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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/06/2026 has been entered.
Responses to Amendments and Arguments
The amendments filed 3/3/2026 have been entered. No claims are amended and Claims 16-20 are newly added. Claims 1-3, 6-10, and 13-20 remain pending in the application.
Applicant's argument and amendments filed 3/3/2026 with respect to
the rejection of claims 1-3, 6-10 and 13-15 directed to a judicial exception under 35 U.S.C. 103 have been fully considered but are not persuasive. (See the detailed response presented below).
On pages 6-8 of the Remarks, Applicant alleges that “Neither Kumar nor Murthy "cluster the series of feature vectors into a plurality of clusters using the first estimated number of occupants as a seed value for a count of the plurality of clusters" as recited in claim 1, by stating that The Office Action asserts that the claimed feature of "cluster the series of feature vectors ... " should be interpreted so broadly as to cover the signal sampling and filtering discussed in Kumar. … Kumar entirely fails to teach or suggest "using the first estimated number of occupants as a seed value for a count of the plurality of clusters" as set forth in claim 1. Moreover, the Office Action fails to provide any evidence that this claim feature was known in the art. … There is nothing of the sort in Kumar. Rather, Kumar makes it clear that its PIR signal and ultrasonic transducer signal are sampled and filtered separately.
Examiner respectfully disagrees.
Note that, under the broadest reasonable interpretation, the limitation of “clustering the series of feature vectors …” is indicative of process to partition/classify/group/filter signals into a plurality of classified/filtered signals or groups, for example, by sampling the audio samples at a specific time period and converting the analogue audio signal to a digital signal, and filtering the sampled signals into the plurality of the filtered signals. Further, under the broadest reasonable interpretation, the limitation of “using the first estimated number of occupants as a seed value for a count of the plurality of clusters” is indicative of determining a reference value such as a sampling rate (i.e., seed value determined by the first estimated number of occupants) to be used for a process to partition/classify/group/filter signals into a plurality of partitioned/classified/filtered signals or groups, for example, by sampling the audio samples at a specific time period at the sampling rate and converting the analogue audio signal to a digital signal, and filtering the sampled signals into the plurality of the filtered signals. Under these interpretations, at least paragraphs 0011 and 0133 of Kumar teach filtering the sampled signals to thereby partition/classify the sampled audio signals into a plurality of filtered signals (i.e., a plurality of clusters) using the refence value such as the sampling rate (Para 0133, “the sensor signal is sampled at an ADC to produce a sampled signal. The ADC may be input a reference voltage, such as 3.3 Volts and sampled at a one kilohertz sampling rate”). Respectfully note that, under the broadest reasonable interpretation, the clustering itself in the limitation is not critical to be distinctly result-effective features but merely indicative of partitioning/classifying/ grouping and/or filtering. If the clustering in a technical filed of audio signal analysis is believed to be a key aspect for the sensor data, as Applicant alleged, at a minimum the claims describe some specific features, structure and/or actions how and/or with what factors/parameters/frequencies/voice signature to perform the cluster process where the audio signals are analyzed. For example, Examiner notes that the feature vectors may be indicative of correlation to a degree of similarity in spatial distance with which voice signatures may be grouped/clustered, where the feature vectors may be clustered/grouped/classified into a plurality of groups/clusters each having a similarity in Fig. 5 and the descriptions in pages 8-9 of the instant application.
On page 8 of the Remarks, Applicant alleges that Second, neither Kumar, Murthy, nor the combination teaches or suggests "generate an output comprising a number of occupants in the room in response to the comparison."
Examiner respectfully disagrees.
Kumar teaches determining an occupancy state, but does not explicitly disclose “a number of occupants” in a room. Note that, under the broadest reasonable interpretation, the limitation of “a number of occupants” is indicative of an occupancy state in a room, because the claimed invention does not present specify how the number of occupants itself in the room are calculated using the detected motion and audio samples. Further, at least paragraphs 0026 and 0037-0040 of MURTHY teaches determining/estimating a number of occupants at a location using multiple sensed signals detected from motion sensor (Para 0026, “The disclosed system may include both a motion detector subsystem including one or more motion sensors, such as a lighting system having one or more embedded PIR sensors, and a radiofrequency (RF) subsystem including one or more RF transceivers, such as a network router. Data gathered by the RF transceivers is used to generate a first occupant estimate with a first algorithm and the data gathered by the motion sensors is used to generate a second occupant estimate with a second algorithm. The estimates produced by the two sensor modalities are fused to produce an accurate count of occupants at a location”).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The current 35 USC 101 analysis is based on the current guidance (Federal Register vol. 79, No. 241. pp. 74618-74633). The analysis follows several steps. Step 1 determines whether the claim belongs to a valid statutory class. Step 2A prong 1 identifies whether an abstract idea is claimed. Step 2A prong 2 determines whether any abstract idea is integrated into a practical application. If the abstract idea is integrated into a practical application the claim is patent eligible under 35 USC 101. Last, step 2B determines whether the claims contain something significantly more than the abstract idea. In most cases the existence of a practical application predicates the existence of an additional element that is significantly more.
