DETAILED ACTION
Claims 1, 4-9, and 12-24 are pending. Claims 2, 3, 10, and 11 were cancelled via amendment. Claims 21-24 were added via amendment.
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
Applicant's arguments filed 11/19/2025 have been fully considered but they are not persuasive.
Response to Arguments
Applicant's arguments filed 11/19/2025 have been fully considered but they are not persuasive.
On page 7 of the remarks, in regard to 35 USC 103, the applicant contends that the combination of Cella et al., Tullberg et al., and Chieh et al. fail to teach or render obvious all of the features in amended claim 1. In particular, the applicant notes that claim 1 now recites subject matter from now rejected claims 2 and 3. Claim 1, as amended, now recites (with emphasis added), “wherein the configuration information indicates one or more of the following content: … an identifier of a device that performs machine learning.” The applicant disagrees with the application of Cella, particularly the reporting packet (see fig. 3B) generated by the edge reporting device (see fig. 3A) as disclosed in paragraphs [0215] and [0216]. The applicant notes that the reporting packet 320 does not encompass received configuration information encompassing an identified of a device performing machine learning, but rather an edge device reporting the result of a measurement. Based on this reasoning, the applicant submits that claim 1 (and independent claims 9 and 18, which recite similar features) are allowable over the prior art.
The examiner respectfully disagrees. The examiner kindly submits that the applicant’s focus on the feature of previous dependent claim 3 takes the entirety of the claim rejection out a context. Notably, the relied upon teachings of Cella must also take into account the teachings of Chieh, which were relied upon in the rejection of previous dependent claim 2. Notably, paragraph [0023] of Chieh teaches a process to initiate/activate sensors (see fig. 4, step 230) and define a data package transmission interval (see step 250). The rationale to combine Chieh and Cella states that the procedure as described in Fig. 2 of Chieh would be performed by the edge router of Cella. In other words, Chieh teaches the additional setup procedure that Cella explicitly lacks. When the two references are seen as combined, the reporting packet of Cella takes on features from the configuration of Chieh, notably information regarding the edge router device that performs machine learning, so the sensor device may report according to the gateway (or edge) device’s preferred interval. Based on this reasoning, the examiner submits that the combined teachings of Chieh and Cella teach the claimed feature.
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, 4-9, 12-20, and 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (US PG Pub 2022/0191284, which was cited in the previous action) in view of Tullberg et al. (US PG Pub 2022/0051139, which was cited in the previous action) and Chieh Tseng et al. (US PG Pub 2020/0336878, hereinafter “Chieh”, which was cited in the previous action).
As per claim 1, Cella et al. teach an apparatus, comprising a processor coupled with a memory, wherein the processor is configured to execute instructions stored in the memory [Cella, Fig. 3, elements 302, 304, and 310, ¶ 0209, “FIG. 3A illustrates an example IoT sensor 102 (or sensor) according to embodiments of the present disclosure. Embodiments of the IoT sensor 102 may include, but are not limited to, one or more sensing components 302, one or more storage devices 304, one or more power supplies 306, one or more communication devices 308, and a processing device 310. In embodiments, the processing device 310 may execute an edge reporting module 312”, The IoT sensor device includes a processor (element 310) and a memory (element 304). See fig. 2A, where sensor devices (see element 102) reporting to an edge device (see element 104 and Fig. 6), where the edge device further reports to a backend system (see element 150 and Fig. 7).] to cause the apparatus to perform steps comprising:
sending a first data segment and first information to a first network device [Cella, ¶ 0214, “In embodiments, the edge reporting module 312 obtains raw sensor data from a sensing component 302 or from a storage device 304 and packetizes the raw sensor data into a reporting packet 320”, The IoT sensor device generates a reporting packet (shown as 322 in Fig. 3). The reporting packet (see also fig. 3B, ¶s 0215 and 0216) includes sensor data (or data segment, see element 326) and a destination of the packet (routing data, see element 324). The reporting packet is sent to the edge device (or first device, see fig. 2A, ¶ 0216). See also fig. 6, step 610 and ¶ 0266.], wherein the first data segment is one of one or more data segments [Cella, ¶ 0215, “a second field 326 indicating the sensor data”, The sensor data reporting is further disclosed in Fig. 6, step 610 (see ¶ 0266).] corresponding to first data used for machine learning [Cella, ¶ 0273, “At 714, the backend system 150 performs one or more backend operations on the decompressed sensor data. The backend operations may include storing the data, filtering the data, performing AI-related tasks on the sensor data, issuing one or more notifications in relation to the results of the AI-related tasks, performing one or more analytics related tasks, controlling an industrial component of the industrial setting 120”, The sensor data is used by the backend system to perform machine learning (or AI related tasks). The sensor data is sent from the edge device to the backend system (see fig. 6, step 616 and ¶ 0269).].
