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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09 April 2026 has been entered.
Response to Amendment
Claims 1-16, 19-21, and 33 are currently pending. Claims 1, 11, and 33 have been amended.
Claim Objections
Claims 1, 3, 11, and 33 are objected to because of the following informalities:
“the ANN system” in line 9 of claim 1 should read as “an artificial neural network (ANN) system”
“an artificial neural network (ANN) system” in line 10 of claim 1 should read as “the ANN system”
“the preprocessing” in line 1 of claim 3 should read as “the image preprocessing”
“the ANN system” in line 10 of claim 11 should read as “an artificial neural network (ANN) system”
“an artificial neural network (ANN) system” in lines 11-12 of claim 11 should read as “the ANN system”
“the ANN system” in line 12 of claim 33 should read as “an artificial neural network (ANN) system”
“an artificial neural network (ANN) system” in lines 13-14 of claim 33 should read as “the ANN system”
Appropriate correction is required.
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 1-16, 19-21, and 33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
STEP 1
Regarding claim 1, the claim recites a series of structural elements, including a camera system. Thus, the claim is directed to a machine, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The steps of:
an image preprocessing system configured to receive via a data communication network one or more images captured by the camera system and preprocess the one or more images by performing one or more of resizing, center cropping, and channel collapsing to modify one or more of a height dimension, a width dimension, and one or more color channels to comply with predetermined image input dimensions of the ANN system;
an artificial neural network (ANN) system configured to receive the one or more preprocessed images complying with the predetermined image input dimensions and output a vector comprising a confidence score for each of five states of the subject simultaneously estimated from the one or more images, wherein the five states of the subject include: an empty room state; an in-bed state; a sitting state; a standing state; and a fall state;
a state machine system configured to receive the estimated confidence scores for each of the five states from the ANN and analyze the estimated confidence scores in combination with one or more previously determined states of the subject and a current state of the subject to determine a new current state of the subject; and
an alert system configured to receive the new current state of the subject and identify one or more alerts corresponding to the new current state of the subject
sets forth a judicial exception. The preprocessing and outputting steps describe a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea. The preprocessing and outputting steps also describe mathematical relationships, mathematical formulas or equations, mathematical calculations. Thus, the claim is also drawn to a Mathematical Concept, which is also an Abstract Idea. The performing and modifying steps describe managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Thus, the claim is also drawn to Organizing Human Activity, which is also an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites the alert system sending the one or more alerts to one or more recipients via the data communication network, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The sending of the one or more alerts does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the sent one or more alerts, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of an image preprocessing system configured to receive one or more images captured by the camera system, an artificial neural network (ANN) system configured to receive the one or more preprocessed images, a state machine system configured to receive the estimated confidence scores for each of the five states from the ANN, and an alert system configured to receive the new current state of the subject. Receiving data is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the receiving steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Regarding claim 1, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The recited camera system having a camera sensor directed toward a portion of a room occupied by a subject having a camera sensor is a generic sensor configured to perform pre-solutional data gathering activity, the alert system is a generic device configured to perform the sending step, which does not integrate the Abstract Idea into a practical application, and the image preprocessing system, the ANN system, and the state machine system are configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application. Furthermore, [0023] of the PGPUB recites that the camera system, image preprocessing system, ANN system, state machine system, and alert system is part of a known computer system. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
The same rationale applies to claims 11 and 33.
The dependent claims also fail to add something more to the abstract independent claims. Claims 2, 12, and 13 merely recite what the one or more captured images are, which is an additional element. Claims 3 and 14 recite steps that add to the Abstract Idea, as the steps are mental processes/organizing human activity. Claims 4-7, 9, 15, 16, and 20 recite steps that do not integrate the Abstract Idea into a practical application. Claims 8, 10, 19, and 21 recite steps that add to the Abstract Idea, as the steps are mental processes. The comparing and calculating steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
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.
Claims 1, 2, 4-6, 8-13, 15, 19, 20, 21, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Derenne et al. ‘179 (US Pub No. 2018/0068179 – previously cited) in view of Eronen et al. ‘159 (US Pub No. 2017/0364159 – previously cited) further in view of Morris et al. ‘565 (US Pub No. 2021/0059565 – previously cited).
