DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This is in response to the applicant response filed on 05/18/2026. In the applicant’s response, claims 1 and 8 were amended, claim 14 was newly added. Accordingly, claims 1-14 are pending and being examined. Claims 1 and 8 are independent form.
Claim Rejections - 35 USC § 101
3. The claim rejections under 35 USC § 101 make in the previous office action are STILL MAINTAINED because applicant’s arguments are unpersuasive and the claimed inventions are directed to non-statutory subject matter (an abstract ideal without significantly more). See Response to Arguments below.
Claim Rejections - 35 USC § 103
4. 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 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.
5. 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 of this title, 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.
6. Claims 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Ushida et al (US Pub 2004/0010409, hereinafter “Ushida”) in view of Huang et al (“AUDIO-VISUAL DEEP LEARNING FOR NOISE ROBUST SPEECH RECOGNITION”, 2013, hereinafter “Huang”).
Regarding claim 1, Ushida discloses a selection server for selecting one or more other communications devices for classifying (the voice/speech recognition system; see abstract and fig.1)
receive, from a communications device for classifying an instance see para.168: “An audio receiving part 112 [of the server 111] receives audio data from the client 101.” It should be noticed that the audio type data from the client 101 is the selection request message from the device 101 for recognizing the audio data pertaining to the device 101.),
select the one or more other communications devices based on therefore, the audio transmit part 105 of the device 101 and the audio receive part 112 of the server 111 are the communication devices selected to transmit and receiving the audio type message),
transmit, to the communications device, a selection response message comprising information identifying the selected one or more other communications devices (see para.172: “the audio recognition engine 114 [of server 111] recognizes the audio data according to the vocabulary and outputs the audio data recognized result to [...] a result transmit part 116 [i.e., the client 101]”.).
As explained above, the mere difference is, Ushida does not discloses using machine learning (ML) for speech recognition. However, using machine learning for speech recognition is a well-known and widely used technique. As evidence, in the same field of endeavor, Huang teaches audio-visual deep learning for robust speech recognition, see the title, wherein the deep belief network (DBN) is trained by both the audio data and the visual data, see fig.1 and Sec.3. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to combine the teachings of Huang and the teachings of Ushida and use the deep belief network (DBN) to classify input data. Suggestion or motivation for doing so would have been to “improve audio-visual speech recognition” and “extract better audio-visual features”, see Sec. 1, Par.5. Therefore, the claim is unpatentable over Ushida in view of Huang.
Regarding claim 2, 9, the combination of Ushida and Huang discloses, being operative to select the one or more other communications devices for classifying an instance using ML by using a second ML model, and being further operative to: receive, from at least one of: the communications device and the one or more other communications devices, a calculated confidence level (Ushida, para.13: “a predetermined case the first transmitting means transmits the audio data to another device is a case a degree of reliability in the recognition result by the first audio recognition means is not more than a predetermined threshold value”); and update the second ML model based on the received calculated confidence level (Huang, see the DBN’s training phase in Sec. 3.1, wherein the “weight layer is trained”).
Regarding claim 3, 10, the combination of Ushida and Huang discloses, wherein the instance is an image or a video frame capturing an object (Huang, see “audio-visual input” in fig.1).
Regarding claim 4, 11, the combination of Ushida and Huang discloses, wherein the instance is an audio recording capturing a sound (Ushida, see “audio data” in fig.1).
Regarding claim 5, 12, the combination of Ushida and Huang discloses the selection server according to claim 1, comprising: a communications module, wherein the communications module is operative to effect communications through a communications network; a processing unit; and a computer-readable storage medium (Ushida, see “the client”, “the server”, and the communication between them shown in fig.1, see para.52, para.105).
Regarding claim 6, the combination of Ushida and Huang discloses the selection server according to claim 1, further comprising: a device selection module; and a messaging module (ibid.).
Regarding claim 7, the combination of Ushida and Huang discloses the selection server according to claim 1, wherein the selection server is maintained by a social-network provider (Ushida, see “the client”, “the server”, and the communication between them shown in fig.1, see para.52, para.105).
