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 1–30 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Analysis is provided for the claims under the guidelines of MPEP 2106.
Regarding claim 1:
Step 1:
The claim recites “[a] sensing node, comprising” the components that follow. Thus, the claim is to a machine, which is a statutory category of invention.
Step 2A prong 1:
The limitation “configure an artificial intelligence (AI) / machine learning (ML) model to be used for sensing” recites a mental process. A person could configure a model for sensing using judgement.
The limitation (bold only) “determine sensing target information by applying the AI/ML model to the sensing measurements” recites a mental process. A person could determine information about a sensed target from sensing measurements, using observation and judgment.
Thus, the claim recites an abstract idea.
Step 2A prong 2:
The further elements “one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to” recites the use of transceivers and computing components at a high level of generality. The elements thus merely recite the use of a computer as a tool to perform the abstract idea, and are equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The further element(bold only) “determine sensing target information by applying the AI/ML model to the sensing measurements” recites configuration and use of a model at a high level of generality. No particular model, method of configuration, or use is described. The element thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The further element “obtain sensing measurements” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
Thus, the additional elements merely recite the use of a computer as a tool to perform the abstract idea or recite insignificant extra-solution activity. Taken alone, the additional elements do not integrate the abstract idea into a practical application. Considering the elements together as an ordered combination adds nothing that is not present from examining the elements individually. The elements, individually or together, do not describe an improvement in the functioning of technology.
Step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
These additional claim elements recite mere instructions to apply the abstract idea:
“one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to”
(bold only) “determine sensing target information by applying the AI/ML model to the sensing measurements”
The further element “obtain sensing measurements” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 2:
For step 2A prong 1, claim 2 further limits claim 1 and the same elements in claim 2 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the sensing node comprises a user equipment (UE) or a base station (BS)” recites the inclusion of computing equipment at a high level of generality. The element thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The additional element “wherein the sensing node comprises a user equipment (UE) or a base station (BS)” recites mere instructions to apply the abstract idea.
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 3:
For step 2A prong 1, claim 3 further limits claim 1 and the same elements in claim 3 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors configured to configure the AI/ML model to be used for sensing comprises the one or more processors, either alone or in combination, configured to receive AI/ML model configuration information, the AI/ML model configuration information comprising the AI/ML model” recites the reception of configuration information at a high level of generality. The element thus recites mere data updating, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to configure the AI/ML model to be used for sensing comprises the one or more processors, either alone or in combination, configured to receive AI/ML model configuration information, the AI/ML model configuration information comprising the AI/ML model” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 4:
For step 2A prong 1, claim 4 further limits claim 3 and the same elements in claim 4 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors configured to receive the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to receive at least one of: information indicating an input to the AI/ML model; information indicating an output from the AI/ML model; information indicating an operating mode of the AI/ML model; or a parameter to be used by the AI/ML model” recites the reception of configuration information at a high level of generality. The element thus recites mere data updating, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to receive the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to receive at least one of: information indicating an input to the AI/ML model; information indicating an output from the AI/ML model; information indicating an operating mode of the AI/ML model; or a parameter to be used by the AI/ML model” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 5:
For step 2A prong 1, claim 5 further limits claim 1 and the same elements in claim 5 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors configured to configure the AI/ML model to be used for sensing comprises the one or more processors, either alone or in combination, configured to receive first information indicating an AI/ML model to be selected from a plurality of configured AI/ML models and to select the AI/ML model to be used for sensing from the plurality of configured AI/ML models based on the first information” recites the reception of configuration information at a high level of generality. The element thus recites mere data updating, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to configure the AI/ML model to be used for sensing comprises the one or more processors, either alone or in combination, configured to receive first information indicating an AI/ML model to be selected from a plurality of configured AI/ML models and to select the AI/ML model to be used for sensing from the plurality of configured AI/ML models based on the first information” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 6:
For step 2A prong 1, claim 6 further limits claim 1 and the same elements in claim 6 still recite an abstract idea.
The limitation (bold only) “wherein the one or more processors configured to configure the AI/ML model to be used for sensing comprises the one or more processors, either alone or in combination, configured to select the AI/ML model to be used for sensing from a plurality of configured AI/ML models based on at least one of: a position of the sensing node; or a speed of the sensing node” recites a mental process. A person could select a model based on the position or speed of a sensing node using observation and judgement.
Thus, the limitation is part of the abstract idea.
For step 2A prong 2, the further element (bold only) “wherein the one or more processors configured to configure the AI/ML model to be used for sensing comprises the one or more processors, either alone or in combination, configured to select the AI/ML model to be used for sensing from a plurality of configured AI/ML models based on at least one of: a position of the sensing node; or a speed of the sensing node” recites the use of processors in performing the mental process. The element thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The additional element (bold only) “wherein the one or more processors configured to configure the AI/ML model to be used for sensing comprises the one or more processors, either alone or in combination, configured to select the AI/ML model to be used for sensing from a plurality of configured AI/ML models based on at least one of: a position of the sensing node; or a speed of the sensing node” recites mere instructions to apply the abstract idea.
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 7:
For step 2A prong 1, claim 7 further limits claim 3 and the same elements in claim 7 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors, either alone or in combination, are further configured to train the AI/ML model” recites model training at a high level of generality. No particular method of training is described. The element thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The additional element “wherein the one or more processors, either alone or in combination, are further configured to train the AI/ML model” recites mere instructions to apply the abstract idea.