Regarding claim 1, Derenne et al. ‘179 teaches a system for detecting a position of a subject (Title, Abstract), comprising:
a camera system having a camera sensor directed toward a portion of a room occupied by a subject (Fig. 1 one or more conventional video cameras 22 and [0064]);
an image preprocessing system configured to receive via a data communication network one or more images captured by the camera system and preprocess the one or more images by modifying one or more of a height dimension, a width dimension, and one or more color channels of a respective image ([0178]; “If computer device 24 is unable to find a match, it is configured to rotate the object detected in the image frames to different orientations and search through database 50 until a match, if any, can be achieved for one of the rotated orientations…computer device 24 is configured to compare color data, size data, and other attribute data to the data stored in database 50 to determine if a match can be located.” One of ordinary skill would understand that rotating the object in the image frame would be modifying the height and width dimensions.) to comply with predetermined image input dimensions of the network system (Fig. 1 computer device 24 and [0065], [0178]);
a network system, configured to receive the one or more preprocessed images complying with the predetermined image input dimensions and output comprising a confidence score ([0175]) for to each of five states of the subject simultaneously estimated from the one or more images (Fig. 7 step 138 and [0175]), wherein the five states of the subject include:
an empty room state ([0296]; “when the bed is empty”);
an in-bed state ([0275]; “please stay in bed”);
a sitting state ([0145]; “sitting up, sitting down”);
a standing state ([0275]; “get back to bed”); and
a fall state ([0280]);
a state machine system configured to receive the estimated confidence scores for each of the five states from the network system and analyze the estimated confidence scores in combination with one or more previously determined states of the subject and a current state of the subject to determine a new current state of the subject (Fig. 7 step 140 and [0175]); and
an alert system configured to receive the new current state of the subject and identify one or more alerts corresponding to the new current state of the subject and send the one or more alerts to one or more recipients via the data communication network (Fig. 7 step 142 and [0175]).
Derenne et al. ‘179 teaches machine learning algorithms to analyze image and depth data ([0095]). Derenne et al. ‘179 teaches all of the elements of the current invention as mentioned above except for wherein the network system is an artificial neural network (ANN) system configured to output a vector comprising the confidence score.
Eronen et al. ‘159 teaches a machine (computer) inference engine or other recognition engine such as an artificial neural network or clustering in the parameter space ([0064]). Motion detection may be achieved using optical flow analysis using a vector-based approach ([0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the network system of Derenne et al. ‘179 to be an ANN system configured to output a vector comprising the confidence score as Eronen et al. ‘159 teaches that ANN is a type of recognition module ([0064]) to perform optical flow analysis ([0053]).
Derenne et al. ‘179 in view of Eronen et al. ‘159 teaches all of the elements of the current invention as mentioned above except for the image preprocessing system configured to preprocess the one or more images by performing one or more of resizing, center cropping, and channel collapsing to modify one or more of a height dimension, a width dimension, and one or more color channels of a respective image.
Morris et al. ‘565 teaches cropping the image around a subject and resizing the cropped image to a fixed size ([0022]) which will save computational cost and normalize the size of the subject in the image, which standardizes the size of the relevant features inside the image, improving accuracy and decreasing the relevance of other attributes ([0034]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the preprocessing system of Derenne et al. ‘179 in view of Eronen et al. ‘159 to include performing one or more of resizing, center cropping, and channel collapsing to modify one or more of a height dimension, a width dimension, and one or more color channels of a respective image as Morris et al. ‘565 teaches that this will aid in saving computational cost and improving accuracy and decreasing the relevance of other attributes.
Regarding claim 2, Derenne et al. ‘179 teaches wherein the one or more captured images comprise a series of still images captured ([0077]) at regular time intervals ([0212]).
Regarding claim 4, Derenne et al. ’179 teaches wherein the alert system is further configured to send at least one of a digital notification, a visual notification, an audible notification, and a haptic notification ([0101]; “displays, speakers, and/or lights” [0188]-[0189]).
Regarding claim 5, Derenne et al. ‘179 teaches wherein the alert system is further configured to send one of: a text message, an email, a push notification, and an application popup ([0189]; “system 20 sends an appropriate message to a nurser’s station”).
Regarding claim 6, Derenne et al. ‘179 teaches wherein the alert system is further configured to send one of: a computer sound and a prerecorded phonecall ([0275]; “warning phrases…or a beeping sound”).
