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 Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-22, and 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over Beiner, (US-PGPUB 20160188763) in view of Ju et al, (US-PGPUB 20210201029)
In regards to claim 21, Beiner discloses a method, (see at least: Par. 0011, a
method for visualization of electrical loads of an electrical installation using a portable device), comprising:
receiving a digital image depicting properties of an electrical panel,
image of the electrical distribution panel 112, [i.e., receiving a digital image depicting properties of an electrical panel, “implicit by using camera’s smartphone to capture a live image of the electrical distribution panel 112”]);
identifying, from the digital image and using an image recognition operation, a type of electrical component of the electrical panel, (see at least: Par. 0046, 0054, identification symbol 318 is chosen so that it can be relatively easily detected by an image analysis algorithm, “i.e., using an image recognition operation”. Further, from Par. 0057, a maximum rating may be detected based on optical character recognition of a corresponding marking of the identified circuit breakers 114, [i.e., implicitly detecting a type of electrical component of the electrical panel, based on performing optical character recognition of a corresponding marking of the identified circuit breakers 11); and
generating a metric for the electrical panel based on the type of electrical component and the geographic-specific attribute, (see at least: Par. 0055-0057, the received electrical load information may also comprises a maximum rating and/or other metadata for each of the circuit breakers 114, where the maximum rating may be detected based on optical character recognition of a corresponding marking of the identified circuit breakers 114; and from Par. 0054, by identifying one or several identification symbols 318, the portable device 196 may determine the number and position of sensor devices 220 within a captured live image of the electrical distribution panel 112, [i.e., generating a metric for the electrical panel, “implicit by detecting maximum rating of the electrical load information”, based on the type of electrical component, “implicit based on performing the optical character recognition of a corresponding marking”, and the geographic-specific attribute, “implicit by determining the number and position of sensor devices 220 within a captured live image of the electrical distribution panel 112, based on identification symbols 318]).
Beiner does not expressly disclose that the digital image having metadata; and determining a geographic-specific attribute of the electrical panel based on the metadata.
Ju et al discloses that the digital image having metadata; and determining a geographic-specific attribute of the electrical panel based on the metadata, (see at least: Par. 0016, the visual data may be accompanied by metadata that may be used by the service provider to identify and track the particular object, [i.e., the digital image, “visual data such as still image or video”, having metadata]. Further, from Par. 0043, using the visual data, virtual tagging application 140 may identifying objects that a user has selected, and particular identifying characteristics with the object, including detecting a geo-location of the object, [i.e., determining a geographic-specific attribute of the object, “detecting a geo-location of the object”, based on the metadata, “the visual data implicitly accompanied by metadata”]).
Beiner and Ju are combinable because they are both concern with object identification/recognition. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Beiner, to include the visual data accompanied with metadata, as though by Ju et al, in order to detect a geo-location of the object, based on visual data, (Ju et al, Par. 0043).
In regards to claim 22, the combine teaching Beiner and Ju as whole discloses the limitations of claim 1.
Ju et al further discloses wherein the metadata includes at least one of global positioning system (GPS) data or latitude-longitude coordinates, (see at least: Par. 0016, communication device capturing visual data at a location may determine a location of the object through a GPS module, “i.e., metadata implicitly includes at least one of global positioning system (GPS) data, within the visual data that is used to identify the object]).
In regards to claim 26, the combine teaching Beiner and Ju as whole discloses the limitations of claim 21.
Beiner further discloses wherein the geographic-specific attribute for the electrical panel comprises a language of the electrical panel, (Par. 0057, the performing optical character recognition of a corresponding marking of the identified circuit breakers 114, results in identifying the marking symbols(s) and/or text, which technically enables the user to identify the language, in which the symbols and/or text is written).
In regards to claim 27, the combine teaching Beiner and Ju as whole discloses the limitations of claim 21.
Beiner further discloses wherein the type of electrical component comprises at least one of: a main breaker switch, a single pole breaker, an empty slot, a double pole breaker, a tandem breaker, an overcurrent protector, a grounding component, a disconnect switch, or a switchgear, (see at least: Par. 0039, where the switch for disconnecting or connecting a corresponding circuit or resetting the circuit breaker 114, arranged at the front surface 212, corresponds to the disconnect switch).
