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 §§ 706.02(l)(1) - 706.02(l)(3) 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).
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Claims 1 - 3, 5 – 10 and 12 – 14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 7 – 8 and 10 – 12 of U.S. Patent No. 12,400,678.
Regarding claim 8 of instant application vs. Claim 7 of patent 12,400,678,
Claim 8 of instant application
Claim 7 of patent 12,400,678
A speech recognition device (SRD) for use in warehouse picking operations comprising:
a microphone;
at least one processor; and
at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the SRD to at least:
receive, at the microphone, an audio input, wherein the audio input is related to a confirmation phrase uttered in response to an instruction to pick an item, wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt, a confirmation of a completion of a task, and a confirmation of an identification of at least of a location and an object;
classify portions of the received audio input based on a machine learning model, wherein the portion of the received audio input is classified as user speech; and
process the portion of the received audio input that is classified as user speech, to generate at least one of words and phrases related to the warehouse picking operations.
A speech recognition device (SRD) for use in warehouse picking operations comprising:
a microphone;
at least one processor; and
at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the SRD to at least:
receive, at the microphone of the SRD, an audio input, wherein the audio input is related to a confirmation phrase uttered in response to an instruction to pick an item;
identify a vocalization in the received audio input;
classify a portion of the identified vocalization based on a machine learning model trained to understand input natural language, wherein the portion of the identified vocalization is classified as at least one of a user speech or a background speech;
in an instance in which the portion of the identified vocalization is classified as the user speech;
process the portion of the identified vocalization, and generate at least one of words or phrases related to the warehouse picking operations; and
in an instance in which the portion of the identified vocalization is classified as the background speech, filter the portion of the identified vocalization.
The claim 8 of instant application discloses each limitation of the claim 7 of patent 12,400,678. Actually, the claimed limitations of claim 8 of instant application is broader than the limitations of the claim 7 of patent 12,400,678.
Claims 9 – 10 and 12 - 14 of instant application are corresponding to claims 7(partial), 8 and 10 – 12 of patent 12,400,678, respectively.
Claims 1 – 3 and 5 - 7 of instant application are corresponding to claims 7, 7(partial), 8 and 10 – 12 of patent 12,400,678, respectively.
Claims 4, 11, 15 – 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 7 – 8 and 10 – 12 of U.S. Patent No. 12,400,678, in view of Braho et al. (US Patent Application Publication 2014/0278391), hereinafter referred as Braho.
Regarding claims 4 and 11 of instant application, claims 4 and 11 claimed “reject a portion of the received audio input that is classified as user speech in an instance which detected background noise exceeds a predetermined threshold of background noise”. This is obvious in view of Braho.
In a similar field of endeavor Braho discloses a method of speech recognition for use in warehouse picking operations (abstract). In addition, Braho discloses the method wherein reject a portion of the received audio input that is classified as user speech in an instance which detected background noise exceeds a predetermined threshold of background noise ([0161 - 0162], “an acceptance or rejection threshold”).
Therefore, it would have been obvious to one of ordinary skill in the art, and reject a portion of the received audio input that is classified as user speech in an instance which detected background noise exceeds a predetermined threshold of background noise. The motivation for doing this is the user speech can be recognized in a better quality.
Regarding claim 15 of instant application vs. Claim 7 of patent 12,400,678,
Claim 15 of instant application
Claim 7 of patent 12,400,678
A method of speech recognition for use in warehouse picking operations, the method comprising:
receiving an audio input;
classifying, via a processor, a portion of the received audio input based on a machine learning model, wherein the portion of the received audio input is classified as user speech related to the warehouse picking operations;
rejecting the portion of the received audio input that is classified as user speech in an instance which detected background noise exceeds a predetermined threshold of background noise; and
processing a remaining portion of the received audio input that is classified as user speech to generate at least one of words and phrases related to the warehouse picking operations.
A speech recognition device (SRD) for use in warehouse picking operations comprising:
a microphone;
at least one processor; and
at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the SRD to at least:
receive, at the microphone of the SRD, an audio input, wherein the audio input is related to a confirmation phrase uttered in response to an instruction to pick an item;
identify a vocalization in the received audio input;
classify a portion of the identified vocalization based on a machine learning model trained to understand input natural language, wherein the portion of the identified vocalization is classified as at least one of a user speech or a background speech;
in an instance in which the portion of the identified vocalization is classified as the user speech;
process the portion of the identified vocalization, and generate at least one of words or phrases related to the warehouse picking operations; and
in an instance in which the portion of the identified vocalization is classified as the background speech, filter the portion of the identified vocalization.