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 8:
For step 2A prong 1, claim 8 further limits claim 1 and the same elements in claim 8 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors configured to obtain the sensing measurements comprises the one or more processors, either alone or in combination, configured to measure a sensing signal transmitted by a transmitting node” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to obtain the sensing measurements comprises the one or more processors, either alone or in combination, configured to measure a sensing signal transmitted by a transmitting node” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 9:
For step 2A prong 1, claim 9 further limits claim 8 and the same elements in claim 9 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors configured to measure the sensing signal comprises the one or more processors, either alone or in combination, configured to measure the sensing signal using a plurality of antennas” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to measure the sensing signal comprises the one or more processors, either alone or in combination, configured to measure the sensing signal using a plurality of antennas” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 10:
For step 2A prong 1, claim 10 further limits claim 8 and the same elements in claim 10 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors configured to measure the sensing signal comprises the one or more processors, either alone or in combination, configured to measure a plurality of sensing signals at a plurality of frequencies and/or times” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to measure the sensing signal comprises the one or more processors, either alone or in combination, configured to measure a plurality of sensing signals at a plurality of frequencies and/or times” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 11:
For step 2A prong 1, claim 11 further limits claim 8 and the same elements in claim 11 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors configured to obtain the sensing measurements comprises the one or more processors, either alone or in combination, configured to obtain at least one of: a quantized amplitude of the sensing signal; a quantized phase of the sensing signal; a correlation coefficient between the sensing signal and codewords in a spatial domain; a correlation coefficient between the sensing signal and codewords in a time domain; a correlation coefficient between the sensing signal and codewords in a frequency domain; a delay-time spectrum of the sensing signal; a delay-Doppler spectrum of the sensing signal; a frequency-Doppler spectrum of the sensing signal; a time-Doppler spectrum of the sensing signal; or an angle of arrival spectrum of the sensing signal” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to obtain the sensing measurements comprises the one or more processors, either alone or in combination, configured to obtain at least one of: a quantized amplitude of the sensing signal; a quantized phase of the sensing signal; a correlation coefficient between the sensing signal and codewords in a spatial domain; a correlation coefficient between the sensing signal and codewords in a time domain; a correlation coefficient between the sensing signal and codewords in a frequency domain; a delay-time spectrum of the sensing signal; a delay-Doppler spectrum of the sensing signal; a frequency-Doppler spectrum of the sensing signal; a time-Doppler spectrum of the sensing signal; or an angle of arrival spectrum of the sensing signal” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 12:
For step 2A prong 1, claim 12 further limits claim 1 and the same elements in claim 12 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors configured to determine the sensing target information comprises the one or more processors, either alone or in combination, configured to determine at least one of: a count of target objects detected; a position of a target object; or a speed of a target object” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to determine the sensing target information comprises the one or more processors, either alone or in combination, configured to determine at least one of: a count of target objects detected; a position of a target object; or a speed of a target object” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 13:
For step 2A prong 1, claim 13 further limits claim 12 and the same elements in claim 13 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors configured to determine the sensing target information comprises the one or more processors, either alone or in combination, configured to determine at least one of: a position of the sensing node; or a speed of the sensing node” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to determine the sensing target information comprises the one or more processors, either alone or in combination, configured to determine at least one of: a position of the sensing node; or a speed of the sensing node” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 14:
For step 2A prong 1, claim 14 further limits claim 1 and the same elements in claim 14 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors, either alone or in combination, are further configured to transmit, via the one or more transceivers, the sensing target information to another node different from the sensing node” recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors, either alone or in combination, are further configured to transmit, via the one or more transceivers, the sensing target information to another node different from the sensing node” which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 15:
For step 2A prong 1, claim 15 further limits claim 14 and the same elements in claim 15 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors configured to transmit the sensing target information to another node different from the sensing node comprises the one or more processors, either alone or in combination, configured to transmit the sensing target information to at least one of a base station or a network entity” recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to transmit the sensing target information to another node different from the sensing node comprises the one or more processors, either alone or in combination, configured to transmit the sensing target information to at least one of a base station or a network entity” which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 16:
Step 1:
The claim recites “[a] network entity, comprising” the components that follow. Thus, the claim is to a machine, which is a statutory category of invention.
Step 2A prong 1:
The limitation “configure, based on the first information, an AI/ML model to be used by the sensing node for sensing” recites a mental process. A person could configure a model for sensing using judgement.
Thus, the claim recites an abstract idea.
Step 2A prong 2:
The further element “one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to” recites the use of transceivers and computing components at a high level of generality. The elements thus merely recite the use of a computer as a tool to perform the abstract idea, and are equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The further element “receive, via the one or more transceivers, first information indicating a capability of a sensing node to support an artificial intelligence (AI) / machine learning (ML) model for sensing” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The further element “send, via the one or more transceivers, to the sensing node, AI/ML model configuration information” recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The additional element “one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to” recites mere instructions to apply the abstract idea.
The element “receive, via the one or more transceivers, first information indicating a capability of a sensing node to support an artificial intelligence (AI) / machine learning (ML) model for sensing” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
The element “send, via the one or more transceivers, to the sensing node, AI/ML model configuration information” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 17:
For step 2A prong 1, claim 17 further limits claim 16 and the same elements in claim 17 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the network entity comprises a base station (BS), a sensing management function (SnMF), an artificial intelligence (AI) server, or a combination thereof” recites the inclusion of computing equipment at a high level of generality. The element thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The additional element “wherein the network entity comprises a base station (BS), a sensing management function (SnMF), an artificial intelligence (AI) server, or a combination thereof” recites mere instructions to apply the abstract idea.
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 18:
For step 2A prong 1, claim 18 further limits claim 16 and the same elements in claim 18 still recite an abstract idea.
