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 .
DETAILED ACTION
This is a F.A.O.M, in responses to Patent Application filed 12/13/2022; is a National Stage entry of PCT/JP2020/023482, International Filing Date: 06/15/2020. Claim(s) 1-11 are pending. Claim(s) 1 and 11 are independent.
In addition, 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 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.
Information Disclosure Statement
A signed and dated copy of applicant’s IDS, which was 01/10/2024, 12/19/2024, 12/13/2024 and 08/22/2025 is/are attached to this Office Action.
Specification
The disclosure is objected to because of the following informalities: The use of the term(s) “IoT”, “NPL1”, “CNN” and “CPU”, ... has been noted in this application. The term(s) should be accompanied by the generic terminology. Appropriate correction is required.
Claim Objections
Claim 5 objected to because of the following informalities: As amended the strike thought line fails to specify the deletion beginning and ending of the amended phrase (i.e., ... . Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The term(s) “more difficult”, “degree of certainty”, and “easier to encode” in claim(s) 2, 3 and 6 is/are a relative term(s) which render the claim(s) indefinite. The term(s) “more difficult”, “degree of certainty”, “easier to encode” is/are not defined by the claim(s) and the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-11 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because: The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more.
Step 1: YES (Claim(s) is/are process, machine, manufacture or composition of the matter). ... the processes and system(s) using an edge device and a server device, wherein the edge device includes processing circuitry configured to: “process processing” target data and “output a processing” result of the processing target data; “determine that the server device” is to “execute processing” related to the processing target data when an evaluation value for “evaluating” which of the edge device and the server device is to process the processing target data “satisfies” a condition, “determine” that the evaluation value is included in a range for determining that processing is to be executed by the edge device when the processing result of the processing target data satisfies a ”predetermined evaluation”, and “output the processing result” of the processing target data processed; and “transmit data” that causes the server device to execute the processing related to the processing target data when determining that the server device is to execute the processing related to the processing target data ... and therefore, fall into one of the four categories of patent eligible subject matter (process, machine, manufacture or composition of the matter).
Step 2A : Prong One: ( whether a claim recites a judicial exception ?) the claim(s) recite processes and system(s) using an edge device and a server device, ...to “process processing” target data and “output a processing” result of the processing target data; “determine that the server device” is to “execute processing” related to the processing target data when an evaluation value for “evaluating” which of the edge device and the server device is to process the processing target data “satisfies” a condition, “determine” that the evaluation value is included in a range for determining that processing is to be executed by the edge device when the processing result of the processing target data satisfies a ”predetermined evaluation”, and “output the processing result” of the processing target data processed; and “transmit data” that causes the server device to execute the processing related to the processing target data when determining that the server device is to execute the processing related to the processing target data ...These limitation(s) recite mental processes and mathematical concepts (mathematical calculations)....since the evaluation value of an edge device using a high-speed and low-precision lightweight model (for example, DNN1) and a cloud (server device) using a low-speed and high-precision model (for example, DNN2) executes processing, by using an evaluation value... (See the Specs USPGPUB 20230224360A1 Para(s) 35-37) is a high level mathematical calculations (See the Specs USPGPUB 20230224360A1 Para(s) 68-82, i.e., the calculation of the “low-precision lightweight model” and “high-precision model” and “loss calculation”...) in order to evaluate the target data “satisfies” a condition ... causes the server device to execute the processing related to the processing target data when determining that the server device is to execute the processing related to the processing target data...moreover, the claim(s) recite only the idea of a solution or outcome i.e., (“caused” by the “predetermined evaluation” [“APPLY IT”]).
Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as “edge device”, “server device” and “processing circuitry”... to “execute processing” related to the processing target data when an evaluation value for “evaluating” which of the edge device and the server device is to process the processing target data “satisfies” a condition, “determine” that the evaluation value is included in a range for determining that processing is to be executed by the edge device when the processing result of the processing target data satisfies a ”predetermined evaluation”, and “output the processing result” of the processing target data processed; and “transmit data” that causes the server device to execute the processing related to the processing target data when determining that the server device is to execute the processing related to the processing target data ......
These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Step 2B: (Whether a Claim Amounts to Significantly More) ? The claim(s) recite additional limitation(s) “edge device”, “server device” and “processing circuitry”......These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not amount to significantly more than the abstract idea itself (MPEP 2106.05, 2106.04(d) and 2106.05(f)).
