DETAILED ACTION
Claims 1-20 are presented for examination.
The present application is being examined under the AIA (America Invents Act) First Inventor to File.
This Office Action is Non-Final.
Claims 1 and 13 are independent claims. Claims 2-12 and 14-20 are dependent claims.
This action is responsive to the following communication: corresponding claims filed on 12-12-2023.
Foreign Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file. It is also noted, that applicant has filed a certified copy on 12-21-2023 as required by 35 U.S.C. 119(b).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12-12-2023 is in compliance with the provisions of 37 CFR 1.97
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4-10, 13, 16-20 are rejected under 35 U.S.C. 102(a)(1)/(a)/(2) as being anticipated by U.S. Publication No. 2021/0381960 (hereinafter, “Na”).
As per claims 1 and 13, Na discloses a micro-controller circuit comprising:
a first sensor circuit sensing a first physical parameter to generate first sense information; (inter alia: Fig. 2 illustrates an apparatus having a wideband sensor 200 for sensing physical attributes that includes at least the following: depth sensing (e.g., i-TOF or d-TOF photodetector), proximity sensing and optical spectroscopy. ¶ [0114] As a result to that, the wideband sensor may generate “output signal SO”; at least Fig’s 1-9)
a first processing circuit performing a first operation on the first sense information to generate a first processed signal; ( Inter alia: Fig. 10 illustrates the wideband sensor having at least an ADC 1050 processor/signal processor 1055 for processing sensed information to “provide the Sensor Output (SO)” ¶ [0165] )
a first storage circuit storing the first processed signal and first predetermined information; and (inter alia: Fig. 10 illustrates memory 1060. ¶ [0165] discloses an ADC having SAR architecture which suggest to a person having ordinary skill that the architecture contains sample and hold logic and SAR register. These teachings results in a memory storing the digitally converted sensed information (i.e., output signal SO that is comprised of first detecting signal and the second detecting signal/ED signal) while the SAR architecture the raw sensed information (i.e., Incident light IL1/IL2.)
a first [calculating] circuit utilizing a machine learning method to process the first processed signal stored in the first storage circuit to generate a first learning result, (Fig. 9 illustrates at least calculation unit that can “can use one or more machine-learned models (e.g., AI)” ¶ [0119] based on the received output signal SO and generate “calculating result ED” signal ¶ [0108]. Also, Fig. 7 )
wherein in response to the first learning result not matching the first predetermined information, the first adjustment circuit adjusts the first predetermined information. (Fig. 9 illustrates an adjustment circuit 106 that is responsible for adjusting several functions based on the type of material the apparatus is sensing. Stated differently, when the sensed information is not associated with a reference1 predetermined material, the apparatus can perform an adjustment by “switching ON/OFF the RF/power/display units/software applications”. This teaching is similar to the invention recited by Applicant’s specification that states “in response to detecting skin (e.g., body tissue of a hand holding the sensing apparatus), the sensing apparatus can adjust from a low-power operational mode to a normal power operational mode” ¶ [100] )
As per claim(s) 4, Na discloses a micro-controller circuit wherein the first adjustment circuit is a neural-network processing unit (NPU). ( ¶ [0195])
As per claim(s) 5, 16, Na discloses a micro-controller circuit further comprising: a central processing unit (CPU) entering a low-power mode according to a sleep command; and (low power mode; ¶ [0128])
a first monitoring circuit determining whether the first processed signal matches the first predetermined information in response to the CPU entering the low-power mode, wherein in response to the first processed signal not matching the first predetermined information, the first monitoring circuit wakes up the CPU. (sensing apparatus can adjust from a low-power operational mode to a normal power operational mode.; ¶ [0100])
As per claim(s) 6, Na discloses a micro-controller circuit wherein: in response to the CPU being woken up, the first adjustment circuit processes the first processed signal stored in the first storage circuit, and in response to the CPU entering the low-power mode, the first adjustment circuit stops processing the first processed signal stored in the first storage circuit. (Na discloses the sensing apparatus to adjust between OFF/ON, low-power and normal power operational mode by stopping/closing various applications; ¶s [0100], [0128])
As per claim(s) 7, Na discloses a micro-controller circuit further comprising: a second sensor circuit sensing a second physical parameter to generate second sense information;
a second processing circuit performing a second operation on the second sense information to generate a second processed signal; (Fig. 4 illustrates the wideband sensor to include a first/second photo-detecting unis 180 and 190, respectively)