Cella et al. do not explicitly teach the first information indicates a target of the first data segment… receiving configuration information from the first network device or a second network device, wherein the configuration information is used to configure the apparatus to collect the first data.
Yet, Cella et al. do teaching a reporting packet, including a destination address of an edge device (see fig. 3, elements 320 and 324) and transmitting sensor data to a backend system for machine learning (see fig. 6, step 616)…wherein the configuration information indicates one or more of the following content: a cell in which the apparatus collects the first data; an area in which the terminal device collects the first data, wherein the area comprises at least one cell; an identifier of a device that performs machine learning [Cella, ¶ 0215, “Additionally, the reporting packet 320 may include additional fields, such as a routing data field 324 indicating a destination of the packet (e.g., an address or identifier of the edge device 104)”, The routing information includes destination of a target device that receives the sensor data. The edge device may also perform machine learning (see 4, element 424, ¶ 0232).]; an identifier of a first task of collecting the first data; or a type of the first task.
However, in an analogous art, Tullberg et al. al teach the first information indicates a target of the first data segment [Tullberg, ¶ 0175, “When a sufficient amount of training data has been collected in the wireless device 120, compressed data is transmitted to the eNB or another central node, such as the core network node 130 or the cloud network node 143. For example, this relates to Action 204 described above”, The wireless device (see fig. 1, element 120) collects data for reporting (see fig. 2, action 201 and ¶s 0089-0091) to a central node (see element 130) or core network node (see element 143). The data is first reported to the network node (see fig. 2, action 204, ¶s 0110 and 0111). The network node (element 110) may receive the data as a packet from the wireless device and forward to the core network node or cloud node (see ¶ 0155). Given this operation, information (or an address) to forward along to the core network or cloud node would be included in the data message from the wireless device (see ¶ 0085).].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify destination field of the reporting packet of Cella et al. with the forwarding capability as taught by Tullberg et al. One would have been motivated to do this, because locating machine learning capabilities within core/cloud network nodes (see Tullberg, ¶s 0080 and 0082) allows for greater computational resources and ML model sharing with a reasonable expectation of success.
Moreover, in analogous art, Chieh et al. teach receiving configuration information from the first network device or a second network device [Chieh, ¶ 0023, “To start the setup routine 210, in step 215, a computer is connected to the gateway device 100,100a, for eg. via USB, wifi or LAN to the connection port 113. Alternatively, the mode switch 112 is turned to Setup mode to scan for available SR wireless sensors/actuators, in step 220. Once a wireless sensor is detected, in step 230, each sensor 150 is registered, an ID 162 is assigned, and an associated name is recorded. The sensor data format is also recorded. This sensor initiation and identification are carried out for all the sensors/actuators/devices 150, 151, 152, etc. that are within SR communication with the gateway device 100,100a”, The gateway device (see fig. 1, element 100) implements a setup procedure for the sensor devices (see elements 150-155), which includes sending configuration information. ], wherein the configuration information is used to configure the apparatus to collect the first data [Chieh, ¶ 0023, “the setup routine 210 proceeds to, step 250, to discover and then define the specific data package transmission time blocks that each specific sensor/actuator/device advertises within (as illustrated in FIG. 5C). Then, the gateway device 100,100a will be configured to scan during these specific time blocks, before ending the setup routine in step 260”, Once configured, the sensor reports its data at periodic time blocks.].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the sensor setup procedure of Chieh et al. into the configuration model of the edge device of Cella et al. (see fig. 4, element 428, paragraph ¶ 0238). One would have been motivated to do this, because adapting a setup procedure into an edge/gateway device, including defined reporting periods for sensor devices, helps reduce battery power usage in sensor systems (see Chieh, ¶ 0023) with a reasonable expectation of success.
As per claim 4, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 1. Cella et al. also teach wherein the target performs machine learning [Cella, ¶ 0273, “At 714, the backend system 150 performs one or more backend operations on the decompressed sensor data. The backend operations may include storing the data, filtering the data, performing AI-related tasks on the sensor data, issuing one or more notifications in relation to the results of the AI-related tasks, performing one or more analytics related tasks, controlling an industrial component of the industrial setting 120”, The sensor data is used by the backend system to perform machine learning (or AI related tasks). See also fig. 5, element 524, ¶ 0252, 0253.].
