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 .
Response to Arguments
Applicants' arguments filed February 24, 2025 have been fully considered but they are only partially persuasive.
Regarding the “deep learning”, Widmer explicitly states a test/measure functionality to determining how object position affects the magnetic field (par 157-158). Taking what was done by hand and making it computerized is an obvious modification. MPEP §2144.04(III). Claim 22 only broadly refers to “using a deep learning”. There are no detailed recitations (and no detailed explanation in the specification) for how this computerized function is any different than human making the Widmer observations.
The specification does not use the term “deep learning”. The specification uses “machine learning”, which appears to be an equivalent term. The phrases, “machine learning”, “supervised learning” and “neural network” appear throughout pages 30-31 and 33. But in each instance, these computer components are only named. They use an “algorithm” to associate inputs/outputs. Page 30 gives two examples of the algorithm (“CNN or RNN”), but these are only examples and not explicitly recited in the claim (even if they were, they appear to be known algorithms).
There is no indication in the specification for how this is not the application of generic artificial intelligence to replace what was previously done by hand. If the Applicants contend otherwise, they are requested to provide evidentiary support.
The Applicants argue that “Widmer [] does not teach or suggest a deep learning model that is trained on historical data to determine the receiver’s position dynamically” (Remarks, page 11). To the contrary, Widmer discloses placing an object in various locations and testing the effects (fig 10; par 157-158). This is undoubtedly “historical” training (or, as referred to in the claim, “simulation or experiment data”). Widmer’s foreign object detection is clearly able to distinguish between the presence/absence of an object. This makes it “dynamic”. It is unclear what the Applicants contend is included within this term, as no citations have been provided and the word does not appear in claim 22.
The Applicants contend that “Widmer’s testing approach merely determines response thresholds for detection”. No citations have been provided to support this conclusion. It appears that the Applicants have confused Widmer’s real-world object location sensing (fig 12-13) with the experimental object calibration (fig 10), in which Widmer explicitly discloses sensing the effects of the object at different locations (par 157-158 make no mention of “thresholds”).
While it is the Examiner’s position that generically applying Widmer’s “learning” to a computer (or neural network) to make it “deep learning” is obvious, the rejection is updated to cite to Widmer (“Widmer II”; US 2018/0239055) paragraph 203-204. Widmer II discloses that it is known to train foreign object detection using a neural network. As this new reference is not due to any amendments (the changes to claim 22 incorporate previously recited dependent claim limitations and not include any newly added limitations), this Action is made non-final.
Regarding issue 2, the application of a generic “machine learning model” to Widmer’s foreign object location calibration (fig 10; par 157-158) is obvious, as discussed above.
Regarding issue 3, “In the case where the claimed ranges "overlap or lie inside ranges disclosed by the prior art" a prima facie case of obviousness exists. In re Wertheim, 541 F.2d 257, 191 USPQ 90 (CCPA 1976); In re Woodruff, 919 F.2d 1575, 16 USPQ2d 1934 (Fed. Cir. 1990)” MPEP §2144.05. As admitted by the Applicants, part of Widmer’s aspect ratio range overlaps with the claimed range and part of Widmer’s overlap ratio range overlaps with the claimed range. Thus, a prima facie case of obviousness has been established. The Applicants’ purported benefits do not rebut the overlap.
Regarding issue 4, no supporting citations or evidence have been provided to support the argument that the claimed deep learning are “not straightforward optimizations” or “require functionality redesign”. As noted above, Widmer discloses calibrating the foreign object detection (par 157-158). This clearly uses sensed current as an input and the corresponding positions as outputs. This is done so that a detection during real-world applications can make a determination about the object’s position. Generically applying this disclosure to a “deep learning” computer, without detailing what that entails, is routine optimization. MPEP §2144.04(III). The claimed computer model appears to do everything that Widmer discloses. The only difference is that the claim uses a more sophisticated computer (“artificial intelligence”).
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.
Claims 22, 25, 27, 29-31 and 34-35 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 22 has been amended such that the limitation of previous dependent claims are recited at the end of the claim. This creates the situation in which claim 22 recites both the broad limitations of original claim 22 and the narrowing limitations of the original dependent claims.
