”
and generating the item vector based at least in part on combining the first and second item vectors, at least by (paragraph [0080 and 0156] describes the vector(s) representing Acoustic metadata and Cultural metadata representing a particular content item, and in identifying a similarity to a seed vector, the vectors for each media content item are compared, as such vectors for the media content item, is the item vector describes that combines the items acoustic metadata vector and cultural metadata vector)
But Pastor and Heitz fails to specifically recite
However, Glesinger teaches the above limitations at least by (paragraph [0168] “semantic chains are converted to numeric-based representations by a vector embedding process such as a process that applies trained neural networks (e.g., Large Language Models or LLMs)”; and paragraph [0189] “application of statistical-based methods such as neural networks, including long short-term memory (LSTM) deep learning neural networks and/or associated variations of LSTM such as Gated Recurrent Units (GRUs), or transformer-based models”)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Pastor and Heitz with Glesinger to “search for subsets of the content that are most relevant to the one or more semantic chains” (e.g. combined vectors) through “application of statistical-based methods such as neural networks, including long short-term memory (LSTM) deep learning neural networks and/or associated variations of LSTM such as Gated Recurrent Units (GRUs), or transformer-based models”, (Glesinger, para. 0189); “improving the system's interpretations of content,” (Glesinger, para. 0199); and accuracy, “enabling increasingly complex and subtle inferences by the system,” (Glesinger, para. 0192).
As per claim 8, claim 7 is incorporated and Pastor further describes:
wherein, when the corresponding respective item comprises an audio file:the metadata comprises the following song properties: album, artist, title, track number, genre, year, duration, bitrate, sample rate, channels, composer, comment, copyright, encoder, language, publisher, original artist, album artist, disc number, lyrics, mood, tempo, key, ISRC (International Standard Recording Code),recording date, release date, label, BPM (beats per minute), performer, conductor, compilation, part of a set, podcast, podcast URL, podcast ID, podcast feed, episode number, episode ID, episode URL, cover art, custom tags, or some combination thereof, at least by (paragraph [0079] “ Explicit metadata may include album and song titles, artist and composer names, other credits, album cover art, publisher name and product number, and other information”
and the data content comprises the following song characteristics: melody, harmony, rhythm, tempo, meter, lyrics, chorus, verse, bridge, dynamics, instrumentation, arrangement, key, harmonic progression, timbre, form, texture, style, emotion, production, hook, groove, transition, or some combination thereof, at least by (paragraph [0077] “Acoustic metadata may include temporal information such as tempo, rhythm, beats, downbeats, tatums, patterns, sections, or other structures. Acoustic metadata may also include spectral information such as melody, pitch, harmony, timbre, chroma, loudness, vocalness, or other possible features”)
Claim(s) 11 and 14-16, recite equivalent claim limitations as claim(s) 3 and 6-8, above, except that they set forth the claimed invention as a non-transitory computer readable storage medium; Claim(s) 19 and 22-24, recite equivalent claim limitations as claim(s) 3 and 6-8, above, except that they set forth the claimed invention as a server computing device; as such they are rejected for the same reasons as applied hereinabove.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Weston et. al. (US 20140074269 A1). See Abstract, Para. 0016, 0035, 0047, 0054, 0068.
Whitman (US 20080256106 A1): See, Abstract, Para. 0028, 0042-0047, 0052-0053, 0064.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS TRUONG whose telephone number is (571)270-3157. The examiner can normally be reached Monday - Friday 8:30 am - 5:30 pm PT.
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/DENNIS TRUONG/Primary Examiner, Art Unit 2152 05/12/2026
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