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
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5, 7, 10, 14, 15, 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Libeer (KR20220143597A), hereinafter Libeer in view of Modi et al., (US Pub.20140324232A1), hereinafter Modi.
Regarding Claim 1, Libeer discloses an electronic device (Fig. 1, #100) comprising:
at least one first component and at least one second component arranged in
an internal space of the electronic device (Fig. 1, #100) wherein temperatures of the first component and the second component vary differently based on the operation of the electronic device;
a first temperature sensor configured to measure a temperature of the at least
one first component (Fig. 1D, 340A-340E, page 5, lines 49-51, where sensors 340A-340E illustrated in FIG. 1D are attached to components (e g, display assembly 304 , back cover 312, main logic board 332 , etc., page 5, lines 35-37, where device 300 may include additional components such as one or more sensors 340A-340E that detect the temperature of the space immediately surrounding the respective sensor);
a second temperature sensor configured to measure a temperature of the at
least one second component (Fig. 1D, 340A-340E, page 5, lines 49-51, where Sensors 340A-340E illustrated in FIG. 1D are attached to components (e.g, display assembly 304, back cover 312, main logic board 332; page 5, where respective temperature detected by each sensor 340 may be affected or otherwise changed by the operation of the component to which the sensor 340 is attached. For example, sensor 340D may be attached to logic board 332, and may be caused by sensor 340A attached to display assembly 324 as logic board 332 may generate heat during operation. A temperature relatively higher than the detected temperature can be detected);
memory (page 7, lines 24-25, where processor and memory of each electronic device); and
at least one processor (#600, page 7, lines 24-25, where processor and memory of each electronic device),
comprising processing circuitry, operatively connected to the at least one first component, the at least one second component (page 2, lines 16-18, where components within an electronic device, such as a processor, memory, antennas, and other components, may be sealed within the housing to protect the components), the
first temperature sensor, the second temperature sensor (page 2, lines 25-27, where electronic device that may utilize one or more temperature sensors disposed within the electronic device), and the memory, wherein at least one processor is configured to:
identify a prediction model related to prediction of the outside temperature
stored in the memory (page 3, lines 1-2, where the data set to generate models that best or most accurately predict the temperature of the external environment based on the temperatures measured within the electronic device),(page 7, lines 27-29, where data set or database may be stored on a local or remote computer or server, and each generated model may be uploaded from the assembly devices or test structures to the processor and memory of each electronic device);
predict the outside temperature corresponding to the acquired first
temperature and the acquired second temperature based on the identified prediction model (Fig. 3A-5, page 3, lines 1-10, where generate models that best or most accurately predict the temperature of the external environment based on the temperatures measured within the electronic device); (page 7, lines 15-18, where two sensors may be utilized to predict or estimate a temperature of an environment external to electronic device 300 . In other words, any number of sensors 340A - 340E or subsets of sensors 340A - 340E may be utilized to predict or estimate the temperature of the environment external to electronic device 300).
Libeer does not disclose acquire a first temperature of the at least one first component through the first temperature sensor according to a specified period;
acquire a second temperature of the at least one second component through
the second temperature sensor according to a specified period.
Modi discloses acquire a first temperature of the at least one first component through the first temperature sensor according to a specified period (Fig. 9, para [0094], where FIG. 9, the first temperature sensor curve 910, the second temperature sensor curve 912, and the third temperature sensor curve 914 may all begin to increase as the thermostat begins to be heated by components operating in the high-power mode. Note that as described above, the curve corresponding to each temperature sensor changes at different rates and times based on their location and thermal isolation within the thermostat housing relative to the heat-generating components, e.g., from Fig. 9, the first temperature sensor curve 910 corresponds to the time period t1-t2, t2-t3, t3-t4);
acquire a second temperature of the at least one second component through
the second temperature sensor according to a specified period (Fig. 9, para [0094], where FIG. 9, the first temperature sensor curve 910, the second temperature sensor curve 912, and the third temperature sensor curve 914 may all begin to increase as the thermostat begins to be heated by components operating in the high-power mode. Note that as described above, the curve corresponding to each temperature sensor changes at different rates and times based on their location and thermal isolation within the thermostat housing relative to the heat-generating components, e.g., see second temperature sensor curve 912 and corresponding specific time intervals).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide specific period for first and second temperature sensors, as taught by Modi into Libeer in order to enable proactive maintenance, enhance safety, support compliance, and optimize performance.