As to the dependent claim(s) 2-10, further recite, addition limitation(s) such as, more difficult, degree of certainty, a correct answer, time taken..., acquisition deadline, situation of resources, a time of the determination, compared with other data, intermediate output value, easier to encode, server device are optimized, inference using a trained neural network, an intermediate layer, purpose of processing, and stores a result of analyzing the intermediate output value and the code for identifying the edge device in association with each other..., etc., These limitation(s) only amounts to mere instructions to implement the abstract idea ...and do not include elements that amount to significantly more than the abstract idea and are also rejected under the same rational.
Accordingly, claims 1-11 fail to recite statutory subject matter, as defined in 35 U.S.C. 101.
Claims Rejection – 35 U.S.C. 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 of this title, 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) 1-11 rejected under 35 U.S.C. 103 as being unpatentable over Sobol et al., (“US 20190209022 A1” filed 12/27/2018 [hereinafter “Sobol”], in view of Hasegawa et al., (“US 20210192183 A1” filed 09/11/2018 [hereinafter “Hasegawa”],
Independent Claim 1, Sobol teaches: A processing system performed using an edge device and a server device, wherein the edge device includes processing circuitry (in Sobol the Abstract and Para 121, i.e., a wearable electronic device include a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources... The device includes the sensors to collect environmental data, activity data and physiological data. ...then transmit some or all of its acquired data to a larger system, including a cloud-based server (i.e., an edge device and server)...) Sobol further teaches: configured to: process processing target data and output a processing result of the processing target data; ... and output the processing result of the processing target data processed ...; (in Sobol Para(s) 2 and 31, i.e., wearable electronic device and corresponding system for monitoring the location, environmental, activity and physiological (LEAP) data of a wearer of the device, and more particularly to a wearable electronic device that wirelessly communicates such data in a hybrid mode in order to allow such data to be used to proactively identify salient indicators of changing health of the wearer of the device....wherein the machine learning model be a neural network that includes numerous input nodes each of which includes a memory location for storing an input value that corresponds to a portion of a respective acquired LEAP data, of which is connected to at least one of the plurality of input nodes and includes computational instructions, implemented in machine codes of the respective processor, for computing a plurality of output values, respectively, and numerous output nodes each of which includes a memory location for storing the output value produced by the machine learning classification model...) Sobol further teaches: and transmit data that causes the server device to execute the processing related to the processing target data when determining that the server device is to execute the processing related to the processing target data; (in Sobol Para(s) 30-31, 121 and 242, describing the steps said enable edge computing (also referred to as fog computing) where the wearable electronic device functions as an edge device where some or all of the sensors 121 are deployed) that would allow the wearable electronic device 100 to function as an IoT device as part of a low-cost, highly-adaptable private network and corresponding system for monitoring the location, environmental, activity and physiological (LEAP) data of a wearer of the device, and more particularly to a wearable electronic device that wirelessly communicates such data to other equipment either within system 1 or beyond that is acting as a mini server and make all data decisions on its own to then send the data to external devices such as a phone or web browser... Significantly, because the size of the data packages being sent from the wearable electronic device 100 to the gateway 300, backhaul server 400 and cloud 500 is relatively small, communication-based bandwidth problems may be avoided...)