The 35 USC 101 analysis between each element of claims and its combination is presented in the table below
Claim number and elements
Judicial exception (Step 2A Prong one)
Practical application (Step 2A Prong two)/ Significantly more (Step 2B)
Claim 1
Step2A Prong one: Yes
Step 2A Prong two: No / Step 2B: No
A system for estimating a number of occupants in a room, the system comprising:
a motion sensor configured to generate motion samples;
a microphone configured to generate audio samples;
a computing system;
a communication interface configured to communicate with the computing system;
Step 1: Yes, statutory class
“a motion sensor”, “a microphone”, “a computing system” and “a communication interface” are high level of generalities.
“generate motion samples” and “generate audio samples” are insignificant extra-solution activities performed by a generic computer component.
at least one processor configured to:
detect motion events from the motion samples to determine a first estimated number of occupants in the room based on at least one of a number of the detected motion events and a type of the detected motion events;
analyze the audio samples to derive a series of feature vectors from the analyzed audio samples;
cluster the series of feature vectors into a plurality of clusters using the first estimated number of occupants as a seed value for a count of the plurality of clusters;
count the plurality of clusters to determine a second estimated number;
compare the first estimated number to the second estimated number; and
abstract idea
math
math
math or mental
math
math
“processor” is a high level of generality.
“detect motion events …” is an insignificant pre-solution activity to collect routine data used to perform a math process based on the collected data.
“to determine a first estimated number of occupants …” is a math process performed based on the collected data.
“analyze …to derive a series of feature vectors ….”, “cluster the series of feature vectors … ” , “count … to determine …”, and “compare …” are math processes.
generate an output comprising a number of occupants in the room in response to the comparison.
mental process or mathematical concept
“generate an output” is a math process.
Claims 1-3, 6-10, 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-3, 6-10, 13-20 are directed to an abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as addressed below and presented in the above table.
Step 2A: Prong One
Regarding Claim 1, the limitations recited in Claim 1, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mathematical calculations and/or the mind, as presented in the above table. Nothing in the claim elements precludes the step from practically being performed in the mind and/or the mathematical calculations. For example, “… to determine a first estimated number of occupants in the room based on at least one of a number of the detected motion events and a type of the detected motion events” in the context of this claim may encompass manually calculating or inferring a first estimated number of occupants based on routine data (i.e., the detected motion events). Similarly, “analyze the audio samples to derive a series of feature vectors from the analyzed audio samples” in the context of this claim may encompass manually calculating or inferring the series of feature vectors, where the feature vectors are indicative of mathematical values/factors/characteristics which are performed by a mathematical algorithm and/or an arithmetic computer program. The limitations of “cluster the series of feature vectors into a plurality of clusters using the first estimated number of occupants as a seed value for a count of the plurality of clusters” may encompass manually calculating or inferring a plurality of clusters using the series of feature vectors, where the clustering is indicative of mathematical calculations/relationship such as partitioning/grouping/classifying/filtering processes which are performed by a mathematical algorithm and/or an arithmetic computer program. The limitations of “count the plurality of clusters to determine a second estimated number; compare the first estimated number to the second estimated number” in the context of this claim may encompass manually calculating or inferring the second estimated number by counting the plurality of clusters, where “compare first estimated number to the second estimated number” is indicative of mathematical calculation which are performed by a mathematical algorithm and/or an arithmetic computer program.
