DETAILED ACTION
This office action is in response to the remarks and amendments filed on 1/20/2026. Claims 1-42 are pending. Claims 1-5, 7-29, 36-37, and 41 are rejected.
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 Applicant's remarks and amendments
Regarding double patenting rejections, Applicant argues on pages 11-17 of remarks dated 1/20/26 that that the patented claims of US Patent 9,635,953 do not contain the limitations of the instant application. This is true for claim 6, which has been indicated as allowable. Independent claims 1 and 20 contain structural limitations that are found in the patent of Nunn with additional limitations regarding machine learning which are taught by Schultz, and would be obvious in view of Schultz. Applicant incorrectly cites MPEP§804(II)(B)(1) with the conclusion that “no part of the reference patent or application may be used as if it were prior art.” This prohibition applies (as the section heading states), to “construing the claim” and determining the scope of the claims. In the instant case, the claims of Nunn are construed to read on (predominantly) the structural limitations of Applicant’s instant application (as was affirmed in the Patent Board Decision dated 4/30/2024). The further analysis of obvious type double patenting requires the steps of a Graham v. John Deere Co analysis, as is described in MPEP§804(II)(B)(3). The steps are as follows:
(A) Determine the scope and content of a patent claim relative to a claim in the application at issue;
(B) Determine the differences between the scope and content of the patent claim as determined in (A) and the claim in the application at issue;
(C) Determine the level of ordinary skill in the pertinent art
And,
Any nonstatutory double patenting rejection made under the obviousness analysis should make clear:
(A) The differences between the inventions defined by the conflicting claims — a claim in the patent compared to a claim in the application; and
(B) The reasons why a person of ordinary skill in the art would conclude that the invention defined in the claim at issue would have been an obvious variation of the invention defined in a claim in the patent.
In the instant case, the scope and content of the patent claims relative to the claims at issue are that Nunn claims the structures of the instant claims, and the difference is that Nunn does not claim the machine learning limitations. The level of ordinary skill in the art is that one of ordinary skill would understand that these machine learning steps exists (Examiner notes that a whole field of computer science exists and that Applicant did not invent machine learning), and finally, that it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use the machine learning techniques of Schultz to perform the data analysis of Nunn in order to more accurately determine when snoring is occurring, and to provide a response that can “operate the bed system according to the determined presence state” in order to maximize a user’s comfort and optimize a user’s quality of sleep.
Examiner disagrees with Applicant’s allegations that the correct analysis for double patenting has been disregarded. Applicant states in footnote 1 on page 11 of remarks that only the “claimed subject matter of Nunn and Schultz” may be used in the obviousness analysis, but this is incorrect. Only the claimed subject matter (as interpreted in view of the disclosure) may be used when determining the scope of the claimed subject matter of the instant application and the conflicting patent, in order to also determine the differences between the claims. In the instant case this applies to the scope of the claims of Nunn, however this does NOT apply to the teaching of Schultz (or any other prior art that one of ordinary skill would know of). To be clear, the reasons why a person of ordinary skill in the art would conclude that the invention defined in the claim at issue would have been an obvious variation of the invention defined in a claim in the patent, are because Schultz teaches the limitations that are not recited in the claims of Nunn.
Aside from general allegations of patentability, Applicant has not specifically and particularly pointed out which limitations are not found in the claims of Nunn and which would not be made obvious by the prior art knowledge contained in Schultz. Furthermore, Applicant’s arguments regarding double patenting and claim interpretation have already been addressed in prior Office Actions, including the Final Rejection dated 8/18/2025, and Examiner’s Answer to Appeal Brief dated 10/4/2023.
Regarding rejections under 35 USC §103 and Declaration of Wade Palashewski, Applicant argues that “detecting bed presence from pressure changes is less challenging than detecting snoring using similar techniques.” However, neither the claimed nor the disclosed invention discuss detecting snoring by using pressure changes. The declaration has been reviewed and Examiner thanks Applicant for providing this additional information. The declaration provides information that appears to be substantially opinion based. To be of probative value, an affidavit must provide evidence of unexpected results, commercial success, or other secondary evidence types. Opinions of the applicant cannot take the place of evidence. Declarant states that “compared to presence detection, snore detection with a sensor is more difficult. And cardiac detection with a sensor is even more difficult.” As an initial matter, Applicant’s disclosure does not discuss snore detection. Furthermore, while it may be true that snore and cardiac detection is difficult, this does not mean that it hasn’t been done. In fact the there are numerous prior art references in which snore detection and cardiac detection are disclosed, including the prior art disclosure of Shultz. However there are other references as well; US Patent Application Publication 2005/0190065 to Ronnholm (previously cited on PTO-892) discloses both detecting snoring and detecting cardiac information. Therefore the state of the prior art, and the level of skill of one of ordinary skill in the prior art, is that snoring detection and cardiac detection are known and within the skill of one of ordinary skill in the art. Note that the limitations of the claimed invention are anticipated or made obvious by the teachings of Nunn and Schultz, and that Ronnholm is merely discussed herein to describe the state of the prior art. Declarant also states that it would not have been obvious to detect presence using snoring, however this is not even discussed in Applicant’s instant application, either in the specification or the claims. Therefore, the affidavit does not appear to have a preponderance of evidence that would point to the claimed invention being non-obvious. See MPEP §716.01(c).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-5, 7-25, 27, and 36-42 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-18 of US Patent 9,635,953 to Nunn, in view of US Patent Application Publication 2013/0245389 to Schultz et al. (“Schultz”).
Regarding claim 1, the patent to Nunn teaches (with the exception of limitations directed toward machine learning):
“A bed system with machine learning, the bed system comprising: a first bed comprising: a first mattress; a first pressure sensor in communication with the first mattress to sense pressure applied to the first mattress; and a first controller in data communication with the first pressure sensor, the first controller configured to: receive, from the first pressure sensor, first pressure readings indicative of the sensed pressure of the first mattress; and transmit the first pressure readings to a remote server such that the remote server is able to generate one or more machine learning presence classifiers using at least one or more machine learning techniques using training data created with the first pressure readings such that, when the one or more machine learning presence classifiers are run by a controller on incoming pressure readings, the one or more machine learning presence classifiers provide a machine learning presence vote; a second bed comprising: a second mattress; a second pressure sensor in communication with the second mattress to sense pressure applied to the second mattress; and a second controller in data communication with the second pressure sensor, the second controller configured to: receive the one or more machine learning presence classifiers; run the received machine learning presence classifiers on second pressure readings in order to collect one or more machine learning presence votes from the running machine learning presence classifiers; determine, from the one or more machine learning presence votes, a presence state of a user on the second bed; and responsive to the determined presence state, operate the bed system according to the determined presence state.”
Nunn teaches an air mattress with a first and second air chamber (Fig. 1, #’s 14B and 14B) with pressure sensors (transducers, Fig. 2, #46), which then uses the collected pressure data to determine if one or more users are present (“presence classifiers” in Applicant’s claim language); also see at least Nunn, column 9, lines 1-5. Regarding the use of “second pressure readings”, Nunn discloses this limitation at least in column 5, lines 37-39, which discusses “two different beds (e.g., two twin beds placed next to each other)… may include more than one zone that may be independently adjusted.” Therefore, second pressure readings are considered to be those pressure readings associated with a second bed and/or a second zone. Additional discussion of specific limitations that are not discussed herein can be found in Examiner’s Answer to Appeal Brief dated 10/4/2023. Regarding the claimed presence classifiers being “machine learning presence classifiers,” Schultz teaches a system that monitors a user for snoring, and discusses in paragraphs [0023]-[0025] using machine learning to perform these actions. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use the machine learning techniques of Schultz to perform the data analysis of Nunn in order to more accurately determine when snoring is occurring, and to provide a response that can “operate the bed system according to the determined presence state” in order to maximize a user’s comfort and optimize a user’s quality of sleep.
Therefore, the patent discloses substantially the same subject matter as is recited in the claims of the instant application. Although the conflicting claims are not identical, they are not patentably distinct from each other because the patent recites an apparatus with the same structures as claimed in the instant application, including, inter alia, an air mattress with pressure sensors that determine if a user is present, and then provide some functionality based on this presence knowledge. The patent does not recite in the claims, nor disclose in the specification, Applicant’s specific recitations with respect to “machine learning.” The patent discloses all other aspects of the claimed invention of the instant application (as are discussed in Examiner’s Answer to Appeal Brief dated 10/4/2023). Regarding Applicant’s terminology directed toward “machine learning,” one of ordinary skill would recognize that the claims and disclosures of Nunn are directed toward data analysis steps and methods (and Applicant admits this in the response dated 7/12/22 when arguing against enablement rejections under 35 USC §112 that were presented in the Office action dated 1/12/22). Nunn does not use the term “machine learning.” However, Schultz teaches a system that monitors a user for snoring, and discusses in paragraphs [0023]-[0025] using machine learning to perform these actions. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use the machine learning techniques of Schultz to perform the data analysis of Nunn in order to more accurately determine when snoring is occurring, and to provide a response that can “operate the bed system according to the determined presence state” in order to maximize a user’s comfort and optimize a user’s quality of sleep.
Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter of the recited claims can be found entirely within the subject matter of the claims of the claims of the co-pending applications, or would be obvious in view of Nunn and Schultz. The claims of the instant application are therefore fully encompassed by (and anticipated or made obvious by) the claims of the co-pending applications regardless of the differing scope of the claims. Furthermore, to the degree to which the claims are different from the co-pending claims, the changes would have been obvious to one of ordinary skill in the art at the time the invention was made.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 21 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. new claim 41 recites “wherein the second controller is configured to determine, from the one or more machine learning presence votes, the presence state of the user on the second bed without detecting snoring.” However, there is no discussion of this feature in Applicant’s disclosure. Moreover, there is no discussion of detecting snoring in Applicant’s disclosure. Claim 21 is considered to be new matter that is not supported by the originally filed disclosure, and must be removed from the application.
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 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 of this title, 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 1-5 and 7-25, 27, 36-37, and 41 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent 9,635,953 to Nunn et al. (“Nunn”) in view of US Patent Application Publication 2013/0245389 to Schultz et al. (“Schultz”).
Claim 1. Nunn teaches (with the exception of limitations directed toward machine learning):
“A bed system with machine learning, the bed system comprising: a first bed comprising: a first mattress; a first pressure sensor in communication with the first mattress to sense pressure applied to the first mattress; and a first controller in data communication with the first pressure sensor, the first controller configured to: receive, from the first pressure sensor, first pressure readings indicative of the sensed pressure of the first mattress; and transmit the first pressure readings to a remote server such that the remote server is able to generate one or more machine learning presence classifiers using at least one or more machine learning techniques using training data created with the first pressure readings such that, when the one or more machine learning presence classifiers are run by a controller on incoming pressure readings, the one or more machine learning presence classifiers provide a machine learning presence vote; a second bed comprising: a second mattress; a second pressure sensor in communication with the second mattress to sense pressure applied to the second mattress; and a second controller in data communication with the second pressure sensor, the second controller configured to: receive the one or more machine learning presence classifiers; run the received machine learning presence classifiers on second pressure readings in order to collect one or more machine learning presence votes from the running machine learning presence classifiers; determine, from the one or more machine learning presence votes, a presence state of a user on the second bed; and responsive to the determined presence state, operate the bed system according to the determined presence state.”
Nunn teaches an air mattress with a first and second air chamber (Fig. 1, #’s 14B and 14B) with pressure sensors (transducers, Fig. 2, #46), which then uses the collected pressure data to determine if one or more users are present (“presence classifiers” in Applicant’s claim language); also see at least Nunn, column 9, lines 1-5. Regarding the use of “second pressure readings”, Nunn discloses this limitation at least in column 5, lines 37-39, which discusses “two different beds (e.g., two twin beds placed next to each other)… may include more than one zone that may be independently adjusted.” Therefore, second pressure readings are considered to be those pressure readings associated with a second bed and/or a second zone. Additional discussion of specific limitations that are not discussed herein can be found in Examiner’s Answer to Appeal Brief dated 10/4/2023. Regarding the claimed presence classifiers being “machine learning presence classifiers,” Schultz teaches a system that monitors a user for snoring, and discusses in paragraphs [0023]-[0025] using machine learning to perform these actions. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use the machine learning techniques of Schultz to perform the data analysis of Nunn in order to more accurately determine when snoring is occurring, and to provide a response that can “operate the bed system according to the determined presence state” in order to maximize a user’s comfort and optimize a user’s quality of sleep.
Regarding claims 2-5, and 7-18: these claims are directed toward aspects of data analysis techniques and methods that are well known in the art of computer science (as disclosed by Nunn or by Schultz). Nunn teaches performing data analysis of data provided by sensors, and using pressure data to determine whether a user is present on the mattress (Nunn, column 9, lines 1-5), then based on that information, the bed of Nunn initiates some change in the bed (Nunn, Figs. 4-5). While Applicant’s terminology may differ slightly from the terminology used by Nunn, the fundamental aspects of data analysis and the functionality that they provide are taught by Nunn, despite any differences in terminology, Nunn and/or Schultz is considered to make obvious the data analysis techniques claimed by Applicant. Furthermore, Applicant has not provided explicit detail as to how to perform various data analysis techniques such as how to “generate one or more presence classifiers,” “provide a presence vote,” “mapping the training data to a kernel space,” self-organizing mapping,” manipulating a “feature set,” “training a classifier with the feature set,” how to perform “supervised” or “unsupervised” training, or how to create “a feature set from the training data”, “mapping the training data to a kernel space”, or “training a classifier with the feature set so that, based on the training data in kernel space, the classifier is able to classify unseen data.” Applicant has also not provided details regarding how to perform “k-means clustering, mixture modeling, hierarchical clustering, self-organizing mapping, and hidden Markov modelling,” or how to determine presence votes and determine classification or confidence of these votes. Applicant has additionally not provided a teaching of how “deep learning” as it relates to neural networks is carried out, and specifically what steps are performed in a “gradient descent process.” Moreover, knowledge of all of these types of data manipulation is well known in the art of computer science, and Applicant is not required to provide a disclosure of that which is well known in the art. MPEP §2164.05(a) states that “the specification need not disclose what is well-known to those skilled in the art and preferably omits that which is well-known to those skilled and already available to the public.” Therefore, because Applicant has not explicitly provided an enabling disclosure for each of these data analysis techniques, prior art knowledge of these techniques is considered to be Applicant Admitted Prior Art, and therefore, it would have been obvious to perform any of these various data analysis techniques with the apparatus of Nunn and/or Schultz and with the data collected by the sensors of Nunn. Furthermore, to the degree that Applicant’s claim amendments and explicit recitation of “machine learning” may differentiate the instantly claimed invention from the prior art of Nunn, the changes would have been obvious in view of Schultz to use the machine learning techniques of Schultz to perform the data analysis of Nunn in order to more accurately determine when snoring is occurring, and to provide a response that can “operate the bed system according to the determined presence state” in order to maximize a user’s comfort and optimize a user’s quality of sleep.
Claim 19. The system of claim 1, wherein: the first mattress comprises a first inflatable chamber; the first pressure sensor is in fluid communication with the first inflatable chamber to sense the pressure applied to the first mattress; the second mattress comprises a second inflatable chamber; and the second pressure sensor is in fluid communication with the second inflatable chamber to sense the pressure applied to the second mattress (regarding two inflatable chambers, see Nunn, Fig. 1, #’s 14A and 14B).
Regarding claim 20, Nunn also teaches “means for supporting the first mattress” at least as a “foundation that supports the bed” in column 5, lines 34-42.
Claim 21. The bed system of claim 1, wherein the second controller is configured to determine, from the one or more machine learning presence votes, the presence state of the user on the second bed without detecting snoring (Nunn column 9, lines 11-24 discusses determining presence and does not discuss snoring being detected at the same time).
Claim 22. The bed system of claim 9, wherein the supervised training comprises generating one or more features from the first pressure readings to create the training data used to generate the one or more machine learning presence classifiers (Schultz detects pressure, discussed in paragraph [0014], and in paragraph [0016], “Learning processor 25 in step 319 employs training datasets of data from the patient”).
Claim 23. The bed system of claim 22, wherein generating the one or more features from the first pressure readings comprises separating the first pressure readings into one or more buffers, each of the one or more buffers comprising a period of time corresponding to the first pressure readings (Shultz paragraph [0036], “The system learns from historical patient specific data derived from multiple patient attached and patient room sensors over a time period and diagnoses new data and acts upon it”).
Claim 24. The bed system of claim 23, wherein the one or more features are generated in at least one of a time domain and a frequency domain using the one or more buffers (Shultz paragraph [0036], “data derived … over a time period).
Claim 25. The bed system of claim 23, wherein the one or more features include at least one of a maximum pressure value, a minimum pressure value, and a random pressure value derived from the first pressure readings (Shultz paragraph [0036], “The system learns from historical patient specific data”; furthermore Shultz and Nunn both disclose collecting pressure data).
Claim 27. The bed system of claim 9, wherein the supervised training comprises identifying one or more instances where the first pressure readings match a pattern (Schultz discusses pattern matching in paragraphs [0018]-[0019]).
Claim 36. The bed system of claim 1, wherein the one or more machine learning presence classifiers comprise a plurality of machine learning presence classifiers (Nunn discloses determining that a user is present over a period of time, which reads on “a plurality” of presence determinations).