For example, claim 22 recites “(c), determining, by the wireless power transfer device, a position of the adjacent wireless power pickup device…” and the claim also recites, “determining of the position of the adjacent wireless power pickup is performed by the power device pickup position determining unit”. The claim is indefinite because it assigns the same functionality to two different components – the transmitter broadly and the position determining unit narrowly. It is unclear if the Applicants are seeking patent protection over the detection by the transmitter or by the position determining unit.
The purpose of a dependent claim is to narrowly define the limitation of a previous independent claim. By moving the dependent claims into claim 22, the independent claims now recites both the broad and narrow limitations for the same component.
This is repeated for several limitations that have been incorporated wholesale onto the end of claim 22. To overcome this rejection, the Applicants should incorporate the dependent claims limitations into the body of claim 22 (so that the method claim retains its ordered step flow).
Claims 25, 27, 29-31 and 34-35 are similarly rejected as they depend from, and inherit the deficiencies of, claim 22.
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.
Claims 22-30 and 32-35 are rejected under 35 U.S.C. 103 as being unpatentable over Covic (US 2015/0236513) in view of Widmer (US 2014/0015329) and in further view of Widmer (“Widmer II”; US 2018/0239055).
With respect to claim 22, Covic discloses a method of controlling a wireless power transmission system (fig 5-12; par 126-168) including a wireless power transfer device (fig 6-7; reference repeatedly refers to primary coils – a transmitter, see at least par 157) and a wireless power pickup device (fig 8-9; reference repeatedly refers to secondary coils – a receiver, see at least par 157), the wireless power transfer device including four primary coils each of which partially overlaps other adjacent primary coils and is electrically independent from other primary coils (see fig 5, 11-12; par 133, “one or more of the at least three coils may be energized” – indicates four coils and that they are electrically independent; the figures show the partial overlap), the wireless power pickup device including four pickup coils each of which partially overlaps other adjacent pickup coils and is electrically independent from other pickup coils (par 153 – the receiver has the same structure as the transmitter), the method comprising:
(a) supplying, by the wireless power transfer device, power to the four primary coils such that each of the four primary coils generates a magnetic field having a same intensity in a same direction (par 147);
(c) determining, by the wireless power transfer device, a position of the adjacent wireless power pickup (par 147-150);
(d) deciding, by the wireless power transfer device, an operation mode of each primary coil based on the position of the adjacent wireless power pickup device determined in the step (c) (par 125-126, 147-150, 164);
(e) controlling, by the wireless power transfer device, an operation of each primary coil based on the operation mode of each primary coil decided in the step (d) (par 125-126, 147-150, 164);
(f) sensing, by the wireless power pickup device, state of each pickup coil (par 154, 156 – power outputs of each coil is sensed);
(g) determining, by the wireless power pickup device, a position of the wireless power pickup device based on information including changes in the state of each pickup coil sensed in the step (f) (par 154, 156; a low power output of a coil indicates misalignment, which informs a “position” of that coil);
(h) deciding, by the wireless power pickup device, an operation mode of each pickup coil based on information including the position of the wireless power pickup device determined in the step (g) (par 154, 156); and
(i) controlling, by the wireless power pickup device, an operation of each pickup coil based on the operation mode of each pickup coil decided in the step (h) (par 154, 156 – the low power coils are electrically disconnected – their “operation mode” is decided and controlled to be “off”);
wherein information for determining the position of the adjacent wireless power pickup device in the step (c) includes information about a kind of the adjacent wireless power pickup device (par 147) and about a state of a current of a pickup coil of the adjacent wireless power pickup device induced from the magnetic flux formed by the wireless power transfer device (par 147).
wherein each of the four primary coils has a rectangular shape (par 164, they are square, which is a type of rectangle) with an aspect ratio in a range of 1.0 to 1.1 (squares have a ratio of exactly 1), and wherein a ratio of overlapping one side of each primary coil with another adjacent primary coil is in a range of 0.47 to 0.58 (par 163, Widmer’s range is 0.5-2.0); and
wherein each of the four pickup coils has a rectangular shape (they’re square) with an aspect ratio in the range of 1.0 to 1.25, (squares have a ratio of exactly 1), and wherein a ratio of overlapping one side of each pickup coil with another adjacent primary coil is in a range of 0.5 to 0.8 (par 163, Widmer’s range is 0.5-2.0).