Step 2A: Prong Two
This judicial exception is abstract ideal itself and not integrated into a practical application. In particular, the specification details use of a processor to perform the mathematical calculations using a mathematical algorithm and/or an arithmetic computer program to perform “… to determine a first estimated number of occupants in the room based on at least one of a number of the detected motion events and a type of the detected motion events”, “analyze the audio samples to derive a series of feature vectors from the analyzed audio samples”, “cluster the series of feature vectors into a plurality of clusters using the first estimated number of occupants as a seed value for a count of the plurality of clusters” and “count the plurality of clusters to determine a second estimated number; compare the first estimated number to the second estimated number”. The motion sensor, the microphone, the computing system, the processor and the communication interface are recited at a high-level of generality (i.e., as generic computer components and techniques performing a generic computer function of generating the motion samples and the audio samples which are indicative of collecting routine data used to perform mathematical calculations such that it amounts no more than mere instructions to apply the exception using a generic computer component.
The limitations of “generate motion samples … generate audio samples … communicate with the computing system” are insignificant extra-solution activities to collect routine data (i.e., motion and audio samples) used for mathematical calculation, and to be performed by a generic computer component. The limitation of “detect motion events …” is an insignificant extra-solution activity to collect routine data (i.e., a number and type of motion events) used to perform a math process based on the collected data. There is no showing of integration into a practical application such as an improvement to the functioning of a computer, or to any other technology or technical field, or use of a particular machine.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of “a motion sensor configured to generate motion samples; a microphone configured to generate audio samples; a computing system; a communication interface configured to communicate with the computing system; … at least one processor configured to: detect motion events from the motion samples” are insignificant extra-solution activity to collect routine data (i.e., samples and a number and type of motion events) used to perform a math process based on the collected data, and are well-understood, routine, conventional activities previously known to the industry, as at least paragraphs 0009 and 0028, and Figs. 4, 5 of Kumar reference (US 20140379305 A1) teach. See MPEP 2106.05(d).
As discussed above, with respect to integration of the abstract idea into a practical application, using the processor to perform the processes of “… to determine a first estimated number of occupants in the room based on at least one of a number of the detected motion events and a type of the detected motion events”, “analyze the audio samples to derive a series of feature vectors from the analyzed audio samples”, “cluster the series of feature vectors into a plurality of clusters using the first estimated number of occupants as a seed value for a count of the plurality of clusters” and “count the plurality of clusters to determine a second estimated number; compare the first estimated number to the second estimated number” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept cannot provide statutory eligibility. Claim 1 is not patent eligible.
Regarding Claims 2-3, 6-7 and 16-20, the limitations are further directed to an abstract idea, as described in claim 1. The additional elements of “a luminaire” and “passive infrared sensor” are high level of generalities. The limitations of “k-means clustering” in claim 6 and “vectors of Mel Frequency Cepstral Coefficients” in claim 7 are indicative of a mathematical value/concept/factor. The limitations of “the clustering of the series of feature vectors into the plurality of clusters is based on degrees of similarity in vocal signature between the feature vectors” in claim 16, “wherein pairs of the feature vectors are separated from each other by a distance that correlates to a degree of similarity in vocal signature between the pair” in claim 17, “wherein the number of clusters corresponds to a number of different vocal signatures in the audio samples” in claim 18, and “wherein the generating of the output comprising the number of occupants in the room is in response to determining that the first estimated number of occupants is the same as the second estimated number of occupants” in claim 19, may encompass mathematical processes related to manually calculating or inferring the plurality of clusters by clustering/grouping/classifying the feature vectors and calculating or inferring the similarity where the similarity is indicative of a mathematical concept/value/factor. (MPEP 2106.04(a)(2)). The limitation of “in response to determining that the first estimated number of occupants is greater than the second estimated number of occupants, adjusting one or more parameters that define how to detect the motion events” in claim 20 may encompass manually calculating or inferring the parameters, where “adjusting one or more parameters …” is an insignificant post-solution activity based on result of the mathematical calculations without tangible or physical elements/components and/or integration of improvements to be indicative of specific features/structure/acts how and or with what to adjust the parameters. (MPEP 2106.04(a)(2)). For the reasons described above with respect to Claim 1, the judicial exceptions are not meaningfully integrated into a practical application, or amount to significantly more than the abstract idea
Regarding Claim 8, it is a method type claim having similar limitations as of claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above.
Regarding Claims 9-10 and 13-15, the limitations are further directed to an abstract idea, as described in claim 8. The additional element of “a speaker device” in claim 15 is a high level of generality to perform a generic computer function of a generic computer component. For the reasons described above with respect to Claims 2-3 and 6-7, the judicial exceptions are not meaningfully integrated into a practical application, or amount to significantly more than the abstract idea.
