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 .
Responses to Amendments and Arguments
The amendments filed 7/21/2026 have been entered. Claims 1 and 8 are amended, Claim 17 is canceled and Claim 21 is newly added. Claims 1-3, 6-10, 13-16, and 18-21 remain pending in the application.
Applicant's argument and amendments filed 7/21/2026 with respect to
the rejection of claims 1-3, 6-10 and 13-20 directed to a judicial exception under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection under 35 U.S.C. 103 is withdrawn.
Applicant's argument and amendments filed 7/21/2026 with respect to
the rejection of claims 1-3, 6-10, and 13-21 directed to a judicial exception under 35 U.S.C. 101 have been fully considered but are not persuasive.
On pages 6-8 of the Remarks, Applicant alleges that Even assuming, only for purposes of discussion, that certain recited processing operations involve mathematical concepts, the claims are not directed merely to mathematical calculations or mental activity. … At Step 2A, Prong Two … The claims reflect that disclosed technical improvement by reciting the particular interaction between motion-based occupancy estimation and audio-based clustering. In particular, the motion-derived first estimate is not merely displayed or stored but it is used as a seed value for clustering audio-derived feature vectors, and the resulting audio-based estimate is compared against the motion-based estimate to generate the occupancy output. … The claims therefore use any alleged mathematical operations in a concrete sensor-processing architecture that improves occupancy estimation and spatial analysis, rather than generally linking a calculation to a technological environment. … the claimed comparison and output are part of a concrete spatial analysis process that verifies and improves occupancy estimation using two sensor modalities. … Applicant respectfully disagrees at least because the claims, read as a whole and in light of the specification, are directed to processing motion samples and audio samples generated by physical sensors, deriving feature vectors from audio samples, clustering those feature vectors using a seed value, clustering based on degrees of similarity in vocal signatures between the feature vectors, calculating distances between pairs of feature vectors in the series and generating a number of occupants in the room from the comparison of sensor-derived estimates. These operations are not merely a person thinking about numbers in the abstract. … For similar reasons, the claims also recite significantly more under Step 2B. Examiner respectfully disagrees.
The claimed features 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, wherein the clustering is based on degrees of similarity in vocal signature between the feature vectors and includes calculating distances between pairs of feature vectors in the series, each distance correlating to a degree of similarity in vocal signature between a pair of feature vectors” 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, where the clustering is a mathematical degree/concept/value indicative of degrees of similarity in vocal signature between the feature vectors, and the clustering is a mathematical concept/relationship which is calculated based on the pairs of feature vectors, which are mathematical values. The claimed features 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 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.
The claimed features 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.
The additional elements of the motion sensor, the microphone, the computing system, the processor and the communication interface are recited at a high-level of generality recited to be 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, without any specific or tangible feature/operation/act related to generating motion samples, generating audio samples and communicating with the computing system. (See MPEP 2106.05(f) and 2106.05(g)).
Claim 1 does not present tangible or physical elements/components and/or integration of, and a technical solution to a technical problem by providing, improvements to be indicative of specific features/structure/acts, for example, how and or with what to estimate a number of occupants. (See MPEP 2106.04(d)). (See the detailed response presented below).
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 wherein the clustering is based on degrees of similarity in vocal signature between the feature vectors and includes calculating distances between pairs of feature vectors in the series, each distance correlating to a degree of similarity in vocal signature between a pair of feature vectors;
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.
“the clustering is based on degrees of similarity in vocal signature between the feature vectors …” is a math process.
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, 13-16 and 18-21 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, 13-16 and 18-21 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, wherein the clustering is based on degrees of similarity in vocal signature between the feature vectors and includes calculating distances between pairs of feature vectors in the series, each distance correlating to a degree of similarity in vocal signature between a pair of feature vectors” 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, where the clustering is a mathematical degree/concept/value indicative of degrees of similarity in vocal signature between the feature vectors, and the clustering is a mathematical concept/relationship which is calculated based on the pairs of feature vectors, which are mathematical values.
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, 16 and 18-21, 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 the number of clusters corresponds to a number of different vocal signatures in the audio samples” in claim 18, “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, and “wherein the series of feature vectors from the analyzed audio samples are filtered to select a predetermined set of frequencies directed to a human voice” in claim 21 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.
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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BYUNG RO LEE whose telephone number is (571)272-3707. The examiner can normally be reached on Monday-Friday 8:30am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lee Rodak can be reached on (571) 270-5628. The fax phone number for the organization where this application or proceeding is assigned is 571-273-2555.
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/BYUNG RO LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858
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