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 .
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 rejected on the grounds of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12236160 although the claims at issue are not identical, they are not patentably distinct from each other because the patent claim 1 and the application claim 1 claim the same system with obvious variations.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-15,19, is/are rejected under 35 U.S.C. 102a1 as being anticipated by Blacker et al (US 20160379274 A1).
As per claim 1, Blacker discloses a method comprising:
accessing, by a computer system, a first graph of an audio signal, wherein the first graph is a graph of audio parameters relative to time (para 21 emotional graph);
accessing, by the computer system, a second graph of the audio signal, wherein the second graph is a signal graph of the audio parameters relative to beat (energy level graph over time para 21, also the tempo parameter as used by the processor per para 21 is a graph over time as related to the other graphs cited in para 28);
accessing, by the computer system, playback music content (the audio clip para 45); and
modifying, by the computer system, the audio parameters in the playback music content to generate new music content, wherein the audio parameters are modified based on a combination of the first graph and the second graph (the combinations of any of those graphs and the other graphs of para 45 into the neural network per para 46, which uses the graphs, where a neural network by nature learns which is a modification of the parameters,) (new music content is generated per para 46: automatically selecting an audio clip asset of the plurality of audio clip assets based on the advertisement campaign objectives. The automatically selecting method may use artificial intelligence and machine learning to optimize the selecting) (also the merging processing per para 8 can also read on the parameter modification as applied to the streamed audio and also the selected/generated additional audio clip/advertisement).
As per claim 2, the method of claim 1, wherein the first graph and the second graph are accessed from a cloud-based server (para 16 servers in the cloud).
As per claim 3, the method of claim 1, wherein the audio parameters in the first graph and the second graph are defined by nodes in the graphs that determine changes in properties of the audio signal (the nodes are the various parameters at various points in time noting they are processed digitally, as such they must be processed as a collection of nodes by the processor, noting the parameters are shown over time, as such they reflect changes in the properties over time).
As per claim 4, the method of claim 3, wherein modifying the audio parameters in the playback music content to generate the new music content includes:determining a first node in the first graph corresponding to an audio signal in the playback music content; determining a second node in the second graph that corresponds to the first node (the parameters as read by the processor to perform the functions as cited above at various points in time is done through determining nodes);
determining one or more specified audio parameters based on the second node (the parameters as read by the processor to perform the functions as cited above at various points in time is done through determining nodes); and
modifying one or more properties of an audio signal in the playback music content by modifying the specified audio parameters (via the merging process per para 25).
As per claim 5, the method of claim 4, further comprising: determining one or more additional specified audio parameters based on the first node (via the neural network learning as applied to the graphs as cited above, which learns and characterizes/creates parameters over time); and
modifying one or more properties of an additional audio signal in the playback music content by modifying the additional specified audio parameters (the neural network continues to learn over time/modify the additional parameters).
As per claim 6, the method of claim 4, wherein determining the one or more specified audio parameters includes:
determining a portion of the second graph to implement for the audio parameters based on a position of the second node in the second graph (the values of the parameters at various points in time as read by the processor to perform the functions cited above); and
selecting the audio parameters from the determined portion of the second graph as the one or more specified audio parameters (the parameters developed via the training functions in para 46).
As per claim 7, the method of claim 6, wherein modifying the one or more specified audio parameters modifies a portion of the playback music content that corresponds to the determined portion of the second graph (the neural network learning modifies parameters which are then used in selecting the advertisement clip, which is then merged/modified per para 7).
As per claim 8, the method of claim 4, wherein the modified properties of the audio signal in the playback music content include signal amplitude, signal frequency, or a combination thereof (para 36: creation engine 240 detects the duration of the visual and/or audio content and automatically adjusts a list of audio clips of varying durations to match the duration and the content of the visual and/or audio content) (noting that matching the duration of an audio signal comprises changing the amplitude and/or frequency parameters of the audio signal at a particular point in time).
As per claim 9, the method of claim 1, further comprising applying one or more automations to the audio parameters, wherein at least one of the automations is a pre-programmed temporal manipulation of at least one of the audio parameters (the automatic duration matching per para 36).
As per claim 10, the method of claim 9, further comprising applying one or more modulations to the audio parameters (the modulation of the parameters as modified by the neural network cited above),
wherein at least one of the modulations modifies at least one of the audio parameters multiplicatively on top of at least one automation (this limitation is not mapped as it is drawn to the non-selected alternative recited above, however para 36 discloses duration matching, and further the neural network as cited above) ).