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.
1. Claims 1-3, 7-10, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (US 20140379305 A1, hereinafter referred to as “Kumar” cited in IDS dated 03/09/2022) in view of MURTHY et al. (US 20210041523 A1, hereinafter referred to as “MURTHY”).
Regarding Claim 1, Kumar teaches a system for estimating a number of occupants in a room (Para 0011, “detecting an occupancy state of a monitored area”), the system comprising:
a motion sensor (Fig. 5, 105 passive infrared sensor) configured to generate motion samples (Para 0098-0100, “the occupancy sensor comprises a low-voltage passive infrared (PIR) sensor and ultrasonic transducer sensor. … generates an occupancy signal based on the occupancy state of that monitored area”);
a microphone (Fig. 2, 102a, 102b ultrasonic transducer; Fig. 5, 106 audio sensor) configured to generate audio samples (Para 0009, “ultrasonic sensor generates a signal based on sensed ultrasonic echo of the monitored area”; Para 0098-0100; Para 0113, “The first and second pair 102a, 102b of ultrasonic transducer sensors generate high frequency sound waves and evaluate the echoes which is received back by the sensors.”);
a computing system (Fig. 4, 407 computer);
a communication interface (Fig. 4, 111 wireless communication interface) configured to communicate with the computing system (Para 0106, “communicate on the control network 40 via one or more communication interfaces”);
at least one processor (Fig. 4, 104) configured to:
detect motion events from the motion samples to determine a first estimated number of occupants in the room based on at least one of a number of the detected motion events and a type of the detected motion events (Para 0009, “receiving at least one signal from the passive infrared sensor and the ultrasonic transducer sensor”; Para 0098-0100, “the occupancy sensor comprises a low-voltage passive infrared (PIR) sensor and ultrasonic transducer sensor. … generates an occupancy signal based on the occupancy state of that monitored area”;
analyze the audio samples to derive a series of feature vectors from the analyzed audio samples (Para 0009, “receiving at least one signal from the passive infrared sensor and the ultrasonic transducer sensor …. the processor cause acts to be performed including: receiving at least one signal from the passive infrared sensor and the ultrasonic transducer sensor; sampling the at least one signal at an analog to digital converter to generate at least one sampled signal; passing the at least one sampled signal through a digital bandpass filter to produce at least one filtered signal”; Para 0133, “the sensor signal is sampled at an ADC to produce a sampled signal. The ADC may be input a reference voltage, such as 3.3 Volts and sampled at a one kilohertz sampling rate”; Note that, under the broadest reasonable interpretation, the step of “analyze the audio sample to derive a series of feature vectors ~” is indicative of processing audio signals such as sampling at a specific time interval and converting the analogue audio signal to a digital signal, which is taught by at least paragraphs 0009 and 0133. Under this interpretation, “a series of feature vectors are indicative of sampled signals and/or digitally converted signal processed by sampling process.);
cluster the series of feature vectors into a plurality of clusters using the first estimated number of occupants as a seed value for a count of the plurality of clusters (Para 0011, “sampling a first signal from a passive infrared sensor and a second signal from an ultrasonic transducer sensor at an analog to digital converter to produce a first sampled signal and a second sampled signal; filtering the first sampled signal and the second sampled signal through a digital bandpass filter to produce a first filtered signal and a second filtered signal”; Para 0133, “the sensor signal is sampled at an ADC to produce a sampled signal. The ADC may be input a reference voltage, such as 3.3 Volts and sampled at a one kilohertz sampling rate”; Note that, under the broadest reasonable interpretation, the limitation of “clustering the series of feature vectors …” is indicative of process to partition/classify/group/filter signals into a plurality of partitioned/classified/filtered signals or group, for example, by sampling the audio samples at a specific time period and converting the analogue audio signal to a digital signal, and filtering the sampled signals into the plurality of the filtered signals, which is taught by at least paragraph 0011. Further, under the broadest reasonable interpretation, the limitation of “using the first estimated number of occupants as a seed value for a count of the plurality of clusters” is indicative of determining a reference value such as a sampling rate (i.e., seed value determined by the first estimated number of occupants) to be used for a process to partition/classify/group/filter signals into a plurality of partitioned/classified/filtered signals or groups, for example, by sampling the audio samples at a specific time period at the sampling rate and converting the analogue audio signal to a digital signal, and filtering the sampled signals into the plurality of the filtered signals. Under these interpretations, at least paragraphs 0011 and 0133 of Kumar teach filtering the sampled signals to thereby classify the sampled audio signals into a plurality of filtered signals (i.e., a plurality of clusters) using the refence value such as the sampling rate);
…
generate an output comprising a number of occupants in the room … (Para 0011, “determining the occupancy state of the monitored area …”).