a second storage circuit storing the second processed signal and second predetermined information; and (output signal SO that is comprised of first detecting signal and the second detecting signal based on the IL1 and IL2 sensed from photo-detecting unis 180 and 190; Fig’s 1-9 )
a second adjustment circuit utilizing the machine learning method to process the second processed signal stored in the second storage circuit to generate a second learning result, wherein in response to the second learning result not matching the second predetermined information, the second adjustment circuit adjusts the second predetermined information. (first and second AI modeling performed by the calculation unit; Fig. 7)
As per claim(s) 8, Na discloses a micro-controller circuit further comprising: a second monitoring circuit determining whether the second processed signal matches the second predetermined information in response to the CPU entering the low-power mode, wherein in response to the second processed signal not matching the second predetermined information, the second monitoring circuit wakes up the CPU. (Na discloses the sensing apparatus to adjust between OFF/ON, low-power and normal power operational mode by stopping/closing various applications based on sensed information not matching a reference value; ¶s [0100], [0128])
As per claim(s) 9, Na discloses a micro-controller circuit wherein the first physical parameter is different from the second physical parameter. (IL1 and IL2 detected from different photo detecting units; Fig’s 1-9)
As per claim(s) 10, Na discloses a micro-controller circuit wherein the first operation is different from the second operation. (output signal SO that is comprised of first detecting signal and the second detecting signal based on the IL1 and IL2 sensed from photo-detecting unis 180 and 190; Fig’s 1-9)
As per claim(s) 16, Na discloses a micro-controller circuit, further comprising: determining whether the processed signal matches the predetermined information in response to a CPU entering a low-power mode; and (switching devices to a power status that includes low-power and ON/FF power. ¶ [0176] By way of example, switching the display to an OFF state, indicates to a POSITA that the display controller is operated in low/off power mode. Similarly, transition the display to an ON state cause the display controller to transition to a normal power mode.)
waking up the CPU so that the CPU exits the low-power mode and enters a normal mode in response to the processed signal not matching the predetermined information. (switching devices to a power status that includes low-power and ON/FF power. ¶ [0176] By way of example, switching the display to an OFF state, indicates to a POSITA that the display controller is operated in low/off power mode. Similarly, transition the display to an ON state cause the display controller to transition to a normal power mode.)
As per claim(s) 18, Na discloses a micro-controller circuit further comprising: stopping the determination of whether the processed signal matches the predetermined information in response to the CPU entering the low-power mode. (Na: switching devices to a power status that includes low-power and ON/FF power based on the sensed information matching a reference value. ¶ [0176], Fig’s 1-9)
As per claim(s) 19, Na discloses a micro-controller circuit further comprising: stopping the adjustment of the predetermined information in response to the learning result matching the predetermined information. ( ¶ [0106] states “The adjustment circuit 106 can then use the property of the objects 104 to determine the authenticity of food. For example, the adjustment circuit 106 may compare the measured sugar content of the fruit with a reference value, and if the difference in the sugar content exceeds a threshold value, the adjustment circuit 106 may determine that the fruit is a counterfeit.” It would be apparent to a person having ordinary skill that when the threshold value is not exceeded, the fruit is not a counterfeit. Therefore, one may conclude that the object matches the reference value as expected in recognizing the material, thus there would be no need for the adjusting circuit to perform a power state change. )
As per claim(s) 20, Na as modified discloses a micro-controller circuit wherein the machine learning method is a backpropagation neural network method, (Na: ¶ [203] backwards propagation) a support vector machine method, an adaptive boosting method or a decision tree method. (Na: vector machines, decision trees; ¶ [0195] )
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-3, 11-12 and 14-15, 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2021/0381960 (hereinafter, “Na”) in view of U.S. Publication No. 2022/0329613 (hereinafter, “Abbaszadeh”).
As per claim(s) 2, 14, Na discloses a micro-controller circuit wherein in response to the first learning result not matching the first predetermined information, the first adjustment circuit writes the first learning result to the first storage circuit (Na discloses modeling result signals for which the adjustment circuit determines whether the information signal received meets a reference value because a adjustment function is made. Fig’s 1-9, ¶s [0136])
Na does not distinctly disclose replace the first predetermined information.