Regarding claim 8, Derenne et al. ‘179 teaches wherein a first of the one or more images includes two or more individuals ([0149], [0304]), and wherein the state machine system is further configured to:
determine the new current state of the subject to be the fall state when one individual has their torso or hand on a floor ([0277], [0280]);
if the fall state is not determined, determine the new current state of the subject to be the in-bed state when an individual is in-bed ([0277]-[0279]);
if one of the fall state and the in-bed state is not determined, determine the new current state of the subject to be the sitting state when an individual is sitting ([0145], [0278]); and
if one of the fall state, the in-bed state, and the sitting state is not determined, determine the new current state of the subject to be the standing state when all individuals are standing ([0036], [0172], [0279]).
Regarding claim 9, Derenne et al. ‘179 teaches wherein the alert system is further configured to alert one or more individuals when the state of the subject has not been in-bed for a predetermined duration of time ([0301]; “determin[ing] which rooms are available is carried out…by detecting not only the absence of a patient from a room for a particular time period…”).
Regarding claim 10, Derenne et al. ‘179, as modified by Eronen et al. ‘159, teaches wherein the ANN is further configured to receive a plurality of images captured of the room over a period of time (Fig. 7 facial detection algorithm 84 and [0174]) and a validated state corresponding to each image and process the plurality of images and their corresponding validated states to retrain the ANN (Fig. 7 step 142 and [0175], [0095]).
Regarding claim 11, Derenne et al. ‘179 teaches a method for detecting a position of a subject (Title, Abstract), comprising:
capturing one or more images by a camera system having a camera sensor directed toward a portion of a room occupied by a subject (Fig. 1 one or more conventional video cameras 22 and [0064]);
transmitting the one or more images to an image preprocessing system via a data communication network (Fig. 1 database 50 and [0065]);
preprocessing the one or more preprocessed images captured by the camera system by modifying one or more of a height dimension, a width dimension, and one or more color channels of a respective image ([0178]; “If computer device 24 is unable to find a match, it is configured to rotate the object detected in the image frames to different orientations and search through database 50 until a match, if any, can be achieved for one of the rotated orientations…computer device 24 is configured to compare color data, size data, and other attribute data to the data stored in database 50 to determine if a match can be located.” One of ordinary skill would understand that rotating the object in the image frame would be modifying the height and width dimensions.) to comply with predetermined image input dimensions of the network system (Fig. 1 computer device 24 and [0065], [0178]);
transmitting the one or more preprocessed images to a network system configured to process images complying with the predetermined image input dimensions (Fig. 7 step 138 and [0175], [0178]);
processing the one or more preprocessed images to output a confidence score for each of five states of the subject simultaneously estimated from the one or more images (Fig. 7 step 138 and [0175]), wherein the five states of the subject include:
an empty room state ([0296]; “when the bed is empty”);
an in-bed state ([0275]; “please stay in bed”);
a sitting state ([0145]; “sitting up, sitting down”);
a standing state ([0275]; “get back to bed”); and
a fall state ([0280]);
analyzing the estimated confidence scores for each of the five states in combination with one or more previously determined states of the subject and a current state of the subject to determine a new current state of the subject (Fig. 7 step 140 and [0175]);
identifying one or more alerts corresponding to the new current state of the subject (Fig. 7 step 142 and [0175]); and
sending the one or more alerts to one or more recipients via a data communication network (Fig. 7 step 142 and [0175]).
Derenne et al. ‘179 teaches machine learning algorithms to analyze image and depth data ([0095]). Derenne et al. ‘179 teaches all of the elements of the current invention as mentioned above except for wherein the network system is an artificial neural network (ANN) system configured to output a vector comprising the confidence score.
Eronen et al. ‘159 teaches a machine (computer) inference engine or other recognition engine such as an artificial neural network or clustering in the parameter space ([0064]). Motion detection may be achieved using optical flow analysis using a vector-based approach ([0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the network system of Derenne et al. ‘179 to be an ANN system as Eronen et al. ‘159 teaches that ANN is a type of recognition module ([0064]) to perform optical flow analysis ([0053]).