Claim 37. The bed system of claim 36, wherein the second controller is configured to run the plurality of machine learning presence classifiers to collect one or more machine learning presence votes from each of the running plurality of machine learning presence classifiers (Examiner notes that a “vote” is simply a binary determination performed in data analysis; in view of Applicant Admitted Prior Art, discussed with respect claims 2-5 and 7-18 above, it would have been obvious to use the concept of “votes” when analyzing machine learning classifiers).
Claim 41. The bed system of claim 1, wherein the second controller is configured to: collect one or more non-machine-learning presence votes from at least one non- machine-learning presence classifier; and determine, from the one or more machine learning presence votes and the one or more non-machine-learning presence votes, the presence state of the user on the second bed (Applicant’s paragraph [0217] discloses that non-machine-learning presence classifier may include “a threshold value (e.g., pressure, pressure change over time)”; Nunn discusses this feature in at least the Abstract).
Claims 26 and 28 is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-18 of US Patent 9,635,953 to Nunn and US Patent Application Publication 2013/0245389 to Schultz et al. (“Schultz”), in view of US Patent Application Publication 2016/0192866 to Nunn et al. (“Nunn ‘886”).
Claim 26. The bed system of claim 23, wherein the one or more features include at least one of an average pressure value, a standard deviation, and a slope value indicating a pressure increase or decrease over time within at least one of the one or more buffers, derived from the first pressure readings (Nunn does not teach using average pressure values, however Nunn ‘886 discusses this in at least paragraph [0008]; it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to determine an average pressure value in order to more accurately calculate pressure data and to minimize erroneous outlying data points).
Claim 28. The bed system of claim 27, wherein the pattern is an elbow pattern (Nunn ‘889 paragraph [0092] discusses “sharp spike and oscillation” which reads on Applicant’s definition of an elbow pattern in Applicant’s paragraph [0195], it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to determine an elbow pattern in order to more accurately calculate pressure data and to minimize erroneous outlying data points).
Claim 29 is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-18 of US Patent 9,635,953 to Nunn and US Patent Application Publication 2013/0245389 to Schultz et al. (“Schultz”), in view of US Patent Application Publication 2013/0245502 to Lange et al. (“Lange”).
Claim 29. The bed system of claim 22, wherein the supervised training comprises combining the one or more features using principal component analysis (Nunn and Schultz do not discuss principal component analysis, however this techniques is well known in the prior art as discussed by Lange in paragraph [0420], it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use the technique of principal component analysis to “to extract typical symptomatic and asymptomatic nightly behavior from historical readings of the patient”).
Discussion of allowable subject matter
Claim 6 is allowable. The claim has been rewritten in independent form, with inclusion of additional limitations that are not found in the prior art. Specifically, the claim recites details regarding performing training of machine learning presence classifiers, including such steps as converting the incoming pressure readings into a vector and providing a machine learning presence vote based at least in part on a similarity of the vector to portions of the first pressure reading and the corresponding labels (i.e. classifiers). While these machine learning steps are known in the prior art (for example US Patent Application Publication 2016/0092793 to Garrow et al., paragraph [0062]), the claimed invention comprises a combination of limitations beyond these machine learning steps which is not found in the prior art of Garrow. Additionally, the recited machine learning limitations are found in a different context in the prior art of Garrow, and it would not be obvious to combine the prior art with the teachings of Nunn to yield the claimed invention.
Claims 30-35 are directed toward details of principal component analysis (PCA) which are not found in the prior art. The closest prior art is considered to be US Patent Application Publication 2013/0245502 to Lange et al. (“Lange”), which discusses PCA, however Lange does not teach the specific claimed details of PCA, and there is no motivation to further modify Nunn, Schultz, and Lange with other prior art that might yield the claimed invention.
Claims 38-40 are directed toward details of using machine learning presence votes. While the concept of using machine learning presence votes is made obvious in view of the prior art and Applicant Admitted Prior Art, the details of the presence votes are not found in the prior art in the same analytical procedure, nor is there any motivation to further modify Nunn, and Schultz with other prior art that might yield the claimed invention.
Claim 42 is directed toward details of using a neural network for presence determination. Applicant’s disclosure discusses these aspects in paragraphs [0222]-[0234]. While neural networks are known in the prior art, the degree to which they are applied to the determination of a user in a bed are not as detailed as Applicant’s claimed invention, nor is there any motivation to further modify Nunn, and Schultz with other prior art that might yield the claimed invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MYLES A THROOP whose telephone number is (571)270-5006. The examiner can normally be reached on 8:00 am to 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Troutman can be reached on 571-270-3654. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MYLES A THROOP/Primary Examiner, Art Unit 3673