However, Abbaszadeh explicitly discloses replace the first predetermined information. ( ¶ [0052] states “rigger element 314 may be the input to the adaptive thresholding system 145 when there is a change in the operation of the system/node, which in turn outputs a corresponding adaptive threshold level 140. The determination modules 120, 130 may then compare the anomaly score 126/132 to this output adaptive threshold level 140 to classify 316 the node/system”)
It would have been obvious before the effective filing date of the claimed invention to modify the teachings of Na and Abbaszadeh because both references are in the same field of endeavor. Abbaszadeh’s teaching of adaptive threshold would enhance Na's system by training the system to changing conditions, thus reducing false positives/negatives.
As per claims 3, 15, Nas as modified discloses a micro-controller circuit further comprising: a first timer circuit triggering the first processing circuit at every fixed time interval to direct the first processing circuit to perform the first operation on the first sense information. (Abbaszadeh: processing may be performed over a “specific duration of time” or “sliding window” ; ¶ [0042] )
It would have been obvious before the effective filing date of the claimed invention to modify the teachings of Na and Abbaszadeh because both references are in the same field of endeavor. Abbaszadeh’s teaching of processing information at fixed time interval or sliding window would enhance Na's system by optimizing the monitoring system, thus enhancing computer detection.
As per claim(s) 11, Na as modified discloses a micro-controller circuit wherein: the first predetermined information comprises a first threshold value and a second threshold value, and in response to the first learning result not being within the first and second threshold values, the first adjustment circuit updates at least one of the first and second threshold values according to the first learning result. (Abbaszadeh discloses assigning a first and second ranges for which trigger A or trigger B is applied based on the determined ranges; ¶ [0053] Also, adaptive thresholding by changing from one threshold to a second threshold. ¶s [0056]-[0064] )
It would have been obvious before the effective filing date of the claimed invention to modify the teachings of Na and Abbaszadeh because both references are in the same field of endeavor. Abbaszadeh’s teaching a plurality of thresholds would enhance Na's system by optimizing the monitoring system, thus enhancing computer detection.
As per claim(s) 12, Na as modified discloses a micro-controller circuit wherein in response to the first learning result matching the first predetermined information, the first adjustment circuit does not adjust the first adjustment circuit. ( ¶ [0106] states “The adjustment circuit 106 can then use the property of the objects 104 to determine the authenticity of food. For example, the adjustment circuit 106 may compare the measured sugar content of the fruit with a reference value, and if the difference in the sugar content exceeds a threshold value, the adjustment circuit 106 may determine that the fruit is a counterfeit.” It would be apparent to a person having ordinary skill that when the threshold value is not exceeded, the fruit is not a counterfeit. Therefore, one may conclude that the object matches the reference value as expected in recognizing the material, thus there would be no need for the adjusting circuit to perform a power state change. ) & (Abbaszadeh: based the comparison of the anomaly score 132 (generated by the local status determination module 130) to the adaptive threshold 140.; ¶ [0030])
As per claim(s) 17, Na as modified discloses a micro-controller circuit wherein in response to the CPU entering the low-power mode, the specific operation is performed at every fixed time interval. (Na: switching devices to a power status that includes low-power and ON/FF power based on the sensed information matching a reference value. ¶ [0176], Fig’s 1-9) & (Abbaszadeh: processing may be performed over a “specific duration of time” or “sliding window” ; ¶ [0042]
It would have been obvious before the effective filing date of the claimed invention to modify the teachings of Na and Abbaszadeh because both references are in the same field of endeavor. Abbaszadeh’s teaching of processing information at fixed time interval or sliding window would enhance Na's system by optimizing the monitoring system, thus enhancing computer detection.
Conclusion
With respect to any newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims. See MPEP §714.02 and § 2163.06. For example, when responding to this office action, applicants are advised to provide the examiner with the line numbers and page numbers in the application and/or references cited to assist the examiner in locating appropriate paragraphs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AUREL PRIFTI whose telephone number is (571)270-1743. The examiner can normally be reached on M-F 8 a.m.- 6 p.m..
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew J. Jung can be reached on 571-270-3779. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AUREL PRIFTI/Primary Examiner, Art Unit 2175
Aurel Prifti
Primary Examiner
Art Unit 2175
Tel. (571) 270-1743
Fax (571) 270-2743
aurel.prifti@uspto.gov
1 “The adjustment circuit 106 can then use the property of the objects 104 to determine the authenticity of food. For example, the adjustment circuit 106 may compare the measured sugar content of the fruit with a reference value, and if the difference in the sugar content exceeds a threshold value, the adjustment circuit 106 may determine that the fruit is a counterfeit.” ¶ [0136]