Derenne et al. ‘179 in view of Eronen et al. ‘159 teaches all of the elements of the current invention as mentioned above except for preprocessing the one or more images captured by the camera system by performing one or more of resizing, center cropping, and channel collapsing to modify one or more of a height dimension, a width dimension, and one or more color channels of a respective image.
Morris et al. ‘565 teaches cropping the image around a subject and resizing the cropped image to a fixed size ([0022]) which will save computational cost and normalize the size of the subject in the image, which standardizes the size of the relevant features inside the image, improving accuracy and decreasing the relevance of other attributes ([0034]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the preprocessing of the one or more images captured by the camera system of Derenne et al. ‘179 in view of Eronen et al. ‘159 to include performing one or more of resizing, center cropping, and channel collapsing to modify one or more of a height dimension, a width dimension, and one or more color channels of a respective image as Morris et al. ‘565 teaches that this will aid in saving computational cost and improving accuracy and decreasing the relevance of other attributes.
Regarding claim 12, Derenne et al. ‘179 teaches wherein the one or more captured images provide a video feed of the room occupied by the subject ([0064]).
Regarding claim 13, Derenne et al. ‘179 teaches wherein the one or more captured images comprise a series of still images captured ([0077]) at regular time intervals ([0212]).
Regarding claim 15, Derenne et al. ’179 teaches wherein the alert system is further configured to send at least one of a digital notification, a visual notification, an audible notification, and a haptic notification ([0101]; “displays, speakers, and/or lights” [0188]-[0189]).
Regarding claim 19, Derenne et al. ‘179 teaches wherein a first of the one or more images includes two or more individuals ([0149], [0304]), further comprising:
determining the new current state of the subject to be the fall state when one individual has their torso or hand on a floor ([0277], [0280]);
if the fall state is not determined, determining the new current state of the subject to be the in-bed state when an individual is in-bed ([0277]-[0279]);
if one of the fall state and the in-bed state is not determined, determining the new current state of the subject to be the sitting state when an individual is sitting ([0145], [0278]); and
if one of the fall state, the in-bed state, and the sitting state is not determined, determining the new current state of the subject to be the standing state when all individuals are standing ([0036], [0172], [0279]).
Regarding claim 20, Derenne et al. ‘179 teaches wherein the alert system is further configured to alert one or more individuals when the state of the subject has not been in-bed for a predetermined duration of time ([0301]; “determin[ing] which rooms are available is carried out…by detecting not only the absence of a patient from a room for a particular time period…”).
Regarding claim 21, Derenne et al. ‘179 teaches receiving a plurality of images captured of the room over a period of time (Fig. 7 facial detection algorithm 84 and [0174]) and a validated state corresponding to each image and processing the plurality of images and their corresponding validated states to retrain a network configured to process the one or more preprocessed images to estimate confidence scores corresponding to a state of the subject. (Fig. 7 step 142 and [0175], [0095]).
Derenne et al. ‘179 teaches machine learning algorithms to analyze image and depth data ([0095]). Derenne et al. ‘179 teaches all of the elements of the current invention as mentioned above except for wherein the network is an artificial neural network.
Eronen et al. ‘159 teaches a machine (computer) inference engine or other recognition engine such as an artificial neural network or clustering in the parameter space ([0064]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the network of Derenne et al. ‘179 to be an ANN system as Eronen et al. ‘159 teaches that ANN is a type of recognition module ([0064]).