The claim 15 of instant application discloses each limitation of the claim 7 of patent 12,400,678 except for “rejecting the portion of the received audio input that is classified as user speech in an instance which detected background noise exceeds a predetermined threshold of background noise”. This is obvious in view of Braho.
In a similar field of endeavor Braho discloses a method of speech recognition for use in warehouse picking operations (abstract). In addition, Braho discloses the method wherein rejecting the portion of the received audio input that is classified as user speech in an instance which detected background noise exceeds a predetermined threshold of background noise ([0099], “the classifications may be used to adjust a rejection threshold (i.e., threshold to which a word's confidence score is compared) by the threshold adjustment module 468, again to prevent background sounds (i.e., noise) from erroneously being recognized as speech”).
Therefore, it would have been obvious to one of ordinary skill in the art, and rejecting the portion of the received audio input that is classified as user speech in an instance which detected background noise exceeds a predetermined threshold of background noise. The motivation for doing this is the user speech can be recognized in a better quality.
Claims 16 – 18 and 20 of instant application are corresponding to claims 8 – 11 of patent 12,400,678, respectively.
Regarding claim 19 of instant application, claim 19 claimed “the background noise comprises at least one of an operation of a vehicle in a warehouse and a movement of pallets in the warehouse”. This is obvious in view of Braho.
In a similar field of endeavor Braho discloses a method of speech recognition for use in warehouse picking operations (abstract). In addition, Braho discloses the method wherein the background noise comprises at least one of an operation of a vehicle in a warehouse and a movement of pallets in the warehouse ([0005], driving a vehicle).
Therefore, it would have been obvious to one of ordinary skill in the art, and the background noise comprises at least one of an operation of a vehicle in a warehouse and a movement of pallets in the warehouse. The motivation for doing this is that the instant application can be extended to focus on some specific noise types.
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) 1, 4 – 8 and 11 – 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braho et al. (US Patent Application Publication 2014/0278391), hereinafter referred as Braho, in view of El Dokor et al. (US Patent Application Publication 2014/0277936), hereinafter referred as El Dokor, and in further view of Liu et al. (US Patent Application Publication 2016/0307565), hereinafter referred as Liu.
Regarding claim 8, Braho discloses a speech recognition device (SRD) for use in warehouse picking operations (Figs. 1 - 3) comprising:
a microphone (Fig. 1, #120a, 120b);
at least one processor (Fig. 2, #214); and
at least one memory (Fig. 2, #218, 220) including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the SRD to at least:
receive, at the microphone, an audio input (Fig. 5, 504, [0110]), wherein the audio input is related to a phrase uttered to an instruction to pick an item, and an identification of at least of a location and an object ([0007], “the user may receive voice instructions, …, and/or receive directions such as location information specifying locations for picking up or delivering goods”);
classify portions of the received audio input, wherein the portion of the received audio input is classified as user speech (Fig. 5, 514 - 516, [0114]); and
process the portion of the received audio input that is classified as user speech (Fig. 5, 518, [0119], “This may advantageously limit the information being sent to be information which has been classified (i.e., determined to likely be) as speech rather than noise”), to generate at least one of words and phrases related to the warehouse picking operations (Fig. 5, 520 - 522, [0120 - 0122], “digitized audio to recognize speech”; [0162], “outputs recognized text”; [0007]).
However, Braho fails to explicitly disclose the SRD wherein the audio input is related to a confirmation phrase uttered in response to an instruction wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt, a confirmation of a completion of a task, and a confirmation of an identification of at least of a location and an object.
However, in a similar field of endeavor El Dokor discloses a system of audio input and control ([0025]). In addition, El Dokor discloses the system wherein the audio input is related to a confirmation phrase uttered in response to an instruction wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt ([0025]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Braho, and the audio input is related to a confirmation phrase uttered in response to an instruction wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt, a confirmation of a completion of a task, and a confirmation of an identification of at least of a location and an object. The motivation for doing this is that audio input can be confirmed without an error so that process can be more accurate.
