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 Objections
Claims 17-20 are objected to because of the following informalities:
Under a broadest reasonable interpretation, “One or more physically manufactured computer-readable storage media” includes transitory forms of signal transmissions. The recommended change is to change the claim language to “a non-transitory physically manufactured computer-readable storage medium”.
Appropriate correction is required.
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) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, the limitations “determining that a compute task on a primary device is a resource-intensive AI task requiring one or more resources above a threshold”, “determining that the resource-intensive AI task can be delegated to one or more proximate computing devices”, “determining weighted scores for the one or more proximate devices based on the device advertisement packets”, and “selecting one of the one or more proximate devices based on the weighted scores” as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. These limitations encompass a human mind carrying out the function through observation, evaluation, judgment, and/or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fail within the “Mental Processes” grouping of abstract ideas under Prong 1.
Under Prong 2, the judicial exception is not integrated into a practical application. The additional elements “scanning one or more proximate devices to receive device advertisement packets” and “communicating a compute task delegation request to the selected proximate device” are mere gathering data, and outputting data which the courts have identified as well-understood, routine conventional activity. See for example Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362, MPEP 2106.05(d). Therefore, mere data gathering and outputting data do not amount to significantly more, thus, cannot provide an inventive concept. Accordingly, the claims are not patent eligible under 35 USC 101. For Berkheimer evidence, “scanning” is a well-known function as evidenced by Oh (US 20100295993 A1, [par. 0040]), rendering it insignificant extra solution activity under Prong 2.
Regarding claims 10 and 17, the limitations “determining that a compute task on a primary device is a resource-intensive AI task requiring one or more resources above a threshold”, “determining that the resource-intensive AI task can be delegated to one or more proximate computing devices”, “determining weighted scores for the one or more proximate devices based on the device advertisement packets”, “selecting one of the one or more proximate devices based on the weighted scores”, and “delegating the resource-intensive AI task to the selected proximate device” as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. These limitations encompass a human mind carrying out the function through observation, evaluation, judgment, and/or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fail within the “Mental Processes” grouping of abstract ideas under Prong 1.
Under Prong 2, the judicial exception is not integrated into a practical application. The additional element “scanning one or more proximate devices to receive device advertisement packets” is mere data gathering and outputting data which the courts have identified as well-understood, routine conventional activity. See for example Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362, MPEP 2106.05(d). Therefore, mere data gathering and outputting data do not amount to significantly more, thus, cannot provide an inventive concept. Accordingly, the claims are not patent eligible under 35 USC 101. For Berkheimer evidence, “scanning” is a well-known function as evidenced by Oh (US 20100295993 A1, [par. 0040]), rendering it insignificant extra solution activity under Prong 2.
Claim 10 additionally recites “memory” and “one or more processor units” These elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer, and/or generic computer components. See MPEP 2106.05(f). Therefore, the additional elements recited in claim 10 do not integrate the judicial exception into a practical application under prong 2, nor amount to significantly more under step 2B.
Claim 17 additionally recites “One or more physically manufactured computer-readable storage media, encoding computer-executable instructions for executing on a computer system a computer process”. These elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer, and/or generic computer components. See MPEP 2106.05(f). Therefore, the additional elements recited in claim 17 do not integrate the judicial exception into a practical application under prong 2, nor amount to significantly more under step 2B.
Regarding dependent claims 2, 11, and 18, the limitation “wherein the resource-intensive AI task is an artificial (AI) task” merely narrows the subject matter of data that is mentally evaluated, and does not recite any additional element. Thus, this limitation is not indicative of either a practical application under Prong 1, nor an inventive concept under Step 2B, for the reasons explained in the rejection of claim 1.
Regarding dependent claim 3, the limitation “receiving an acknowledgment … “ is mere data gathering which is not indicative of either a practical application under Prong 1, nor an inventive concept under step 2B for the reasons explained in the rejection of claim 1. The additional element “delegating … “merely uses a generic computer or computer components as a tool to output/transmit data, and this is insignificant extra-solution activity that is not indicative of either a practical application under Prong 1, nor an inventive concept under step 2B, for the reasons explained in the rejection of claim 1.
Regarding dependent claims 4, 13, and 19, the limitation “wherein the device advertisement packets comprising …” merely narrows the subject matter of data that is gathered. Thus, the limitation is not indicative of either a practical application under Prong 1, nor an inventive concept under Step 2B for the reasons explained in the rejection of claim 1.
