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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Such claim limitation(s) is/are: image acquiring means in claims 1 and 9, information acquiring means in claim 1, and generating means in claims 1 and 5-6.
The structure described in the specification paragraph[0026] for the image acquiring means is described as a hardware camera that is part of the monitor device 10.
The structure described in the specification paragraph[0028] for the information acquiring means is described as part of a transceiver that is part of the monitor device 10.
The structure described in the specification paragraph[0103] for the generating means is described as a transmitter/generator that is part of the monitor device 10.
Therefore, Examiner finds the claims are reasonably supported by the structure described in the specification pertaining to 35 U.S.C. 112 (a) and (b) (or 35 U.S.C. 112, first and second paragraphs, pre-AIA ) whether 35 U.S.C. 112(f) is invoked or not.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites the limitation "each of the alert target swimmer" in line 4. It is unclear and indefinite about the term “each” since there is only one alert target swimmer that is recited in claim 1. Does the Applicant recites the term “each” to emphasize there are many alert target swimmer? Or each is associated with one alert target swimmer?.
Claim 6 recites the limitation "each of the alert target swimmer" in lines 4-5. It is unclear and indefinite about the term “each” since there is only one alert target swimmer that is recited in claim 1. Does the Applicant recites the term “each” to emphasize there are many alert target swimmer? Or each is associated with one alert target swimmer?.
Claim 6 recites the limitation "the alert target swimmers" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "each of the alert target swimmer" in line 3. It is unclear and indefinite about the term “each” since there is only one alert target swimmer that is recited in claim 1. Does the Applicant recites the term “each” to emphasize there are many alert target swimmer? Or each is associated with one alert target swimmer?.
Claim 16 recites the limitation "each of the alert target swimmer" in lines 3-4. It is unclear and indefinite about the term “each” since there is only one alert target swimmer that is recited in claim 1. Does the Applicant recites the term “each” to emphasize there are many alert target swimmer? Or each is associated with one alert target swimmer?.
Claim 16 recites the limitation "the alert target swimmers" in lines 6-7. There is insufficient antecedent basis for this limitation in the claim.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
1. Claim(s) 1-4 and 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gama et al. (JP2019215740) hereafter Gama, in view of Barton et al. (US2020/0394804A1) hereafter Barton.
Regarding claim 1, Gama discloses a swimmer monitoring device comprising:
an image acquiring means configured to acquire a monitor image obtained by capturing an aquatic location from a position higher than a surface of water (fig 1:13 & fig 2:13a, 13b; par[0043]: The monitoring cameras 13a and 13b are installed such that the combined range of the respective imaging ranges is a range corresponding to the entire site of the pool. As shown in FIG. 4, at least one of the monitoring cameras 13a and 13b is installed at a relatively high position (for example, a position 5 meters from the upper surface of the poolside) so that an image can be captured from a viewpoint that looks down at the pool area.);
an information acquiring means configured to acquire swimming-state information outputted from a learned by inputting the acquired monitor image into the learned (fig 1:14; fig 2:30; par[0012], [0052]: The analysis unit analyzes the image captured by the imaging unit, detects a person inside the hall, and detects a behavior of the person inside the hall. Existing techniques can be applied to image analysis. It is desirable that the analysis means is configured to be able to execute image analysis using deep learning technically equivalent to the trained learning model. The image analysis unit 31 of the main PC 30 analyzes the monitoring images transmitted from each of the monitoring cameras 13a and 13b. Here, it is desirable that the image analysis unit 31 is configured to be able to execute analysis using deep learning. The analysis means may include at least (1) a vertical movement of the head near the water surface, (2) a diving for a first predetermined time or more, and (3) a second predetermined time or more for a narrow area of the water area, Stagnation, is detected. );
and
a generating means configured to generate drowning alert information on a swimmer on a basis of the acquired swimming-state information (fig 1:15; par[0016], [0020], [0026]: the notification means may add (1) a vertical movement of the head near the water surface to the detected behavior, An alarm is issued when at least one of (2) diving for a first predetermined time or more and (3) stagnation for a second predetermined time or more in a narrow range of a water area is included. The notifying means includes: (1) vertical movement of the head near the water surface; (2) diving for the first predetermined time or more; And (3) when at least one of the stagnation for the second predetermined time or more within the narrow range of the water area is included, issue an alarm and, based on the specific information and the position information, (2) a person in the field where the vertical movement of the head near the water surface is detected as the behavior, (2) a person in the field where the diving for the first predetermined time or more is detected, and / or (3). The information and the position of the inside of the stadium where the stagnation for the second predetermined time or more in the narrow range of the water area as the behavior may be notified. With this configuration, the supervisor can relatively easily find the rescuer in need.), wherein
the swimming-state information can include detection information on an alert target swimmer in such a manner as to enable a first-drowning-state (par[0064], [0075]: A drowning (or drowning) person desperately tries to come to the surface of the water to breathe, but often loses buoyancy and sinks in the water. On the other hand, people who are drowning (or drowning) often stay in a small area) and a second-drowning-state to be distinguished from each other (par[0065]: The image analysis unit 31 may detect “splashing around the head” in addition to “up and down movement of the head near the water surface” as the behavior of the occupant technically equivalent to an action of trying to lift the head above the water surface to breathe. People who are drowning and have relatively little physical strength often struggle to get their bodies out of the water. This often results in splashes around the head. However, even those who are swimming with will, depending on their swimming style, may have splashes around their heads. For this reason, it is desirable not to detect “splashing around the head” alone but to detect it in combination with “head up / down movement near the water surface”), the first-drowning-state being a state of drowning and sinking in water (par[0064], [0075]: A drowning (or drowning) person desperately tries to come to the surface of the water to breathe, but often loses buoyancy and sinks in the water. On the other hand, people who are drowning (or drowning) often stay in a small area), a second-drowning-state involving a movement of a body and being differing from the first-drowning-state (par[0065]: The image analysis unit 31 may detect “splashing around the head” in addition to “up and down movement of the head near the water surface” as the behavior of the occupant. People who are drowning and have relatively little physical strength often struggle to get their bodies out of the water. This often results in splashes around the head. However, even those who are swimming with will, depending on their swimming style, may have splashes around their heads. For this reason, it is desirable not to detect “splashing around the head” alone but to detect it in combination with “head up / down movement near the water surface”), the alert target swimmer including at least a first-state swimmer estimated to be in the first-drowning-state (par[0075]: For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected. The determination unit 32 determines that an alarm is to be issued when the behavior of the occupant detected by the image analysis unit 31 includes “stagnation for a second predetermined time or more in a narrow range of the water area”. At this time, based on the specific information generated by the image analysis unit 31, the determination unit 32 determines from the list generated by the recognition terminal 22 that the behavior is “stagnation for a second predetermined time or more within a narrow range of the water body”. The determination unit 32 displays an alarm message such as “There is a person staying at one place for a long time” and an image (see FIG. 5) including the face image and the position of the rescuer, and issues a warning. The notification monitor 40 is controlled so as to emit a sound.) and a second-state swimmer estimated to be in the second- drowning-state (par[0066], [0068]: The determination unit 32 determines to issue an alarm when the behavior of the person inside the hall detected by the image analysis unit 31 includes “head up / down movement near the water surface”. The judging unit 32 outputs a warning message such as “Some people splash on the surface of the water or repeatedly move their heads up and down” and a face image of a rescuer (or a candidate for a rescuer). And the position are displayed (see FIG. 5), and the notification monitor 40 is controlled to emit a warning sound).
