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 .
Response to Arguments
Applicant’s arguments, see page 6-7, filed on 1/20/2026, with respect to objection of claims 12, 14, and 35 U.S.C. 112(b) rejection of claims 4, 12, 14-21 have been fully considered and are persuasive. The objection of claims 12, 14, and 35 U.S.C. 112(b) rejection of claims 4, 12, 14-21 has been withdrawn.
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
Computing device, subject classifier, and activity classifier in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
(1) Regarding claim 1:
Claim limitation “computing device”, “subject classifier”, and “activity classifier” recite in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Claim 1 recites elements “computing device”, “subject classifier”, and “activity classifier” are non-structural term without any structural modifier, modified by functional language, and does not include the structure necessary to perform the claimed function, therefore, the limitation invokes 112, 35 U.S.C. 112, sixth paragraph.
The claimed “computing device”, “subject classifier”, and “activity classifier” correspond to blocks 415, 422, and 424 respectively as shown in figure 4, and is describe in paragraph 0030 in the specification of the instant application, however, there is no detail structure of this blocks 415, 422, and 424. As the claims limitations invoke 112, 6th paragraph, the claim scope of the limitations is limited to the structure specifically disclosed in the specification for achieving the recited function and equivalent to that structure. Since the specification does not provide the detail structure of the “computing device”, “subject classifier”, and “activity classifier” for performing its function, the claim scope is unclear.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
(2) Regarding claims 2-12:
Claims 2-12 fails to provide the detail structure of the “computing device”, “subject classifier”, and “activity classifier”, therefore, claims 2-12 are also rejected under 35 U.S.C, 112(b).
Allowable Subject Matter
Claims 13-21 are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
(1) Regarding claims 13:
The present invention describes a method of operating a classification engine, comprising during a meta-learning phase, training an embedding network and a classifier jointly; during an optimization phase, training the classifier independent from the embedding network via a training dataset; and during an operational phase: via the embedding network, mapping an input tensor to a latent vector, the input tensor corresponding to an input dataset; and via the classifier, identifying a classification corresponding to the input tensor by mapping the latent vector to a label. The closest prior art, Inti et al. (US 11,894,902 B1) discloses a method of using feedback CSI data to monitor motion condition; but fails to disclose meta-learning phase, training an embedding network and a classifier jointly; during an optimization phase, training the classifier independent from the embedding network via a training dataset; and during an operational phase: via the embedding network, mapping an input tensor to a latent vector, the input tensor corresponding to an input dataset; and via the classifier, identifying a classification corresponding to the input tensor by mapping the latent vector to a label.
(2) Regarding claims 14-21:
The present invention describes a method of sensing an environment, comprising transmitting a wireless signal through the environment; receiving, via a plurality of wireless receivers, the wireless signal at a distinct location within the environment via at least one respective antenna, each of the wireless receivers positioned at the distinct location within the environment, each of the plurality of wireless receivers being located closest to a respective subject of a plurality of subjects; generating, via the plurality of wireless receivers, a channel state information (CSI) packet indicating a state of a wireless communications channel associated with the wireless signal; processing the CSI packets from the plurality of wireless receivers to generate a CSI dataset as a function of the CSI packets; identifying a target subject of the plurality of subjects based on the CSI dataset via a subject machine learning (ML) model trained on a first training dataset; and identifying an activity exhibited by the target subject based on the CSI dataset via an activity ML model trained on a second training dataset. The closest prior art, Inti et al. (US 11,894,902 B1) discloses a method of using feedback CSI data to monitor motion condition; but fails to disclose processing the CSI packets from the plurality of wireless receivers to generate a CSI dataset as a function of the CSI packets; identifying a target subject of the plurality of subjects based on the CSI dataset via a subject machine learning (ML) model trained on a first training dataset; and identifying an activity exhibited by the target subject based on the CSI dataset via an activity ML model trained on a second training dataset.
Conclusion
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/SIU M LEE/Primary Examiner, Art Unit 2632 3/13/2026