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 filed 4/20/2026 have been fully considered but they are not persuasive. Applicant amended the claims to perform data fusion and decision fusion to provide a technical solution and argued that the claims require specific operations performed by specific hardware elements (a processor coupled with sensor modules). The Examiner respectfully disagrees. The sensors and computer are recited in the claims that would amount to nothing more than a generic computer and sensors (see Specification, Para. 10 and 43). The sensors collecting data and raw sensor fusion are extrasolution activities of mere data gathering and processing (see MPEP 2106.05(g)). Genetic Technologies Limited v. Merial LLC (Fed Cir., 2016) tells us that the inventive concept of step 2 of the Alice/Mayo analysis cannot be supplied by the abstract idea. The inventive concept necessary at step two of the Mayo/Alice analysis cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself. That is, under the Mayo/Alice framework, a claim directed to a newly discovered law of nature (or natural phenomenon or abstract idea) cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility; instead, the application must provide something inventive, beyond mere “well-understood, routine, conventional activity.” Mayo, 132 S. Ct. at 1294; see also Myriad, 133 S. Ct. at 2117; Ariosa, 788 F.3d at 1379. Regarding the decision fusion, the specification involves feeding sensor data a generic AI and calculating a probability of a sleep stage based on different sensor outputs, which are considered mathematical calculations and data analysis which can be performed by a trained physician. The Examiner recommends adding more language about how the raw/decision fusions work and technical improvements to the AI model into the claims. Regarding the “perform an event to facilitate the user’s sleep”, the claim language needs to be more specific on the treatment step (see 101 Subject Matter Eligibility Example 49). The 101 rejection is maintained below.
Applicant’s arguments and amendments, see Pages 17-19, filed 4/20/2026, with respect to the rejection(s) of claim(s) 1-20 under USC 112 and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection under USC 103 is made in view of Jiangsu (US 2021/0212630 A1) in view of Liu (CN 115581435 A), further in view of Luo (CN 110151169 A), further in view of Garcia (US 2022/0386947 A1).
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 1-20 are rejected under 35 U.S.C. 101 because of the following analysis:
Step 1: Do the claims recite one of the statutory categories of matter (i.e. method, apparatus, etc.)? YES, claims 1-7 and 15-20 recite an apparatus and claims 8-14 recite a method.
Step 2A Prong 1: Is there an abstract idea involved? YES, the claim language recites performing decision fusion for the plurality of sensor modules (data analysis and making judgments) determining a sleep stage of a user based on the fusion decision (making determinations/judgments).
Step 2a Prong 2: Do the claims recite additional elements that integrate the exception into a practical application? NO, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claims recite sensor modules, transceiver, and a processor, which (i.e., output) are recited at a high level of generality and is recited as performing generic computer functions. i.e., data processing and display. The elements amount to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.04(d) and 2106.05(f)). Accordingly, each of the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. Additionally the generically recited sensor modules are considered a mere extrasolution activity of necessary data gathering (see MPEP 2106.05(g)).
The dependent claims merely recite the same abstract idea as the independent claims. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the mental process). The elements amount to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.04(d) and 2106.05(f)).
Step 2B: Do the additional elements amount to “Significantly More” than the judicial exception? NO, The emphasized elements cited above do not amount to significantly more than the judicial exception because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’I, 110 USPQ2d 1976 (2014)). In view of the above, the additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4-8, 11-15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiangsu (US 2021/0212630 A1) in view of Liu (CN 115581435 A) further in view of Luo (CN 110151169 A), further in view of Garcia (US 2022/0386947 A1).
Regarding claims 1, 8, and 15, Jiangsu discloses a sleep monitoring apparatus comprising: a plurality of sensor modules (eg. Para. 48, 93); a transceiver (eg. claim 2, Para. 14, 45, 48-49, 52, 73, Fig. 1); and a processor operatively coupled with the plurality of sensor modules and the transceiver (eg. claim 1-2, Para. 14, 45, 48-49, 52, 73, Fig. 1), the processor configured to: receive, from the plurality of sensor modules, raw sensor data for each of the plurality of sensor modules related to a sleep session of a user of the sleep monitoring apparatus (eg. claim 1, Fig. 3, Para. 82-108); and determine a sleep stage of the user based on the decision fusion (eg. Para. 11-12, 23, 38, 93, 101, 108, claim 1), but does not disclose perform raw data fusion of the raw sensor data wherein the raw data fusion generates a fused raw data signal; based on the fused raw data signal, perform feature extraction for the plurality of sensor modules; based on the feature extraction, perform feature fusion for the plurality of sensor modules; based on the feature fusion, perform decision fusion for the plurality of sensor modules and based on the determined sleep stage of the user, perform an event to facilitate the user's sleep.
Liu teaches a sleep monitoring multi-sensor data fusion device that fuses multi-sensing data and obtaining the turn-over times and night times based on video, obtaining snoring frequency, sound size, breathing frequency, heart rate, and oxygen saturation rate, performing fusion on the data, and combining particle swarm optimization and radial basis function (eg. Para. 65, page 4-5, 8, 11-12, claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Jiangsu with the raw sensor fusion and decision fusion as taught by Liu as a known alternative method of fusing data with PSO and RBF to provide a sleep state determination based on multi-sensor parameters.