Regarding Claims 5 and 15, Libeer and Modi disclose the electronic device of claim 1, further Libeer discloses wherein the determined mathematical model is determined based on temperature data related to the at least one first component, temperature data related to the at least one second component, a third temperature configured to correspond to the outside temperature, and the prediction parameter data (page 2, lines 31-48, where temperature measurements taken by a temperature sensor to monitor an operating temperature of a component (e.g, a processor) are also used to estimate a temperature of an external environment (i.e, outside a housing of an electronic device). can be utilized. Thus, a single measurement taken by the sensor can be utilized to determine the temperature proximate to the sensor as well as the temperature outside the electronic device… data set may also include a respective temperature measurement for each of the sensors, and an external environment temperature at which the respective temperature measurements were taken, page 3, lines 1-6, where the data set to generate models that best or most accurately predict the temperature of the external environment based on the temperatures measured within the electronic device. ), etc.) and machine learning techniques can be used to correlate or compare the temperature of such environments. In other words, adjustment factors and scalers may be applied to temperatures measured by one or more temperature sensors inside the electronic device to determine a temperature of an environment external to the electronic device), and
wherein the learning scenario comprises a scenario where heat is generated inside the electronic device in lines response to at least one of the at least one first component and the at least one second component are driven (page 3, para 4, where the electronic device 100 can include a housing 102 that can accommodate operating components).
Regarding Claim 14, Libeer and Modi disclose the method of claim 10, further Libeer discloses wherein the specified mathematical models is determined based on temperature data related to the at least one first component, temperature data related to the at least one second component, a third temperature configured to correspond to the outside temperature, and the prediction parameter data (page 2, lines 31-48, where temperature measurements taken by a temperature sensor to monitor an operating temperature of a component (e g, a processor) are also used to estimate a temperature of an external environment (I e, outside a housing of an electronic device). can be utilized. Thus, a single measurement taken by the sensor can be utilized to determine the temperature proximate to the sensor as well as the temperature outside the electronic device…data set may also include a respective temperature measurement for each of the sensors, and an external environment temperature at which the respective temperature measurements were taken, page 3, lines 1-6, where the data set to generate models that best or most accurately predict the temperature of the external environment based on the temperatures measured within the electronic device), etc.) and machine learning techniques can be used to correlate or compare the temperature of such environments. In other words, adjustment factors and scalers may be applied to temperatures measured by one or more temperature sensors inside the electronic device to determine a temperature of an environment external to the electronic device).
Regarding Claims 7 and 17, Libeer and Modi disclose the electronic device of claim 1, wherein at least one processor is configured to:
Libeer discloses identify a first temperature change range in which the first temperature of the at least one first component varies and a second temperature change range in which the second temperature of the at least one second component varies (page 10, para 3, where first predicted temperature may be compared to a range of predicted temperature values. If the first predicted temperature is within the range of predicted temperature values, the first predicted temperature may be verified or verified… if the first predicted temperature is outside (e.g, above or below the range) of expected temperature values, then process 600 returns to a first subset of sensors, e.g., subset of sensor corresponds to the predicted temperature values within a range); (abstract, where set of temperature sensors may be located adjacent to or attached to components of the portable electronic device); and
at least partially initialize an application related to the prediction of the outside temperature based on at least one of the first temperature change range and the second temperature change range exceeding a specified threshold value (page 10, para 4, if the first predicted temperature is outside the range of expected temperature values, the process 600 may include selecting a second subset of sensors from among the set of temperature sensors, e.g., subset of sensors is comprising the temperature of first and second sensors, range corresponds to the each sensor of the subset and further thresholds values corresponds to the range value);(page 10, para 3, If the first predicted temperature is within the range of predicted temperature values, the first predicted temperature may be verified or verified. However, if the first predicted temperature is outside (e g, above or below the range) of expected temperature values).