Regarding claim 5, the limitation “evaluating the device identifiers … “ recites an additional abstract idea of a mental process since this limitation encompasses a human mind carrying out the function through observation through observation, evaluation, judgment and/or opinion, or even with the aid of pen and paper. Thus, this limitation recites and falls within the “Mental Processes” grouping of abstract ideas under Prong 1, and is not indicative of either a practical application under Prong 1, nor an inventive concept under Step 2B for the reasons explained in the rejection of claim 1.
Regarding dependent claims 6 and 12, the limitation “evaluating the device identifiers of a proximate device … to determine that the proximate device is not a trusted device” recites an additional abstract idea of a mental process since this limitation encompasses a human mind carrying out the function through observation through observation, evaluation, judgment and/or opinion, or even with the aid of pen and paper. Thus, this limitation recites and falls within the “Mental Processes” grouping of abstract ideas under Prong 1. The additional element “communication a request … transiently trusted device” merely uses a generic computer or computer components as a tool to transmit data, and is not indicative of either a practical application under Prong 1, nor an inventive concept under Step 2B for the reasons explained in the rejection of claim 1. Claim 12 further recites the additional element of claim 4 discussed above, which for the same reasons is mere data gathering and is not indicative of eligibility.
Regarding claims 7, 14, and 20, the limitation “wherein the device resource levels … “ is mere data gathering, which is not indicative of either a practical application under Prong 1, nor an inventive concept under Step 2B for the reasons explained in the rejection of claim 1.
Regarding dependent claims 8 and 15, the limitation “wherein the device advertisement packets received … “ is mere data gathering, which is not indicative of either a practical application under Prong 1, nor an inventive concept under Step 2B for the reasons explained in the rejection of claim 1.
Regarding dependent claim 16, the limitation “determining weighted scores … “ recites an additional abstract idea of a mental process since this limitation encompasses a human mind carrying out the function through observation, evaluation, judgment, and/or opinion, or even with the aid of pen and paper. Thus, this limitation recites and falls within the “Mental Processes” grouping of abstract ideas under Prong 1, and is not indicative of either a practical application under Prong 1, nor an inventive concept under Step 2B for the reasons explained in the rejection of claim 1.
Regarding claim 9, the additional element “wherein scanning … “ merely recites well-understood, routine, and conventional wireless networking technologies used to gather data, and generally links the abstract idea to a particular technological environment. This additional element does not impose any meaningful limit on the abstract idea and does not integrate the judicial exception into a practical application under Prong 1, nor does it amount to significantly more than the judicial exception under Step 2B. See MPEP 2106.05(d) and (h). Accordingly, claim 9 is not patent eligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4, 7, 9-11, 13-14, and 17-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20250138888 A1 (hereinafter referred to as Maker).
As per claim 1 – Maker teaches a method, comprising: determining that a compute task on a primary device is a resource-intensive AI task requiring one or more resources above a threshold (In some cases, ML-based tasks can be especially resource intensive and such device can sometimes have limited computing resources. For these and/or other reasons, in some instances, it may be desirable for these devices to offload such ML-based tasks to one or more other devices – [0020]); determining that the resource-intensive AI task can be delegated to one or more proximate computing devices (The method is for use in connection with a local area network (LAN) system comprising a communication network and a group of multiple devices connected to the communication network, wherein the group of multiple devices includes a first device and a separate set of devices, the method comprising: (i) the first device determining that a machine learning (ML)-based task is to be performed; (ii) the first device broadcasting to the separate set of devices, a ML-based task request for the ML-based task – [0002]; The devices all belonging to a LAN teach “proximate devices”. The first device receiving a ML task and broadcasting to the separate set of devices corresponds to “determining that a resource-intensive AI task can be delegated …”); scanning one or more proximate devices to receive device advertisement packets determining weighted scores for the one or more proximate devices based on the device advertisement packets; selecting one of the one or more proximate devices based on the weighted scores ((a) wherein the separate set of devices are configured to perform an arbitration process to select a second device from among the separate set of devices based at least on the second device having favorable computing resource availability as compared to respective computing resource availability of any other devices of the separate set of devices – [0002]; The separate set of devices performing an arbitration process to select a device from the set based on favorable computing resources corresponds to “scanning one or more proximate devices … selecting one or more proximate devices based on weighted scores. The devices having the ability to perform a selection on some criteria (computing resource availability) means they must receive information from each device. The information they receive corresponds to “advertisement packets” and the criteria corresponds to the “weighted scores”); and communicating a compute task delegation request to the selected proximate device (and (b) wherein the second device is configured to perform the ML-based task in accordance with the ML-based task request, thereby generating ML-based task output – [0002]).