Regarding claim 33, Derenne et al. ‘179 teaches a non-transitory computer readable medium having stored thereon one or more sequences of instructions for causing one or more processors to perform steps for detecting a position of a subject (Title, Abstract), the steps comprising:
capturing one or more images by a camera system having a camera sensor directed toward a portion of a room occupied by a subject (Fig. 1 one or more conventional video cameras 22 and [0064]);
transmitting the one or more images to an image preprocessing system via a data communication network (Fig. 1 database 50 and [0065]);
preprocessing the one or more preprocessed images captured by the camera system by modifying one or more of a height dimension, a width dimension, and one or more color channels of a respective image ([0178]; “If computer device 24 is unable to find a match, it is configured to rotate the object detected in the image frames to different orientations and search through database 50 until a match, if any, can be achieved for one of the rotated orientations…computer device 24 is configured to compare color data, size data, and other attribute data to the data stored in database 50 to determine if a match can be located.” One of ordinary skill would understand that rotating the object in the image frame would be modifying the height and width dimensions.) to comply with predetermined image input dimensions of the network system (Fig. 1 computer device 24 and [0065], [0178]);
transmitting the one or more preprocessed images to a network system configured to process images complying with the predetermined image input dimensions (Fig. 7 step 138 and [0175]);
processing the one or more preprocessed images by the network system to output a confidence score for each of five states of the subject simultaneously estimated from the one or more images (Fig. 7 step 138 and [0175]), wherein the five states of the subject include:
an empty room state ([0296]; “when the bed is empty”);
an in-bed state ([0275]; “please stay in bed”);
a sitting state ([0145]; “sitting up, sitting down”);
a standing state ([0275]; “get back to bed”); and
a fall state ([0280]);
analyzing the estimated confidence scores for each of the five states in combination with one or more previously determined states of the subject and a current state of the subject to determine a new current state of the subject (Fig. 7 step 140 and [0175]);
identifying one or more alerts corresponding to the new current state of the subject (Fig. 7 step 142 and [0175]); and
sending the one or more alerts to one or more recipients via a data communication network (Fig. 7 step 142 and [0175]).
Derenne et al. ‘179 teaches machine learning algorithms to analyze image and depth data ([0095]). Derenne et al. ‘179 teaches all of the elements of the current invention as mentioned above except for wherein the network system is an artificial neural network (ANN) system configured to output a vector comprising a confidence score.
Eronen et al. ‘159 teaches a machine (computer) inference engine or other recognition engine such as an artificial neural network or clustering in the parameter space ([0064]). Motion detection may be achieved using optical flow analysis using a vector-based approach ([0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the network system configured to output a vector comprising a confidence score of Derenne et al. ‘179 to be an ANN system as Eronen et al. ‘159 teaches that ANN is a type of recognition module ([0064]) to perform optical flow analysis ([0053]).
Derenne et al. ‘179 in view of Eronen et al. ‘159 teaches all of the elements of the current invention as mentioned above except for preprocessing of the one or more images captured by the camera system by performing one or more of resizing, center cropping, and channel collapsing to modify one or more of a height dimension, a width dimension, and one or more color channels of a respective image.
Morris et al. ‘565 teaches cropping the image around a subject and resizing the cropped image to a fixed size ([0022]) which will save computational cost and normalize the size of the subject in the image, which standardizes the size of the relevant features inside the image, improving accuracy and decreasing the relevance of other attributes ([0034]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the preprocessing of the one or more images captured by the camera system of Derenne et al. ‘179 in view of Eronen et al. ‘159 to include performing one or more of resizing, center cropping, and channel collapsing to modify one or more of a height dimension, a width dimension, and one or more color channels of a respective image as Morris et al. ‘565 teaches that this will aid in saving computational cost and improving accuracy and decreasing the relevance of other attributes.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 further in view of Ziaie et al. ‘231 (US Pub No. 2015/0196231 – previously cited).
Regarding claim 3, Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 teaches all of the elements of the current invention as mentioned above except for wherein the preprocessing system is further configured to:
crop a length of the one or more images;
crop a height of the one or more images; and
adjust a color of the one or more images.
Morris et al. ‘565 teaches cropping the image around a subject and resizing the cropped image to a fixed size ([0022]) which will save computational cost and normalize the size of the subject in the image, which standardizes the size of the relevant features inside the image, improving accuracy and decreasing the relevance of other attributes ([0034]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the preprocessing system of Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 to include cropping a length of the one or more images and cropping a height of the one or more images as Morris et al. ‘565 teaches that this will aid in saving computational cost and improving accuracy and decreasing the relevance of other attributes.
Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 teaches all of the elements of the current invention as mentioned above except for adjusting a color of the one or more images.
Ziaie et al. ‘231 teaches capturing raw images in the RGB format, which is then converted to a HSV image to provide more intuitive and perceptual relevance ([0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the preprocessing system of Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 to include adjusting a color of the one or more images as Ziaie et al. ‘231 teaches that this will aid in providing more intuitive and perceptual relevance.
Regarding claim 14, Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 teaches all of the elements of the current invention as mentioned above except for wherein the preprocessing further comprises:
cropping a length of the one or more images;
cropping a height of the one or more images; and
adjusting a color of the one or more images.