Kumar teaches determining an occupancy state, but does not explicitly disclose “a number of occupants” in a room. Note that, under the broadest reasonable interpretation, the limitation of “a number of occupants” is indicative of an occupancy state in a room, because the claimed invention does not present specify how the number of occupants itself in the room are calculated using the detected motion and audio samples. Further, MURTHY teaches determining/estimating a number of occupants at a location using multiple sensed signals detected from motion sensor (Para 0026, “The disclosed system may include both a motion detector subsystem including one or more motion sensors, such as a lighting system having one or more embedded PIR sensors, and a radiofrequency (RF) subsystem including one or more RF transceivers, such as a network router. Data gathered by the RF transceivers is used to generate a first occupant estimate with a first algorithm and the data gathered by the motion sensors is used to generate a second occupant estimate with a second algorithm. The estimates produced by the two sensor modalities are fused to produce an accurate count of occupants at a location.”; Para 0037-0040).
Kumar fails to explicitly disclose, but MURTHY teaches
count the plurality of clusters to determine a second estimated number (Para 0007 (“determining a number of occupants at a location using multiple modalities … calculating a first occupant estimate from the first set of data … calculating a second occupant estimate from the second set of data using a second algorithm … to create a fused occupant estimate corresponding to the number of occupants at the location); compare the first estimated number to the second estimated number (Para 0007, “fusing the first occupant estimate and the second occupant estimate to create a fused occupant estimate corresponding to the number of occupants at the location”; Para 0009, “comparing the first set of data and the coordinates of each of the one or more motion sensors to the second set of data to localize positions of the distant occupants … comparing the first data set to the second data set to form one or more new data points in which the true occupant count is set as the second occupant estimate”); and generate an output comprising a number of occupants in the room in response to the comparison (Para 0007, “… fusing the first occupant estimate and the second occupant estimate to create a fused occupant estimate corresponding to the number of occupants at the location).
Kumar and MURTHY are both considered to be analogous to the claimed invention because they are in the same field of occupancy sensors and determining the number of occupants in a location. Note that, under the broadest reasonable interpretation, the limitation of “count the plurality of clusters … compare … and generate an output …” is indicative of estimating a second occupant number based on the second set data (i.e., the plurality of the clusters) and estimating/creating final occupants by comparing a first occupant estimate to a second occupant estimate. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kumar to incorporate the teachings of MURTHY by providing operations for determining a number of occupants in a room by detecting and analyzing sensed signals from two types of occupancy sensors such as a motion sensor and ultrasonic sensor, estimating a second occupant number based on the second set data (i.e., the plurality of the clusters) and estimating/creating final occupants by comparing a first occupant estimate to a second occupant estimate, taught by MURTHY at least at paragraphs 0007, 0009, 0026 and 0037-0040.
Regarding Claim 2, Kumar teaches comprising a luminaire (occupancy sensor), the luminaire comprising the motion sensor and microphone (Para 0009, “occupancy sensor includes a passive infrared sensor, an ultrasonic transducer sensor,”).
Regarding Claim 3, Kumar teaches wherein the motion sensor comprises a passive infrared sensor and wherein the motion events comprise at least a minor motion event and a major motion event (Para 0009, “occupancy sensor includes a passive infrared sensor, an ultrasonic transducer sensor,”; Para 0098-0100, “the occupancy sensor comprises a low-voltage passive infrared (PIR) sensor and ultrasonic transducer sensor. … generates an occupancy signal based on the occupancy state of that monitored area”).
Regarding Claim 7, Kumar teaches wherein the series of feature vectors comprise vectors of Mel Frequency Cepstral Coefficients (Para 0038, “In some embodiments, an audio waveform or speech signal may be represented by a sequence of speech parameter vectors or speech feature vectors. In speech analysis, the speech waveform can be converted in a series of feature vectors which represent a subsequence of the speech waveform”; Para 0041, “feature vectors are extracted every 10 ms using an overlapping analysis window of around 25 ms. In some embodiments, the encoding scheme may be based on mel-frequency cepstral coefficients (MFCCs)”). Note that, under the broadest reasonable interpretation, paragraph 0134 teaches that the sampled signal comprises frequencies coefficients).
Note that, under the broadest reasonable interpretation, vectors of Mel Frequency Cepstral Coefficients are indicative of frequencies of sampled signals sampled at a specific time interval, which is taught by Kumar’s sampling process in ADC operation at least at paragraphs 0113 and 0133-0134.
Kumar in view of MURTHY fails to explicitly disclose “Mel Frequency Cepstral Coefficients”. However, Examiner takes OFFICIAL NOTICE that “Mel Frequency Cepstral Coefficients” used for a process related to audio/speech analysis is merely indicative or well known in the art at the effective filing date, where “Mel Frequency Cepstral Coefficients” are used for representing feature vectors in audio/speech analysis and/or its data processing model as taught by KEMPANNA et al. (US 20190221317 A1 (see at least paragraphs 0038 and 0041, “feature vectors are extracted every 10 ms using an overlapping analysis window of around 25 ms. In some embodiments, the encoding scheme may be based on mel-frequency cepstral coefficients (MFCCs)”), as the “Mel Frequency Cepstral Coefficients” itself is not critical to be distinctly result-effective features but may be selected by routine experimentation and/or a user’s interest/preference.
Regarding Claim 8, it is a method type claim having similar limitations as of claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above.
Regarding Claim 9, it is dependent on claim 8 and has similar limitations as of claim 2 above. Therefore, it is rejected under the same rationale as of claim 2 above.
Regarding Claim 10, it is dependent on claim 8 and has similar limitations as of claim 3 above. Therefore, it is rejected under the same rationale as of claim 3 above.
Regarding Claim 14, it is dependent on claim 8 and has similar limitations as of claim 7 above. Therefore, it is rejected under the same rationale as of claim 7 above.
Regarding Claim 19, Kumar fails to explicitly disclose, but MURTHY teaches wherein the generating of the output comprising the number of occupants in the room is in response to determining that the first estimated number of occupants is the same as the second estimated number of occupants (As presented above in rationale of Claim 1, at least paragraph 0046 of MURTHY teach determining/estimating a number of occupants at a location using multiple sensed signals detected from motion sensor, and paragraphs 0007 and 0009 teach creating a fused occupant estimate based on considering the first occupant estimate and the second occupant).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kumar to incorporate the teachings of MURTHY by providing operations for determining a number of occupants in a room by considering a first occupant estimate to a second occupant estimate which may include a comparison result between two occupant estimates such as being same therebetween, taught by MURTHY at least at paragraphs 0007, 0009, 0026 and 0037-0040.
2. Claims 6, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar in view of MURTHY, and further in view of Sidhu et al. (US 20180007431 A1, hereinafter referred to as “Sidhu”).
Regarding Claim 6, Kumar in view of MURTHY fails to explicitly disclose, but Sidhu teaches wherein the feature vectors are clustered according to k-means clustering (Para 0134, “non-hierarchical clustering can be performed using the K-means method, at least because the sample size (1,065) is large”).
Note that, under the broadest reasonable interpretation, k-means method in the field of clustering the feature vectors is well-known in the art at the effective filing date, where k-means method is used for data processing of audio signals in audio signal analysis, taught by Sidhu at least at paragraph 0134.
Sidhu is considered to be analogous to the claimed invention because it is in the same field of receiving the image data and the audio data and determining an identity of the video displayed on the display based on the audio data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kumar in view of MURTHY to incorporate the teachings of Sidhu by providing audio data to be received from a speaker and to thereby be sampled and cluster the feature vectors using k-means method, taught by Sidhu at least at paragraphs 0134.
Regarding Claim 13, it is dependent on claim 8 and has similar limitations as of claim 6 above. Therefore, it is rejected under the same rationale as of claim 6 above.
Regarding Claim 15, Kumar in view of MURTHY fails to explicitly disclose, but Sidhu teaches further comprising determining whether a cluster among the clusters corresponds to audio originating from a speaker device (Abstract, Para 0004, “microphone is disposed in proximity to the display to acquire audio data emitted by a speaker coupled to the display”; Para 0065).
Sidhu is considered to be analogous to the claimed invention because it is in the same field of receiving the image data and the audio data and determining an identity of the video displayed on the display based on the audio data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kumar in view of MURTHY to incorporate the teachings of Sidhu by providing audio data to be received from a speaker and to thereby be sampled, taught by Sidhu at least at paragraphs 0004 and 0065.