It is noted, Sobol is related to wearable electronic device include a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources... signal strength are passed to the cloud 500 through the wearable electronic device 100, gateway 300 and the backhaul server 400, the cloud 500 is able to determine a ground truth through mapping the UUID of a corresponding one of the BLE beacons 200 to a physical location... However, Sobol fail to teach the limitations, said, when an evaluation value for evaluating which of the edge device and the server device is to process the processing target data satisfies a condition, determine that the evaluation value is included in a range for determining that processing is to be executed by the edge device when the processing result of the processing target data satisfies a predetermined evaluation, and output the processing result of the processing target data processed;
But the combination of Sobol and Hasegawa teach these limitation in (Hasegawa Para(s) 43-49, which is describing the steps said, when an evaluation value for evaluating which of the edge device and the server device is to process the processing target data satisfies a condition, determine that the evaluation value is included in a range for determining that processing is to be executed by the edge device when the processing result of the processing target data satisfies a predetermined evaluation, ...; (See Hasegawa in Para(s) 35-38 and Fig. 1, which is illustrated describing the edge server is formed as a deceive that is integrated with the terminal apparatus (edge device), wherein the edge server is arranged within a local network within which the terminal apparatus is located, and the cloud server is arranged in an external network...Moreover, in Hasegawa Para(s) 43-49, further mentions the detection data that has been acquired by the sensor(s) (i.e., edge devices), the learning data transmission unit transmits only the detection data that satisfies predetermined conditions instead of transmitting all the detection data, wherein the server device is to process the processing detection data that satisfied,(i.e., determine that the evaluation value is included in a range for determining that processing is to be executed by the edge device) when the processing result of the processing target data satisfied a predetermined evaluation, and in this case where a matching degree that exceeds a predetermined determination reference value is acquired as a result of the verification, the verification is determined to be successful (registration data that matches the detection data exists within the verification data storage unit), or otherwise the verification is determined to have failed (registration data that matches the detection data does not exist within the verification data storage unit)...)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Sobol’s wearable electronic device and server, to include a means of said, when an evaluation value for evaluating which of the edge device and the server device is to process the processing target data satisfies a condition, determine that the evaluation value is included in a range for determining that processing is to be executed by the edge device when the processing result of the processing target data satisfies a predetermined evaluation, ...as taught by Hasegawa, that transmits only the detection data that satisfies predetermined conditions instead of transmitting all the detection data...[Hasegawa Para 43]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 2 , Sobol and Hasegawa further teach: wherein the evaluation value has a stronger tendency to fall in a range for determining that evaluation is to be executed by the server device when the processing for the processing target data becomes more difficult; (in Hasegawa Para(s) 43-49, describing the steps said, detection data that has been acquired by the sensor(s) (i.e., edge devices), the learning data transmission unit transmits only the detection data that satisfies predetermined conditions instead of transmitting all the detection data, wherein the server device is to process the processing detection data that satisfied,(i.e., determine that the evaluation value is included in a range for determining that processing is to be executed by the edge device) when the processing result of the processing target data satisfied a predetermined evaluation, and in this case where a matching degree that exceeds a predetermined determination reference value is acquired as a result of the verification, the verification is determined to be successful (registration data that matches the detection data exists within the verification data storage unit), or otherwise the verification is determined to have failed (registration data that matches the detection data does not exist within the verification data storage unit)...). Moreover, Hasegawa Para(s) 40-42, further mentions the verification is determined to have failed (...data that matches the detection data does not exist within the verification data storage unit 202). In addition, the verification unit 203 is configured to request a verification to the cloud server 100 so as to gain the verification results in the case where the verification has failed; wherein the detection data transmission unit 205 transmits the detection data that has been acquired by the sensor 300 (or the data on which preprocessing for data analysis has been carried out) to the cloud server 100. Here, the detection data transmission unit 205 transmits the detection data only in the case where the verification thereof is determined to have failed instead of transmitting all the detection data). (seer the 112 rejections for support of these interpretation).
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Sobol’s wearable electronic device and server, to include a means of said, wherein the evaluation value has a stronger tendency to fall in a range for determining that evaluation is to be executed by the server device when the processing for the processing target data becomes more difficult, ...as taught by Hasegawa, that transmits only the detection data that satisfies predetermined conditions instead of transmitting all the detection data...[Hasegawa Para 43]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 3, Sobol and Hasegawa further teach: wherein the evaluation value is a value indicating a degree of certainty of whether a result of processing the processing target data in the edge device is a correct answer; (in Hasegawa Para(s) 43-49, describing the steps said, detection data that has been acquired by the sensor(s) (i.e., edge devices), the learning data transmission unit transmits only the detection data that satisfies predetermined conditions instead of transmitting all the detection data, wherein the server device is to process the processing detection data that satisfied,(i.e., determine that the evaluation value is included in a range for determining that processing is to be executed by the edge device) when the processing result of the processing target data satisfied a predetermined evaluation, and in this case where a matching degree that exceeds a predetermined determination reference value is acquired as a result of the verification, the verification is determined to be successful (registration data that matches the detection data exists within the verification data storage unit), or otherwise the verification is determined to have failed (registration data that matches the detection data does not exist within the verification data storage unit (seer the 112 rejections for support of these interpretation). Moreover, Hasegawa Para(s) 40-42, further mentions the verification is determined to have failed (...data that matches the detection data does not exist within the verification data storage unit 202). In addition, the verification unit 203 is configured to request a verification to the cloud server 100 so as to gain the verification results in the case where the verification has failed; wherein the detection data transmission unit 205 transmits the detection data that has been acquired by the sensor 300 (or the data on which preprocessing for data analysis has been carried out) to the cloud server 100. Here, the detection data transmission unit 205 transmits the detection data only in the case where the verification thereof is determined to have failed instead of transmitting all the detection data). (seer the 112 rejections for support of these interpretation).
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Sobol’s wearable electronic device and server, to include a means of said, wherein the evaluation value is a value indicating a degree of certainty of whether a result of processing the processing target data in the edge device is a correct answer, ...as taught by Hasegawa, that transmits only the detection data that satisfies predetermined conditions instead of transmitting all the detection data...[Hasegawa Para 43]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 4, Sobol and Hasegawa further teach: wherein the evaluation value is determined based on ANY ONE OF a time taken to obtain the processing result of the processing target data, an acquisition deadline of the processing result of the processing target data, a use situation of resources of the edge device at a time of the determination, and whether the processing target data is data where an event occurs as compared with other data; (in Sobo Para 32, i.e., the wearable electronic device and the non-transitory computer readable medium of the backhaul server and that are operated upon by the respective processor further includes a machine code that compares at least a portion of the LEAP data to baseline data that forms a data structure that is stored on one or both of the non-transitory computer readable medium of the wearable electronic device and the non-transitory computer readable medium of the backhaul server....).
Claim 5, Sobol and Hasegawa further teach: wherein the evaluation value is calculated based on an intermediate output value of processing of outputting the processing result of the processing target data, the processing being performed, and the processing circuitry is further configured to transmit the intermediate output value to the server device; (in Sobo Para 28, i.e., the data structures are made to correspond to various nodes of a machine learning model, such as the input, intermediate and output nodes of a neural network or the input and output nodes of a K-means clustering approach, and in one form may be implemented in the memory of the respective computer readable medium or mediums. Such data structures may be operated upon by a search algorithm implemented in machine codes for the respective one of the wearable electronic device processor or backhaul server processor. Moreover, in Sobol Para(s) 30-31, 121 and 242, further describing the steps said enable edge computing (also referred to as fog computing) where the wearable electronic device functions as an edge device where some or all of the sensors 121 are deployed) that would allow the wearable electronic device 100 to function as an IoT device as part of a low-cost, highly-adaptable private network and corresponding system for monitoring the location, environmental, activity and physiological (LEAP) data of a wearer of the device, and more particularly to a wearable electronic device that wirelessly communicates such data to other equipment either within system 1 or beyond that is acting as a mini server and make all data decisions on its own to then send the data to external devices such as a phone or web browser ...)
Claim 6, Sobol and Hasegawa further teach: wherein the processing circuitry is further configured to encode data to be transmitted to the server device, wherein as the intermediate output value, (in Sobo Para 28, i.e., the data structures are made to correspond to various nodes of a machine learning model, such as the input, intermediate and output nodes of a neural network or the input and output nodes of a K-means clustering approach, and in one form may be implemented in the memory of the respective computer readable medium or mediums. Such data structures may be operated upon by a search algorithm implemented in machine codes for the respective one of the wearable electronic device processor or backhaul server processor.)
Moreover, Hasegawa further teach: .... a value that is easier to encode than other intermediate output values is selected from among a plurality of the intermediate output values output in the processing of outputting the processing result of the processing target data; (in Hasegawa Para(s) 43-49, describing the steps said, detection data that has been acquired by the sensor(s) (i.e., edge devices), the learning data transmission unit transmits only the detection data that satisfies predetermined conditions instead of transmitting all the detection data, wherein the server device is to process the processing detection data that satisfied,(i.e., determine that the evaluation value is included in a range for determining that processing is to be executed by the edge device) when the processing result of the processing target data satisfied a predetermined evaluation, and in this case where a matching degree that exceeds a predetermined determination reference value is acquired as a result of the verification, the verification is determined to be successful (registration data that matches the detection data exists within the verification data storage unit), or otherwise the verification is determined to have failed (registration data that matches the detection data does not exist within the verification data storage unit (seer the 112 rejections for support of these interpretation).
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Sobol’s wearable electronic device and server, to include a means of said, wherein ..., a value that is easier to encode than other intermediate output values is selected from among a plurality of the intermediate output values output in the processing of outputting the processing result of the processing target data, ...as taught by Hasegawa, that transmits only the detection data that satisfies predetermined conditions instead of transmitting all the detection data...[Hasegawa Para 43]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 7, Sobol and Hasegawa further teach: wherein there are a plurality of edge devices, and processing performed by each edge device and processing performed by the server device are optimized such that the server device performs the processing related to the processing target data on data transmitted from at least one of the plurality of edge devices; (in Sobol the Abstract and Para 121, i.e., a wearable electronic device include a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources... The device includes the sensors to collect environmental data, activity data and physiological data. ...then transmit some or all of its acquired data to a larger system, including a cloud-based server (i.e., an edge device and server)...Moreover, Sobo Para 265, further describing the hyperparameter optimization involves finding the hyperparameters of a particular machine learning algorithm that produce optimum performance when measured on a validation data set...Moreover, Sobol Para(s) 30-31, 121 and 242, further describing the steps said enable edge computing (also referred to as fog computing) where the wearable electronic device functions as an edge device where some or all of the sensors 121 are deployed) that would allow the wearable electronic device 100 to function as an IoT device as part of a low-cost, highly-adaptable private network and corresponding system for monitoring the location, environmental, activity and physiological (LEAP) data of a wearer of the device, and more particularly to a wearable electronic device that wirelessly communicates such data to other equipment either within system 1 or beyond that is acting as a mini server and make all data decisions on its own to then send the data to external devices such as a phone or web browser... Significantly, because the size of the data packages being sent from the wearable electronic device 100 to the gateway 300, backhaul server 400 and cloud 500 is relatively small, communication-based bandwidth problems may be avoided...)
Claim 8, Sobol and Hasegawa further teach: wherein the processing of outputting the processing result of the processing target data is inference using a trained neural network,...; (in Sobo Para 260, i.e., machine learning model used to predict an individual's health condition based on the acquired LEAP data can take in the raw input from sensors and pass the data to one or more of these intermediate hidden layer filters to process through weighted kernels that are trained to detect specific features with a high degree of correlation to a known quantity.... in order to provide direct output for use in multi-step forecasting.... Moreover, in Sobol Para 265, further mentions the training data set predicts an outcome... In this way, the algorithm is being trained without learning from the validation data set 1620....wherein when the machine learning model is in the form of the neural network 2000 of FIG. 7, the validation data set 1620 may also be used to tune the model's hyperparameters (that is to say, the number of hidden units) in view of the fact that the correct answer... and the intermediate output value is an output value of an intermediate layer of the trained neural network (in Sobo Para 28, i.e., the data structures are made to correspond to various nodes of a machine learning model, such as the input, intermediate and output nodes of a neural network or the input and output nodes of a K-means clustering approach, and in one form may be implemented in the memory of the respective computer readable medium or mediums. Such data structures may be operated upon by a search algorithm implemented in machine codes for the respective one of the wearable electronic device processor or backhaul server processor.
Claim 9, Sobol and Hasegawa further teach: wherein there are a plurality of server devices, and the processing circuitry is further configured to select one of the plurality of server devices to which data that causes the server device to execute the processing related to the processing target data is to be transmitted according to a purpose of processing the processing target data. (in Sobol the Abstract and Para 121, i.e., a wearable electronic device include a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources... The device includes the sensors to collect environmental data, activity data and physiological data. ...then transmit some or all of its acquired data to a larger system, including a cloud-based server (i.e., an edge device and server)...Moreover, Sobo Para 265, further describing the hyperparameter optimization involves finding the hyperparameters of a particular machine learning algorithm that produce optimum performance when measured on a validation data set...Moreover, Sobol Para(s) 30-31, 121 and 242, further describing the steps said enable edge computing (also referred to as fog computing) where the wearable electronic device functions as an edge device where some or all of the sensors 121 are deployed) that would allow the wearable electronic device 100 to function as an IoT device as part of a low-cost, highly-adaptable private network and corresponding system for monitoring the location, environmental, activity and physiological (LEAP) data of a wearer of the device, and more particularly to a wearable electronic device that wirelessly communicates such data to other equipment either within system 1 or beyond that is acting as a mini server and make all data decisions on its own to then send the data to external devices such as a phone or web browser... Significantly, because the size of the data packages being sent from the wearable electronic device 100 to the gateway 300, backhaul server 400 and cloud 500 is relatively small, communication-based bandwidth problems may be avoided...and further mentions in Sobo Para 154, i.e., allows the wearable electronic device 100 to have varying degrees of sensing functionality, depending on the end-use needs. For example, if a larger number (or a large number of different types) of physiological sensors 121C (shown in FIG. 2F) are needed for particular forms of bodily function monitoring, different modular packages or options made of differing combinations of such sensors may be placed within the housing 110 and support tray 120. In one form, this modularity may be enhanced through structure that can accept various smaller components or component sets. For example, and as shown in FIG. 2E, a ledge 114 may act as a mounting surface to a complementary-sized and shaped underside of the top plate 130, thereby promoting a volumetric space for the secure placement of one or more smaller components...)
Claim 10, Sobol and Hasegawa further teach: wherein the intermediate output value is irreversible with respect to the processing target data, the edge device transmits the intermediate output value together with a code for identifying the edge device, (in Sobo Para(s) 28, i.e., the data structures are made to correspond to various nodes of a machine learning model, such as the input, intermediate and output nodes of a neural network, ....Such data structures may be operated upon by a search algorithm implemented in machine codes for the respective one of the wearable electronic device processor or backhaul server processor. Moreover, Sobo Para 266, i.e., the testing data set 1630 is used to provide an unbiased evaluation of the performance of the final version of the algorithm that was fitted to the original training data set 1610. The testing data set 1630 is independent of—but statistically similar to—the training data set 1610, thereby minimizing any adverse effects from discrepancies in the data,..(in the BRI, is recognized as the intermediate output value is irreversible as claimed.). and the server device stores a result of analyzing the intermediate output value and the code for identifying the edge device in association with each other. (in Sobol Para 28, i.e., wearable electronic device and the backhaul server include a non-transitory computer readable medium, a processor and a set of machine codes selected from a native instruction set and operated upon by the processor. In addition, at least a portion of the set of machine codes are stored in the respective non-transitory computer readable medium or mediums. Data structures are made to correspond to various nodes of a machine learning model, such as the input, intermediate and output nodes of a neural network or the input and output nodes of a K-means clustering approach, and in one form may be implemented in the memory of the respective computer readable medium or mediums. Such data structures may be operated upon by a search algorithm implemented in machine codes for the respective one of the wearable electronic device processor or backhaul server processor...)
Regarding Independent claim 11 is fully incorporated similar subject of claim 1 cited above, and is similarly rejected along the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wantabe et al.,(“US 20200082225 A1 ” filed 09/11/2019, discloses a machine learning device that performs machine learning on the basis of state data acquired from an inspection target and label data indicating an inspection result related to the inspection target to generate a learning model; a learning model evaluation index calculation unit that calculates a learning model evaluation index related to the learning model generated by the machine learning device as an evaluation index used to evaluate the learning model; an inspection index acquisition unit that acquires an inspection index used in an inspection; and an index value determination unit that determines whether the learning model generated by the machine learning device satisfies the inspection index on the basis of the learning model evaluation index and the inspection index and outputs a result of the determination... [the Abstract].
Pagliari et al., NPL (“Optimal Input-Dependent Edge-Cloud Partitioning for RNN Inference” Published 2019 – Total 4 Pages By IEEE, relates to Recurrent Neural Networks (RNNs) such as those based on the Long-Short Term Memory (LSTM) architecture are state-of-the-art deep learning models for sequence analysis. Given the complexity of RNN-based inference, IoT devices typically offload this task to a cloud server. However, the complexity of RNN inference strongly depends on the length of the processed input sequence. Therefore, when communication time is taken into account, it may be more convenient to process short input sequences locally and only offload long ones to the cloud. In this paper, we propose a low-overhead runtime tool that performs this decision automatically. Results based on performance profiling of real edge and cloud devices show that our method is able to reduce the total execution time of the system by up to 20% compared to solutions that execute the RNN inference fully locally or fully in the cloud......[the Abstract].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUOC A TRAN whose telephone number is (571)272-8664. The examiner can normally be reached Monday-Friday 9am-5pm EST.
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/QUOC A TRAN/ Primary Examiner, Art Unit 2145