Luo teaches determining a sleep stage of a user using a decision fusion module (eg. Pg. 1, 3, 5, 7-8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Jiansu and Liu with the decision fusion module as taught by Luo since the art is related to sleep detection and the use of decision fusion methods are well-known in the art for classifying sleep stages.
Garcia teaches a sleep stage determination and maintenance/encouragement to a different sleep stage by controlling different peripheral device to adjust environmental conditions such as temperature and bed pressure (eg. Para. 14, 36, 56-59, 66-70, 205, 234).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Jiangsu, Liu and Luo with the peripheral control as taught by Garcia to provide the predictable result of improving sleep quality (eg. Garcia, Para. 4-5, and 14).
Regarding claims 4, 11, and 18, the combined invention of Jiangsu, Liu, Luo, and Garcia discloses to perform the feature fusion, the processor is further configured to: determine a feature for each sensor module of the plurality of sensor modules; fuse the features, wherein to fuse the features, the processor is further configured to: concatenate the features; treat each feature as a different channel; or process the features; and based on the fused features, determine a sleep stage of the user (eg. Jiangsu, Para. 11-12, 23, 38, 93, 101, 108, claim 1 and Liu, eg. Para. 65, page 4-5, 8, 11-12, claim 1).
Regarding claims 5, 12, and 19, the combined invention of Jiangsu, Liu, Luo, and Garcia discloses to perform the decision fusion, the processor is further configured to:determine hard sleep stage decision for each sensor module of the plurality of sensor modules; perform a hard fusion of the hard sleep stage decisions; and generate a final sleep stage decision based on the hard fusion (eg. Jiangsu, Para. 11-12, 23, 38, 93, 101, 108, claim 1 and Liu, eg. Para. 65, page 4-5, 8, 11-12, claim 1).
Regarding claims 6 and 13 the combined invention of Jiangsu, Liu, Luo, and Garcia discloses to perform the decision fusion, the processor is further configured to: determine a soft sleep stage decision for each sensor module of the plurality of sensor modules; perform a soft fusion of the soft sleep stage decisions; determine a hard decision based on the soft fusion; and generate a sleep stage decision based on the hard decision (eg. Jiangsu, Para. 11-12, 23, 38, 93, 101, 108, claim 1 and Liu, eg. Para. 65, page 4-5, 8, 11-12, claim 1).
Regarding claim 7, the combined invention of Jiangsu, Liu, Luo, and Garcia discloses to perform the soft fusion, the processor is further configured to perform at least one of: a mean fusion; a most confidence decision; a Bayesian method-based fusion; and a learning-based fusion (eg. Jiangsu, Para. 32, 63-64, 98, extracted features that are fused, Liu, Page. 4 and 8-12).
Regarding claim 20, the combined invention of Jiangsu, Liu, Luo, and Garcia discloses to perform the decision fusion, the computer program further comprises program code that, when executed by the processor of the device causes the device to: determine a soft sleep stage decision for each sensor module of the plurality of sensor modules; perform a soft fusion of the soft sleep stage decisions, (eg. Jiangsu, Para. 11-12, 23, 38, 93, 101, 108, claim 1 and Liu, eg. Para. 65, page 4-5, 8, 11-12, claim 1) wherein to perform the soft fusion the computer program further comprises program code that, when executed by the processor of the device causes the device to perform at least one of: a mean fusion, a most confidence decision, a Bayesian method-based fusion, and a learning-based fusion; determine a hard decision based on the soft fusion; and generate a sleep stage decision based on the hard decision (eg. Jiangsu, Para. 32, 63-64, 98, extracted features that are fused, Liu, Page. 4 and 8-12).
Claim(s) 2-3, 9-10, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiangsu (US 2021/0212630 A1) in view of Liu (CN 115581435 A), further in view of Luo (CN 110151169 A), further in view of Garcia (US 2022/0386947 A1), further in view of Zhang (US 2020/0397365 A1).
Regarding claims 2, 9, and 16, the combined invention of Jiangsu, Liu, Luo, and Garcia discloses to perform the raw data fusion on the raw sensor data, the processor is further configured to: filter the raw sensor data from each sensor module (eg. Jiangsu, Para. 55-57, 67, 103, and claim 6); but does not disclose perform an auto correlation function (ACF) on the filtered raw sensor data from each sensor module, wherein the ACF generates ACF processed sensor data; and generate the fused raw data signal based on the ACF processed sensor data.
Zhang teaches using ACF and principal component analysis on multi-sensor data to monitor sleep (eg. Para. 119, 141, 147, 152-153, 160, 223, 239-257, 271).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the invention of Jiangsu and Liu with the ACF processing as taught by Zhang to allow for circumventing the use of noise phase and usually handcrafted CSI denoising procedure and eliminating the frequency offsets to synchronize breathing signal over different subcarriers, allowing to perform MRC to combine multiple subcarriers to combat measurement noises and maximize breathing signals in an optimal way (eg. Zhang, Para. 223).
Regarding claims 3, 10, and 17, the combined invention of Jiangsu, Liu, Luo, Garcia, and Zhang discloses to generate the fused raw data signal based on the ACF, the processor is further configured to: sum the ACF processed sensor data; or perform a principal component analysis (PCA) on the ACF processed sensor data (eg. Zhang, eg. Para. 119, 141, 147, 152-153, 160, 223, 239-257, 271).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J LAU whose telephone number is (571)272-2317. The examiner can normally be reached 8-5:30 PM.
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/MICHAEL J LAU/Examiner, Art Unit 3796
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