As per claim 5, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 1. Cella et al. also teach wherein the target is a third network device [Cella, ¶ 0273, “At 714, the backend system 150 performs one or more backend operations on the decompressed sensor data. The backend operations may include storing the data, filtering the data, performing AI-related tasks on the sensor data, issuing one or more notifications in relation to the results of the AI-related tasks, performing one or more analytics related tasks, controlling an industrial component of the industrial setting 120”, The sensor data is used by the backend system to perform machine learning (or AI related tasks). The backend system is the third device.].
As per claim 6, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 5. Cella et al. do not explicitly teach wherein the first information indicates one or more of the following content: an identifier of the third network device; an identifier of a cell managed by the third network device; or an identifier of an area, wherein the area comprises at least one cell, and the at least one cell comprises the cell managed by the third network device.
However, in an analogous art, Tullberg et al. teach wherein the first information indicates one or more of the following content: an identifier of the third network device [Tullberg, ¶ 0175, “When a sufficient amount of training data has been collected in the wireless device 120, compressed data is transmitted to the eNB or another central node, such as the core network node 130 or the cloud network node 143. For example, this relates to Action 204 described above”, The wireless device (see fig. 1, element 120) collects data for reporting (see fig. 2, action 201 and ¶s 0089-0091) to a central node (see element 130) or core network node (see element 143). The data is first reported to the network node (see fig. 2, action 204, ¶s 0110 and 0111). The network node (element 110) may receive the data as a packet from the wireless device and forward to the core network node or cloud node (see ¶ 0155). Given this operation, information (or an address) to forward along to the core network or cloud node would be included in the data message from the wireless device (see ¶ 0085).]; an identifier of a cell managed by the third network device; or an identifier of an area, wherein the area comprises at least one cell, and the at least one cell comprises the cell managed by the third network device.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify destination field of the reporting packet of Cella et al. with the forwarding capability as taught by Tullberg et al. One would have been motivated to do this, because locating machine learning capabilities within core/cloud network nodes (see Tullberg, ¶s 0080 and 0082) allows for greater computational resources and ML model sharing with a reasonable expectation of success.
As per claim 7, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 1. Cella et al. do not explicitly teach wherein the first information is carried in a first message comprising second information that indicates one or more of the following content: the identifier of the first task; a sequence number of the first data segment in the one or more data segments; information indicating whether the first data segment is a last data segment in the one or more data segments; information indicating whether a second cell is in the second area, wherein the second cell is managed by the first network device, the second cell is a serving cell of the terminal, and the second area comprises at least one cell; or a first duration from a moment at which the terminal device generates the first data segment to a moment at which the terminal sends the first data segment.
However, in an analogous art, Chieh et al. teach wherein the first information is carried in a first message comprising second information that indicates one or more of the following content: the identifier of the first task; a sequence number of the first data segment in the one or more data segments; information indicating whether the first data segment is a last data segment in the one or more data segments; information indicating whether a second cell is in the second area, wherein the second cell is managed by the first network device, the second cell is a serving cell of the terminal, and the second area comprises at least one cell; or a first duration from a moment at which the terminal generates the first data segment to a moment at which the terminal sends the first data segment [Chieh, ¶ 0023, “the setup routine 210 proceeds to, step 250, to discover and then define the specific data package transmission time blocks that each specific sensor/actuator/device advertises within (as illustrated in FIG. 5C). Then, the gateway device 100,100a will be configured to scan during these specific time blocks, before ending the setup routine in step 260”, Once configured, the sensor reports its data at periodic time blocks.].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the sensor setup procedure of Chieh et al. into the configuration model of the edge device of Cella et al. (see fig. 4, element 428, paragraph ¶ 0238). One would have been motivated to do this, because adapting a setup procedure into an edge/gateway device , including defined reporting periods for sensor devices, helps reduce battery power usage in sensor systems (see Chieh, ¶ 0023) with a reasonable expectation of success.
As per claim 8, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 1. Cella et al. teach wherein the first data comprises one or more of training data [Cella, ¶ 0247, “the sensor kit data store 510 stores data relating to respective sensor kits 100. In embodiments, the sensor kit data store 510 may store sensor kit data corresponding to each installed sensor kit 100. In embodiments, the sensor kit data may indicate the devices in a sensor kit 100, including each sensor 102 (e.g., a sensor ID) in the sensor kit 100. In some embodiments, the sensor kit data may indicate the sensor data captured by the sensor kit 100”, The sensor data is compiled by the backend system and used for AI model training and tuning (see ¶s 0249, 0250, and 0251).], a model parameter gradient, or an inference result.