However, Braho in view of El Dokor fails to explicitly disclose the SRD wherein classify portions of the received audio input is based on a machine learning model.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classify portions of the received audio input is based on a machine learning model ([0014]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Braho, and classify portions of the received audio input is based on a machine learning model. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence.
Regarding claim 11 (depends on claim 8), Braho discloses the SRD wherein the at least one memory and the computer program code are further configured to, with the at least one processor cause the SRD to at least reject a portion of the received audio input that is classified as user speech in an instance which detected background noise exceeds a predetermined threshold of background noise ([0161 - 0162], “an acceptance or rejection threshold”).
Regarding claim 12 (depends on claim 8), Braho discloses the SRD further comprising a headset, wherein the microphone is mounted on the headset and is configured for use in a warehouse environment (Fig. 1, #120a and 120b).
Regarding claim 13 (depends on claim 8), Braho discloses the SRD wherein the classification is processing based on a corpus comprising at least one word and phrase related to warehouse picking operations ([0007], “the user may receive voice instructions, …, and/or receive directions such as location information specifying locations for picking up or delivering goods”).
However, Braho in view of El Dokor fails to explicitly disclose the SRD wherein the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase ([0049 - 0052]).
There was some teaching, suggestion, or motivation in Braho and Liu or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings teachings to achieve the claimed limitations; and there was reasonable expectation of success to achieve the claimed limitations (KSG scenario G).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Braho, and the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence.
Regarding claim 14 (depends on claim 13), Braho discloses the SRD wherein the
classification is processing based on a corpus and the audio input received during warehouse picking operations ([0007], “the user may receive voice instructions, …, and/or receive directions such as location information specifying locations for picking up or delivering goods”; Fig. 1, [0046 - 0053]).
However, Braho in view of El Dokor fails to explicitly disclose the SRD wherein the classification is based on the machine learning model that is trained based on training corpus and the audio input.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classification is based on the machine learning model that is trained based on training corpus and the audio input ([0049 - 0052]).
There was some teaching, suggestion, or motivation in Braho and Liu or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings to achieve the claimed limitations; and there was reasonable expectation of success to achieve the claimed limitations (KSG scenario G).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Braho, and the classification is based on the machine learning model that is trained based on training corpus and the audio input. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence.
Regarding claims 1 and 4 – 7, they are corresponding to claims 8 and 11 – 14, respectively, thus, they are interpreted and rejected for a same reason set forth for claims 8 and 11 – 14.
Claim(s) 2 – 3, 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braho in view of El Dokor, in further view of Liu, and Yen et al. (US Patent Application Publication 2009/0271187), hereinafter referred as Yen.
Regarding claim 9 (depends on claim 8), Braho discloses the SRD wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the SRD to at least classify a portion of the received audio input as background noise and reject the portion of the received audio input that is classified as background noise ([0161 – 0162]).
However, Braho in view of El Dokor fails to explicitly disclose the SRD wherein classify portions of the received audio input is based on a machine learning model; and background noise is background speech.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classify portions of the received audio input is based on a machine learning model ([0014]).
In a similar field of endeavor Yen discloses a system of audio signal processing (abstract). In addition, Yen discloses the system wherein background noise is background speech ([0057]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Braho, and classify portions of the received audio input is based on a machine learning model; and background noise is background speech. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence, and a specific type of background noise can be rejected so that the Application of Braho can be extended.
Regarding claim 10 (depends on claim 8), Braho discloses the SRD wherein the
classification is to distinguish user speech from background noise ([0161 – 0162]).
However, Braho in view of El Dokor fails to explicitly disclose the machine learning model is at least one of a neural network system, a support vector machine, and an inductive logic system and wherein the machine learning model is trained to distinguish user speech from background speech.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classification is based on a machine learning model is at least one of a neural network system, a support vector machine, and an inductive logic system and wherein the machine learning model is trained to distinguish sounds ([0014]).
In a similar field of endeavor Yen discloses a system of audio signal processing (abstract). In addition, Yen discloses the system wherein background noise is background speech ([0057]).