As per claim 2 – Maker teaches the method of claim 1. Maker additionally teaches:
wherein the resource-intensive AI task is an artificial intelligence (AI) task (the method comprising: (i) the first device determining that a machine learning (ML)-based task is to be performed – [0002]; ML-based tasks correspond to “resource intensive AI task”).
As per claim 4 – Maker teaches the method of claim 1. Maker additionally teaches:
wherein the device advertisement packets comprising one or more of device identifiers, device configuration specification, and device resource availability levels ((a) wherein the separate set of devices are configured to perform an arbitration process to select a second device from among the separate set of devices based at least on the second device having favorable computing resource availability as compared to respective computing resource availability of any other devices of the separate set of devices – [0002]).
As per claim 7 – Maker teaches the method of claim 4. Maker additionally teaches:
wherein the device resource availability levels including one or more of a device power availability level, a device operations per second (OPS) level, and a cache availability level (In addition, each device 102 may also have respective computing resource availability information, which the device may store in its data storage. This information may include an indication of the availability of one or more computing resources of the device 102. For example, this information may include information relating to the theoretical maximum availability and/or current availability of resources such as processing power, memory, battery life, among various other examples – [0047]).
As per claim 9 – Maker teaches the method of claim 4. Maker additionally teaches:
wherein scanning one or more proximate devices further comprising scanning over at least one of a Bluetooth low energy (BLE) network, an ultra-wideband (UWB) network, and a wi-fi direct communication network (To support this or other such inter-device communication, each device 102 of the collection could include one or more communication modules and associated communication logic. For instance, each device 102 could include a wireless communication module, such as a WiFi, BLUETOOTH, or ZIGBEE module, and/or a wired communication module such as an Ethernet or Powerline network adapter, and could include a processor programmed with an associated communication stack and/or other logic that governs its communications – [0032]).
As per claim 10 – Maker teaches a system, comprising: memory; one or more processor units; and a compute task delegation system stored in the memory and executable by the one or more processor units; the compute task delegation system encoding computer-executable instructions on the memory for executing on the one or more processor units a computer process, the computer process comprising (The processor 202 could comprise one or more general purpose processors (e.g., microprocessors) and/or one or more special purpose processors (e.g., application specific integrated circuits). Further, the data storage 204 could comprise one or more non-transitory storage/memory components, such as optical, flash, magnetic, RAM, ROM, or other storage, possibly integrated in whole or in part with the processor 202. As shown, the data storage could hold or be configured to hold program instructions 208 and/or other data. The program instructions 208 could define or constitute controller code, which could be executable or interpretable by the processor 202 to cause the device 102 to carry out various device operations described here – [0038]).
determining that the resource-intensive AI task can be delegated to one or more proximate computing devices (The method is for use in connection with a local area network (LAN) system comprising a communication network and a group of multiple devices connected to the communication network, wherein the group of multiple devices includes a first device and a separate set of devices, the method comprising: (i) the first device determining that a machine learning (ML)-based task is to be performed; (ii) the first device broadcasting to the separate set of devices, a ML-based task request for the ML-based task – [0002]; The devices all belonging to a LAN teach “proximate devices”. The first device receiving a ML task and broadcasting to the separate set of devices corresponds to “determining that a resource-intensive AI task can be delegated …”); scanning one or more proximate devices to receive device advertisement packets determining weighted scores for the one or more proximate devices based on the device advertisement packets; selecting one of the one or more proximate devices based on the weighted scores ((a) wherein the separate set of devices are configured to perform an arbitration process to select a second device from among the separate set of devices based at least on the second device having favorable computing resource availability as compared to respective computing resource availability of any other devices of the separate set of devices – [0002]; The separate set of devices performing an arbitration process to select a device from the set based on favorable computing resources corresponds to “scanning one or more proximate devices … selecting one or more proximate devices based on weighted scores. The devices having the ability to perform a selection on some criteria (computing resource availability) means they must receive information from each device. The information they receive corresponds to “advertisement packets” and the criteria corresponds to the “weighted scores”); and communicating a compute task delegation request to the selected proximate device (and (b) wherein the second device is configured to perform the ML-based task in accordance with the ML-based task request, thereby generating ML-based task output – [0002]).
