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 .
Examiner’s Comments
“an electrical signal embodied on a carrier wave and propagated on an electrical medium” are recited in claim 13 is drawn to the structure of software stored in nontransitory memory user by a processor
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.
The following claims 1-7,9-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Manjunath et al (US 20220138300 A1) in view of Von Brasch et al(US 20170359659 A1).
As per claim 1, Manjunath discloses a computer-implemented method for snore attribution, comprising:
capturing audio using a microphone (12) (para 109);
detecting a plurality of snores in the captured audio (469 the snoring pattern);
determining that a first set of snores (42a) of said plurality of snores belongs to a first individual (28a) and that a second set of snores (42b) of said plurality of snores belongs to a second individual (28b) ( para 475) using a trained model (44) (para 475: The device or devices are able to continuously learn or be used to learn as the device is used);
wherein a multidimensional vector space embedding of each detected snore is formed (the multidimensional space as per the processor required to implement the cited steps is multidimensional. Time ((or freq)) and amplitude define a set of embedded vectors which identify the data to the computer/processor processing it), and
wherein determining that a first set of snores of said plurality of snores belongs to a first individual and that a second set of snores of said plurality of snores belongs to a second individual using a trained model includes:
the trained model (44) causing the embeddings (46a) of the snores of the first individual (28a) to be located in a first subspace in a vector space (48) and the embeddings (46b) of the snores of the second individual to be located in a second, different subspace in said vector space (The vectors identified and stored via the learning for USER A/ first subspace, and for USER B/ second subspace);
clustering the embeddings (46a) located in the first subspace to form said first set of snores (42a) (the machine learning comprises clustering of the data/parameters/embeddings per para 435); and
clustering the embeddings (66b) located in the second subspace to form said second set of snores (42b) (the clustering as applied first to user A then as applied to user B).
Manujunath discloses a Turing test used for training per para 178 but does not specify
playing for a user a subset of the snores of said first set (42a) and a subset of the snores of said second set (42b);
for each played snore, prompting the user via a user interface (18) to provide input whether or not the played snore belongs to the user;
receiving said input from the user via the user interface; and
attributing the first set of snores or the second set of snores to the user based on said input
Von Brasch discloses an audio device that classifies and teaches that it can be configured to prompt the recipient indicating a given classification (para 98), such as the sound scene corresponds to jazz music (which in this case is incorrect), and the recipient could provide feedback indicating that this is an incorrect classification. Which allows the recipient can provide input indicative of dissatisfaction with respect to the sound processing. It would have been obvious to one skilled in the art that the Turing test of Manujunath could implement a well known process of obtaining feedback from the user from eat set of snores for USER A and USER B for the purpose of training the device and allowing the user to express dissatisfaction with an improved user interface.
As per claim 2, a computer-implemented method according to claim 1, further comprising attributing the other of the first and second sets of snores to another user (Manjunath user A and User B per para 475).
As per claim 3, a computer-implemented method according to claim 1 or 2, performed on a single device (10) (Manjunath smartphone fig. 3),
(such as a smartphone, comprising said microphone- this claim limitation is not mapped as it not positively recited).
As per claim 4, a computer-implemented method according to claim 1,wherein the audio is captured when the first individual (28a) and the second individual (28b) are in the same room ( Manjunath per para 475, two people sleeping).
As per claim 5, a computer-implemented method according to claim 1, wherein the user interface for each played snore prompts the user to provide input whether the played snore belongs to the user (52a), does not belong to the user (52b), or belongs to multiple individuals or is inaudible (52c) (per the turing test cited above).
As per claim 6, A computer-implemented method according to claim 1,wherein the user interface is a graphical user interface implemented on a touch screen (20) (per fig. 3 Manjunath).
As per claim 7, a computer-implemented method according to claim 1, wherein during training of the trained model (44) two embeddings of snores known to belong to the same person are moved closer together in a vector space and another embedding of a snore known to belong to a different person is moved further away from one of said two embeddings (per the learning based clustering cited above).
As per claim 9, a computer-implemented method according to claim 1, wherein training data for the trained model (44) includes multiple sleep audio clips annotated as snores by multiple users (the training cited above in view of the scenario described in para 475).
As per claim 10, a computer-implemented method according to claim 1, wherein the method does not know anything snoring-related about the user beforehand (para 475: when a new voice is detected, the device is able to prompt the user to input an associated name with the voice or the device is able to assign the user a name).
As per claim 11, A computer program product (26) comprising computer program code to perform, when executed on a computer (10), the method according to claim 1 (the device of the claim 1 rejection requires software, processors and memory in order to be implemented).
As per claim 12, a computer-readable storage medium comprising the computer program product according to claim 11 (per the claim 11 rejection).
As per claim 13, an electrical signal embodied on a carrier wave and propagated on an electrical medium, the electrical signal comprising the computer program product according to claim 11 (per the claim 11 rejection).
As per claim 14, the claim 1 rejection discloses a device (10) comprising a microphone (12), a processor, audio playback functionality, and a user interface (the phone manjunath Fig. 3), wherein the device is configured to:
capture audio using the microphone (12); detect, using the processor (14), a plurality of snores in the captured audio; determine, using the processor (14) and a trained model (44), that a first set of snores (42a) of said plurality of snores belongs to a first individual (28a) and that a second set of snores (42b) of said plurality of snores belongs to a second individual (28b), wherein the device is configured to form a multidimensional vector space embedding of each detected snore, the trained model (44) causing the embeddings (46a) of the snores of the first individual (28a) to be located in a first subspace in a vector space (48) and the embeddings (46b) of the snores of the second individual to be located in a second, different subspace in said vector space (48), wherein the device is configured to cluster the embeddings (46a) located in the first subspace to form said first set of snores (42a), and wherein the device is configured to clustering the embeddings (66b) located in the second subspace to form said second set of snores (42b); play for a user a subset of the snores of said first set (42a) and a subset of the snores of said second set (42b) using the audio playback functionality (16); for each played snore, prompt the user via the user interface (18) to provide input whether or not the played snore belongs to the user; receive said input from the user via the user interface; and attribute, using the processor (14), the first set of snores or the second set of snores to the user based on said input. (per the claim 1 rejection).
As per claim 15, a device according to claim 14, wherein the device does not know anything snoring-related about the user beforehand (per the claim 10 rejection).
The following claims, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Manjunath et al (US 20220138300 A1) in view of Von Brasch et al (US 20170359659 A1) as applied to claim 1, and further in view of Zhang et al (US 20210174592 A1).
As per claim 8, Manjunath and Von Brasch disclose a computer-implemented method according to claim 1, but do not specify wherein the trained model (44) is based on a triplet loss function.
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
September 2, 2026
Prosecution Insights