There was some teaching, suggestion, or motivation in Braho, Liu and Yen or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings to achieve the claimed limitations; and there was reasonable expectation of success to achieve the claimed limitations (KSG scenario G).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Braho, and the machine learning model is at least one of a neural network system, a support vector machine, and an inductive logic system and wherein the machine learning model is trained to distinguish user speech from background speech. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence, and a specific type of background noise can be rejected so that the Application of Braho can be extended.
Regarding claims 2 – 3, they are corresponding to claims 9 – 10, respectively, thus, they are interpreted and rejected for a same reason set forth for claims 9 – 10.
Claim(s) 15 – 16 and 18 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braho in view of Liu.
Regarding claim 15, Braho discloses a method of speech recognition for use in warehouse picking operations (abstract), the method comprising:
receiving an audio input (Fig. 5, 504, [0110]);
classifying, via a processor, a portion of the received audio input, wherein the portion of the received audio input is classified as user speech (Fig. 5, 514 - 516, [0114]) related to the warehouse picking operations ([0007], “the user may receive voice instructions, …, and/or receive directions such as location information specifying locations for picking up or delivering goods”);
rejecting the portion of the received audio input that is classified as user speech in an instance which detected background noise exceeds a predetermined threshold of background noise ([0099], “the classifications may be used to adjust a rejection threshold (i.e., threshold to which a word's confidence score is compared) by the threshold adjustment module 468, again to prevent background sounds (i.e., noise) from erroneously being recognized as speech”); and
processing a remaining portion of the received audio input that is classified as user speech (Fig. 5, 518, [0119], “This may advantageously limit the information being sent to be information which has been classified (i.e., determined to likely be) as speech rather than noise”) to generate at least one of words and phrases related to the warehouse picking operations (Fig. 5, 520 - 522, [0120 - 0122], “digitized audio to recognize speech”; [0162], “outputs recognized text”; [0007]).
However, Braho fails to explicitly disclose the method wherein classify portions of the received audio input is based on a machine learning model.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classify portions of the received audio input is based on a machine learning model ([0014]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Braho, and classify portions of the received audio input is based on a machine learning model. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence.
Regarding claim 16 (depends on claim 15), Liu discloses the method wherein the machine learning model is at least one of a neural network system, a support vector machine, and an inductive logic system ([0014]).
Regarding claim 18 (depends on claim 15), Braho discloses the method further comprising a microphone, wherein the microphone is mounted on a headset and is configured for use in a warehouse environment (Fig. 1, #120a and 120b).
Regarding claim 19 (depends on claim 15), Braho discloses the method wherein the background noise comprises at least one of an operation of a vehicle in a warehouse and a movement of pallets in the warehouse ([0005], driving a vehicle).
Regarding claim 20 (depends on claim 15), Braho discloses the method wherein the classification is processing based on a corpus comprising at least one word and phrase related to warehouse picking operations ([0007], “the user may receive voice instructions, …, and/or receive directions such as location information specifying locations for picking up or delivering goods”).
However, Braho in view of El Dokor fails to explicitly disclose the SRD wherein the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase ([0049 - 0052]).
There was some teaching, suggestion, or motivation in Braho and Liu or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings teachings to achieve the claimed limitations; and there was reasonable expectation of success to achieve the claimed limitations (KSG scenario G).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Braho, and the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braho in view of Liu, and in further view of El Dokor.
Regarding claim 17 (depends on claim 15), Braho fails to explicitly disclose the method wherein the audio input is related to a confirmation phrase, and wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt, a confirmation of a completion of a task, and a confirmation of an identification of at least of a location and an object.
However, in a similar field of endeavor El Dokor discloses a system of audio input and control ([0025]). In addition, El Dokor discloses the system wherein the audio input is related to a confirmation phrase uttered in response to an instruction wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt ([0025]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Braho, and the audio input is related to a confirmation phrase uttered in response to an instruction wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt, a confirmation of a completion of a task, and a confirmation of an identification of at least of a location and an object. The motivation for doing this is that audio input can be confirmed without an error so that process can be more accurate.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QIAN YANG whose telephone number is (571)270-7239. The examiner can normally be reached on Monday-Thursday 8am-6pm.
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/QIAN YANG/
Primary Examiner, Art Unit 2677