Morris et al. ‘565 teaches cropping the image around a subject and resizing the cropped image to a fixed size ([0022]) which will save computational cost and normalize the size of the subject in the image, which standardizes the size of the relevant features inside the image, improving accuracy and decreasing the relevance of other attributes ([0034]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the preprocessing of Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 to include cropping a length of the one or more images and cropping a height of the one or more images as Morris et al. ‘565 teaches that this will aid in saving computational cost and improving accuracy and decreasing the relevance of other attributes.
Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 teaches all of the elements of the current invention as mentioned above except for adjusting a color of the one or more images.
Ziaie et al. ‘231 teaches capturing raw images in the RGB format, which is then converted to a HSV image to provide more intuitive and perceptual relevance ([0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the preprocessing of Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 to include adjusting a color of the one or more images as Ziaie et al. ‘231 teaches that this will aid in providing more intuitive and perceptual relevance.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 further in view of Receveur et al. ‘309 (US Pub No. 2023/0123309 – previously cited).
Regarding claim 7, Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 teaches all of the elements of the current invention as mentioned above except for wherein the alert system is further configured to send a haptic vibration.
Receveur et al. ‘309 an alert notification that may be visual, haptic, audible, or a combination thereof ([0119]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the alert system of Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 to include sending a haptic vibration and Receveur et al. ‘309 teaches that each type of alert if similar ([0119]).
Regarding claim 16, Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 teaches wherein sending one or more digital notifications comprises sending one of: a text message, an email, a push notification, and an application popup ([0189]; “system 20 sends an appropriate message to a nurser’s station”), and wherein sending one or more audible notifications comprises sending one of: a computer sound and a prerecorded phonecall ([0275]; “warning phrases…or a beeping sound”).
Derenne et al. ‘179 in view of Eronen et al. ‘159 teaches all of the elements of the current invention as mentioned above except for wherein sending one or more haptic notifications comprises sending a vibration.
Receveur et al. ‘309 an alert notification that may be visual, haptic, audible, or a combination thereof ([0119]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the one or more alerts of Derenne et al. ‘179 in view of Eronen et al. ‘159 further in view of Morris et al. ‘565 to include a haptic notification, wherein sending one or more haptic notifications comprises sending a vibration and Receveur et al. ‘309 teaches that each type of alert if similar ([0119]).
Response to Arguments
Applicant argues that the step of performing one or more of resizing, center cropping, and channel collapsing cannot be performed mentally or by hand. However, this step is considered to be organizing human activity, which is an Abstract Idea. Regarding outputting the vector, this is seen as a mathematical process as the images are input into the ANN to output a vector. Thus, the independent claims recite Abstract Ideas.
Applicant argues that the claims recite a specific technical solution that eliminates the need for wearable devices. However, the camera system, image preprocessing system, ANN system, state machine system, and alert system is part of a known computer system, as mentioned in [0023] of the PGPUB. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Applicant argues that the amended claims recite specific preprocessing operations and shows integration with the particular machine. Again, the “particular machine” is a known and generic computer. Furthermore, the resizing, center cropping, and channel collapsing are Abstract Ideas (see 35 U.S.C. 101 rejection). It is noted that section 2106.05(a) II. of the MPEP states that “…it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
Applicant argues that outputting the vector comprising a confidence score is a specific technical implementation. However, this is merely outputting a data point. It is unclear how outputting a data point (vector comprising confidence score) provides an improvement to the technology or effects a change. Furthermore, the outputting of the vector is a result of performing a mathematical process of using the ANN system, which is an Abstract Idea. It is noted that section 2106.05(a) II. of the MPEP states that “…it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
Applicant argues that the additional elements are not merely generic computer components. Examiner respectfully disagrees, as the camera system, image preprocessing system, ANN system, state machine system, and alert system is part of a known computer system, as mentioned in [0023] of the PGPUB. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application. The receiving data steps are also seen as pre-solutional activity of data gathering necessary to perform the Abstract Idea. As such, Applicant’s arguments are not persuasive and the 35 U.S.C. 101 rejection has been maintained.
Applicant’s arguments with respect to the 35 U.S.C. 103 rejections for claims 1, 11, and 33 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.
Conclusion
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/AURELIE H TU/ Primary Examiner, Art Unit 3791
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