Covic discloses a transmitter with four overlapping coils and a receiver with four overlapping coils. Covic discloses values within the recited ranges (for aspect ratio and overlap) and, therefore, obviously teaches the claimed ranges. MPEP §2144.05.
The Covic transmitter coils can be independently activated and their phases are controlled as a direct consequence of the relative location of a receiver. The transmitter detects the location of a receiver and activates the closest (best aligned) coils (par 147-148). The receiver operates in a similar manner – it detects the current through each coil and turns off those that are misaligned. Covic discloses the need to detect the location of the receiver, but does not expressly disclose how the location is detected or any type of calibration.
Widmer discloses a method of controlling a wireless power transmission system (fig 2, 8c, 10, 11a, 12-13; par 145-151, 157-200) including a wireless power transfer device (202) and a wireless power pickup device 214), the wireless power transfer device including four primary coils each of which partially overlaps other adjacent primary coils (fig 11a) and is electrically independent from other primary coils (via multiplexer 1228 and 1328 shown in fig 12-13; par 175), the method comprising:
(a) supplying, by the wireless power transfer device, power to the four primary coils such that each of the four primary coils generates a magnetic field having a same intensity in a same direction (via multiplexer 1228 or 1328; par 175. See also step 3302, par 145, 321);
(b) sensing, by the wireless power transfer device, a state change of each primary coil as a magnetic field formed by the wireless power transfer device is changed by an adjacent wireless power pickup device (via measuring unit 1234 or 1334 and/or comparator 1236 or 1336; discussed in par 145, 175); and
(c) determining, by the wireless power transfer device, a position of the adjacent wireless power pickup (fig 12-13; item 1238 or 1338; par 175-178).
wherein the wireless power transfer device includes a power pickup device position determining unit using am algorithm (fig 10; par 157-158) based on simulation or experiment data,
determining of the position of the adjacent wireless power pickup device is performed by the power pickup device position determining unit (Widmer makes the determination and, therefore, obviously discloses a “unit” to do so),
wherein, in the algorithm, a change of a current generated in each primary coil of the wireless power transfer device by the adjacent wireless power pickup device is provided as an input and the position of the adjacent wireless power pickup device as a label (par 157-158), and
the power pickup device position determining unit outputs information including the position of the adjacent wireless power pickup device when information including the change of the current generated in each primary coil of the wireless power transfer device is provided as inputs (par 157-158), and
wherein information for determining the position of the adjacent wireless power pickup device includes information about a kind of the adjacent wireless power pickup device (par 317 includes a “type” of object).
Widmer discloses that it is known to sense the position of a receiver by monitoring “state changes” in the primary coils. Widmer discloses that the position detection (fig 12-13) is first calibrated (fig 10; par 157-158) by testing the effects of an object at various locations.
The last four limitations before the wherein clauses that define the aspect ratio and overlap are a nearly verbatim copy of the first four limitations added after step (i). The only difference is the first set of limitations define the transmitter and the second set of limitations define the pickup. Covic discloses that the receiver has the same structure as the transmitter (par 153). Thus, the combination of references would provide for the algorithm and power pickup position determining unit to be duplicated for the combinations’ pickup as well. They are not repeated here solely for brevity.
Covic and Widmer are analogous to the claimed invention because they are from the same field of endeavor, namely wireless power transmitters that need to know/detect the receiver’s position. At the time of the earliest priority date of the application, it would have been obvious to one skilled in the art to modify Covic to include the state change sensing taught by Widmer. The motivation for doing so would have been to fill in the gaps missing in the Covic disclosure. Covic discloses that the receiver’s position is critical to activating the coils, but does not explain how to calibrate it or detect it. Thus, the skilled artisan would have looked to other references (like Widmer) to determine a suitable manner by which a receiver’s position can be accurately determined, with a reasonable expectation of success.
Widmer discloses experimenting with different positions to determine the resulting current (fig 10) and then using that information to configure the threshold to distinguish between a pickup being present or absent (fig 12-13). This appears to be done by hand and is not a “deep learning” computerized process. Widmer II discloses a wireless power transmission system with foreign object detection that is trained with a neural network (par 203-204). In the combination, this neural network (i.e. “deep learning”) is applied to both the transmitter and pickup.