Libeer does not disclose identify a first temperature and a second temperature according to a specified period.
Modi discloses identify a first temperature and a second temperature according to a specified period (Fig. 9, para [0094], where FIG. 9, the first temperature sensor curve 910, the second temperature sensor curve 912, and the third temperature sensor curve 914 may all begin to increase as the thermostat begins to be heated by components operating in the high-power mode. Note that as described above, the curve corresponding to each temperature sensor changes at different rates and times based on their location and thermal isolation within the thermostat housing relative to the heat-generating components, e.g., from Fig. 9, the first temperature sensor curve 910 corresponds to the time period t1-t2, t2-t3, t3-t4).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide specific period for first and second temperature sensors, as taught by Modi into Libeer in order to enables proactive maintenance, enhances safety, supports compliance, and optimizes performance.
Regarding Claims 10 and 20, Libeer discloses a method of predicting an outside temperature by an electronic device, the method comprising:
identifying a prediction model related to prediction of the outside temperature stored in a memory (page 3, lines 1-2, where the data set to generate models that best or most accurately predict the temperature of the external environment based on the temperatures measured within the electronic device.)),(page 7, lines 27-29, where data set or database may be stored on a local or remote computer or server, and each generated model may be uploaded from the assembly devices or test structures to the processor and memory of each electronic device);
predicting the outside temperature corresponding to the acquired first and second temperatures based on the identified prediction model, wherein the temperatures of the at least one first component and the at least one second component vary differently based on the operation of the electronic device Fig. 3A-5, page 3, lines 1-10, where generate models that best or most accurately predict the temperature of the external environment based on the temperatures measured within the electronic device); (page 7, lines 15-18, where two sensors may be utilized to predict or estimate a temperature of an environment external to electronic device 300 . In other words, any number of sensors 340A - 340E or subsets of sensors 340A - 340E may be utilized to predict or estimate the temperature of the environment external to electronic device 300).
Libeer does not discloses acquiring a first temperature of at least one first component through a first temperature sensor according to a specified period;
acquiring a second temperature of at least one second component through a second temperature sensor according to a specified period.
Modi discloses acquiring a first temperature of the at least one first component through the first temperature sensor according to a specified period (Fig. 9, para [0094], where FIG. 9, the first temperature sensor curve 910, the second temperature sensor curve 912, and the third temperature sensor curve 914 may all begin to increase as the thermostat begins to be heated by components operating in the high-power mode. Note that as described above, the curve corresponding to each temperature sensor changes at different rates and times based on their location and thermal isolation within the thermostat housing relative to the heat-generating components, e.g., from Fig. 9, the first temperature sensor curve 910 corresponds to the time period t1-t2, t2-t3, t3-t4;
acquiring a second temperature of the at least one second component through
the second temperature sensor according to a specified period (Fig. 9, para [0094], where FIG. 9, the first temperature sensor curve 910, the second temperature sensor curve 912, and the third temperature sensor curve 914 may all begin to increase as the thermostat begins to be heated by components operating in the high-power mode. Note that as described above, the curve corresponding to each temperature sensor changes at different rates and times based on their location and thermal isolation within the thermostat housing relative to the heat-generating components, e.g., see second temperature sensor curve 912 and corresponding specific time intervals).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide specific period for first and second temperature sensors, as taught by Modi into Libeer in order to enables proactive maintenance, enhances safety, supports compliance, and optimizes performance.