3. Claims 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar in view of MURTHY, and further in view of LeBoeuf et al. (US 20110075851 A1, hereinafter referred to as “LeBoeuf”).
Regarding Claim 16, Kumar in view of MURTHY fails to explicitly disclose, but LeBoeuf teaches wherein the clustering of the series of feature vectors into the plurality of clusters is based on degrees of similarity in vocal signature between the feature vectors (at least paragraphs 0007, 0056-0057 and 0110 teach a sound-similarity between the feature vectors of audio selection by calculating the distance between the clustered feature vectors).
LeBoeuf is considered to be analogous to the claimed invention because it is in the same field of audio signal processing, and sound object recognition and labeling. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kumar in view of MURTHY to incorporate the teachings of LeBoeuf by providing process for determining similarity of the feature vectors of audio selection by calculating the distance between the clustered feature vectors, taught by LeBoeuf at least at paragraphs 0007, 0056-0057 and 0110.
Regarding Claim 17, Kumar in view of MURTHY fails to explicitly disclose, but LeBoeuf teaches wherein pairs of the feature vectors are separated from each other by a distance that correlates to a degree of similarity in vocal signature between the pair (at least paragraphs 0007-0009, 0056-0057 and 0110 teach the feature vectors separated or partitioned by time-domain values (sound amplitude measures) and frequency-domain values (sound spectral content) which are indicative distance in view of a sound-similarity).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kumar in view of MURTHY to incorporate the teachings of LeBoeuf by providing the feature vectors separated or partitioned by time-domain values (sound amplitude measures) and frequency-domain values (sound spectral content) which are indicative distance in view of a sound-similarity, taught by LeBoeuf at least at paragraphs 0007-0009, 0056-0057 and 0110.
4. Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar in view of MURTHY and LeBoeuf, and further in view of CAHILL et al. (US 20160277863 A1, hereinafter referred to as “CAHILL”).
Regarding Claim 18, Kumar in view of MURTHY fails to explicitly disclose, but CAHILL teaches wherein the number of clusters corresponds to a number of different vocal signatures in the audio samples (at least paragraphs 0049, 0053 and 0114 teach the multi-dimensional event signature for each respective sound event by classifying/clustering each sound event into feature vectors).
CAHILL is considered to be analogous to the claimed invention because it is in the same field of acoustic imaging and computer audio vision processes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kumar in view of MURTHY and LeBoeuf to incorporate the teachings of CAHILL by providing the multi-dimensional event signature for each respective sound event by classifying/clustering each sound event into feature vectors, taught by CAHILL at least at paragraphs 0049, 0053 and 0114.
5. Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar in view of MURTHY, and further in view of Chemel et al. (US 9014829 B2, hereinafter referred to as “Chemel”).
Regarding Claim 20, Kumar in view of MURTHY fails to explicitly disclose, but Chemel teaches in response to determining that the first estimated number of occupants is greater than the second estimated number of occupants, adjusting one or more parameters that define how to detect the motion events (at least abstract and Col. 4 lines 13-30 teach adjusting the sensing parameters f the occupancy sensor based on the analysis; “The processor can partition the n-dimensional array into clusters corresponding to different types of occupancy events and adjust the sensing parameters, which include, but are not limited to sensor timeout, gain, threshold, offset, and/or sensitivity, based on the partitioning. Alternatively, or in addition, the processor can determine a distribution of a frequency (e.g., a histogram) with which the occupancy sensor detects occupancy events and, optionally, adjust the sensing parameters based on the frequency distribution”).
Chemel is considered to be analogous to the claimed invention because it is in the same field of occupancy sensing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kumar in view of MURTHY to incorporate the teachings of Chemel by providing operation for adjusting the sensing parameters f the occupancy sensor based on the analysis, taught by Chemel at least at abstract and Col. 4 lines 13-30.
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hyman et al. (US 20150177716 A1) teaches occupancy detection system including a plurality of sensors located within an area, communication links to be established between each of the sensors and a controller, and the controller operative to receive sense data from the plurality of sensors, group the data according to identified groupings of the plurality of sensors, and sense occupancy within at least a portion of the area based on data analytics processing of one or more of the groups of sensed data.
Conclusion
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/BYUNG RO LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858
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