Widmer and Widmer II are analogous to the claimed invention because they are from the same field of endeavor, namely foreign object detection training/calibration. At the time of the earliest priority date of the application, it would have been obvious to one skilled in the art to modify Widmer’s figure 10 training to be completed by a “neural network”, as taught by Widmer II. The motivation for doing so would have been the obvious of automation (MPEP §2144.04(III)) and the generally accepted and known trend to use artificial intelligence. The claim only broadly names “deep learning” without reciting any specifics for how it is programmed or used in any manner to distinguish over the interpretation that it is a generic application of artificial intelligence to complete a known functionality (Widmer’s figure 10). Widmer II teaches that applying artificial intelligence to foreign object training was known prior to the Applicants’ earliest priority date.
With respect to claim 25, Widmer discloses inputs provided for the supervised learning of the power pickup device position determining unit include information about the kind of the adjacent wireless power pickup device (par 115, 157). Widmer (par 157) tests the response for a coin. Paragraph 115 discloses that the object’s size and conductivity (among other parameters) determine its effect on the magnetic field. Thus, the Widmer input includes information about different “kinds” of receivers (different coins, different metals, different actual receivers, etc.). The Examiner notes that claim 25, while reciting that receiver “kind” is an input to the learning – the claim does not recite how this information is actually used to create any benefit. Claim 24 recites inputting current and outputting position. Claim 25, however, only recites inputting “kind”. There is no indication in the claim of how this input affects the output.
With respect to claim 27, the references combine to disclose inputs provided for the (Widmer) supervised learning of the power pickup device position determining unit comprise information about (Covic’s) a state of a current induced to a pickup coil of the adjacent wireless power pickup device (see Covic ;par 147 – alignment is a function of how much current is actually received by the pickup coils). Widmer’s learning uses sensed current that reacts to characteristics of the receiver/object. Covic improves on this learning by adding its disclosed input of receiver state of induced current.
With respect to claims 29-30, the combination teaches the recites limitations, as discussed above in the art rejections of claims 22 and 25, respectively. Widmer, modified by Widmer II, disclose the transmitter includes the recited functionality. And Covic discloses that the receiver has the same structure as the transmitter (par 153). Thus, the combination would obviously provide the recited limitations within the pickup.
With respect to claims 34-35, Widmer discloses both the wireless power transfer device and the wireless power pickup device each comprise a communication unit configured to communicate with each other (par 78).
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Covic in view of Widmer, in further view of Widmer II, and Partovi (US 2007/0279002).
Covic discloses that power transfer mode is initiated before step (h). This is because Covic senses the power collected at each pickup coil to determine if it is aligned or misaligned (misaligned coils are then turned off). Covic does not expressly disclose requesting a power transfer mode. Partovi discloses a wireless power transfer system comprising a wireless power transfer device (transmitter) and wireless power pickup device (receiver) that executes a method step of requesting, by the wireless power pickup device, the wireless power transfer device to switch to a power transfer mode (par 75-77). Partovi discloses the receiver requests specific power levels. This is interpreted as requesting a switch to a power transfer mode. The Examiner notes that the claim does not recite any other modes (namely, the mode the transmitter was in before it was requested to switch to a power transfer mode).
When combined, Partovi’s request would come before Covic’s step (h), wherein information about the power transfer mode of the wireless power transfer device is further used to determine the operation mode of each pickup coil in the (Covic) step (h) (the information about the power transfer mode is the amount of power received at each Covic coil).
Covic and Partovi are analogous to the claimed invention because they are from the same field of endeavor, namely wireless power transmission systems. At the time of the earliest priority date of the application, it would have been obvious to one skilled in the art to modify Covic to include the request, as taught by Partovi. The motivation for doing so would have been to conserve energy and protect receivers (Partovi par 77).
The Examiner notes that Partovi also discloses that the transmitter detects a “type” of receiver and includes a learning algorithm to keep track of how much power each type of receiver requires (par 77-78). This Partovi would also appear to render obvious some of the other dependent claims.
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/ADI AMRANY/ Primary Examiner, Art Unit 2836