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Libeer in view of Modi, as applied above and further in view of Im et al., (US Pub. 20130259092 A1), hereinafter Im.
Regarding claims 2 and 11, Libeer and Modi disclose the electronic device of claim 1, wherein at least one processor is configured to:
Libeer and Modi do not disclose execute an application related to prediction of the outside temperature stored in the memory in response to a booting operation of the electronic device; and
identify the prediction model based on the executed application.
Im discloses an application related to prediction of the outside temperature stored in the memory in response to a booting operation of the electronic device (para [0051], where non-memory chip of the device used during the execution of a particular operation (e.g., booting the device), and with respect to a node corresponding to a memory chip of the device used during particular operations (e.g., storing data in memory of the device and erasing data from memory of the device); para [0069], where the device is a CPU, the CPU may perform a booting operation).
identify the prediction model based on the executed application (para [008], where temperature prediction circuit may be provided inside or outside the device, and may be mounted on a substrate with the device).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide a booting operation of the electronic device, as taught by Im in combination of Libeer and Modi in order to provide the system initialization, hardware verification, and error detection.
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Libeer in view of Modi, as applied above and further in view of Franzini et al., (US Pat. 11613240B2), hereinafter Franzini and Qiu et al., (CN105388928A), hereinafter Qiu.
Regarding Claim 3, Libeer and Modi disclose the electronic device of claim 1, wherein at least one processor is configured to:
Libeer and Modi do not disclose identify a difference value between the first temperature and the second temperature; and
reflect the identified difference value in the prediction model to predict the outdoor temperature based on the prediction model.
Qiu discloses identify a difference value between the first temperature and the second temperature (Page 4, last paragraph, where the internal temperature is based at 321 the sensed temperature of the first temperature sensor, and the temperature difference is sensed by first temperature sensor 321 and second temperature sensor 322 of the temperature difference value).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide difference value between the first temperature and the second temperature, as taught by Qiu in combination of Libeer and Modi in order to more accurately detect the change of temperature inside the device and predict the operation of the component under heat.
Franzini discloses reflect the identified [difference value]/(combining values) in the prediction model to predict the outdoor temperature based on the prediction model (Claim 1, where inputting actual temperature sensor measurement data of said component and said predicted temperature into an estimation algorithm, wherein said estimation algorithm combines said actual temperature sensor data and said predicted temperature and generates an estimated brake temperature of said component based on said combined inputs, e.g., brake temperature corresponds to the outdoor temperature).
Franzini discloses predict the outdoor temperature based on the prediction model (based on the combined inputs), not based on a difference value.
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to predict outdoor temperature, as taught by Qiu based on the indifference temperature values of Qiu and further in combination of Libeer and Modi in order to improve accuracy of modeling and enhanced thermal management, helps maintain optimal operating temperatures and improve energy efficiency.
Regarding Claim 12, Libeer and Modi disclose the method of claim 10,
Libeer and Modi do not disclose wherein the predicting of the outside temperature comprises:
identifying a difference value between the first temperature and the second
temperature; and
predicting the outside temperature based on the prediction model by
reflecting a conformed difference value in the prediction model.
Qiu discloses identify a difference value between the first temperature and the second temperature (Page 4, last paragraph, where the internal temperature is based at 321 the sensed temperature of the first temperature sensor, and the temperature difference is sensed by first temperature sensor 321 and second temperature sensor 322 of the temperature difference value).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide difference value between the first temperature and the second temperature, as taught by Qiu in combination of Libeer and Modi in order to more accurately detect the change of temperature inside the device and predict the operation of the component under heat.
Franzini discloses predicting the outside temperature based on the prediction model by
reflecting a conformed[ difference value]/(combining values) in the prediction model (Claim 1, where inputting actual temperature sensor measurement data of said component and said predicted temperature into an estimation algorithm, wherein said estimation algorithm combines said actual temperature sensor data and said predicted temperature and generates an estimated brake temperature of said component based on said combined inputs, e.g., brake temperature corresponds to the outdoor temperature).