As per claim 9, Cella et al. teach an apparatus, comprising a processor coupled with a memory, wherein the processor is configured to execute instructions stored in the memory [Cella, fig. 4, elements 402 and 406, ¶ 0220] to cause the apparatus to perform steps comprising:
receiving a first data segment and first information from a terminal [Cella, ¶ 0266, “At 610, the edge device 104 receives sensor data from one or more sensors 102 of the sensor kit 100 via a sensor kit network 200. In embodiments, the sensor data from a respective sensor 102 may be received in a reporting packet. Each reporting packet may include a device identifier of the sensor 102 that generated the reporting packet and one or more instances of sensor data captured by sensor 102. The reporting packet may include additional data, such as a timestamp or other metadata”, The edge device receives the reporting packet from the sensor device using communication module 404 (see fig. 4).], wherein the first data segment is one of one or more data segments [Cella, ¶ 0215, “a second field 326 indicating the sensor data”, The sensor data reporting is further disclosed in Fig. 6, step 610 (see ¶ 0266).] corresponding to first data used for machine learning [Cella, ¶ 0273, “At 714, the backend system 150 performs one or more backend operations on the decompressed sensor data. The backend operations may include storing the data, filtering the data, performing AI-related tasks on the sensor data, issuing one or more notifications in relation to the results of the AI-related tasks, performing one or more analytics related tasks, controlling an industrial component of the industrial setting 120”, The sensor data is used by the backend system to perform machine learning (or AI related tasks). The sensor data is sent from the edge device to the backend system (see fig. 6, step 616 and ¶ 0269).].
Cella et at do not explicitly teach the first information indicates a target of the first data segment; and sending the first data segment to the target device…sending, to the terminal, configuration information used to configure the apparatus to collect the first data.
Yet, Cella et al. do teach wherein the configuration information indicates one or more of the following content: a cell in which the terminal device collects the first data; an area in which the terminal device collects the first data, wherein the area comprises at least one cell; an identifier of a device that performs machine learning [Cella, ¶ 0215, “Additionally, the reporting packet 320 may include additional fields, such as a routing data field 324 indicating a destination of the packet (e.g., an address or identifier of the edge device 104)”, The routing information includes destination of a target device that receives the sensor data. The edge device may also perform machine learning (see 4, element 424, ¶ 0232).]; an identifier of a first task of collecting the first data; or a type of the first task.
However, in an analogous art, Tullberg et al. al teach the first information indicates a target of the first data segment; and sending the first data segment to the target [Tullberg, ¶ 0175, “When a sufficient amount of training data has been collected in the wireless device 120, compressed data is transmitted to the eNB or another central node, such as the core network node 130 or the cloud network node 143. For example, this relates to Action 204 described above”, The wireless device (see fig. 1, element 120) collects data for reporting (see fig. 2, action 201 and ¶s 0089-0091) to a central node (see element 130) or core network node (see element 143). The data is first reported to the network node (see fig. 2, action 204, ¶s 0110 and 0111). The network node (element 110) may receive the data as a packet from the wireless device and forward to the core network node or cloud node (see ¶ 0155). Given this operation, information (or an address) to forward along to the core network or cloud node would be included in the data message from the wireless device (see ¶ 0085).].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify destination field of the reporting packet of Cella et al. with the forwarding capability as taught by Tullberg et al. One would have been motivated to do this, because locating machine learning capabilities within core/cloud network nodes (see Tullberg, ¶s 0080 and 0082) allows for greater computational resources and ML model sharing with a reasonable expectation of success.
Moreover, in an analogous art, Chieh et al. teach sending, to the terminal, configuration information used to configure the apparatus to collect the first data [Chieh, ¶ 0023, “To start the setup routine 210, in step 215, a computer is connected to the gateway device 100,100a, for eg. via USB, wifi or LAN to the connection port 113. Alternatively, the mode switch 112 is turned to Setup mode to scan for available SR wireless sensors/actuators, in step 220. Once a wireless sensor is detected, in step 230, each sensor 150 is registered, an ID 162 is assigned, and an associated name is recorded. The sensor data format is also recorded. This sensor initiation and identification are carried out for all the sensors/actuators/devices 150, 151, 152, etc. that are within SR communication with the gateway device 100,100a”, The gateway device (see fig. 1, element 100) implements a setup procedure for the sensor devices (see elements 150-155), which includes sending configuration information.], used to configure the apparatus to collect the first data [Chieh, ¶ 0023, “the setup routine 210 proceeds to, step 250, to discover and then define the specific data package transmission time blocks that each specific sensor/actuator/device advertises within (as illustrated in FIG. 5C). Then, the gateway device 100,100a will be configured to scan during these specific time blocks, before ending the setup routine in step 260”, Once configured, the sensor reports its data at periodic time blocks.].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the sensor setup procedure of Chieh et al. into the configuration model of the edge device of Cella et al. (see fig. 4, element 428, paragraph ¶ 0238). One would have been motivated to do this, because adapting a setup procedure into an edge/gateway device, including defined reporting periods for sensor devices, helps reduce battery power usage in sensor systems (see Chieh, ¶ 0023) with a reasonable expectation of success.
As per claim 12, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 9. Cella et al. also teach wherein the target performs machine learning [Cella, ¶ 0273, “At 714, the backend system 150 performs one or more backend operations on the decompressed sensor data. The backend operations may include storing the data, filtering the data, performing AI-related tasks on the sensor data, issuing one or more notifications in relation to the results of the AI-related tasks, performing one or more analytics related tasks, controlling an industrial component of the industrial setting 120”, The sensor data is used by the backend system to perform machine learning (or AI related tasks). See also fig. 5, element 524, ¶ 0252, 0253.].
As per claim 13, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 9. Cella et al. also teach wherein the target is a third network device [Cella, ¶ 0273, “At 714, the backend system 150 performs one or more backend operations on the decompressed sensor data. The backend operations may include storing the data, filtering the data, performing AI-related tasks on the sensor data, issuing one or more notifications in relation to the results of the AI-related tasks, performing one or more analytics related tasks, controlling an industrial component of the industrial setting 120”, The sensor data is used by the backend system to perform machine learning (or AI related tasks). The backend system is the third device.].
As per claim 14, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 13. Cella et al. do not explicitly teach wherein the first information indicates one or more of the following content: an identifier of the third network device; an identifier of a cell managed by the third network device; or an identifier of an area, wherein the area comprises at least one cell, and the at least one cell comprises the cell managed by the third network device.
However, in an analogous art, Tullberg et al. teach wherein the first information indicates one or more of the following content: an identifier of the third network device [Tullberg, ¶ 0175, “When a sufficient amount of training data has been collected in the wireless device 120, compressed data is transmitted to the eNB or another central node, such as the core network node 130 or the cloud network node 143. For example, this relates to Action 204 described above”, The wireless device (see fig. 1, element 120) collects data for reporting (see fig. 2, action 201 and ¶s 0089-0091) to a central node (see element 130) or core network node (see element 143). The data is first reported to the network node (see fig. 2, action 204, ¶s 0110 and 0111). The network node (element 110) may receive the data as a packet from the wireless device and forward to the core network node or cloud node (see ¶ 0155). Given this operation, information (or an address) to forward along to the core network or cloud node would be included in the data message from the wireless device (see ¶ 0085).]; an identifier of a cell managed by the third network device; or an identifier of an area, wherein the area comprises at least one cell, and the at least one cell comprises the cell managed by the third network device.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify destination field of the reporting packet of Cella et al. with the forwarding capability as taught by Tullberg et al. One would have been motivated to do this, because locating machine learning capabilities within core/cloud network nodes (see Tullberg, ¶s 0080 and 0082) allows for greater computational resources and ML model sharing with a reasonable expectation of success.
As per claim 15, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 12. Cella et al. do not explicitly teach wherein the first information is carried in a first message comprising second information that indicates one or more of the following content: the identifier of the first task; a sequence number of the first data segment in the one or more data segments; information indicating whether the first data segment is a last data segment in the one or more data segments; information indicating whether a second cell is in the second area, wherein the second cell is managed by a first network device, the second cell is a serving cell of the terminal device, and the second area comprises at least one cell; or a first duration length from a moment at which the terminal generates the first data segment to a moment at which the terminal sends the first data segment.
However, in an analogous art, Chieh et al. teach wherein the first information is carried in a first message comprising second information that indicates one or more of the following content: the identifier of the first task; a sequence number of the first data segment in the one or more data segments; information indicating whether the first data segment is a last data segment in the one or more data segments; information indicating whether a second cell is in the second area, wherein the second cell is managed by a first network device, the second cell is a serving cell of the terminal device, and the second area comprises at least one cell; or a first duration length from a moment at which the terminal generates the first data segment to a moment at which the terminal sends the first data segment [Chieh, ¶ 0023, “the setup routine 210 proceeds to, step 250, to discover and then define the specific data package transmission time blocks that each specific sensor/actuator/device advertises within (as illustrated in FIG. 5C). Then, the gateway device 100,100a will be configured to scan during these specific time blocks, before ending the setup routine in step 260”, Once configured, the sensor reports its data at periodic time blocks.].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the sensor setup procedure of Chieh et al. into the configuration model of the edge device of Cella et al. (see fig. 4, element 428, paragraph ¶ 0238). One would have been motivated to do this, because adapting a setup procedure into an edge/gateway device, including defined reporting periods for sensor devices, helps reduce battery power usage in sensor systems (see Chieh, ¶ 0023) with a reasonable expectation of success.
As per claim 16, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 15. Cella et al. do not explicitly teach wherein the instructions, when executed, further cause the apparatus to send the second information to the target.
However, in an analogous art, Tullberg et al. teach wherein the instructions, when executed, further cause the apparatus to send the second information to the target [Tullberg, ¶ 0175, “When a sufficient amount of training data has been collected in the wireless device 120, compressed data is transmitted to the eNB or another central node, such as the core network node 130 or the cloud network node 143. For example, this relates to Action 204 described above”, The wireless device (see fig. 1, element 120) collects data for reporting (see fig. 2, action 201 and ¶s 0089-0091) to a central node (see element 130) or core network node (see element 143). The data is first reported to the network node (see fig. 2, action 204, ¶s 0110 and 0111). The network node (element 110) may receive the data as a packet from the wireless device and forward to the core network node or cloud node (see ¶ 0155). Given this operation, information (or an address) to forward along to the core network or cloud node would be included in the data message from the wireless device (see ¶ 0085).].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify destination field of the reporting packet of Cella et al. with the forwarding capability as taught by Tullberg et al. One would have been motivated to do this, because locating machine learning capabilities within core/cloud network nodes (see Tullberg, ¶s 0080 and 0082) allows for greater computational resources and ML model sharing with a reasonable expectation of success.
As per claim 17, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 9. Cella et al. also teach wherein the first data comprises one or more of training data [Cella, ¶ 0247, “the sensor kit data store 510 stores data relating to respective sensor kits 100. In embodiments, the sensor kit data store 510 may store sensor kit data corresponding to each installed sensor kit 100. In embodiments, the sensor kit data may indicate the devices in a sensor kit 100, including each sensor 102 (e.g., a sensor ID) in the sensor kit 100. In some embodiments, the sensor kit data may indicate the sensor data captured by the sensor kit 100”, The sensor data is compiled by the backend system and used for AI model training and tuning (see ¶s 0249, 0250, and 0251).], a model parameter gradient, or an inference result.
As per claim 18, Cella et al. teach an apparatus, comprising a processor coupled with a memory, wherein the processor is configured to execute instructions stored in the memory to cause the apparatus to perform steps comprising:
receiving a first data segment [Cella, ¶ 0214, “In embodiments, the edge reporting module 312 obtains raw sensor data from a sensing component 302 or from a storage device 304 and packetizes the raw sensor data into a reporting packet 320”, The IoT sensor device generates a reporting packet (shown as 322 in Fig. 3). The reporting packet (see also fig. 3B, ¶s 0215 and 0216) includes sensor data (or data segment, see element 326) and a destination of the packet (routing data, see element 324). The reporting packet is sent to the edge device (or first device, see fig. 2A, ¶ 0216). See also fig. 6, step 610 and ¶ 0266.]…wherein the first data segment is one of one or more data segments corresponding to first data used for machine learning [Cella, ¶ 0273, “At 714, the backend system 150 performs one or more backend operations on the decompressed sensor data. The backend operations may include storing the data, filtering the data, performing AI-related tasks on the sensor data, issuing one or more notifications in relation to the results of the AI-related tasks, performing one or more analytics related tasks, controlling an industrial component of the industrial setting 120”, The sensor data is used by the backend system to perform machine learning (or AI related tasks). The sensor data is sent from the edge device to the backend system (see fig. 6, step 616 and ¶ 0269).].
Cella et al. do not explicitly teach receiving a first data segment from a first network device… sending, to the terminal, configuration information used to configure the apparatus to collect the first data…and sending the first data segment to a device that performs machine learning.
Yet, Cella et al. do teach wherein the configuration information indicates one or more of the following content: a cell in which the terminal device collects the first data; an area in which the terminal device collects the first data, wherein the area comprises at least one cell; an identifier of a device that performs machine learning [Cella, ¶ 0215, “Additionally, the reporting packet 320 may include additional fields, such as a routing data field 324 indicating a destination of the packet (e.g., an address or identifier of the edge device 104)”, The routing information includes destination of a target device that receives the sensor data. The edge device may also perform machine learning (see 4, element 424, ¶ 0232).]; an identifier of a first task of collecting the first data; or a type of the first task.
However, in an analogous art, Tullberg et al. teach receiving a first data segment from a first network device…and sending the first data segment to a device that performs machine learning [Tullberg, ¶ 0175, “When a sufficient amount of training data has been collected in the wireless device 120, compressed data is transmitted to the eNB or another central node, such as the core network node 130 or the cloud network node 143. For example, this relates to Action 204 described above”, The wireless device (see fig. 1, element 120) collects data for reporting (see fig. 2, action 201 and ¶s 0089-0091) to a central node (see element 130) or core network node (see element 143). The data is first reported to the network node (see fig. 2, action 204, ¶s 0110 and 0111). The network node (element 110) may receive the data as a packet from the wireless device and forward to the core network node or cloud node (see ¶ 0155). Given this operation, information (or an address) to forward along to the core network or cloud node would be included in the data message from the wireless device (see ¶ 0085).].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify destination field of the reporting packet of Cella et al. with the network device forwarding capability as taught by Tullberg et al. One would have been motivated to do this, because locating machine learning capabilities within core/cloud network nodes (see Tullberg, ¶s 0080 and 0082) allows for greater computational resources and ML model sharing with a reasonable expectation of success.
Moreover, in an analogous art, Chieh et al. teach sending, to the terminal, configuration information used to configure the apparatus to collect the first data [Chieh, ¶ 0023, “To start the setup routine 210, in step 215, a computer is connected to the gateway device 100,100a, for eg. via USB, wifi or LAN to the connection port 113. Alternatively, the mode switch 112 is turned to Setup mode to scan for available SR wireless sensors/actuators, in step 220. Once a wireless sensor is detected, in step 230, each sensor 150 is registered, an ID 162 is assigned, and an associated name is recorded. The sensor data format is also recorded. This sensor initiation and identification are carried out for all the sensors/actuators/devices 150, 151, 152, etc. that are within SR communication with the gateway device 100,100a”, The gateway device (see fig. 1, element 100) implements a setup procedure for the sensor devices (see elements 150-155), which includes sending configuration information.], used to configure the apparatus to collect the first data [Chieh, ¶ 0023, “the setup routine 210 proceeds to, step 250, to discover and then define the specific data package transmission time blocks that each specific sensor/actuator/device advertises within (as illustrated in FIG. 5C). Then, the gateway device 100,100a will be configured to scan during these specific time blocks, before ending the setup routine in step 260”, Once configured, the sensor reports its data at periodic time blocks.].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the sensor setup procedure of Chieh et al. into the configuration model of the edge device of Cella et al. (see fig. 4, element 428, paragraph ¶ 0238). One would have been motivated to do this, because adapting a setup procedure into an edge/gateway device, including defined reporting periods for sensor devices, helps reduce battery power usage in sensor systems (see Chieh, ¶ 0023) with a reasonable expectation of success.
As per claim 19, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 18. Cella et al. do not explicitly teach wherein the processor is configured to execute further instructions stored in the memory to cause the apparatus to further perform: receiving configuration information from a terminal that performs machine learning.
However, in an analogous art, Chieh et al. teach wherein the processor is configured to execute further instructions stored in the memory to cause the apparatus to further perform: receiving configuration information from the terminal that performs machine learning [Chieh, ¶ 0023, “To start the setup routine 210, in step 215, a computer is connected to the gateway device 100,100a, for eg. via USB, wifi or LAN to the connection port 113. Alternatively, the mode switch 112 is turned to Setup mode to scan for available SR wireless sensors/actuators, in step 220. Once a wireless sensor is detected, in step 230, each sensor 150 is registered, an ID 162 is assigned, and an associated name is recorded. The sensor data format is also recorded. This sensor initiation and identification are carried out for all the sensors/actuators/devices 150, 151, 152, etc. that are within SR communication with the gateway device 100,100a”, The gateway device (see fig. 1, element 100) implements a setup procedure for the sensor devices (see elements 150-155), which includes sending configuration information. ], wherein the configuration information is used to configure a terminal device to collect the first data [Chieh, ¶ 0023, “the setup routine 210 proceeds to, step 250, to discover and then define the specific data package transmission time blocks that each specific sensor/actuator/device advertises within (as illustrated in FIG. 5C). Then, the gateway device 100,100a will be configured to scan during these specific time blocks, before ending the setup routine in step 260”, Once configured, the sensor reports its data at periodic time blocks.].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the sensor setup procedure of Chieh et al. into the configuration module of the backend device of Cella et al. (see fig. 5, element 534, ¶ 0261). One would have been motivated to do this, because adapting a setup procedure into an edge/gateway device, including defined reporting periods for sensor devices, helps reduce battery power usage in sensor systems (see Chieh, ¶ 0023) with a reasonable expectation of success.