For step 2A prong 2, “wherein the one or more processors configured to send the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to send the AI/ML model to be used” recites the transmission of a model or a model selection at a high level of generality. Theus the element recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to send the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to send the AI/ML model to be used” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 19:
For step 2A prong 1, claim 19 further limits claim 17 and the same elements in claim 19 still recite an abstract idea.
For step 2A prong 2, “wherein the one or more processors configured to send the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to send at least one of: information indicating an input to the AI/ML model; information indicating an output from the AI/ML model; information indicating an operating mode of the AI/ML model; or a parameter to be used by the AI/ML model” recites the transmission of a model or a model selection at a high level of generality. Theus the element recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to send the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to send at least one of: information indicating an input to the AI/ML model; information indicating an output from the AI/ML model; information indicating an operating mode of the AI/ML model; or a parameter to be used by the AI/ML model” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 20:
For step 2A prong 1, claim 20 further limits claim 16 and the same elements in claim 20 still recite an abstract idea.
For step 2A prong 2, “wherein the one or more processors configured to send the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to send information indicating an AI/ML model to be selected from a plurality of configured AI/ML models” recites the transmission of a model at a high level of generality. Theus the element recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors configured to send the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to send information indicating an AI/ML model to be selected from a plurality of configured AI/ML models” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 21:
For step 2A prong 1, claim 21 further limits claim 16 and the same elements in claim 21 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the one or more processors, either alone or in combination, are further configured to train one or more AI/ML models for sensing” recites model training at a high level of generality. No particular method of training is described. The element thus merely recites the use of a computer as a tool to perform the abstract idea, and is equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The additional element “wherein the one or more processors, either alone or in combination, are further configured to train one or more AI/ML models for sensing” recites mere instructions to apply the abstract idea.
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 22:
For step 2A prong 1, claim 22 further limits claim 21 and the same elements in claim 22 still recite an abstract idea.
For step 2A prong 2, “wherein the one or more processors, either alone or in combination, are further configured to send, via the one or more transceivers, to the sensing node, at least one of the one or more AI/ML models” recites the transmission of a model at a high level of generality. Theus the element recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors, either alone or in combination, are further configured to send, via the one or more transceivers, to the sensing node, at least one of the one or more AI/ML models” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 23:
For step 2A prong 1, claim 23 further limits claim 16 and the same elements in claim 23 still recite an abstract idea.
For step 2A prong 2, “wherein the one or more processors, either alone or in combination, are further configured to: transmit, via the one or more transceivers, a sensing signal; and receive, from the sensing node via the one or more transceivers, a sensing report comprising sensing target information generated according to the AI/ML model configuration information from measurements, by the sensing node, of the sensing signal” recites the transmission of a model at a high level of generality. Theus the element recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The element “wherein the one or more processors, either alone or in combination, are further configured to: transmit, via the one or more transceivers, a sensing signal; and receive, from the sensing node via the one or more transceivers, a sensing report comprising sensing target information generated according to the AI/ML model configuration information from measurements, by the sensing node, of the sensing signal” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claims 24–27:
For step 1, the claims recite “A method of radio frequency (RF) sensing performed by a sensing node, the method comprising” the steps that follow. Thus, the claims are to a process, which is a statutory category of invention. Claims 24–27 are otherwise analogous to claims 1, 3, and 5–6, respectively, and are found ineligible under 35 U.S.C. 101 by the same arguments.
Regarding claims 28–30:
For step 1, the claims recite “A method of radio frequency (RF) sensing performed by a network entity, the method comprising” the steps that follow. Thus, the claims are to a process, which is a statutory category of invention. Claims 28–30 are otherwise analogous to claims 16, 18, and 20, respectively, and are found ineligible under 35 U.S.C. 101 by the same arguments.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1–6, 8, 10, 12–15, and 24–27 rejected under 35 U.S.C. 102(a) (1) as being anticipated by Pijl, US Pre-Grant Publication No. 2015/0173037 (hereafter Pijl).
Regarding claim 1 and analogous claim 24:
Pijl teaches:
“A sensing node, comprising: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to”: Pijl, paragraphs 0039–0040, "As the device 2 [sensing node] in this embodiment is a mobile telephone or smartphone, the device 2 further comprises transceiver circuitry 10 [one or more transceivers] and associated antenna 12 for communicating wirelessly with a mobile communication network. The device 2 further comprises a memory module 14 [one or more memories] that can store program code for execution by the processor [one or more processors communicatively coupled to the one or more memories ] 8 to cause the processor 8 to perform the processing required to control the device 2 according to the invention. The memory module 14 can also store one or more recent measurements of the position of the device 2 obtained by the GPS module 4."
“configure an artificial intelligence (AI) / machine learning (ML) model to be used for sensing”: Pijl, paragraph 0012, "In alternative embodiments, the step of predicting the position of the device comprises selecting the one of the plurality of models corresponding to the determined state of motion of the user; and using the selected one of the plurality of models to determine the predicted position of the device [configure an artificial intelligence (AI) / machine learning (ML) model to be used for sensing]."
“obtain sensing measurements”: Pijl, paragraph 0009, "Preferably, the step of determining the state of motion of the user comprises analysing signals from one or more sensors and/or one or more earlier measured positions of the device. In some embodiments, the signals from one or more sensors comprise signals from one or more of an accelerometer, magnetometer and gyroscope [obtain sensing measurements]."
“determine sensing target information by applying the AI/ML model to the sensing measurements”: Pijl, paragraph 0012, "In alternative embodiments, the step of predicting the position of the device comprises selecting the one of the plurality of models corresponding to the determined state of motion of the user; and using the selected one of the plurality of models to determine the predicted position of the device [determine sensing target information by applying the AI/ML model to the sensing measurements]."