Regarding claims 8, 13, each of them is an inherent variation of claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
Regarding claim 14, the combination of Ushida and Huang discloses the selection server according to claim 1, being further operative to select the or more other communications devices based on a contact list of a user of the communications (Ushida, wherein the audio transmit part 105 of the device 101 and the audio receive part 112 of the server 111 are selected based on the recognized audio of the user; see para.105: “The client 101 comprising a mobile phone, a PDA or the like recognizes audio received from a user.”) , wherein the one or more other communications devices are associated with users identified in the contact list whose respective local first ML models are trained for classifying instances encountered by a communications devices of a related user (Huang, wherein the trained deep belief network (DBN) can identify L different contacts of the current owner via the audio and visual data recognition; see fig.1 and col. 3, lines 47-63).
Response to Arguments
7. Applicant’s arguments, filed on 05/18/2026, have been fully considered but they are not persuasive.
7-1. Regarding the claim rejections under 35 U.S.C. 101
On pg. 9 of applicant’s response, applicant argues: “The selection criteria, as amended, are highly technical ML-specific parameters. [...] These criteria are not concepts that fall within the "organizing human activity" grouping because they do not concern managing personal behavior, social activities, or relationships between people.”
The examiner respectfully disagrees with the arguments. The element of “select[ing] the one or more other communications devices based on [at least one of:] the type of data comprised in the feature vector [...]” recited by claim 1, in the context of the claim, encompasses mental observation, evaluations, judgments, opinions, and/or activities that can be performed in human mind. For instance, one can select a respective audio or visual communication device corresponding to audio or visual data. Claim 1 therefore recites an abstract idea. If a claim limitation is directed to organizing human activity, can be practically performed in human mind, or falls within mathematical concepts, then the claim recites an abstract idea. See MPEP 2106.04(a)(2).
Further, on page 10, applicant argues “The claim recites additional elements that integrate the judicial exception into a practical application”.
The examiner respectfully disagrees with the applicant’s arguments. The examiner respectfully points out that claim 1 does not “recite additional elements that integrate the exception into a practical application of that exception” as argued by applicant. Rather, claim 1 broadly recites selecting one or more communication devices by receiving a selection request message including a type of data comprised in a feature vector representing an instance. It is apparent that this type of “message” does not integrate into any practical particular application at all but instead broadly includes any data comprised in a feature vector representing an instance. Therefore, claim1 as a whole does not integrate the judicial exception into a practical application.
Further, on page 12, applicant argues “The additional limitations recited in the claim amount to significantly more than the judicial exception”
The examiner respectfully disagrees with the applicant’s arguments. Claim 1 recites a selection server for selecting one or more communications devices for classifying an instance using ML, is at best the equivalent of merely adding the words “apply it” to the judicial exception. The “receiv[ing]” in step [1] was considered insignificant extra-solution activity. These conclusions should be reevaluated in Step 2B. The limitations are mere data gathering and/or output recited at high level of generality and amount to receiving (i.e., acquiring), accessing, or transmitting data over a network, which is well-understood, routine, conventional activity. In the instant case, “receiv[ing] a selection request message” is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The limitations remain insignificant extra-solution activity even upon reconsideration. Even when considered in combination, the additional elements present mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim therefore is ineligible.
7-2. Regarding the claim rejections under 35 U.S.C. 103, on pg. 17 of applicant’s response, applicant argues:
Ushida discloses that the only data sent from the client to the server is raw audio data for speech recognition. (See Ushida, [0168] ("An audio receiving part 112 [of the server 111] receives audio data from the client 101.")). But Ushida's audio data does not in any way comprise information pertaining to any of: (i) a type of data comprised in a feature vector, (ii) an origin of a feature vector, (iii) a classification of an instance using a local first ML model, or (iv) classified instances related to the instance represented by a feature vector. Indeed, the Office's mapping relied entirely on Ushida's audio data being mapped to "a location of the communications device," which is a criterion that has now been removed from the claim as a result of the amendments described above. (See also Office Action, p. 10).
The examiner respectfully disagrees with the applicant’s arguments. Ushida's audio data represents the audio of a user. For example, the audio of a user comprises the voice feature of the user. See para. 105: “[t]he client 101 comprising a mobile phone, a PDA or the like recognizes audio received from a user.” The applicant’s arguments therefore are not persuasive.
Conclusion
8. THIS ACTION IS MADE FINAL. 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 extension fee 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.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached on (571)272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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