In regards to claim 28, the combine teaching Beiner and Ju as whole discloses the limitations of claim 21.
Beiner further discloses wherein the metric comprises at least one of an overall electrical power capacity of the electrical panel, an electrical load of the electrical panel, or an unused electrical power capacity of the electrical panel, (see at least: Par. 0055-0057, the received electrical load information may also comprises a maximum rating and/or other metadata for each of the circuit breakers 114, which the electrical load information, corresponds to the electrical load of the electrical panel).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Beiner, and Ju et al, as applied to claim 22 above; and further in view of Petrisor et al, (US-PGPUB 20080204313)
The combine teaching Beiner and Ju as whole discloses the limitations of claim 22.
the combine teaching Beiner and Ju as whole does not expressly disclose GPS data or the latitude-longitude coordinates indicates that the geographic-specific attribute for the electrical panel comprises one of a residential electrical panel, a commercial electrical panel, or an industrial electrical panel.
Petrisor discloses wherein the GPS data or the latitude-longitude coordinates indicates that the geographic-specific attribute for the electrical panel comprises one of a residential electrical panel, a commercial electrical panel, or an industrial electrical panel, (see at least: Fig. 1, where the radio navigation satellite system (RNSS) component may be GPS components, which implicitly indicates the location of building 103, which the Building 103 may be a residential or commercial structure).
Beiner, Ju, and Petrisor are combinable because they are all concern with object identification/recognition. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Beiner and Ju, to include the radio navigation satellite system (RNSS) component, as though by Petrisor, to implicitly determining whether the building 103, is a residential or commercial structure, based on its location.
Claims 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Beiner, and Ju et al, as applied to claim 22 above; and further in view of Kincade, (US-Patent 11,372,033)
In regards to claim 24, the combine teaching Beiner and Ju as whole discloses the limitations of claim 21.
The combine teaching Beiner and Ju as whole does not expressly disclose wherein the geographic-specific attribute includes an attribute of a building in which the electrical panel is located.
Kincade discloses wherein the geographic-specific attribute includes an attribute of a building in which the electrical panel is located, (see at least: col. 5, lines 49-56, where the one or more regions where the electric power distribution system 115, is located, corresponds to the attribute of the building in which the electrical panel is located, as the electric power distribution system 115, includes the electrical panel 116)
Beiner, Ju, and Kincade are combinable because they are all concern with object identification/recognition. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Beiner and Ju, to use the one or more regions where the electric power distribution system 115 is located, as though by Kincade, in order to locate the electrical panel 116, (Kincade, col. 5, lines 49-56).
In regards to claim 25, the combine teaching Beiner, Ju, and Kincade as whole discloses the limitations of claim 24.
Kincade further discloses wherein the attribute of the building includes a regional specific attribute of the building, a construction date of the building, or a type of utility service at the building, (see at least: col. 5, lines 49-56, where the region, where the electric power distribution system 115 is located, corresponds to the regional specific attribute of the building).
Claim 29 is rejected under 35 U.S.C. 103 as being unpatentable over Beiner, and Ju et al, as applied to claim 22 above; and further in view of Smolenaers, (US-PGPUB 20220402390)
The combine teaching Beiner and Ju as whole discloses the limitations of the claim 21.
Beiner does not expressly disclose generating a recommendation regarding whether a specific charging station is suitable for installation at the electrical panel.
However, Oh et al discloses generating a recommendation regarding whether a specific charging station is suitable for installation at the electrical panel, (see at least: Par. 0290, When electric vehicle 201 connects to station 500 at DC input 601 and communication between vehicle and charging station takes place to ensure compatibility; and from Par. 0336, the charging station 500 includes a communication module for communicating with EV 200 to determine voltage and conversion compatibilities, [i.e., generating a recommendation, “implicit by determining the voltage and conversion compatibilities”, regarding whether a specific charging station is suitable for installation at the electrical panel, “implicit by determining the voltage and conversion compatibilities, between the charging station 500 and EV 200”).