Regarding claim 2:
Pijl teaches “[t]he sensing node of claim 1.”
Pijl further teaches “wherein the sensing node comprises a user equipment (UE) or a base station (BS)”: Pijl, paragraph 0021, "In some embodiments, the apparatus is configured as a device to be worn or carried by a user [wherein the sensing node comprises a user equipment (UE)], with the device further comprising the position measurement means for obtaining measurements of the position of the device. In alternative embodiments, the apparatus is a separate device to the device that is worn or carried by a user."
Regarding claim 3 and analogous claim 25:
Pijl teaches “[t]he sensing node of claim 1.”
Pijl further teaches “wherein the one or more processors configured to configure the AI/ML model to be used for sensing comprises the one or more processors, either alone or in combination, configured to receive AI/ML model configuration information, the AI/ML model configuration information comprising the AI/ML model”: Pijl, paragraph 0052, "In alternative embodiments, multiple Kalman filters 20 can be provided, each having a prediction function and output function corresponding to a particular type of behaviour (state of motion) of the user. In these embodiments, the estimated behaviour of the user is used to select the most appropriate one of the filters 20 to use to determine if the measurement is an outlier. In this embodiment, it will be appreciated that multiple filters 20 will be provided, and the 'update model weights' block 32 in FIG. 2 will be replaced by a 'model selection' or 'model enabling' block [the one or more processors, either alone or in combination, configured to receive AI/ML model configuration information, the AI/ML model configuration information comprising the AI/ML model]. The residual 28 of the selected filter 20 will be used in subsequent processing to determine if the GPS position measurement 26 is an outlier."
Regarding claim 4:
Pijl teaches “[t]he sensing node of claim 3.”
Pijl further teaches “wherein the one or more processors configured to receive the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to receive at least one of: information indicating an input to the AI/ML model; information indicating an output from the AI/ML model; information indicating an operating mode of the AI/ML model; or a parameter to be used by the AI/ML model”: Pijl, paragraph 0052, "In alternative embodiments, multiple Kalman filters 20 can be provided, each having a prediction function and output function corresponding to a particular type of behaviour (state of motion) of the user. In these embodiments, the estimated behaviour of the user is used to select the most appropriate one of the filters 20 to use to determine if the measurement is an outlier. In this embodiment, it will be appreciated that multiple filters 20 will be provided, and the 'update model weights' block 32 in FIG. 2 will be replaced by a 'model selection' or 'model enabling' block [information indicating an operating mode of the AI/ML model]. The residual 28 of the selected filter 20 will be used in subsequent processing to determine if the GPS position measurement 26 is an outlier."
Regarding claim 5 and analogous claim 26:
Pijl teaches “[t]he sensing node of claim 1.”
Pijl further teaches “wherein the one or more processors configured to configure the AI/ML model to be used for sensing comprises the one or more processors, either alone or in combination, configured to receive first information indicating an AI/ML model to be selected from a plurality of configured AI/ML models and to select the AI/ML model to be used for sensing from the plurality of configured AI/ML models based on the first information”: Pijl, paragraph 0012, "In alternative embodiments, the step of predicting the position of the device comprises selecting the one of the plurality of models corresponding to the determined state of motion of the user [configured to receive first information indicating an AI/ML model to be selected from a plurality of configured AI/ML models]; and using the selected one of the plurality of models to determine the predicted position of the device [select the AI/ML model to be used for sensing from the plurality of configured AI/ML models based on the first information]."
Regarding claim 6 and analogous claim 27:
Pijl teaches “[t]he sensing node of claim 1.”
Pijl further teaches “wherein the one or more processors configured to configure the AI/ML model to be used for sensing comprises the one or more processors, either alone or in combination, configured to select the AI/ML model to be used for sensing from a plurality of configured AI/ML models based on at least one of: a position of the sensing node; or a speed of the sensing node”: Pijl, paragraph 0012, "In alternative embodiments, the step of predicting the position of the device comprises selecting the one of the plurality of models corresponding to the determined state of motion of the user [configured to select the AI/ML model to be used for sensing from a plurality of configured AI/ML models based on at least one of: … a speed of the sensing node]; and using the selected one of the plurality of models to determine the predicted position of the device."
Regarding claim 8:
Pijl teaches “[t]he sensing node of claim 1.”
Pijl further teaches “wherein the one or more processors configured to obtain the sensing measurements comprises the one or more processors, either alone or in combination, configured to measure a sensing signal transmitted by a transmitting node”: Pijl, paragraph 0009, “Preferably, the step of determining the state of motion of the user comprises analysing signals from one or more sensors and/or one or more earlier measured positions of the device. In some embodiments, the signals from one or more sensors comprise signals from one or more of an accelerometer, magnetometer and gyroscope [measure a sensing signal transmitted by a transmitting node].”
Regarding claim 10:
Pijl teaches “[t]he sensing node of claim 8.”
Pijl further teaches “wherein the one or more processors configured to measure the sensing signal comprises the one or more processors, either alone or in combination, configured to measure a plurality of sensing signals at a plurality of frequencies and/or times”: Pijl, paragraph 0042, “The current behaviour (state of motion) of the user of the device 2 can be estimated by analysing the signals from the one or more sensors 16, and/or by analysing previous measurements of the position of the device 2. For example, it is possible to identify steps by the user of the device 2 (e.g. in the form of the impact of the user's foot with the ground) from a signal from an accelerometer 16 and to determine from the magnitude and frequency of the steps whether the user is walking or running [measure a plurality of sensing signals at a plurality of … times].”
Regarding claim 12:
Pijl teaches “[t]he sensing node of claim 1.”
Pijl further teaches “wherein the one or more processors configured to determine the sensing target information comprises the one or more processors, either alone or in combination, configured to determine at least one of: a count of target objects detected; a position of a target object; or a speed of a target object”: Pijl, paragraph 0012, “In alternative embodiments, the step of predicting the position of the device comprises selecting the one of the plurality of models corresponding to the determined state of motion of the user; and using the selected one of the plurality of models to determine the predicted position of the device [determine at least one of: … a position of a target object].”
Regarding claim 13:
Pijl teaches “[t]he sensing node of claim 12.”
Pijl further teaches “wherein the one or more processors configured to determine the sensing target information comprises the one or more processors, either alone or in combination, configured to determine at least one of: a position of the sensing node; or a speed of the sensing node”: Pijl, paragraph 0012, “In alternative embodiments, the step of predicting the position of the device comprises selecting the one of the plurality of models corresponding to the determined state of motion of the user; and using the selected one of the plurality of models to determine the predicted position of the device [determine at least one of: a position of the sensing node].”
Regarding claim 14:
Pijl teaches “[t]he sensing node of claim 1.”
Pijl further teaches “wherein the one or more processors, either alone or in combination, are further configured to transmit, via the one or more transceivers, the sensing target information to another node different from the sensing node”: Pijl, paragraph 0062, “Although FIG. 2 illustrates that the processing according to the invention is performed by the processor 8 in the device 2, it will be appreciated that in alternative embodiments, the GPS module 4 can be configured to implement the processing according to the invention to identify outlier position measurements. In a further alternative embodiment, the processing according to the invention can be performed in a device that is remote from the device 2 that is carried or worn by the user, in which case the device 2 can be configured to transmit the position measurement data and any data collected using the one or more sensors 16 to the remote device [transmit, via the one or more transceivers, the sensing target information to another node different from the sensing node].”
Regarding claim 15:
Pijl teaches “[t]he sensing node of claim 14.”
Pijl further teaches “wherein the one or more processors configured to transmit the sensing target information to another node different from the sensing node comprises the one or more processors, either alone or in combination, configured to transmit the sensing target information to at least one of a base station or a network entity”: Pijl, paragraph 0062, “Although FIG. 2 illustrates that the processing according to the invention is performed by the processor 8 in the device 2, it will be appreciated that in alternative embodiments, the GPS module 4 can be configured to implement the processing according to the invention to identify outlier position measurements. In a further alternative embodiment, the processing according to the invention can be performed in a device that is remote from the device 2 that is carried or worn by the user, in which case the device 2 can be configured to transmit the position measurement data and any data collected using the one or more sensors 16 to the remote device [configured to transmit the sensing target information to at least one of … a network entity].”
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.
Claims 7 and 9 rejected under 35 U.S.C. 103 over Pijl in view of Li et al., US Patent No. 11,884,292 (hereafter Li).
Regarding claim 7:
Pijl teaches “[t]he sensing node of claim 3.”
Pijl does not explicitly teach “wherein the one or more processors, either alone or in combination, are further configured to train the AI/ML model.”
Li teaches “wherein the one or more processors, either alone or in combination, are further configured to train the AI/ML model”: Li, col. 31, lines 8–19, “In some implementations, the second computing system 40 or the first computing system 20 can train one or more machine-learned models of the model(s) 26 or the model(s) 46 through the use of one or more model trainers 47 and
training data 48. The model trainer(s) 47 can train any one of the model(s) 26 or the model(s) 46 using one or more training or learning algorithms [configured to train the AI/ML model]. One example training technique is backwards propagation of errors. In some implementations, the model trainer(s) 47 can perform supervised training techniques using labeled training data. In other implementations, the model trainer(s) 47 can perform unsupervised training techniques using unlabeled training data.”
Li and Pijl are analogous arts as they are both related to models for the analysis of sensor data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the trained models of Li with the teachings of Pijl to arrive at the present invention, in order to improve model generalization, as stated in Li, col. 31, lines 31–35, “In some implementations, the model trainer(s) 47 can perform a number of generalization techniques to improve the generalization capability of the model(s) being trained. Generalization techniques include weight decays, dropouts, or other techniques.”
Regarding claim 9:
Pijl teaches “[t]he sensing node of claim 8.”
Pijl does not explicitly teach “wherein the one or more processors configured to measure the sensing signal comprises the one or more processors, either alone or in combination, configured to measure the sensing signal using a plurality of antennas.”
Li teaches “wherein the one or more processors configured to measure the sensing signal comprises the one or more processors, either alone or in combination, configured to measure the sensing signal using a plurality of antennas”: Li, col. 2, lines 8–24, “For example, in an aspect, the present disclosure provides a radio detection and ranging (RADAR) sensor system for vehicles, such as autonomous vehicles. The RADAR sensor system includes a first RADAR sensor configured to generate first RADAR data descriptive of an environment of a vehicle having a first antenna configured to output a first RADAR beam having a first azimuthal component over a first angular range and a second RADAR sensor configured to provide second RADAR data descriptive of the environment of the vehicle. The second RADAR sensor includes a second antenna configured to output a second RADAR beam having a second azimuthal component that is narrower than the first azimuthal component of the first RADAR beam [measure the sensing signal using a plurality of antennas]. The second RADAR sensor is configured to sweep the second RADAR beam over a second angular range closer to a rear of the vehicle than a front of the vehicle to obtain the second RADAR data.”
Li and Pijl are analogous arts as they are both related to models for the analysis of sensor data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the multiple antennas of Li with the teachings of Pijl to arrive at the present invention, in order to improve sensing, as stated in Li, col. 1, lines 48–54, “For instance, the use of a second RADAR sensor having a second antenna configured to output a second RADAR beam having a second (e.g., narrow) azimuthal component can reduce the effects of multipath interference from portions of the autonomous platform, thereby increasing the accuracy of sensor data from the RADAR system.”
Claim 11 rejected under 35 U.S.C. 103 over Pijl in view of Fripp et al., US Pre-Grant Publication No. 2023/0184102 (hereafter Fripp).
Pijl teaches “[t]he sensing node of claim 8.”
Pijl does not explicitly teach “wherein the one or more processors configured to obtain the sensing measurements comprises the one or more processors, either alone or in combination, configured to obtain at least one of: a quantized amplitude of the sensing signal; a quantized phase of the sensing signal; a correlation coefficient between the sensing signal and codewords in a spatial domain; a correlation coefficient between the sensing signal and codewords in a time domain; a correlation coefficient between the sensing signal and codewords in a frequency domain; a delay-time spectrum of the sensing signal; a delay-Doppler spectrum of the sensing signal; a frequency-Doppler spectrum of the sensing signal; a time-Doppler spectrum of the sensing signal; or an angle of arrival spectrum of the sensing signal.”
Fripp teaches “wherein the one or more processors configured to obtain the sensing measurements comprises the one or more processors, either alone or in combination, configured to obtain at least one of: a quantized amplitude of the sensing signal; a quantized phase of the sensing signal; a correlation coefficient between the sensing signal and codewords in a spatial domain; a correlation coefficient between the sensing signal and codewords in a time domain; a correlation coefficient between the sensing signal and codewords in a frequency domain; a delay-time spectrum of the sensing signal; a delay-Doppler spectrum of the sensing signal; a frequency-Doppler spectrum of the sensing signal; a time-Doppler spectrum of the sensing signal; or an angle of arrival spectrum of the sensing signal”: Fripp, paragraph 0038, “In block 602, the sensor 206 may obtain measurements from a wellbore. Examples of measurements include, but are not limited to, pressure, temperature, acoustic vibration, fluid composition, static strain, dynamic strain, flow rate, tool passage, tool operation, tool health, magnetic flux, electrical field, or gravitational acceleration. Measurements may be converted into a signal representing the measurements by a processor 208 within the sensor module 106. The signal representing the measurements may be converted, by the processor 208, to a digital signal by quantizing amplitudes comprising an original signal and assigning the values of the quantized amplitudes to regular time intervals [a quantized amplitude of the sensing signal]. The processor 208 may include instructions for downhole tools within the signal representing the measurements. The instructions for downhole tools may be based on the measurements.”
Fripp and Pijl are analogous arts as they are both related to processing of sensor signals. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the quantized amplitudes of Fripp with the teachings of Pijl to arrive at the present invention, in order to process the data by a digital computer, as stated in Fripp, paragraph 0038, “Measurements may be converted into a signal representing the measurements by a processor 208 within the sensor module 106. The signal representing the measurements may be converted, by the processor 208, to a digital signal by quantizing amplitudes comprising an original signal and assigning the values of the quantized amplitudes to regular time intervals.”
Claims 16–21, 23, and 28–30 rejected under 35 U.S.C. 103 over Pijl in view of Poomachandran et al., US Pre-Grant Publication No. 2019/0049912 (hereafter Poomachandran ).
Regarding claim 16 and analogous claim 28:
Pijl teaches:
“A network entity, comprising: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to”: Pijl, paragraphs 0039–0040, "As the device 2 [network entity] in this embodiment is a mobile telephone or smartphone, the device 2 further comprises transceiver circuitry 10 [one or more transceivers] and associated antenna 12 for communicating wirelessly with a mobile communication network. The device 2 further comprises a memory module 14 [one or more memories] that can store program code for execution by the processor [one or more processors communicatively coupled to the one or more memories and the one or more transceivers] 8 to cause the processor 8 to perform the processing required to control the device 2 according to the invention. The memory module 14 can also store one or more recent measurements of the position of the device 2 obtained by the GPS module 4."
(bold only) “receive, via the one or more transceivers, first information indicating a capability of a sensing node to support an artificial intelligence (AI) / machine learning (ML) model for sensing”: Pijl, paragraph 0012, "In alternative embodiments, the step of predicting the position of the device comprises selecting the one of the plurality of models corresponding to the determined state of motion of the user; and using the selected one of the plurality of models to determine the predicted position of the device [receive, …, first information indicating a capability of a sensing node to support an artificial intelligence (AI) / machine learning (ML) model for sensing]."
“configure, based on the first information, an AI/ML model to be used by the sensing node for sensing”: Pijl, paragraph 0012, "In alternative embodiments, the step of predicting the position of the device comprises selecting the one of the plurality of models corresponding to the determined state of motion of the user; and using the selected one of the plurality of models to determine the predicted position of the device [configure, based on the first information, an AI/ML model to be used by the sensing node for sensing].”
(bold only) “send, via the one or more transceivers, to the sensing node, AI/ML model configuration information”: Pijl, paragraph 0062, “Although FIG. 2 illustrates that the processing according to the invention is performed by the processor 8 in the device 2, it will be appreciated that in alternative embodiments, the GPS module 4 can be configured to implement the processing according to the invention to identify outlier position measurements. In a further alternative embodiment, the processing according to the invention can be performed in a device that is remote from the device 2 that is carried or worn by the user, in which case the device 2 can be configured to transmit the position measurement data and any data collected using the one or more sensors 16 to the remote device [send, via the one or more transceivers, to the sensing node, AI/ML model configuration information].”
