DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communications: RCE filed on 9/23/2025.
Claims 1-20 are pending. Claims 1, and 11 are independent.
The Double patenting rejection of claims 1-6, 9, 11-16, and 19 has been maintained in view of the amendment.
The previous rejection of claims 1-20 under 35 USC § 103 have been withdrawn in view of the amendment.
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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-6, 9, 11-16, and 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 3, and 5 of U.S. Patent No. 10,973,344 in view of Vahdatpour et al. (US2012/0277637) and in further view of Perlian (US2009/0256817). Although the claims at issue are not identical, they are not patentably distinct from each other because both the current application and the patent ‘344 are directed towards a method of receiving pressure information about a body on a support surface, using machine learning to identify the location of joints, and analyzing pressure on the joints over time. Claim 1 of patent ‘344 does not explicitly disclose tracking joint pressure over time, and transmitting an alert when the pressure on a joint exceeds a threshold. However Vahdatpour et al. substantially discloses tracking joint pressure over time, and sending an alert when the pressure on a joint exceeds a threshold (Vahdatpour et al. par[0069]). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have modified the claimed invention of patent ‘344 to provide an alert in order to avoid potentially harmful conditions (Vahdatpour et al. para[0065]). Claim 1 of patent ‘344 does not explicitly disclose inputting the set of pressure data measured by pressure sensors into the machine learning model. However Perlin et al. substantially discloses inputting the set of pressure data measured by pressure sensors into one or more machine learning models (Perlin et al. para[0274]). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to modified the claimed invention of patent ‘344 with to input sensor pad pressure information in to a machine learning model to determine how a user interacts with a surface (Perlin et al. para[0004]). Claim 1 of patent ‘344 does not explicitly disclose determining average pressure data derived from the set of pressure data and contact area pressure values at the coordinate locations of the joint, wherein the average pressure data corresponds to a firmness level of the body support system; normalizing the contact area pressure values based on the average pressure data to account for the firmness level of the body support system. However Terawaki et al. substantially discloses Determining average pressure data derived from the set of pressure data and contact area pressure values at the coordinate locations of the joint, wherein the average pressure data corresponds to a firmness level of the body support system (Terawaki et al. para [0093]); normalizing the contact area pressure values based on the average pressure data to account for the firmness level of the body support system (Terawaki et al. para[0094],). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have modified the claimed invention of patent ‘344 with the pressure and position control method of Terawaki et al. in order to identify and control contact between the body and the mattress (Terawaki et al. para[0008]).
Claims 2-4 of current application recite additional limitations found in claim 1 of patent ‘344.
Claims 11-16, and 19 recite substantially similar limitations to claims 1-6, and 9. Thus claims 11-16, and 19 are rejected along the same rationale as claims 1-6, and 9.
Current Application
Patent 10,973,344
1. A computer-implemented method, comprising:
receiving a set of pressure data measured by pressure sensors of a body support system, the set of pressure data comprising pressure values corresponding to different locations of the body support system, the pressure values taken when the body support system supports an individual;
1. A method for adjusting a bedding system, comprising:
receiving a two-dimensional pressure image of a sleeper on a bedding system while the sleeper is sleeping on the bedding system; and
inputting the set of pressure data measured by pressure sensors into one or more machine learning models identify locations of a joint of the individual;
Determining average pressure data derived from the set of pressure data and contact area pressure values at the coordinate locations of the joint, wherein the average pressure data corresponds to a firmness level of the body support system;
Normalizing the contact area pressure values based on the average pressure data to account for the firmness level of the body support system.
applying a machine vision process to analyze the pressure image, comprising:
selecting, by a first machine learned model, a position classification for a body position of the sleeper, the position classification selected from a set of predetermined position classifications, wherein the first machine learned model comprises a convolutional neural network, the convolutional neural network trained using training data comprising pressure images tagged with body position labels, and wherein the convolutional neural network comprises at least three convolution layers each of which is coupled to a pooling layer,
determining, by a second machine learned model, one or more joint locations of the sleeper, the one or more joints selected from a set of predetermined joints, and
determining pressure values at the one or more joints using the pressure image and the one or more joint locations.
analyzing normalized contact area pressure values at coordinate locations of the joint to track pressure exerted on the joint over time; and
transmitting an alert in response to the pressure exerted on the joint exceeding a threshold level.
3. The method of claim 1, wherein the machine vision process further comprises:
Tracking the pressure values at the one or more joints over time.
2. The computer-implemented method of claim 1, wherein the set of pressure data corresponds to a two-dimensional pressure image.