As per claim 11, the method of claim 1, wherein the first graph is a tempo variant graph of the audio signal (the energy level graph).
As per claim 12, the method of claim 1, wherein the second graph is a tempo invariant graph of the audio signal (para 21 the emotional graph).
As per claim 13, a non-transitory computer-readable medium having instructions stored thereon that are executable by a computing device to perform operations comprising: accessing a first graph of an audio signal, wherein the first graph is a graph of audio parameters relative to time; accessing a second graph of the audio signal, wherein the second graph is a signal graph of the audio parameters relative to beat; accessing playback music content; and modifying the audio parameters in the playback music content to generate new music content, wherein the audio parameters are modified based on a combination of the first graph and the second graph. (the system per the claim 1 rejection requires non-transitory memory and software in order to be implemented).
As per claim 14, the non-transitory computer-readable medium of claim 13, wherein the audio parameters in the first graph and the second graph are defined by nodes in the graphs that determine changes in properties of the audio signal, and wherein modifying the audio parameters in the playback music content to generate the new music content includes:determining a first node in the first graph corresponding to an audio signal in the playback music content;determining a second node in the second graph that corresponds to the first node;determining one or more specified audio parameters based on the second node; and modifying one or more properties of an audio signal in the playback music content by modifying the specified audio parameters (per the claim 4 rejection).
As per claim 15, the non-transitory computer-readable medium of claim 14, wherein the modified properties of the audio signal in the playback music content include signal amplitude, signal frequency, or a combination thereof (per the claim 8 rejection).
As per claim 19, an apparatus, comprising: one or more processors; and one or more memories having program instructions stored thereon that are executable by the one or more processors to: access a first graph of an audio signal, wherein the first graph is a graph of audio parameters relative to time; access a second graph of the audio signal, wherein the second graph is a signal graph of the audio parameters relative to beat/tempo; access playback music content; and modify the audio parameters in the playback music content to generate new music content, wherein the audio parameters are modified based on a combination of the first graph and the second graph (per the claim 1 rejection).
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) 16-18,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Blacker et al (US 20160379274 A1) as applied to claim 1-13 above, and further in view of Masputra (US 20190306282 A1).
As per claim 16, Blacker discloses the non-transitory computer-readable medium of claim 13, but does not specify wherein at least one of: the audio signal, the first graph, and the second graph are access as one or more objects associated with a set of music content stored in a heap allocated memory.
Masputra teaches an audio network streaming system, and teaches that a well known TCP protocol can use heap memory to avoid data inconsistencies (para 280). It would have been obvious to one skilled in the art at the time of filing, that the cloud server based processing cited by Blacked could implement a TCP protocol using the header to transfer the audio signals and graphs, while storing them in a heap allocated memory, for the purpose of avoiding data inconsistencies.
As per claim 17, the system of Blacker further teaches the non-transitory computer-readable medium of claim 13, further comprising adding an identifier to the new music content (the parameters and functions as cited in the claim 1 rejection require the steps of adding identifiers to each data object in order for the processor to recognize each data object), and
Masputra teaches to store music content in at least one circular buffer in a static array of circular buffers in order to transfer data (para 233). It would have been obvious to one skilled in the art at the time of filing to use a well known mechanism for transferring the data per the claim 1 rejection, for the purpose of compatibility with known processing standards.
As per claim 18, the non-transitory computer-readable medium of claim 17, wherein the static array of circular buffers is accessible by a single user (end user 120 in Blacker fig. 1).
As per claim 20, the apparatus of claim 19, further comprising a static array of circular buffers, wherein the program instructions stored on the one or more memories are executable by the one or more processors to store the new music content in at least one circular buffer in the static array of circular buffers (per the teachings of the claim 17 rejection).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER KRZYSTAN whose telephone number is 571-272-7498, and whose email address is alexander.krzystan@uspto.gov
The examiner can usually be reached on m-f 7:30-4:00 est.
If attempts to reach the examiner by telephone or email are unsuccessful, the examiner’s supervisor, Carolyn Edwards can be reached on (571) 270-7136.
The fax phone numbers for the organization where this application or proceeding is assigned are 571-273-8300 for regular communications and 571-273-8300 for After Final communications.
/ALEXANDER KRZYSTAN/Primary Examiner, Art Unit 2653
Examiner Alexander Krzystan
August 8, 2026
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