Pijl does not explicitly teach:
(bold only) “receive, via the one or more transceivers, first information indicating a capability of a sensing node to support an artificial intelligence (AI) / machine learning (ML) model for sensing”
(bold only) “send, via the one or more transceivers, to the sensing node, AI/ML model configuration information”
Poomachandran teaches (bold only) “receive, via the one or more transceivers, first information indicating a capability of a sensing node to support an artificial intelligence (AI) / machine learning (ML) model for sensing” and (bold only) “send, via the one or more transceivers, to the sensing node, AI/ML model configuration information”: Poomachandran, paragraph 0119, “Example twenty-three includes a system to implement functional safety control logic (FSCL) comprising a field-programmable gate array (FPGA) comprising logic elements to be partitioned into a first section to implement one or more safety cores and a second section to implement one or more non-safety cores, a memory to couple to the safety core or to the non-safety core, a radio-frequency (RF) transceiver, and a trusted execution environment (TEE) to couple to a remote administrator over a network via the RF transceiver, and to apply a configuration received via the RF transceiver [via the one or more transceivers] from the remote administrator to the FPGA.”
Poomachandran and Pijl are analogous arts as they are both related to configurable sensor logic devices. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the transceiver-based configuration of Poomachandran with the teachings of Pijl to arrive at the present invention, in order to allow remote configuration of a device, as stated in Poomachandran, paragraph 0119, “Example twenty-three includes a system to implement functional safety control logic (FSCL) comprising a field-programmable gate array (FPGA) comprising logic elements to be partitioned into a first section to implement one or more safety cores and a second section to implement one or more non-safety cores, a memory to couple to the safety core or to the non-safety core, a radio-frequency (RF) transceiver, and a trusted execution environment (TEE) to couple to a remote administrator over a network via the RF transceiver, and to apply a configuration received via the RF transceiver [via the one or more transceivers] from the remote administrator to the FPGA.”
Regarding claim 17:
Pijl as modified by Poomachandran teaches “[t]he network entity of claim 16.”
Poomachandran further teaches “wherein the network entity comprises a base station (BS), a sensing management function (SnMF), an artificial intelligence (AI) server, or a combination thereof”: Poomachandran, paragraph 0066, “The RAN 1010 can include one or more access nodes that enable the connections 1003 and 1004. These access nodes (ANs) can be referred to as base stations (BSs) [comprises a base station (BS)], NodeBs, evolved NodeBs (eNBs), next Generation NodeBs (gNB), RAN nodes, and so forth, and can comprise ground stations ( e.g., terrestrial access points) or satellite stations providing coverage within a geographic area (e.g., a cell). The RAN 1010 may include one or more RAN nodes for providing macrocells, e.g., macro RAN node 1011, and one or more RAN nodes for providing femtocells or picocells (e.g., cells having smaller coverage areas, smaller user capacity, or higher bandwidth compared to macrocells ), e.g., low power (LP) RAN node 1012.”
Poomachandran and Pijl are combinable for the rationale given under claim 16.
Regarding claim 18 and analogous claim 29:
Pijl as modified by Poomachandran teaches “[t]he network entity of claim 16.”
Pijl further teaches “wherein the one or more processors configured to send the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to send the AI/ML model to be used”: Pijl, paragraph 0012, "In alternative embodiments, the step of predicting the position of the device comprises selecting the one of the plurality of models [configured … the AI/ML model to be used] corresponding to the determined state of motion of the user; and using the selected one of the plurality of models to determine the predicted position of the device.”
Poomachandran further teaches (bold only) “wherein the one or more processors configured to send the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to send the AI/ML model to be used”: Poomachandran, paragraph 0119, “Example twenty-three includes a system to implement functional safety control logic (FSCL) comprising a field-programmable gate array (FPGA) comprising logic elements to be partitioned into a first section to implement one or more safety cores and a second section to implement one or more non-safety cores, a memory to couple to the safety core or to the non-safety core, a radio-frequency (RF) transceiver, and a trusted execution environment (TEE) to couple to a remote administrator over a network via the RF transceiver, and to apply a configuration received via the RF transceiver [to send] from the remote administrator to the FPGA.”
Poomachandran and Pijl are combinable for the rationale given under claim 16.
Regarding claim 19:
Pijl as modified by Poomachandran teaches “[t]he network entity of claim 17.”
Pijl further teaches “wherein the one or more processors configured to send the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to send at least one of: information indicating an input to the AI/ML model; information indicating an output from the AI/ML model; information indicating an operating mode of the AI/ML model; or a parameter to be used by the AI/ML model”: Pijl, paragraph 0052, "In alternative embodiments, multiple Kalman filters 20 can be provided, each having a prediction function and output function corresponding to a particular type of behaviour (state of motion) of the user. In these embodiments, the estimated behaviour of the user is used to select the most appropriate one of the filters 20 to use to determine if the measurement is an outlier. In this embodiment, it will be appreciated that multiple filters 20 will be provided, and the 'update model weights' block 32 in FIG. 2 will be replaced by a 'model selection' or 'model enabling' block [information indicating an operating mode of the AI/ML model]. The residual 28 of the selected filter 20 will be used in subsequent processing to determine if the GPS position measurement 26 is an outlier."
Regarding claim 20 and analogous claim 30:
Pijl as modified by Poomachandran teaches “[t]he network entity of claim 16.”
Pijl further teaches “wherein the one or more processors configured to send the AI/ML model configuration information comprises the one or more processors, either alone or in combination, configured to send information indicating an AI/ML model to be selected from a plurality of configured AI/ML models”: Pijl, paragraph 0012, "In alternative embodiments, the step of predicting the position of the device comprises selecting the one of the plurality of models corresponding to the determined state of motion of the user [configured to receive first information indicating an AI/ML model to be selected from a plurality of configured AI/ML models]; and using the selected one of the plurality of models to determine the predicted position of the device."
Regarding claim 21:
Pijl as modified by Poomachandran teaches “[t]he network entity of claim 16.”
Poomachandran further teaches “wherein the one or more processors, either alone or in combination, are further configured to train one or more AI/ML models for sensing”: , paragraph 0112, “FIG. 13 illustrates training and deployment of a deep neural network. Once a given network has been structured for a task the neural network is trained using a training dataset 1302. Various training frameworks have been developed to enable hardware acceleration of the training process. For example, the machine learning framework 1204 of FIG. 12 may be configured as a training framework 1304. The training framework 1304 can hook into an untrained neural network 1306 and enable the untrained neural net to be trained using the parallel processing resources described herein to generate a trained neural network 1308 [further configured to train one or more AI/ML models for sensing]. To start the training process the initial weights may be chosen randomly or by pre-training using a deep belief network. The training cycle then be performed in either a supervised or unsupervised manner.”
Poomachandran and Pijl are analogous arts as they are both related to configurable sensor logic devices. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the trained models of Poomachandran with the teachings of Pijl to arrive at the present invention, in order to reach a desired level of accuracy, as stated in Poomachandran, paragraph 0113, “The training process can continue until the neural network reaches a statistically desired accuracy associated with a trained neural network 1308. The trained neural network 1308 can then be deployed to implement any number of machine learning operations.”
Regarding claim 23:
Pijl as modified by Poomachandran teaches “[t]he network entity of claim 16.”
Pijl further teaches (bold only) “wherein the one or more processors, either alone or in combination, are further configured to: transmit, via the one or more transceivers, a sensing signal; and receive, from the sensing node via the one or more transceivers, a sensing report comprising sensing target information generated according to the AI/ML model configuration information from measurements, by the sensing node, of the sensing signal”: Pijl, paragraph 0009, "Preferably, the step of determining the state of motion of the user [according to the AI/ML model configuration information] comprises analysing signals from one or more sensors [transmit, …, a sensing signal] and/or one or more earlier measured positions of the device. In some embodiments, the signals from one or more sensors comprise signals from one or more of an accelerometer, magnetometer and gyroscope [receive, from the sensing node … a sensing report comprising sensing target information generated according to the AI/ML model configuration information from measurements, by the sensing node, of the sensing signal]
Poomachandran further teaches (bold only) “wherein the one or more processors, either alone or in combination, are further configured to: transmit, via the one or more transceivers, a sensing signal; and receive, from the sensing node via the one or more transceivers, a sensing report comprising sensing target information generated according to the AI/ML model configuration information from measurements, by the sensing node, of the sensing signal”: Poomachandran, paragraph 0119, “Example twenty-three includes a system to implement functional safety control logic (FSCL) comprising a field-programmable gate array (FPGA) comprising logic elements to be partitioned into a first section to implement one or more safety cores and a second section to implement one or more non-safety cores, a memory to couple to the safety core or to the non-safety core, a radio-frequency (RF) transceiver, and a trusted execution environment (TEE) to couple to a remote administrator over a network via the RF transceiver, and to apply a configuration received via the RF transceiver [via the one or more transceivers] from the remote administrator to the FPGA.”
Poomachandran and Pijl are combinable for the rationale given under claim 16.
Claim 22 rejected under 35 U.S.C. 103 over Pijl as modified by Poomachandran in view of Shimamura, US Pre-Grant Publication No. 2024/0005672 (hereafter Shimamura).
Pijl as modified by Poomachandran teaches “[t]he network entity of claim 21.”
Pijl further teaches (bold only) “wherein the one or more processors, either alone or in combination, are further configured to send, via the one or more transceivers, to the sensing node, at least one of the one or more AI/ML models”: Pijl, paragraph 0061, “This alert can comprise an audible and/or visible warning to the user of the device 2 that they are outside the permitted area, and/or it can
comprise initiating a call or alert to a remote monitoring station (such as a call centre) using the transceiver circuitry 10 and associated antenna [send, via the one or more transceivers].”
Pijl as modified by Poomachandran does not explicitly teach (bold only) “wherein the one or more processors, either alone or in combination, are further configured to send, via the one or more transceivers, to the sensing node, at least one of the one or more AI/ML models.”
Shimamura teaches “wherein the one or more processors, either alone or in combination, are further configured to send, via the one or more transceivers, to the sensing node, at least one of the one or more AI/ML models”: Shimamura, paragraph 0025, “A recognition model selection means 21 of the server 20 selects a recognition model for identifying that a vehicle is in a situation corresponding to a specific scene on the basis of sensor information. The transmission means 22 transmits the recognition model selected by the recognition model selection means 21 to the vehicle 30 [, to the sensing node, at least one of the one or more AI/ML models].”
Shimamura and Pijl are analogous arts as they are both related to sensor data processing models. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the remote transmission of a selected model of Shimamura with the teachings of Pijl to arrive at the present invention, in order to employ a model adapted to a particular situation, as stated in Shimamura, paragraph 0014, “The information collection system, server, vehicle, information collection method, information transmission method, and computer readable medium according to the present disclosure can cause a server to collect data when the vehicle is in a situation corresponding to a specific scene.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gopalakrishnan et al., US Pre-Grant Publication No. 2024/0179671, discloses a method for sensing that includes the transmission of sensor data and neural network parameters between user equipment and a base station, in which the devices are equipped with transceivers.
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/VAS/Examiner, Art Unit 2129
/HAL SCHNEE/Primary Examiner, Art Unit 2129
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