Franzini discloses predict the outdoor temperature based on the prediction model (based on the combined inputs), not based on a difference value.
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to predict outdoor temperature, as taught by Qiu based on the indifference temperature values of Qiu and further in combination of Libeer and Modi in order to improve accuracy of modeling and enhanced thermal management, helps maintain optimal operating temperatures and improve energy efficiency
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Libeer in view of Modi, as applied above and further in view of Kim (KR 20210155642), hereinafter Kim and Cho (KR20100005880A), hereinafter Cho.
Regarding Claims 4 and 13, Libeer and Modi disclose the electronic device of claim 1, further but Libeer and Modi do not disclose wherein at least one processor is configured to:
drive at least one of the at least one first component and the at least one second component based on a configured learning scenario within a test space configured to a third temperature;
identify a first test temperature of the at least one first component and a second test temperature of the at least one second component according to a specified period;
reflect the first test temperature and the second test temperature in a specified mathematical model;
determine prediction parameter data included in the specified mathematical model;
implement the prediction model by substituting the determined prediction parameter data into the specified mathematical model; and
store the implemented prediction model in the memory.
Kim discloses drive at least one of the at least one first component and the at least one second component based on a configured learning scenario( page 3, para 1, where learning algorithm may include, for example, supervised learning, unsupervised learning, semi supervised learning, or reinforcement learning, and further see page 9, para 7 and 8, where performing various scenarios in the specific region)) configured to a third temperature (page 8, para 8(last paragraph on page 8), where the temperature measured by the temperature sensors 311 to 316 and the surface temperature of the electronic device 310 may not match, the specific regions 321 to 328 to analyze the temperature are selected from the electronic device 310) may be an area in which the temperature sensor is disposed. For example, the specific regions 321 to 328 in which the temperature is to be predicted may be regions in which the temperature of the electronic device 310 can be increased, e.g., surface temperature (temperature of electronic device 310) corresponds to the third temperature(e.g., temperature in specific region see Fig. 3, # 321-328));
reflect the first test temperature and the second test temperature in a specified mathematical model (page 9, paragraph 2-7, where According to various embodiments, the temperatures Y1 to Y8 of the specific regions 321 to 328 may be derived through [Equation 1].Y1 = a1X1 + b1X2 + c1X3 + d1X4 + e1X5 + f1X6 + g1
.Math.Y8 = a8X1 + b8X2 + c8X3 + d8X4 + e8X5 + f8X6 + g8 Here, X1 to X6 may be temperatures measured using the temperature sensors 311 to 316 );
determine prediction parameter data included in the specified mathematical model(page 9, para 2, where the temperatures Y1 to Y8 of the specific regions 321 to 328 may be derived through equation 1, Y1 = a1X1 + b1X2 + c1X3 + d1X4 + e1X5 + f1X6 + g1 e.g., a1, b1, c1, d1, e1 f1 is prediction parameters);
implement the prediction model by substituting the determined prediction parameter data into the specified mathematical model (page 11, last paragraph and Page 12, paragraph 1, where electronic device 101 may derive (e g, predict) the temperature of a specific region of the electronic device 101 using a linear regression method. The external electronic device 108 may derive the temperature of another specific region or/and the entire region of the electronic device 101 by using the temperature of the specific region of the electronic device 101 derived by the electronic device 101 . . The external electronic device 108 may transmit the temperature of another specific region or/and the entire region derived to the electronic device 101 . electronic device 101 may generate a heating image ((b) of FIG. 5) using the received temperature of another specific region or/and the entire region) (Page 9, para 8, where according to various embodiments, the electronic device 310 may check (e.g, predict) a heat generation image of the electronic device 310 using the temperature sensors 311 to 316 based on modeling);
store the implemented prediction model in the memory.(page 9, para 8, where the electronic device 310 may store the temperature (e g, the temperature of the thermal image 320 or the temperature measured by the temperature sensors 311 to 316 ) measured for each scenario in the specific regions 321 to 328).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to drive at least one of the at least one first component and the at least one second component based on a configured a third temperature, as taught by Kim in combination of Libeer and Modi in order to identifies safe operating limits.