Beiner, Ju, and Oh et al are combinable because they are all concern with object identification/recognition. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Beiner and Ju, to use the communication module of the charging station 500 , as though by Oh et al, in order to determine voltage and conversion compatibilities between the charging station 500 and the EV 200, (Oh et al, Par. 0336)
Claims 30-31, and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Beiner, (US-PGPUB 20160188763) in view of Lee et al, (US-PGPUB 20130335356)
In regards to claim 30, Beiner discloses a computing device, (196 in Fig. 1), comprising:
a processor; memory in electronic communication with the processor, wherein the memory stores computer-executable instructions that, when executed by the processor, (see at least: Par. 0038, the portable device 196 is a conventional smartphone, which implicitly comprises a processor; and memory in electronic communication with the processor. See also, Par. 0012, “a smartphone app, stored in a non-volatile memory device”, [i.e., memory]; …and “at least one processor of a smartphone”; [i.e., processor]), cause the processor to:
receive a digital image, (see at least: Par. 0038, the portable device 196 is a conventional smartphone having a built-in camera; and from Par. 0054, using the camera, the smartphone 400 may capture a live image of the electrical distribution panel 112, [i.e., receiving a digital image, “implicitly by using the camera, of the smartphone 400 to capture a live image of the electrical distribution panel 112”]);
identify, from the digital image, a type of an electrical component of an electrical panel, (see at least: Par. 0057, a maximum rating may be detected based on optical character recognition of a corresponding marking of the identified circuit breakers 114, [i.e., identify, from the digital image, a type of an electrical component of an electrical panel, “implicit by identifying maximum rating of a corresponding marking of the identified circuit breakers 114”]);
perform optical character recognition electrical load information may also comprises a maximum rating and/or other metadata for each of the circuit breakers 114, where the maximum rating may be detected based on optical character recognition of a corresponding marking of the identified circuit breakers 114, [i.e., perform optical character recognition to determine text associated with the electrical component; “implicit by optical character recognition of a corresponding marking of the identified circuit breakers 114”, and generate a metric for the electrical panel based on the type of the electrical component and the text, “implicit by receiving the electrical load information, which may also comprises a maximum rating and/or other metadata for each of the circuit breakers 114”]).
Beiner does not expressly disclose modifying a portion of the digital image including the electrical component to generate a modified image portion; and performing optical character recognition of the modified image portion.
However, Lee et al discloses modifying a portion of the digital image including the electrical component to generate a modified image portion; and performing optical character recognition of the modified image portion, (see at least: Par. 0047-0048, image text extraction application 125 rotates 520 the image (or the surrounding region) to correct the skew; … cropping540 the text region; … and transmitting 440 the cropped image containing the text region to the server 110, which processes the cropped image using OCR technology and returns OCR'ed text, [i.e., modifying a portion of the digital image to generate a modified image portion, “rotating 520 the image (or the surrounding region)”; and performing optical character recognition of the modified image portion, “using OCR technology and returns OCR'ed text”]).
Beiner and Lee et al are combinable because they are both concerned with object/text detection, Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Beiner, to use the image text extraction application 125, as though by Beiner, in order to rotate the surrounding region of the image, cropping the text region, and processes the cropped image using OCR technology for detecting the text, (Lee, Par. 0047-0048).
In regards to claim 31, the combine teaching Beiner and Lee as whole discloses the limitations of claim 30.
Lee further discloses wherein modifying the portion of the digital image comprises rotating the portion of the digital image to a first rotated orientation, (see at least: Par. 0047-0048, implicit by rotating the image (or the surrounding region)).
In regards to claim 35, the combine teaching Beiner and Lee as whole discloses the limitations of claim 30.
Beiner further discloses wherein the digital image comprises an image of the electrical panel captured via a portable electronic device, (see at least: Par. 0054, using the camera, the smartphone 400 may capture a live image of the electrical distribution panel 112, “electrical panel”).
Claims 32-34 are rejected under 35 U.S.C. 103 as being unpatentable over Beiner, and Lee et al, as applied to claim 30 above; and further in view of Lorie et al, (US-Patent 6,993,205)
In regards to claim 32, the combine teaching Beiner and Lee as whole discloses the limitations of claim 30.
The combine teaching Beiner and Lee as whole does not expressly disclose wherein at the first rotated orientation, a value of the text is unable to be determined by the optical character recognition.
Lorie et al discloses wherein at the first rotated orientation, a value of the text is unable to be determined by the optical character recognition, (see at least: Fig. 1, step 14, “is text block oriented correctly”, (No, rotate image and perform OCR again), and also col. 5, lines 4-48, where the text could be classified as confidence a (e.g., correct orientation), confidence b (e.g., incorrect orientation), [i.e., wherein at the first rotated orientation, a value of the text is unable to be determined by the optical character recognition, “implicit when text block is classified as incorrect orientation”).
Beiner, Lee, and Lorie are combinable because they are all concerned with object/text detection, Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Beiner and Lee, to use the step 14, as though by Lorie, in order to makes a judgment as to whether the block is oriented correctly, (Lorie, col. 5, lines 4-5).
In regards to claim 33, the combine teaching Beiner, Lee, and Lorie as whole discloses the limitations of claim 32.
Lorie further discloses wherein the memory further comprises computer-executable instructions that, when executed by the processor, cause the processor to rotate the portion of the digital image to a second rotated orientation that differs from the first rotated orientation, (see at least: Fig. 1, step 14, “is text block oriented correctly”, (No, rotate image and perform OCR again), [i.e., implicitly rotating the portion of the digital image to a second rotated orientation that differs from the first rotated orientation]).
In regards to claim 34, the combine teaching Beiner, Lee, and Lorie as whole discloses the limitations of claim 33.
Lorie further discloses wherein the second rotated orientation is based on a rotated orientation of another modified image portion, (col. 5, lines 30-38, other levels of incorrect orientation can be explored with the invention by only rotating the image a limited amount and repeating the process on the slightly rotated image, [i.e., the second rotated orientation, “180 degrees”, is based on a rotated orientation of another modified image portion, “implicit by rotating the image a limited amount and repeating the process on the slightly rotated image”]).
Claims 36-37, and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Beiner, (US-PGPUB 20160188763) in view of Han et al, (US-PGPUB 20210073597); and further in view of Snopek et al, (US-PGPUB 20230394587); and further in view of Sohmshetty et al, (US-PGPUB 20220254055)
In regards to claim 36, Beiner discloses a non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by a processor, (see at least: Par. 0046, implicit by using an image analysis algorithm executed by a processor of the portable device), cause the processor to:
receive a digital image of an electrical panel, (see at least: Par. 0038, the portable device 196 is a conventional smartphone having a built-in camera; and from Par. 0054, using the camera, the smartphone 400 may capture a live image of the electrical distribution panel 112, [i.e., receiving a digital image, “implicitly by using the camera, of the smartphone 400 to capture a live image of the electrical distribution panel 112”]);
generate a metric for one or more of plurality of electrical components, (see at least: Par. 0055-0057, receiving an electrical load information comprising a maximum rating and/or other metadata for each of the circuit breakers 114, where the maximum rating may be detected based on optical character recognition of a corresponding marking of the identified circuit breakers 114, [i.e., analyzing the digital image to identify text and a plurality of electrical components, based implicitly on performing optical character recognition of a corresponding marking of the identified circuit breakers 114, and generating the maximum rating and/or other metadata for each of the circuit breakers 114, “generate a metric for the plurality of electrical components”]).
Beiner does not expressly disclose generating a plurality of bounding boxes and apply the plurality of bounding boxes to respective portions of the digital image; analyzing each bounding box of the plurality of bounding boxes to identify text and a plurality of electrical components respectively positioned within the plurality of bounding boxes; and generate a metric for one or more bounding boxes of the plurality of bounding boxes of plurality of electrical components.