Gama does not explicitly disclose the learned model, (“since Deep learning is the overall process or methodology used to train a system, whereas the learned model is the actual product or mathematical structure that results from that training”, technically in general Not the same.).
Barton discloses the learned model (par[0065]: FIG. 1A, classifier 122 may be invoked to aid in identifying objects detected by sensors 126 and/or 144 in a body of water. In some examples, classifier 122 may be configured to implement segmentation of captured images and videos from sensors 126 and/or 144. For example, classifier 122 may be configured to classify features of detected objects into person and non-person classes, which may include pose detector features, body part size and shape features, and thermal intensity differentials. By using deep or machine learning algorithms such as those implemented by deep learning module 108 (which can be trained against various types and groups of data (e.g., model data 130)), non-person objects can be identified apart from persons detected in an aquatic environment such as a swimming pool by segmenting captured images and video. As used herein, “deep learning” may include machine learning models, both of which may refer to data models and algorithms that are used to process various types of input data to perform other processes and functions such as those described herein or others. Collectively, “deep learning” and “machine learning” may be referred to as “models.”).
One of ordinary skill in the art would be aware of both the Gama and the Barton references since both pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the system of Gama with the machine learning model feature as disclosed by Barton to achieve predictable results and gain the functionality of providing data models and algorithms that are used to process various types of input data to perform non-person objects that can be identified apart from persons detected in an aquatic environment such as a swimming pool by segmenting captured images and videos.
Regarding claim 2, Gama in view of Barton discloses the swimmer monitoring device according to claim 1, wherein the learned model is capable of distinguishing and detecting each of plural types of individual motions as the second-drowning-state in the monitor image (Gama par[0012]: The analysis unit analyzes the image captured by the imaging unit, detects a person inside the hall, and detects a behavior of the person inside the hall. Existing techniques can be applied to image analysis. It is desirable that the analysis means is configured to be able to execute image analysis using deep learning. The behavior of a person inside the venue detected by the analysis means may be limited to a specific behavior such as a behavior indicating a sign of a water spill accident. The analysis means may include at least (1) a vertical movement of the head near the water surface technically equivalent to the second drowning state, (2) a diving for a first predetermined time or more, and (3) a second predetermined time or more for a narrow area of the water area, Stagnation, is detected), and
the detection information that can be included in the swimming-state information can include information that makes it possible to identify each of the plural types of individual motions as the second-drowning-state (Gama par[0065]: The image analysis unit 31 may detect “splashing around the head” in addition to “up and down movement of the head near the water surface” as the behavior of the occupant technically equivalent to an action of trying to lift the head above the water surface to breathe. People who are drowning and have relatively little physical strength often struggle to get their bodies out of the water. This often results in splashes around the head. However, even those who are swimming with will, depending on their swimming style, may have splashes around their heads. For this reason, it is desirable not to detect “splashing around the head” alone but to detect it in combination with “head up / down movement near the water surface”).
Regarding claim 3, Gama in view of Barton discloses the swimmer monitoring device wherein the learned model is capable of distinguishing and detecting each of plural types of individual motions as a dangerous motion that poses a risk that possibly results in a drowning state in the monitor image (Gama par[0012], [0075]: The analysis unit analyzes the image captured by the imaging unit, detects a person inside the hall, and detects a behavior of the person inside the hall. Existing techniques can be applied to image analysis. It is desirable that the analysis means is configured to be able to execute image analysis using deep learning. The behavior of a person inside the venue detected by the analysis means may be limited to a specific behavior such as a behavior indicating a sign of a water spill accident. The analysis means may include at least (1) a vertical movement of the head near the water surface technically equivalent to the second drowning state, (2) a diving for a first predetermined time or more, and (3) a second predetermined time or more for a narrow area of the water area, Stagnation, is detected),
the alert target swimmer indicated by the detection information that can be included in the swimming-state information further includes a dangerous-action swimmer estimated to perform the dangerous motion (Gama par[0075]: "stagnation for a second predetermined time or more within a narrow range of a water area" can be said to be a behavior indicating a sign of a water accident. For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected.), and
the detection information that can be included in the swimming-state information can include information that makes it possible to identify each of the plural types of individual motions as the dangerous motion (Gama par[0075], [0076]: "stagnation for a second predetermined time or more within a narrow range of a water area" can be said to be a behavior indicating a sign of a water accident. For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected. The determination unit 32 determines that an alarm is to be issued when the behavior of the occupant detected by the image analysis unit 31 includes “stagnation for a second predetermined time or more in a narrow range of the water area”. At this time, based on the specific information generated by the image analysis unit 31, the determination unit 32 determines from the list generated by the recognition terminal 22 that the behavior is “stagnation for a second predetermined time or more within a narrow range of the water body”. The information on the rescuer who needs to be a rescuer who is a person in the hall where is detected is acquired).
Regarding claim 4, Gama in view of Barton discloses the swimmer monitoring device wherein the swimming-state information can include the detection information on the alert target swimmer in such a manner as to enable the first-drowning-state, the second-drowning-state and a third-drowning-state to be distinguished from each other, the third-drowning-state being a state of drowning and floating with a face being below a surface of water (Gama par[0075]: "stagnation for a second predetermined time or more within a narrow range of a water area" can be said to be a behavior indicating a sign of a water accident. For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected.), the alert target swimmer including the first-state swimmer and the second-state swimmer and further including a third- state swimmer estimated to be in the third-drowning-state (Gama par[0075]: "stagnation for a second predetermined time or more within a narrow range of a water area" can be said to be a behavior indicating a sign of a water accident. For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected.).
Regarding claim 11, Gama discloses a swimmer monitoring method performed by one or more processors capable of using a learned model, wherein the one or more processors execute:
acquiring a monitor image obtained by capturing an aquatic location from a position higher than a surface of water (fig 1:13 & fig 2:13a, 13b; par[0043]: The monitoring cameras 13a and 13b are installed such that the combined range of the respective imaging ranges is a range corresponding to the entire site of the pool. As shown in FIG. 4, at least one of the monitoring cameras 13a and 13b is installed at a relatively high position (for example, a position 5 meters from the upper surface of the poolside) so that an image can be captured from a viewpoint that looks down at the pool area.);
acquiring swimming-state information outputted from a learned by inputting the acquired monitor image into the learned (fig 1:14; fig 2:30; par[0012], [0052]: The analysis unit analyzes the image captured by the imaging unit, detects a person inside the hall, and detects a behavior of the person inside the hall. Existing techniques can be applied to image analysis. It is desirable that the analysis means is configured to be able to execute image analysis using deep learning technically equivalent to the trained learning model. The image analysis unit 31 of the main PC 30 analyzes the monitoring images transmitted from each of the monitoring cameras 13a and 13b. Here, it is desirable that the image analysis unit 31 is configured to be able to execute analysis using deep learning. The analysis means may include at least (1) a vertical movement of the head near the water surface, (2) a diving for a first predetermined time or more, and (3) a second predetermined time or more for a narrow area of the water area, Stagnation, is detected. );
and
generating drowning alert information on a swimmer on a basis of the acquired swimming-state information (fig 1:15; par[0016], [0020], [0026]: the notification means may add (1) a vertical movement of the head near the water surface to the detected behavior, An alarm is issued when at least one of (2) diving for a first predetermined time or more and (3) stagnation for a second predetermined time or more in a narrow range of a water area is included. The notifying means includes: (1) vertical movement of the head near the water surface; (2) diving for the first predetermined time or more; And (3) when at least one of the stagnation for the second predetermined time or more within the narrow range of the water area is included, issue an alarm and, based on the specific information and the position information, (2) a person in the field where the vertical movement of the head near the water surface is detected as the behavior, (2) a person in the field where the diving for the first predetermined time or more is detected, and / or (3). The information and the position of the inside of the stadium where the stagnation for the second predetermined time or more in the narrow range of the water area as the behavior may be notified. With this configuration, the supervisor can relatively easily find the rescuer in need.), wherein
the swimming-state information can include detection information on an alert target swimmer in such a manner as to enable a first-drowning-state (par[0064], [0075]: A drowning (or drowning) person desperately tries to come to the surface of the water to breathe, but often loses buoyancy and sinks in the water. On the other hand, people who are drowning (or drowning) often stay in a small area) and a second-drowning-state to be distinguished from each other (par[0065]: The image analysis unit 31 may detect “splashing around the head” in addition to “up and down movement of the head near the water surface” as the behavior of the occupant technically equivalent to an action of trying to lift the head above the water surface to breathe. People who are drowning and have relatively little physical strength often struggle to get their bodies out of the water. This often results in splashes around the head. However, even those who are swimming with will, depending on their swimming style, may have splashes around their heads. For this reason, it is desirable not to detect “splashing around the head” alone but to detect it in combination with “head up / down movement near the water surface”), the first-drowning-state being a state of drowning and sinking in water (par[0064], [0075]: A drowning (or drowning) person desperately tries to come to the surface of the water to breathe, but often loses buoyancy and sinks in the water. On the other hand, people who are drowning (or drowning) often stay in a small area), a second-drowning-state involving a movement of a body and being differing from the first-drowning-state (par[0065]: The image analysis unit 31 may detect “splashing around the head” in addition to “up and down movement of the head near the water surface” as the behavior of the occupant. People who are drowning and have relatively little physical strength often struggle to get their bodies out of the water. This often results in splashes around the head. However, even those who are swimming with will, depending on their swimming style, may have splashes around their heads. For this reason, it is desirable not to detect “splashing around the head” alone but to detect it in combination with “head up / down movement near the water surface”), the alert target swimmer including at least a first-state swimmer estimated to be in the first-drowning-state (par[0075]: For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected. The determination unit 32 determines that an alarm is to be issued when the behavior of the occupant detected by the image analysis unit 31 includes “stagnation for a second predetermined time or more in a narrow range of the water area”. At this time, based on the specific information generated by the image analysis unit 31, the determination unit 32 determines from the list generated by the recognition terminal 22 that the behavior is “stagnation for a second predetermined time or more within a narrow range of the water body”. The determination unit 32 displays an alarm message such as “There is a person staying at one place for a long time” and an image (see FIG. 5) including the face image and the position of the rescuer, and issues a warning. The notification monitor 40 is controlled so as to emit a sound.) and a second-state swimmer estimated to be in the second- drowning-state (par[0066], [0068]: The determination unit 32 determines to issue an alarm when the behavior of the person inside the hall detected by the image analysis unit 31 includes “head up / down movement near the water surface”. The judging unit 32 outputs a warning message such as “Some people splash on the surface of the water or repeatedly move their heads up and down” and a face image of a rescuer (or a candidate for a rescuer). And the position are displayed (see FIG. 5), and the notification monitor 40 is controlled to emit a warning sound).
Gama does not explicitly disclose the learned model, (“since Deep learning is the overall process or methodology used to train a system, whereas the learned model is the actual product or mathematical structure that results from that training”, technically in general Not the same.).
Barton discloses the learned model (par[0065]: FIG. 1A, classifier 122 may be invoked to aid in identifying objects detected by sensors 126 and/or 144 in a body of water. In some examples, classifier 122 may be configured to implement segmentation of captured images and videos from sensors 126 and/or 144. For example, classifier 122 may be configured to classify features of detected objects into person and non-person classes, which may include pose detector features, body part size and shape features, and thermal intensity differentials. By using deep or machine learning algorithms such as those implemented by deep learning module 108 (which can be trained against various types and groups of data (e.g., model data 130)), non-person objects can be identified apart from persons detected in an aquatic environment such as a swimming pool by segmenting captured images and video. As used herein, “deep learning” may include machine learning models, both of which may refer to data models and algorithms that are used to process various types of input data to perform other processes and functions such as those described herein or others. Collectively, “deep learning” and “machine learning” may be referred to as “models.”).
One of ordinary skill in the art would be aware of both the Gama and the Barton references since both pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the system of Gama with the machine learning model feature as disclosed by Barton to achieve predictable results and gain the functionality of providing data models and algorithms that are used to process various types of input data to perform non-person objects that can be identified apart from persons detected in an aquatic environment such as a swimming pool by segmenting captured images and videos.
Regarding claim 12, Gama in view of Barton discloses the swimmer monitoring method wherein the swimming-state information can include the detection information on the alert target swimmer distinguishable between the first-drowning-state, the second-drowning-state and a third-drowning-state (Gama par[0075]: "stagnation for a second predetermined time or more within a narrow range of a water area" can be said to be a behavior indicating a sign of a water accident. For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected.), the third-drowning-state being a state of drowning and floating with a face being below a surface of water (Gama par[0075]: "stagnation for a second predetermined time or more within a narrow range of a water area" can be said to be a behavior indicating a sign of a water accident. For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected.), the alert target swimmer including the first-state swimmer and the second-state swimmer and further including a third- state swimmer estimated to be in the third-drowning-state (Gama par[0075]: "stagnation for a second predetermined time or more within a narrow range of a water area" can be said to be a behavior indicating a sign of a water accident. For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected.).
Regarding claim 13, Gama in view of Barton discloses the swimmer monitoring method wherein the learned model is capable of distinguishing and detecting each of plural types of individual motions as a dangerous motion that poses a risk that possibly results in a drowning state in the monitor image (Gama par[0012], [0075]: The analysis unit analyzes the image captured by the imaging unit, detects a person inside the hall, and detects a behavior of the person inside the hall. Existing techniques can be applied to image analysis. It is desirable that the analysis means is configured to be able to execute image analysis using deep learning. The behavior of a person inside the venue detected by the analysis means may be limited to a specific behavior such as a behavior indicating a sign of a water spill accident. The analysis means may include at least (1) a vertical movement of the head near the water surface technically equivalent to the second drowning state, (2) a diving for a first predetermined time or more, and (3) a second predetermined time or more for a narrow area of the water area, Stagnation, is detected),
the alert target swimmer indicated by the detection information that can be included in the swimming-state information further includes a dangerous-action swimmer estimated to perform the dangerous motion (Gama par[0075]: "stagnation for a second predetermined time or more within a narrow range of a water area" can be said to be a behavior indicating a sign of a water accident. For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected.), and
the detection information that can be included in the swimming-state information can include information that makes it possible to identify each of the plural types of individual motions as the dangerous motion (Gama par[0075], [0076]: "stagnation for a second predetermined time or more within a narrow range of a water area" can be said to be a behavior indicating a sign of a water accident. For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected. The determination unit 32 determines that an alarm is to be issued when the behavior of the occupant detected by the image analysis unit 31 includes “stagnation for a second predetermined time or more in a narrow range of the water area”. At this time, based on the specific information generated by the image analysis unit 31, the determination unit 32 determines from the list generated by the recognition terminal 22 that the behavior is “stagnation for a second predetermined time or more within a narrow range of the water body”. The information on the rescuer who needs to be a rescuer who is a person in the hall where is detected is acquired).
Regarding claim 14, Gama in view of Barton discloses the swimmer monitoring method wherein the swimming-state information can include the detection information on the alert target swimmer in such a manner as to enable the first-drowning-state, the second-drowning-state and a third-drowning-state to be distinguished from each other, the third-drowning-state being a state of drowning and floating with a face being below a surface of water (Gama par[0075]: "stagnation for a second predetermined time or more within a narrow range of a water area" can be said to be a behavior indicating a sign of a water accident. For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected.), the alert target swimmer including the first-state swimmer and the second-state swimmer and further including a third- state swimmer estimated to be in the third-drowning-state (Gama par[0075]: "stagnation for a second predetermined time or more within a narrow range of a water area" can be said to be a behavior indicating a sign of a water accident. For this reason, if the image analysis unit 31 is configured to detect “stagnation for a second predetermined time or more in a narrow range of the water area” as the behavior of the occupant, even if the water surface cannot be detected, A person who is likely to be drowning (or drowning) can be detected.).
2. Claim(s) 5-6 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gama in view of Barton, and further in view of Nemo et al. (US2021/0056829A1) hereafter Nemo.
Regarding claim 5, Gama in view of Barton does not explicitly disclose the swimmer monitoring device wherein the detection information that can be included in the swimming-state information further includes a probability value for each of the alert target swimmer, and the generating means determines whether or not to generate the drowning alert information on a basis of the probability value included in the detection information of the swimming-state information.
Nemo discloses the swimmer monitoring device wherein
the detection information that can be included in the swimming-state information further includes a probability value for each of the alert target swimmer (par[0012], [0051]: The program being configured to use the detection probabilities to identify, in video sequences of a video stream provided, preferably by sensors, in particular a camera, and preferably in real time, drowning situations or situations presenting a risk of drowning. A third layer (230) comprising a final classifier trained using video sequences from a specific database of videos comprising simulated or real drowning situations or situations presenting a risk of drowning, the video sequences being annotated and classified, the classifier being configured to provide as a result a probability of detecting a situation presenting a risk based on video sequences, The program being configured to use the probabilities to identify, in video sequences of a video stream provided, preferably by sensors, in particular a camera, and preferably in real time, drowning situations or situations presenting a risk of drowning.), and
the generating means determines whether or not to generate the drowning alert information on a basis of the probability value included in the detection information of the swimming-state information (par[0094], [006795]: The classifier, specialized for detecting situations presenting a risk of drowning, processes the data received as input in order to provide, as output, a probability of detection (232) of a situation presenting a risk. Based on the probability, the detection device (2) is configured to trigger or not trigger the sending of an alert, for example by a notification module to an information device (2). The alert may thus contain a simple notification of danger, an augmented image illustrating the area in which the situation presenting a risk is taking place, and/or GPS coordinates or other information liable to help a user of the device (2), for example a lifeguard. [0095] In some embodiments, it is possible to level the alert based on the value of the probability of detection (232) of a situation presenting a risk. Thus, if a first video sequence gives a probability of detection (232) of 50%, a low-level alert can be sent. Based on the probability of detection (232) of the following video sequences, the alert level can increase, or conversely the alert can be stopped.).
One of ordinary skill in the art would be aware of the Gama, Barton and Nemo references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the machine learning model feature as disclosed by Nemo to achieve predictable results and gain the functionality of providing optimized emergency response by recognizing subtle signs of distress early and alert lifeguards before an incident becomes life-threatening, minimizing false alarms, contextual awareness, and continuous monitoring with real-time calculation of swimming-state data for every swimmer.
Regarding claim 6, Gama in view of Barton does not explicitly disclose the swimmer monitoring device wherein the detection information that can be included in the swimming-state information further includes image area information on the alert target swimmer in the monitor image for each of the alert target swimmer, and the generating means generates the drowning alert information including a monitor image in which an image area of one or a plurality of the alert target swimmers is designated, on a basis of the image area information included in the detection information of the swimming-state information.
Nemo discloses the swimmer monitoring device wherein the detection information that can be included in the swimming-state information further includes image area information on the alert target swimmer in the monitor image for each of the alert target swimmer (par[0066]: In some embodiments, the program of the device (2) is further configured to process the image or the image stream received in order to produce an augmented image or a stream of augmented images, displaying for example an area containing the situation presenting a risk. This advantageously makes it possible to provide, as output from the device (2), an image or a stream of images (video stream) of the situation, to which additional information is added by the program, for example GPS coordinates, an illustration of the area where the detected event is, or else the type of event identified or the probability of the risk of drowning. This image or this stream of images can thus be communicated to the user for example on a device for receiving (3) alerts and/or for displaying to a user, such as a connected watch comprising a screen.), and
the generating means generates the drowning alert information including a monitor image in which an image area of one or a plurality of the alert target swimmers is designated, on a basis of the image area information included in the detection information of the swimming-state information (par[0069]: the device (2) is part of a system (1) for monitoring and detecting individuals in a situation presenting a risk of drowning or situations presenting a risk of drowning, said system (1) further comprising at least one image sensor, preferably a sensor for images (4) and video sequences, for example a camera, the sensor being configured to provide video sequences to the program of the detection device (2), and at least one information and/or alert receiving device (3) configured to inform a user that a drowning situation or a situation presenting a risk of drowning has been detected, the detection device (2) being configured to send a message by a notification module to the information device (2) when one of the video sequences provided by the image sensor (4) is considered to correspond to a drowning situation or a situation presenting a risk of drowning from its database.).
One of ordinary skill in the art would be aware of the Gama, Barton and Nemo references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the machine learning model feature as disclosed by Nemo to achieve predictable results and gain the functionality of providing optimized emergency response by recognizing subtle signs of distress early and alert lifeguards before an incident becomes life-threatening, minimizing false alarms, contextual awareness, and continuous monitoring with real-time calculation of swimming-state data for every swimmer.
Regarding claim 15, Gama in view of Barton discloses the swimmer monitoring method wherein
the detection information that can be included in the swimming-state information further includes a probability value for each of the alert target swimmer, and the generating means determines whether or not to generate the drowning alert information on a basis of the probability value included in the detection information of the swimming-state information.
Nemo discloses the swimmer monitoring device wherein
the detection information that can be included in the swimming-state information further includes a probability value for each of the alert target swimmer (par[0012], [0051]: The program being configured to use the detection probabilities to identify, in video sequences of a video stream provided, preferably by sensors, in particular a camera, and preferably in real time, drowning situations or situations presenting a risk of drowning. A third layer (230) comprising a final classifier trained using video sequences from a specific database of videos comprising simulated or real drowning situations or situations presenting a risk of drowning, the video sequences being annotated and classified, the classifier being configured to provide as a result a probability of detecting a situation presenting a risk based on video sequences, The program being configured to use the probabilities to identify, in video sequences of a video stream provided, preferably by sensors, in particular a camera, and preferably in real time, drowning situations or situations presenting a risk of drowning.), and
generating the drowning alert information on a basis of the probability value included in the detection information of the swimming-state information (par[0094], [006795]: The classifier, specialized for detecting situations presenting a risk of drowning, processes the data received as input in order to provide, as output, a probability of detection (232) of a situation presenting a risk. Based on the probability, the detection device (2) is configured to trigger or not trigger the sending of an alert, for example by a notification module to an information device (2). The alert may thus contain a simple notification of danger, an augmented image illustrating the area in which the situation presenting a risk is taking place, and/or GPS coordinates or other information liable to help a user of the device (2), for example a lifeguard. [0095] In some embodiments, it is possible to level the alert based on the value of the probability of detection (232) of a situation presenting a risk. Thus, if a first video sequence gives a probability of detection (232) of 50%, a low-level alert can be sent. Based on the probability of detection (232) of the following video sequences, the alert level can increase, or conversely the alert can be stopped.).
One of ordinary skill in the art would be aware of the Gama, Barton and Nemo references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the machine learning model feature as disclosed by Nemo to achieve predictable results and gain the functionality of providing optimized emergency response by recognizing subtle signs of distress early and alert lifeguards before an incident becomes life-threatening, minimizing false alarms, contextual awareness, and continuous monitoring with real-time calculation of swimming-state data for every swimmer.
Regarding claim 16, Gama in view of Barton does not explicitly disclose the swimmer monitoring method wherein the detection information that can be included in the swimming-state information further includes image area information on the alert target swimmer in the monitor image for each of the alert target swimmer, and the generating means generates the drowning alert information including a monitor image in which an image area of one or a plurality of the alert target swimmers is designated, on a basis of the image area information included in the detection information of the swimming-state information.
Nemo discloses the swimmer monitoring device wherein the detection information that can be included in the swimming-state information further includes image area information on the alert target swimmer in the monitor image for each of the alert target swimmer (par[0066]: In some embodiments, the program of the device (2) is further configured to process the image or the image stream received in order to produce an augmented image or a stream of augmented images, displaying for example an area containing the situation presenting a risk. This advantageously makes it possible to provide, as output from the device (2), an image or a stream of images (video stream) of the situation, to which additional information is added by the program, for example GPS coordinates, an illustration of the area where the detected event is, or else the type of event identified or the probability of the risk of drowning. This image or this stream of images can thus be communicated to the user for example on a device for receiving (3) alerts and/or for displaying to a user, such as a connected watch comprising a screen.), and
the generating means generates the drowning alert information including a monitor image in which an image area of one or a plurality of the alert target swimmers is designated, on a basis of the image area information included in the detection information of the swimming-state information (par[0069]: the device (2) is part of a system (1) for monitoring and detecting individuals in a situation presenting a risk of drowning or situations presenting a risk of drowning, said system (1) further comprising at least one image sensor, preferably a sensor for images (4) and video sequences, for example a camera, the sensor being configured to provide video sequences to the program of the detection device (2), and at least one information and/or alert receiving device (3) configured to inform a user that a drowning situation or a situation presenting a risk of drowning has been detected, the detection device (2) being configured to send a message by a notification module to the information device (2) when one of the video sequences provided by the image sensor (4) is considered to correspond to a drowning situation or a situation presenting a risk of drowning from its database.).
One of ordinary skill in the art would be aware of the Gama, Barton and Nemo references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the machine learning model feature as disclosed by Nemo to achieve predictable results and gain the functionality of providing optimized emergency response by recognizing subtle signs of distress early and alert lifeguards before an incident becomes life-threatening, minimizing false alarms, contextual awareness, and continuous monitoring with real-time calculation of swimming-state data for every swimmer.
3. Claim(s) 7-8 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gama in view of Barton, and further in view of Cutler et al. (US2020/0020221A1) hereafter Cutler.
Regarding claim 7, Gama in view of Barton does explicitly disclose the swimmer monitoring device wherein the learned model is capable of distinguishing and detecting the alert target swimmer with a float and the alert target swimmer without a float in the monitor image, and the detection information that can be included in the swimming-state information further includes information that makes it possible to distinguish the alert target swimmer with a float and the alert target swimmer without a float.
Cutler discloses the swimmer monitoring device wherein the learned model is capable of distinguishing and detecting the alert target swimmer with a float (par[0142], [0148]: A swimmer 20 may also wear a PDID 10 as described above on the swimmer's 20 head, and the associated floating transceiver 1002 attached to the swimmer 20 may receive transmissions from the PDID 10, e.g. via Bluetooth, and relay or transmit those transmissions, and potentially additional information, to a hub 70 or other reception devices and components associated with system 1. The floating transceiver 1002 may make transmissions via any one or more technologies including but not limiting to GPS, Bluetooth, and Long Range (LoRa) radio technology, the latter providing extremely long range radio transmission. Such data sets, potentially including all PDID 10 data, hub data, wearable 60 data, and monitor wearable data, potentially including data from multiple sensors on each PDID 10 such as location tracking, heart rate tracking, etc., and potentially including data such as video camera monitoring data connected with and overseen by the hub 70, may form a brand new, large and dense source of unique data on swimmer behavior, lifeguard performance, drowning risk factors, and drowning detection and prevention, and may thus form a uniquely valuable set of machine learning training data for novel machine learning applications, such as A.I.-powered drowning prediction and prevention.) and the alert target swimmer without a float in the monitor image (par[0120]: In some embodiments, in addition to sending corresponding real-time alerts to lifeguard wearables 60 based on the signals received from PDIDs 10, the hub 70 may also include customizable and optional alerts as well, for example, but not limited to, an embedded flashing strobe light and an adjustable selection of audio alerts and volumes, based on the users' needs or preferences. For example, a hub 70 may send alert signals to lifeguard wearables 60 and/or other monitor systems for audio alerts that are encoded with information, e.g., about a location of a detected potential drowning incident and/or a swimming expertise level of a swimmer registered with a PDID 10 involved in the detected potential drowning incident, such that the audio alerts may be interpreted and understood by lifeguards due to training thereon, but are subtle and do not alarm or panic other guests of the pool. Such data sets, potentially including all PDID 10 data, hub data, wearable 60 data, and monitor wearable data, potentially including data from multiple sensors on each PDID 10 such as location tracking, heart rate tracking, etc., and potentially including data such as video camera monitoring data connected with and overseen by the hub 70, may form a brand new, large and dense source of unique data on swimmer behavior, lifeguard performance, drowning risk factors, and drowning detection and prevention, and may thus form a uniquely valuable set of machine learning training data for novel machine learning applications, such as A.I.-powered drowning prediction and prevention.), and
the detection information that can be included in the swimming-state information further includes information that makes it possible to distinguish the alert target swimmer with a float (par[0143]: In some embodiments, if the floating transceiver 1002 does not receive the radio signals from the swimmer's 20 wearable for a preset duration of time (e.g. 30 seconds or 45 seconds), the floating transceiver 1002 may transmit an alert and the swimmer's 20 real-time location coordinates via, e.g., LoRa (or other long-range radio or other effective transmission technology) to hubs 70 and/or other base stations positioned, e.g., on the shoreline, in support boats, or in a control center. Rescuers may then be alerted, via the floating transceiver 1002, that the swimmer 20 is in possible danger and also know that swimmer's 20 location, enabling a much faster rescue despite the victim being in a large body of water.) and the alert target swimmer without a float (par[0120]: In some embodiments, in addition to sending corresponding real-time alerts to lifeguard wearables 60 based on the signals received from PDIDs 10, the hub 70 may also include customizable and optional alerts as well, for example, but not limited to, an embedded flashing strobe light and an adjustable selection of audio alerts and volumes, based on the users' needs or preferences. For example, a hub 70 may send alert signals to lifeguard wearables 60 and/or other monitor systems for audio alerts that are encoded with information, e.g., about a location of a detected potential drowning incident and/or a swimming expertise level of a swimmer registered with a PDID 10 involved in the detected potential drowning incident, such that the audio alerts may be interpreted and understood by lifeguards due to training thereon, but are subtle and do not alarm or panic other guests of the pool.).
One of ordinary skill in the art would be aware of the Gama, Barton and Cutler references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the floating feature as disclosed by Cutler to achieve predictable results and gain the functionality of isolating muscle groups, promoting proper body alignment, allowing beginners to build confidence and refining form without worrying about sinking in order to target specific areas, isolating the upper body for arm workouts or the legs for kick training.
Regarding claim 8, Gama in view of Barton in view of Cutler discloses the swimmer monitoring device wherein the learned model is capable of detecting motions, in the monitor image, that differ from each other between the second-drowning-state of the second-state swimmer with a float (Cutler par[0142], [0148]: A swimmer 20 may also wear a PDID 10 as described above on the swimmer's 20 head, and the associated floating transceiver 1002 attached to the swimmer 20 may receive transmissions from the PDID 10, e.g. via Bluetooth, and relay or transmit those transmissions, and potentially additional information, to a hub 70 or other reception devices and components associated with system 1. The floating transceiver 1002 may make transmissions via any one or more technologies including but not limiting to GPS, Bluetooth, and Long Range (LoRa) radio technology, the latter providing extremely long range radio transmission. Such data sets, potentially including all PDID 10 data, hub data, wearable 60 data, and monitor wearable data, potentially including data from multiple sensors on each PDID 10 such as location tracking, heart rate tracking, etc., and potentially including data such as video camera monitoring data connected with and overseen by the hub 70, may form a brand new, large and dense source of unique data on swimmer behavior, lifeguard performance, drowning risk factors, and drowning detection and prevention, and may thus form a uniquely valuable set of machine learning training data for novel machine learning applications, such as A.I.-powered drowning prediction and prevention.) and the second-drowning-state of the second-state swimmer without a float (Cutler par[0120]: In some embodiments, in addition to sending corresponding real-time alerts to lifeguard wearables 60 based on the signals received from PDIDs 10, the hub 70 may also include customizable and optional alerts as well, for example, but not limited to, an embedded flashing strobe light and an adjustable selection of audio alerts and volumes, based on the users' needs or preferences. For example, a hub 70 may send alert signals to lifeguard wearables 60 and/or other monitor systems for audio alerts that are encoded with information, e.g., about a location of a detected potential drowning incident and/or a swimming expertise level of a swimmer registered with a PDID 10 involved in the detected potential drowning incident, such that the audio alerts may be interpreted and understood by lifeguards due to training thereon, but are subtle and do not alarm or panic other guests of the pool.).
One of ordinary skill in the art would be aware of the Gama, Barton and Cutler references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the floating feature as disclosed by Cutler to achieve predictable results and gain the functionality of isolating muscle groups, promoting proper body alignment, allowing beginners to build confidence and refining form without worrying about sinking in order to target specific areas, isolating the upper body for arm workouts or the legs for kick training.
Regarding claim 17, Gama in view of Barton does explicitly disclose the swimmer monitoring method wherein the learned model is capable of distinguishing and detecting the alert target swimmer with a float and the alert target swimmer without a float in the monitor image, and the detection information that can be included in the swimming-state information further includes information that makes it possible to distinguish the alert target swimmer with a float and the alert target swimmer without a float.
Cutler discloses the swimmer monitoring method wherein the learned model is capable of distinguishing and detecting the alert target swimmer with a float (par[0142], [0148]: A swimmer 20 may also wear a PDID 10 as described above on the swimmer's 20 head, and the associated floating transceiver 1002 attached to the swimmer 20 may receive transmissions from the PDID 10, e.g. via Bluetooth, and relay or transmit those transmissions, and potentially additional information, to a hub 70 or other reception devices and components associated with system 1. The floating transceiver 1002 may make transmissions via any one or more technologies including but not limiting to GPS, Bluetooth, and Long Range (LoRa) radio technology, the latter providing extremely long range radio transmission. Such data sets, potentially including all PDID 10 data, hub data, wearable 60 data, and monitor wearable data, potentially including data from multiple sensors on each PDID 10 such as location tracking, heart rate tracking, etc., and potentially including data such as video camera monitoring data connected with and overseen by the hub 70, may form a brand new, large and dense source of unique data on swimmer behavior, lifeguard performance, drowning risk factors, and drowning detection and prevention, and may thus form a uniquely valuable set of machine learning training data for novel machine learning applications, such as A.I.-powered drowning prediction and prevention.) and the alert target swimmer without a float in the monitor image (par[0120]: In some embodiments, in addition to sending corresponding real-time alerts to lifeguard wearables 60 based on the signals received from PDIDs 10, the hub 70 may also include customizable and optional alerts as well, for example, but not limited to, an embedded flashing strobe light and an adjustable selection of audio alerts and volumes, based on the users' needs or preferences. For example, a hub 70 may send alert signals to lifeguard wearables 60 and/or other monitor systems for audio alerts that are encoded with information, e.g., about a location of a detected potential drowning incident and/or a swimming expertise level of a swimmer registered with a PDID 10 involved in the detected potential drowning incident, such that the audio alerts may be interpreted and understood by lifeguards due to training thereon, but are subtle and do not alarm or panic other guests of the pool. Such data sets, potentially including all PDID 10 data, hub data, wearable 60 data, and monitor wearable data, potentially including data from multiple sensors on each PDID 10 such as location tracking, heart rate tracking, etc., and potentially including data such as video camera monitoring data connected with and overseen by the hub 70, may form a brand new, large and dense source of unique data on swimmer behavior, lifeguard performance, drowning risk factors, and drowning detection and prevention, and may thus form a uniquely valuable set of machine learning training data for novel machine learning applications, such as A.I.-powered drowning prediction and prevention.), and
the detection information that can be included in the swimming-state information further includes information that makes it possible to distinguish the alert target swimmer with a float (par[0143]: In some embodiments, if the floating transceiver 1002 does not receive the radio signals from the swimmer's 20 wearable for a preset duration of time (e.g. 30 seconds or 45 seconds), the floating transceiver 1002 may transmit an alert and the swimmer's 20 real-time location coordinates via, e.g., LoRa (or other long-range radio or other effective transmission technology) to hubs 70 and/or other base stations positioned, e.g., on the shoreline, in support boats, or in a control center. Rescuers may then be alerted, via the floating transceiver 1002, that the swimmer 20 is in possible danger and also know that swimmer's 20 location, enabling a much faster rescue despite the victim being in a large body of water.) and the alert target swimmer without a float (par[0120]: In some embodiments, in addition to sending corresponding real-time alerts to lifeguard wearables 60 based on the signals received from PDIDs 10, the hub 70 may also include customizable and optional alerts as well, for example, but not limited to, an embedded flashing strobe light and an adjustable selection of audio alerts and volumes, based on the users' needs or preferences. For example, a hub 70 may send alert signals to lifeguard wearables 60 and/or other monitor systems for audio alerts that are encoded with information, e.g., about a location of a detected potential drowning incident and/or a swimming expertise level of a swimmer registered with a PDID 10 involved in the detected potential drowning incident, such that the audio alerts may be interpreted and understood by lifeguards due to training thereon, but are subtle and do not alarm or panic other guests of the pool.).
One of ordinary skill in the art would be aware of the Gama, Barton and Cutler references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the floating feature as disclosed by Cutler to achieve predictable results and gain the functionality of isolating muscle groups, promoting proper body alignment, allowing beginners to build confidence and refining form without worrying about sinking in order to target specific areas, isolating the upper body for arm workouts or the legs for kick training.
Regarding claim 18, Gama in view of Barton in view of Cutler discloses the swimmer monitoring method wherein the learned model is capable of detecting motions, in the monitor image, that differ from each other between the second-drowning-state of the second-state swimmer with a float (Cutler par[0142], [0148]: A swimmer 20 may also wear a PDID 10 as described above on the swimmer's 20 head, and the associated floating transceiver 1002 attached to the swimmer 20 may receive transmissions from the PDID 10, e.g. via Bluetooth, and relay or transmit those transmissions, and potentially additional information, to a hub 70 or other reception devices and components associated with system 1. The floating transceiver 1002 may make transmissions via any one or more technologies including but not limiting to GPS, Bluetooth, and Long Range (LoRa) radio technology, the latter providing extremely long range radio transmission. Such data sets, potentially including all PDID 10 data, hub data, wearable 60 data, and monitor wearable data, potentially including data from multiple sensors on each PDID 10 such as location tracking, heart rate tracking, etc., and potentially including data such as video camera monitoring data connected with and overseen by the hub 70, may form a brand new, large and dense source of unique data on swimmer behavior, lifeguard performance, drowning risk factors, and drowning detection and prevention, and may thus form a uniquely valuable set of machine learning training data for novel machine learning applications, such as A.I.-powered drowning prediction and prevention.) and the second-drowning-state of the second-state swimmer without a float (Cutler par[0120]: In some embodiments, in addition to sending corresponding real-time alerts to lifeguard wearables 60 based on the signals received from PDIDs 10, the hub 70 may also include customizable and optional alerts as well, for example, but not limited to, an embedded flashing strobe light and an adjustable selection of audio alerts and volumes, based on the users' needs or preferences. For example, a hub 70 may send alert signals to lifeguard wearables 60 and/or other monitor systems for audio alerts that are encoded with information, e.g., about a location of a detected potential drowning incident and/or a swimming expertise level of a swimmer registered with a PDID 10 involved in the detected potential drowning incident, such that the audio alerts may be interpreted and understood by lifeguards due to training thereon, but are subtle and do not alarm or panic other guests of the pool.).
One of ordinary skill in the art would be aware of the Gama, Barton and Cutler references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the floating feature as disclosed by Cutler to achieve predictable results and gain the functionality of isolating muscle groups, promoting proper body alignment, allowing beginners to build confidence and refining form without worrying about sinking in order to target specific areas, isolating the upper body for arm workouts or the legs for kick training.
4. Claim(s) 9-10 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gama in view of Barton, and further in view of Katz et al. (Patent5956081) hereafter Katz.
Regarding claim 9, Gama in view of Barton discloses the swimmer monitoring device wherein the monitor image acquired by the image acquiring means is a wide-angle image captured by a wide-angle camera (par[0011], [0021]: In the case where there is only one imaging unit, typically, the imaging unit is installed so as to be able to image the entire specific area. For this reason, in the support system, for example, a camera having a relatively low degree of waterproofing can be used as the imaging unit.).
Gama in view of Barton does explicitly disclose the swimmer monitoring device wherein the learned model is trained at least by using, as training data: the monitor image in which a swimmer in the first-drowning-state is shown in a middle area; the monitor image in which the swimmer in the first-drowning-state is shown in a first partial area; the monitor image in which the swimmer in the first-drowning-state is shown in a second partial area disposed at an opposite side of the middle area from the first partial area; the monitor image in which a swimmer in the second-drowning-state is shown in the middle area; the monitor image in which the swimmer in the second-drowning-state is shown in the first partial area; and the monitor image in which the swimmer in the second-drowning-state is shown in the second partial area.
Katz discloses the swimmer monitoring device wherein
the learned model is trained at least by using, as training data:
the monitor image in which a swimmer in the first-drowning-state is shown in a middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the first-drowning-state is shown in a first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.);
the monitor image in which the swimmer in the first-drowning-state is shown in a second partial area disposed at an opposite side of the middle area from the first partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5);
the monitor image in which a swimmer in the second-drowning-state is shown in the middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the second-drowning-state is shown in the first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.);
the monitor image in which the swimmer in the second-drowning-state is shown in the second partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5);
the monitor image in which a swimmer in the third-drowning-state is shown in the middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the third-drowning state is shown in the first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.); and
the monitor image in which the swimmer in the third-drowning-state is shown in the second partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5).
One of ordinary skill in the art would be aware of the Gama, Barton and Katz references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the display feature as disclosed by Katz to achieve predictable results and gain the functionality of allowing for simultaneous observation of multiple pool zones, providing targeted visual feedback, and enhancing both facility security by eliminating blind spots and athletic performance by allowing swimmers to instantly review their stroke mechanics.
Regarding claim 10, Gama in view of Barton discloses the swimmer monitoring device wherein the monitor image acquired by the image acquiring means is a wide-angle image captured by a wide-angle camera (par[0011], [0021]: In the case where there is only one imaging unit, typically, the imaging unit is installed so as to be able to image the entire specific area. For this reason, in the support system, for example, a camera having a relatively low degree of waterproofing can be used as the imaging unit.).
Gama in view of Barton does explicitly disclose the swimmer monitoring device wherein the learned model is trained at least by using, as training data: the monitor image in which a swimmer in the first-drowning-state is shown in a middle area; the monitor image in which the swimmer in the first-drowning-state is shown in a first partial area; the monitor image in which the swimmer in the first-drowning-state is shown in a second partial area disposed at an opposite side of the middle area from the first partial area; the monitor image in which a swimmer in the second-drowning-state is shown in the middle area; the monitor image in which the swimmer in the second-drowning-state is shown in the first partial area; the monitor image in which the swimmer in the second-drowning-state is shown in the second partial area; the monitor image in which a swimmer in the third-drowning-state is shown in the middle area; the monitor image in which the swimmer in the third-drowning state is shown in the first partial area; and the monitor image in which the swimmer in the third-drowning-state is shown in the second partial area.
Katz discloses the swimmer monitoring device wherein
the learned model is trained at least by using, as training data:
the monitor image in which a swimmer in the first-drowning-state is shown in a middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the first-drowning-state is shown in a first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.);
the monitor image in which the swimmer in the first-drowning-state is shown in a second partial area disposed at an opposite side of the middle area from the first partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5);
the monitor image in which a swimmer in the second-drowning-state is shown in the middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the second-drowning-state is shown in the first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.);
the monitor image in which the swimmer in the second-drowning-state is shown in the second partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5);
the monitor image in which a swimmer in the third-drowning-state is shown in the middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the third-drowning state is shown in the first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.); and
the monitor image in which the swimmer in the third-drowning-state is shown in the second partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5).