As per claim 20, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 19. Cella et al. do not explicitly teach wherein the processor is configured to execute further instructions stored in the memory to cause the apparatus to further perform: sending the configuration information to the terminal.
However, in an analogous art, Chieh et al. teach wherein the processor is configured to execute further instructions stored in the memory to cause the apparatus to further perform: sending the configuration information to the terminal [Chieh, ¶ 0023, “To start the setup routine 210, in step 215, a computer is connected to the gateway device 100,100a, for eg. via USB, wifi or LAN to the connection port 113. Alternatively, the mode switch 112 is turned to Setup mode to scan for available SR wireless sensors/actuators, in step 220. Once a wireless sensor is detected, in step 230, each sensor 150 is registered, an ID 162 is assigned, and an associated name is recorded. The sensor data format is also recorded. This sensor initiation and identification are carried out for all the sensors/actuators/devices 150, 151, 152, etc. that are within SR communication with the gateway device 100,100a”, The gateway device (see fig. 1, element 100) implements a setup procedure for the sensor devices (see elements 150-155), which includes sending configuration information. ].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the sensor setup procedure of Chieh et al. into the communication between network devices of Cella et al. (see fig. 6, element 616, ¶ 0269). One would have been motivated to do this, because adapting a setup procedure into an edge/gateway device, including defined reporting periods for sensor devices, helps reduce battery power usage in sensor systems (see Chieh, ¶ 0023) with a reasonable expectation of success.
As per claim 22, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 1. Cella et al. also teach wherein the configuration information indicates whether collecting the first data is a cell-level task or an area-level task [Cella, ¶ 0011, The reference discloses the usage of sensor kits within an industrial setting (see ¶ 0009), where the sensors within the kit may monitor a certain area of an industrial setting (see ¶s 0005 and 0204). When seen in light with Chieh, it is reasonable to conclude that the sensor kit may be configured with industrial setting specific tasks.].
As per claim 24, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 9. Cella et al. also teach wherein the configuration information indicates whether collecting the first data is a cell-level task or an area-level task [Cella, ¶ 0011, The reference discloses the usage of sensor kits within an industrial setting (see ¶ 0009), where the sensors within the kit may monitor a certain area of an industrial setting (see ¶s 0005 and 0204). When seen in light with Chieh, it is reasonable to conclude that the sensor kit may be configured with industrial setting specific tasks.].
Claims 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (US PG Pub 2022/0191284, which was cited in the previous action) in view of Tullberg et al. (US PG Pub 2022/0051139, which was cited in the previous action), Chieh Tseng et al. (US PG Pub 2020/0336878, hereinafter “Chieh”, which was cited in the previous action), and Rommer et al. (US PG Pub 2023/0327748).
As per claim 21, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 1. Cella et al. do not explicitly teach wherein the first information includes a cell global identifier and a tracking area code of the first cell.
However, in an analogous art, Rommer et al. teach wherein the first information includes a cell global identifier and a tracking area code of the first cell [Rommer, ¶s 0020, 0021, 0023, and 0025, NTN uses a number of network identifiers, including a NR cell global identifier (NCGI) and a tracking area code (TAC). NTN may support IoT devices.].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the NTN network identifiers of Rommer et al. into Cella et al. One would have been motivated to do this because NTN networks are designed to support IoT networks (see Rommer, ¶ 0120) and such identifiers would be assigned and utilized by sensors within such a network with a reasonable expectation of success.
As per claim 23, Cella et al. in view of Tullberg et al. and Chieh et al. teach the apparatus according to claim 9. Cella et al. do not explicitly teach wherein the first information includes a cell global identifier and a tracking area code of the first cell.
However, in an analogous art, Rommer et al. teach wherein the first information includes a cell global identifier and a tracking area code of the first cell [Rommer, ¶s 0020, 0021, 0023, and 0025, NTN uses a number of network identifiers, including a NR cell global identifier (NCGI) and a tracking area code (TAC). NTN may support IoT devices.].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the NTN network identifiers of Rommer et al. into Cella et al. One would have been motivated to do this because NTN networks are designed to support IoT networks (see Rommer, ¶ 0120) and such identifiers would be assigned and utilized by sensors within such a network with a reasonable expectation of success.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Paul H. Masur/
Primary Examiner
Art Unit 2417