3. The computer-implemented method of claim 1, wherein the one or more machine learning models comprise a first machine learning model that determines a position of the individual and a second machine learning model that determines the locations of the joint of the individual.
4. The computer-implemented method of claim 3, wherein the first machine learning model is a convolutional neural network.
5. The computer-implemented method of claim 1, wherein the alert comprising a message that the individual should change a position.
6. The computer-implemented method of claim 1, further comprising: adjusting a comfort and/or support of the body support system based on the pressure exerted on the joint.
2. The method of claim 1, further comprising:
Adjusting a comfort and/or support of the bedding system based at least in part on the position classification
9. The computer-implemented method of claim 1, further comprising: determining a position classification for a body position of the individual, the position classification selected from a list that comprises a leftside-sleeping classification, a rightside-sleeping classification, a prone-sleeping classification, and a supine- sleeping classification.
5. The method of claim 1, wherein the set of predetermined position classifications includes a leftside-sleeping classification, a rightside-sleeping classification, a prone-sleeping classification, and a supine-sleeping classification.
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.
Claim(s) 1-3, 5-9, 11-13, and 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vahdatpour et al. (US2012/0277637) in view of Carneiro et al. (US2009/0093717) and Perlin et al. (US2009/0256817) and Terawaki et al. (US2011/0308019).
In regards to claim 1, Vahdatpour et al. substantially discloses a computer-implemented method, comprising:
receiving a set of pressure data measured by pressure sensors of a body support system, the set of pressure data comprising pressure values corresponding to different locations of the body support system, the pressure values taken when the body support system supports an individual (Vahdatpour et al. para[0039], matrix of sensors provides pressure data of human body 19);
analyzing normalized contact area pressure values at to track pressure exerted on the joint over time (Vahdatpour et al. para[0069], tracks location and pressure on a body parts over time); and
transmitting an alert in response to the pressure exerted on the joint exceeding a threshold level (Vahdatpour et al. par[0069], sends out alarm if pressure areas are above a predetermined limit for a predetermined time period).
Vahdatpour et al. does not explicitly disclose applying one or more machine learning models to analyze the set of pressure data to identify locations of a joint of the individual.
However Carneiro et al. substantially discloses inputting the set of data into one or more machine learning models to analyze the set of pressure data to identify locations of a joint of the individual (Carneiro et al. para[0030], uses machine learning models to identify anatomy from data).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the pressure monitoring system of Vahdatpour et al. with the anatomy identification method of Carneiro et al. in order to identify 3D anatomical structures from range images (Carneiro et al. para[0005]).
Vahdatpour does not explicitly disclose inputting the set of pressure data measured by pressure sensors into one or more machine learning models; and
pressure data of identified coordinate locations of the joint.
However Perlin et al. discloses inputting the set of pressure data measured by pressure sensors into one or more machine learning models (Perlin et al. para[0274], inputs pressure data from pressure sensors to machine learning model to recognize differences between pressure signatures); and
pressure data of identified coordinate locations of the joint (Perlin et al. para[0449], uses pressure data to track coordinate location of finger).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the pressure monitoring system of Vahdatpour et al. with the sensor pad processing of Perlin et al. in order to cost-effectively record pressure information about how a user interacts with a surface (Perlin et al. para[0004]).
Vahdatpour et al. does not explicitly disclose determining average pressure data derived from the set of pressure data and contact area pressure values at the coordinate locations of the joint, wherein the average pressure data corresponds to a firmness level of the body support system;
normalizing the contact area pressure values based on the average pressure data to account for the firmness level of the body support system.
However Terawaki et al. substantially discloses determining average pressure data derived from the set of pressure data and contact area pressure values at the coordinate locations of the joint, wherein the average pressure data corresponds to a firmness level of the body support system (Terawaki et al. para [0093], Specifically, the capacitance C is substituted to the capacitance-body pressure map previously stored in the ROM 44, and then the body pressure is calculated for any detectors A0101 to A1410.);
normalizing the contact area pressure values based on the average pressure data to account for the firmness level of the body support system (Terawaki et al. para[0094], Then, the air cell 21 inflates. Thus, the body pressure can be distributed and the body position can be changed).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the pressure monitoring system of Vahdatpour et al. with the pressure and position control method of Terawaki et al. in order to identify and control contact between the body and the mattress (Terawaki et al. para[0008]).
In regards to claim 2, Vahdatpour et al. as modified by Carneiro et al., Perlin et al., and Terawaki et al. disclose the computer-implemented method of claim 1, wherein the set of pressure data corresponds to a two-dimensional pressure image (Vahdatpour et al. para[0047]).