However, Han et al discloses generating a plurality of bounding boxes and apply the plurality of bounding boxes to respective portions of the digital image, (see at least: Fig. 2, Par. 0039, bounding component 220 may generate the set of bounding boxes using a multilayer object model, and identifying a set of coordinates within images, determines a set of sizes and aspect ratios for the set of bounding boxes, and determines a distribution of the set of bounding boxes within the image, [i.e., generating a plurality of bounding boxes, “generate the set of bounding boxes”, and apply the plurality of bounding boxes to respective portions of the digital image, “implicit by determining a distribution of the set of bounding boxes within the image”];
analyzing each bounding box of the plurality of bounding boxes to identify boxes, “implicit by using one or more algorithms, functions, or operations”, to identify objects positioned within the plurality of bounding boxes”]);
Beiner and Han et al are combinable because they are both concerned with object detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Beiner, to apply the object detection system 160, as though by Han, in order to generate the set of bounding boxes, (Han, Par. 0039), and identifying the objects of interest, within the set of bounding boxes, (Han, Par. 0042).
The combine teaching Beiner and Han as whole does not expressly disclose analyzing each bounding box of the plurality of bounding boxes to identify text positioned within the plurality of bounding boxes.
Snopek discloses analyzing each bounding box of the plurality of bounding boxes to identify text positioned within the plurality of bounding boxes, (Par. 0063, the server 202 may perform the OCR techniques to generate bounding boxes and identify the text included in each bounding box, such as via an OCR module 238B, [i.e., analyzing each bounding box of the plurality of bounding boxes, “implicit by performing the OCR techniques”, to identify text positioned within the plurality of bounding boxes, “identify the text included in each bounding box”]).
Beiner, Han, and Snopek are combinable because they are all concerned with object detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Beiner and Han, to perform the OCR techniques for each bounding box, as though by Snopek, in order to identify the text included in each bounding box, (Snopek, Par. 0063)
The combine teaching Beiner, Han, and Snopek as whole does not expressly disclose generating a metric for one or more bounding boxes of the plurality of bounding boxes of plurality of electrical components.
However, Sohmshetty discloses generating a metric for one or more bounding boxes of the plurality of bounding boxes of plurality of electrical components, (see at least: Par. 0034, defining bounding boxes surrounding various components of the electrical connector assembly; and from Par. 0044-0047, Fig. 1a, the metric module 50 includes a distance module 52 and an angle module 54 configured to determine one or more metrics based on the identified edges of the bounding boxes (e.g., the distal edges and/or the adjacent edges), [i.e., generating a metric for one or more bounding boxes of the plurality of bounding boxes, “determine one or more metrics based on the identified edges of one or more bounding boxes surrounding various components”, of plurality of electrical components, “various components of electrical connector assembly ECA 20)”).
Beiner, Han, Snopek, and Sohmshetty are combinable because they are all concerned with object detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Beiner, Han, and Snopek, to use the metric module 50, as though by Sohmshetty, in order to determine a state of the various components of electrical connector assembly ECA 20, based on the one or more metrics determined by the metric module 50, (Sohmshetty, Par. 0047)
In regards to claim 37, combine teaching Beiner, Han, Snopek, and Sohmshetty as whole discloses the limitations of claim 36.
Sohmshetty further discloses wherein a first bounding box of the plurality of bounding boxes and a second bounding box of the plurality of bounding boxes are independently rotatable to identify the text, (Sohmshetty, see at least: Par. 0038, a deep learning neural network, that is configured to generate rotated bounding boxes of male and female portions of the ECA 20).
In other hand, Snopek discloses identifying text within bounding boxes, (Snopek, Par. 0063, the server 202 may perform the OCR techniques to generate bounding boxes and identify the text included in each bounding box, such as via an OCR module 238B, [i.e., analyzing each bounding box of the plurality of bounding boxes, “implicit by performing the OCR techniques”, to identify text positioned within the plurality of bounding boxes, “identify the text included in each bounding box”]).
In regards to claim 39, the combine teaching Beiner, Han, Snopek, and Sohmshetty, as whole discloses the limitations of claim 37.
Beiner further discloses wherein the text comprises a numerical value representative of at least one of a service amperage of the electrical panel or a service amperage of an individual breaker, (see at least: Par. 0057, implicit by performing the optical character recognition of a corresponding marking of the identified circuit breakers 114).
Claim 38 is rejected under 35 U.S.C. 103 as being unpatentable over Beiner, Han, Snopek, and Sohmshetty, as applied to claim 37 above; and further in view of Dagnino et al, (ES 2688829)
The combine teaching Beiner, Han, Snopek, and Sohmshetty as whole discloses the limitations of claim 37.
The combine teaching Beiner, Han, Snopek, and Sohmshetty as whole does not expressly wherein analyzing the plurality of electrical components respectively positioned within the plurality of bounding boxes comprises using a neural network model trained to identify types of electrical components.
Dagnino discloses using a neural network model trained to identify types of electrical components, (see at least: Page 4, 4th paragraph, plurality of electrical components …generators; and 5th paragraph, the historical data used to train the machine learning algorithm may be related to (e.g., may be derived from) a plurality of different types of electrical components of the power supply system, [i.e., using a neural network model trained to identify types of electrical components, “implicit by training the machine learning algorithm with different types of electrical components”]).
Beiner, Han, Snopek, Sohmshetty, and Dagnino are combinable because they are all concerned with object detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching combine teaching Beiner, Han, Snopek, and Sohmshetty, to train the train the machine learning algorithm, with different types of electrical components, as though by Dagnino, in order to develop the electrical component profile, (Dagnino, Abstract).
Allowable Subject Matter
Claim 40 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
With respect to claim 40, the prior art of record, alone or in reasonable combination, does not teach or suggest, the following underlined limitation(s), (in consideration of the claim as a whole):
“wherein the text comprises one or more additional numerical values, and the processor is further configured to: filter the numerical value and the one or more additional numerical values based on standard amperages; and select a largest numerical value as the service amperage of the electrical panel if multiple numerical values remain after being filtered”
The relevant prior art of record, Beiner, (US-PGPUB 20160188763), discloses a non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by a processor, (see at least: Par. 0046, implicit by using an image analysis algorithm executed by a processor of the portable device), cause the processor to:
receive a digital image of an electrical panel, (see at least: Par. 0038, the portable device 196 is a conventional smartphone having a built-in camera; and from Par. 0054, using the camera, the smartphone 400 may capture a live image of the electrical distribution panel 112, [i.e., receiving a digital image, “implicitly by using the camera, of the smartphone 400 to capture a live image of the electrical distribution panel 112”]);
generate a metric for one or more of plurality of electrical components, (see at least: Par. 0055-0057, receiving an electrical load information comprising a maximum rating and/or other metadata for each of the circuit breakers 114, where the maximum rating may be detected based on optical character recognition of a corresponding marking of the identified circuit breakers 114, [i.e., analyzing the digital image to identify text and a plurality of electrical components, based implicitly on performing optical character recognition of a corresponding marking of the identified circuit breakers 114, and generating the maximum rating and/or other metadata for each of the circuit breakers 114, “generate a metric for the plurality of electrical components”]).
However, while disclosing that the maximum rating may be detected based on optical character recognition of a corresponding marking of the identified circuit breakers 114; Beiner fails to teach or suggest, either alone or in combination with the other cited references, wherein the text comprises one or more additional numerical values, and the processor is further configured to: filter the numerical value and the one or more additional numerical values based on standard amperages; and select a largest numerical value as the service amperage of the electrical panel if multiple numerical values remain after being filtered.
A further prior art of record, Snopek, (US-PGPUB 20230394587), discloses analyzing each bounding box of the plurality of bounding boxes to identify text positioned within the plurality of bounding boxes, (Par. 0063, the server 202 may perform the OCR techniques to generate bounding boxes and identify the text included in each bounding box, such as via an OCR module 238B, [i.e., analyzing each bounding box of the plurality of bounding boxes, “implicit by performing the OCR techniques”, to identify text positioned within the plurality of bounding boxes, “identify the text included in each bounding box”]); fails to teach or suggest, either alone or in combination with the other cited references, wherein the text comprises one or more additional numerical values, and the processor is further configured to: filter the numerical value and the one or more additional numerical values based on standard amperages; and select a largest numerical value as the service amperage of the electrical panel if multiple numerical values remain after being filtered.
Contact Information
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/AMARA ABDI/Primary Examiner, Art Unit 2668 08/08/2026
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