One of ordinary skill in the art would be aware of the Gama, Barton and Katz references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the display feature as disclosed by Katz to achieve predictable results and gain the functionality of allowing for simultaneous observation of multiple pool zones, providing targeted visual feedback, and enhancing both facility security by eliminating blind spots and athletic performance by allowing swimmers to instantly review their stroke mechanics.
Regarding claim 19, Gama in view of Barton discloses the swimmer monitoring device wherein the monitor image acquired by the image acquiring means is a wide-angle image captured by a wide-angle camera (par[0011], [0021]: In the case where there is only one imaging unit, typically, the imaging unit is installed so as to be able to image the entire specific area. For this reason, in the support system, for example, a camera having a relatively low degree of waterproofing can be used as the imaging unit.).
Gama in view of Barton does explicitly disclose the swimmer monitoring device wherein the learned model is trained at least by using, as training data: the monitor image in which a swimmer in the first-drowning-state is shown in a middle area; the monitor image in which the swimmer in the first-drowning-state is shown in a first partial area; the monitor image in which the swimmer in the first-drowning-state is shown in a second partial area disposed at an opposite side of the middle area from the first partial area; the monitor image in which a swimmer in the second-drowning-state is shown in the middle area; the monitor image in which the swimmer in the second-drowning-state is shown in the first partial area; and the monitor image in which the swimmer in the second-drowning-state is shown in the second partial area.
Katz discloses the swimmer monitoring device wherein
the learned model is trained at least by using, as training data:
the monitor image in which a swimmer in the first-drowning-state is shown in a middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the first-drowning-state is shown in a first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.);
the monitor image in which the swimmer in the first-drowning-state is shown in a second partial area disposed at an opposite side of the middle area from the first partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5);
the monitor image in which a swimmer in the second-drowning-state is shown in the middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the second-drowning-state is shown in the first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.);
the monitor image in which the swimmer in the second-drowning-state is shown in the second partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5);
the monitor image in which a swimmer in the third-drowning-state is shown in the middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the third-drowning state is shown in the first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.); and
the monitor image in which the swimmer in the third-drowning-state is shown in the second partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5).
One of ordinary skill in the art would be aware of the Gama, Barton and Katz references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the display feature as disclosed by Katz to achieve predictable results and gain the functionality of allowing for simultaneous observation of multiple pool zones, providing targeted visual feedback, and enhancing both facility security by eliminating blind spots and athletic performance by allowing swimmers to instantly review their stroke mechanics.
Regarding claim 20, Gama in view of Barton discloses the swimmer monitoring device wherein the monitor image acquired by the image acquiring means is a wide-angle image captured by a wide-angle camera (par[0011], [0021]: In the case where there is only one imaging unit, typically, the imaging unit is installed so as to be able to image the entire specific area. For this reason, in the support system, for example, a camera having a relatively low degree of waterproofing can be used as the imaging unit.).
Gama in view of Barton does explicitly disclose the swimmer monitoring device wherein the learned model is trained at least by using, as training data: the monitor image in which a swimmer in the first-drowning-state is shown in a middle area; the monitor image in which the swimmer in the first-drowning-state is shown in a first partial area; the monitor image in which the swimmer in the first-drowning-state is shown in a second partial area disposed at an opposite side of the middle area from the first partial area; the monitor image in which a swimmer in the second-drowning-state is shown in the middle area; the monitor image in which the swimmer in the second-drowning-state is shown in the first partial area; the monitor image in which the swimmer in the second-drowning-state is shown in the second partial area; the monitor image in which a swimmer in the third-drowning-state is shown in the middle area; the monitor image in which the swimmer in the third-drowning state is shown in the first partial area; and the monitor image in which the swimmer in the third-drowning-state is shown in the second partial area.
Katz discloses the swimmer monitoring device wherein
the learned model is trained at least by using, as training data:
the monitor image in which a swimmer in the first-drowning-state is shown in a middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the first-drowning-state is shown in a first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.);
the monitor image in which the swimmer in the first-drowning-state is shown in a second partial area disposed at an opposite side of the middle area from the first partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5);
the monitor image in which a swimmer in the second-drowning-state is shown in the middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the second-drowning-state is shown in the first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.);
the monitor image in which the swimmer in the second-drowning-state is shown in the second partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5);
the monitor image in which a swimmer in the third-drowning-state is shown in the middle area (col 4 ln 55-64: the processor may display the views from the three cameras closest to the camera selected by the operator (assuming an array having four views as shown in either FIG. 2B or FIG. 2C). The processor 28 may also determine the positions of the camera views on the monitor 30, however, the largest viewing area, i.e., grid of cells, of the array is usually reserved for the view selected by the operator. In the example shown inn FIG. 2C, the camera view selected by the operator would usually be positioned in the eight cells, i.e. camera 1 technically equivalent to the middle area);
the monitor image in which the swimmer in the third-drowning state is shown in the first partial area (fig 6:Aisle 6 with 12f; col 7 ln 49-56, col 8 ln 1-11: Accordingly, after the operator selects the camera view corresponding to aisle 5, as the target enters aisle 6, the operator can easily track him with camera 12F technically equivalent to the first partial area “aisle 6”. The operator can select camera 12F by touching the monitor anywhere on the portion of the screen corresponding to camera 12F. Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5.); and
the monitor image in which the swimmer in the third-drowning-state is shown in the second partial area (fig 6:Aisle 7; col 8 ln 1-11: Note that the screen formats are chosen by the operator. Depending on the number of multiplexors used and the size of the physical area being monitored, more or less camera views may be displayed on the monitor. Therefore, if the operator wishes to have the primary camera view occupying half of the monitor and only two other related views occupying the remaining half of the monitor, a target in aisle 5, may appear as shown in FIG. 6. In this format, the surveillance system may display the signals from camera 12F and 12G on the quarter screens on the right half of the monitor since these are the principal aisles connected to aisle 5).
One of ordinary skill in the art would be aware of the Gama, Barton and Katz references since all pertain to the field of swimming systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved the swimmer monitoring device of Gama with the display feature as disclosed by Katz to achieve predictable results and gain the functionality of allowing for simultaneous observation of multiple pool zones, providing targeted visual feedback, and enhancing both facility security by eliminating blind spots and athletic performance by allowing swimmers to instantly review their stroke mechanics.
Conclusion
Patent US12008881B1 to Brand discloses systems and methods for improving water safety are provided. The methods include various features based on monitoring the area around a pool. Benefits are gained by applying artificial intelligence algorithms, as well as various techniques for improving processing speed and eliminating false alarms. A method for promoting water safety includes the steps of: video monitoring an area including around a body of water, and above or at a top surface of the water; detecting a presence of a human suspect in the monitored area through processing images of the video with computer implemented artificial intelligence; storing an image upon detection of the human suspect in the monitored area; processing the stored image to determine if the detection was a false alarm; and if the human suspect is determined to be a human in the monitored area, then transmitting an alert, either to a device in the area being monitored, to a mobile device, or both.
US2021/0374391A1 to Jorasch discloses systems, apparatus, interfaces, methods, and articles of manufacture are provided for providing information about objects, such as background information and task information, and for providing alerts related to objects. In various embodiments, data is captured about an object and about a user via a camera. Based on the data, information about the object may be provided to the user.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMINE BENLAGSIR whose telephone number is (571)270-5165. The examiner can normally be reached (571)270-5165.
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, Steven Lim can be reached at (571) 270-1210. 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.
/AMINE BENLAGSIR/Primary Examiner, Art Unit 2688
Prosecution Insights