In regards to claim 3, Vahdatpour et al. as modified by Carneiro et al., Perlin et al., and Terawaki et al. disclose the computer-implemented method of claim 1, wherein the one or more machine learning models comprise a first machine learning model that determines a position of the individual and a second machine learning model that determines the locations of the joint of the individual (Carneiro et al. fig. 2 para[0054] and [0062]).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the pressure monitoring system of Vahdatpour et al. with the anatomy identification method of Carneiro et al. in order to identify 3D anatomical structures from range images (Carneiro et al. para[0005]).
In regards to claim 5, Vahdatpour et al. as modified by Carneiro et al., Perlin et al., and Terawaki et al. disclose the computer-implemented method of claim 1, wherein the alert comprising a message that the individual should change a position (Vahdatpour et al. para[0069]).
In regards to claim 6, Vahdatpour et al. as modified by Carneiro et al., Perlin et al., and Terawaki et al. disclose the computer-implemented method of claim 1, further comprising: adjusting a comfort and/or support of the body support system based on the pressure exerted on the joint (Vahdatpour et al. para[0018]).
In regards to claim 7, Vahdatpour et al. as modified by Carneiro et al., Perlin et al., and Terawaki et al. disclose the computer-implemented method of claim 1, further comprising: transmitting data for displaying a two-dimensional pressure image of the individual (Vahdatpour et al. fig.4 para[0047]).
In regards to claim 8, Vahdatpour et al. as modified by Carneiro et al., Perlin et al., and Terawaki et al. disclose the computer-implemented method of claim 7, wherein the two-dimensional pressure image displays different levels of pressure measurements with in different shadings and/or colors (Vahdatpour et al. fig.4 para[0047]).
In regards to claim 9, Vahdatpour et al. as modified by Carneiro et al., Perlin et al., and Terawaki et al. disclose the computer-implemented method of claim 1, further comprising: determining a position classification for a body position of the individual, the position classification selected from a list that comprises a leftside-sleeping classification, a rightside-sleeping classification, a prone-sleeping classification, and a supine- sleeping classification (Vahdatpour et al. para[0048]).
Claims 11-13 and 15-19 recites substantially similar limitations to claims 1-3, and 5-9. Thus claims 11-13 and 15-19 is rejected along the same rationale as claims 1-3 and 5-9.
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vahdatpour et al. in view of Carneiro et al., Perlin et al., Terawaki et al. and Williams et al. (US2012/0056800).
In regards to claim 4, Vahdatpour et al. as modified by Carneiro et al., Perlin et al., and Terawaki et al. disclose the computer-implemented method of claim 3, wherein the first machine learning model is a convolutional neural network (Williams et al. para[0124]).
It would have been obvious to one of ordinary skill in the art before he filing date of the invention to have combined pressure monitoring system of Vahdatpour et al. with the skeletal tracking of Williams et al. in order to identify to most probable position of a user’s limbs (Williams et al. para[0006]).
Claim 14 recites substantially similar limitations to claim 4. Thus claim 14 is rejected along the same rationale as claim 4.
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vahdatpour et al. in view of Carneiro et al., Perlin et al., Terawaki et al., and Reynolds et al. (US2004/0011150).
In regards to claim 10, Vahdatpour et al. as modified by Carneiro et al., Perlin et al., and Terawaki et al. disclose the computer-implemented method of claim 1, Vahdatpour et al. does not explicitly disclose further comprising detecting anatomical features comprising one or more of ischial tuberosity, trochanter, or coccyx based on the identified coordinate locations.
However Reynolds et al. substantially discloses further comprising detecting anatomical features comprising one or more of ischial tuberosity, trochanter, or coccyx based on the identified coordinate locations (Reynolds et al. para[0220] pressure mat used to measure occupant sitting in different postures, [0222] pressure data used to estimate location of ischial tuberosities).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the pressure monitoring system of Vahdatpour et al. with the measurement system of Reynolds et al. in order to identify stress and loads placed on a human body (Reynolds et al. para[0010]).
Claim 20 recites substantially similar limitations to claim 10. Thus claim 20 is rejected along the same rationale as claim 10.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the arguments do not apply the current rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS HASTY whose telephone number is (571)270-7775. The examiner can normally be reached Monday-Friday 8:30am-5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matt Ell can be reached at (571)270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.H/Examiner, Art Unit 2141
/TAN H TRAN/Primary Examiner, Art Unit 2141
Prosecution Insights