Modi discloses identify a first test temperature of the at least one first component and a second test temperature of the at least one second component according to a specified period (Fig. 9, para [0094], where FIG. 9, the first temperature sensor curve 910, the second temperature sensor curve 912, and the third temperature sensor curve 914 may all begin to increase as the thermostat begins to be heated by components operating in the high-power mode. Note that as described above, the curve corresponding to each temperature sensor changes at different rates and times based on their location and thermal isolation within the thermostat housing relative to the heat-generating components, e.g., from Fig. 9, the first temperature sensor curve 910 corresponds to the time period t1-t2, t2-t3, t3-t4).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide specific period for first and second temperature sensors, as taught by Modi into Libeer in order to enables proactive maintenance, enhances safety, supports compliance, and optimizes performance.
Cho discloses a test space (page 8, paragraph 10, where meanwhile, FIGS. 4A and 4B illustrate comparative experimental data of a general surface temperature measuring device according to an external temperature change and an internal body temperature estimator according to the present invention, and FIG. 4A is a change in room temperature over time. 4B is a graph showing changes in surface temperature and body temperature according to changes in room temperature).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide a test space, as taught by Cho in combination of Libeer and Modi in order to identifies safe operating limits and improve reliability and field performance.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Libeer (KR20220143597A), hereinafter Libeer in view of Bang et al., (KR 20220020096A), hereinafter Bang.
Regarding Claims 6 and 16, Libeer and Modi disclose the electronic device of claim 1, but do not disclose wherein at least one processor is configured to:
identify that the electronic device is updated in relation to the at least one first component and the at least one second component; and
re-determine prediction parameter data based on the configured learning scenario in response to the update of the electronic device.
Bang discloses identify that the electronic device is updated in relation to the at least one first component and the at least one second component (page 14, paragraph 4, where processor 120 may predict and update the external temperature); (page 14, para 1, where scenario may include a plurality of sub-scenarios in which at least one component among a plurality of components generating heat inside the electronic device 101); and
re-determine prediction parameter data based on the configured learning scenario in response to the update of the electronic device (Page 10, paragraph 5, where the processor 120 may update the scenario using a plurality of machine learning parameters downloaded from the server 108).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide electronic device is updated in relation to the at least one first component and the at least one second component, as taught by Bang in combination of Libeer and Modi in order to identifies safe operating limits and improve reliability and field performance.
Objection / Allowable Subject Matter
Claims 8, 9, 18 and 19 are 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.
1) Examiner note regarding the prior art of the record:
Regarding Claims 8 and 18 :
The prior art of record does not teach or fairly suggest an electronic device having the steps of “the at least one first component comprises a 1-1st component and a 1-2nd component having priority configured to be relatively lower than a priority of the 1-1st component, and wherein at least one processor is configured to:
identify a 1-1st temperature change range in which the temperature of the 1-1st component varies and a 1-2nd temperature change range in which the temperature of the 1-2nd component varies, according to a specified period; reflect the 1-2nd component in the prediction model instead of the 1-1st component based on the 1-1st temperature change range exceeding the specified threshold value; and
predict the outside temperature corresponding to the temperature of the 1-2nd component based on the prediction model.”
Regarding Claims 9 and 19:
The prior art of record does not teach or fairly suggest an electronic device having the steps of “identify a component having a relatively higher priority between the 1-1st component and the 1-2nd component; and
predict the outside temperature corresponding to the temperature of the high-priority component based on the prediction model”.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KALERIA KNOX whose telephone number is (571)270-5971. The examiner can normally be reached M-F 8am-5pm.
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/KALERIA KNOX/
Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights