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 .
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.
Claims 1-11 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. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 1: Statutory Category
The claims are directed to a method for detecting human presence using wireless network signal data and machine learning.
Conclusion: The claims fall within a statutory category (process/method).
Step 2A: Judicial Exception (Abstract Idea) — Prong 1
Identify the focus of the claim (as a whole):
The claims recite a method comprising:
- Deploying wireless transceivers in an area,
- Exchanging wireless signals with status data,
- Training a machine learning model using labeled signal data to distinguish between “human present” and “no human present,”
- Receiving new signal data,
- Using the trained model to infer human presence, number, and/or location,
- Optionally, using the inference to control a second system (e.g., lighting, HVAC, security).
Potential Abstract Idea:
- The core of the claims is **collecting data, analyzing it using mathematical/statistical/machine learning techniques, and making a decision based on the result (i.e., detection/classification).
- The claims also recite training and applying a machine learning model to classify new data.
*Relevant USPTO Guidance:
- Mathematical concepts (including mathematical relationships, formulas, and calculations) are recognized as abstract ideas.
- Certain methods of organizing human activity (e.g., collecting and analyzing information) are also abstract ideas.
Conclusion:
- The claims, as drafted, are directed to an abstract idea: (i) mathematical/statistical processing (machine learning classification), and (ii) collecting and analyzing information to detect human presence.
Step 2A: Prong 2 — Integration Into a Practical Application?
Does the claim integrate the abstract idea into a practical application?
- The claims recite that the machine learning output is used to control a second system (e.g., only after detection, a lighting/HVAC/security system is operated).
- However, the use of generic computer/network components (transceivers, servers, machine learning system) is described at a high level, with no details of a novel hardware implementation or a specific improvement to computer/network technology.
- The steps of collecting data, training a model, and making a decision based on model output are recited at a functional level, without a particular technical solution beyond the abstract idea itself.
USPTO Example:
- Claims that merely use a generic computer to automate data collection and analysis, even if the output is used to trigger a generic action, are typically not “integrated into a practical application” unless the claims provide a specific improvement to computer technology or another technical field.
Conclusion:
- The claims do not appear to integrate the abstract idea into a practical application. The recited steps are implemented using conventional hardware and do not recite a specific improvement to the functioning of a computer, network, or other technology.
Step 2B: Inventive Concept
Do the claims add “significantly more” than the abstract idea?
- The only additional elements are conventional transceivers, generic servers, and a generic machine learning system.
- The claims do not recite a specific, unconventional arrangement of hardware, nor do they solve a technical problem in a novel way.
- The use of machine learning for classification based on wireless signal data is itself abstract and implemented on generic hardware.
Conclusion:
- The claims do not recite an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. All steps are performed using conventional technology in a routine manner.
Sample 101 Rejection Language
Claims 1–11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea and does not recite an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
Specifically, the claims are directed to methods of collecting wireless signal data, training and applying a machine learning model to classify the data as indicating the presence or absence of a human, and optionally using the result to control a generic environmental system (e.g., lighting, HVAC, security). The claims recite the abstract idea of mathematical/statistical analysis and classification of information, which is a judicial exception. The additional elements—such as generic transceivers, servers, and machine learning systems—are recited at a high level of generality and do not amount to significantly more than the abstract idea itself. The claims do not recite a specific improvement to the functioning of a computer or other technology, nor do they effect an unconventional transformation of data or hardware. Accordingly, the claims are not eligible under § 101.
Allowable Subject Matter
Claims 1-11 are allowed.
The following is an examiner’s statement of reasons for allowance:
Prior art such as Clausen (US Pub No. 2016/0044467) discloses a user may
manually indicate his or her location using a first IPS-enabled device and share the location with a second device. The first IPS-enabled device may communicate to an IPS backend the user-specified location and information about beacons and their respective signal strengths detected by the first IPS-enabled device at the time that the user indicated his or her location. The IPS backed may forward the location information to a second device, which may or may not be IPS-enabled. The IPS backend may also use the user-specified location and the beacon information received from the first IPS-enabled device to update and maintain a global beacon database to ensure that the global database remains up-to-date for future indoor positioning operations. See abstract.
Prior art such as Kamlani (US Pub No. 2015/0371139) discloses methods and systems of localizing a device are presented. In an example method, a communication signal from a device is received by a wireless reference point during a period of time. A sequence of values is generated from the communication signal, as received by the wireless reference point, during the period of time. The sequence of values is supplied to a learning model configured to generate an output based on past values of the sequence of values and at least one predicted future value of the sequence of values. The current location of the device is estimated during the period of time based on the output of the learning model. See abstract.
Prior art such as Parvizi et al. (US Pub No. 2014/0018095) discloses a system facilitating the calibration of a map-point grid for an indoor location determination, the grid includes several map points, each having a radio frequency (RF) data fingerprint being associated therewith. At least one of: (i) RF signal data from several RF sources, (ii) a user specified location indication, and (ii) tracking data from a sensor, the tracking data indicating a user's movement relative to a base map point, are received. The map-point grid is updated based on, at least in part, at least one of (i) adjusted RF data, the received RF data being adjusted using systematic analysis thereof, (ii) the tracking data, and (iii) the location indication. A user's location may be determined based on the fingerprints associated with the map-point grid, and sensor data. See abstract.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shin et al. (US Patent No. 10,217,120) discloses a Method And System For In-
store Shopper Behavior Analysis With Multi-modal Sensor Fusion.
Alameh et al. (US Pub No. 2015/0069242) discloses an Electronic Device And
Method For Detecting Presence.
IWATA et al. (US Pub No. 2015/0059248) discloses an AUTOMATIC DOOR SENSOR DEVICE.
Herrala et al. (US Pub No. 2011/0211563) discloses a LOCATION TRACKING SYSTEM.
Cleveland (US Pub No. 2007/0225000) discloses a Method And Procedure For Self Discovery Of Small Office Or Home Interior Structure By Means Of Acoustic Sensing And Profiling.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANTELL LAKETA HEIBER whose telephone number is (571)272-0886. The examiner can normally be reached on M-F from 9am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anthony Addy, can be reached at telephone number 571-272-7795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHANTELL L HEIBER/Primary Examiner, Art Unit 2645
July 24, 2026
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