As per claim 11 – Maker teaches the system of claim 10. Maker additionally teaches:
wherein the resource-intensive AI task is an artificial intelligence (AI) task (the method comprising: (i) the first device determining that a machine learning (ML)-based task is to be performed – [0002]; ML-based tasks correspond to “resource intensive AI task”).
As per claim 13 – Maker teaches the system of claim 10. Maker additionally teaches:
wherein the device advertisement packets comprising one or more of device identifiers, device configuration specification, and device resource availability levels ((a) wherein the separate set of devices are configured to perform an arbitration process to select a second device from among the separate set of devices based at least on the second device having favorable computing resource availability as compared to respective computing resource availability of any other devices of the separate set of devices – [0002]).
As per claim 14 – Maker teaches system of claim 13. Maker additionally teaches:
wherein the device resource availability levels including one or more of a device power availability level, a device operations per second (OPS) level, and a cache availability level (In addition, each device 102 may also have respective computing resource availability information, which the device may store in its data storage. This information may include an indication of the availability of one or more computing resources of the device 102. For example, this information may include information relating to the theoretical maximum availability and/or current availability of resources such as processing power, memory, battery life, among various other examples – [0047]).
As per claim 17 – Maker teaches one or more physically manufactured computer-readable storage media, encoding computer-executable instructions for executing on a computer system a computer process, the computer process comprising (The processor 202 could comprise one or more general purpose processors (e.g., microprocessors) and/or one or more special purpose processors (e.g., application specific integrated circuits). Further, the data storage 204 could comprise one or more non-transitory storage/memory components, such as optical, flash, magnetic, RAM, ROM, or other storage, possibly integrated in whole or in part with the processor 202. As shown, the data storage could hold or be configured to hold program instructions 208 and/or other data. The program instructions 208 could define or constitute controller code, which could be executable or interpretable by the processor 202 to cause the device 102 to carry out various device operations described here – [0038]).
determining that the resource-intensive AI task can be delegated to one or more proximate computing devices (The method is for use in connection with a local area network (LAN) system comprising a communication network and a group of multiple devices connected to the communication network, wherein the group of multiple devices includes a first device and a separate set of devices, the method comprising: (i) the first device determining that a machine learning (ML)-based task is to be performed; (ii) the first device broadcasting to the separate set of devices, a ML-based task request for the ML-based task – [0002]; The devices all belonging to a LAN teach “proximate devices”. The first device receiving a ML task and broadcasting to the separate set of devices corresponds to “determining that a resource-intensive AI task can be delegated …”); scanning one or more proximate devices to receive device advertisement packets determining weighted scores for the one or more proximate devices based on the device advertisement packets; selecting one of the one or more proximate devices based on the weighted scores ((a) wherein the separate set of devices are configured to perform an arbitration process to select a second device from among the separate set of devices based at least on the second device having favorable computing resource availability as compared to respective computing resource availability of any other devices of the separate set of devices – [0002]; The separate set of devices performing an arbitration process to select a device from the set based on favorable computing resources corresponds to “scanning one or more proximate devices … selecting one or more proximate devices based on weighted scores. The devices having the ability to perform a selection on some criteria (computing resource availability) means they must receive information from each device. The information they receive corresponds to “advertisement packets” and the criteria corresponds to the “weighted scores”); and communicating a compute task delegation request to the selected proximate device (and (b) wherein the second device is configured to perform the ML-based task in accordance with the ML-based task request, thereby generating ML-based task output – [0002]).
As per claim 18 – Maker teaches the one or more physically manufactured computer-readable storage media of claim 17. Maker additionally teaches:
wherein the resource-intensive AI task is an artificial intelligence (AI) task (the method comprising: (i) the first device determining that a machine learning (ML)-based task is to be performed – [0002]; ML-based tasks correspond to “resource intensive AI task”).
As per claim 19 – Maker teaches the one or more physically manufactured computer-readable storage media of claim 17. Maker additionally teaches:
wherein the device advertisement packets comprising one or more of device identifiers, device configuration specification, and device resource availability levels ((a) wherein the separate set of devices are configured to perform an arbitration process to select a second device from among the separate set of devices based at least on the second device having favorable computing resource availability as compared to respective computing resource availability of any other devices of the separate set of devices – [0002]).
As per claim 20 - Maker teaches the one or more physically manufactured computer-readable storage media of claim 17. Maker additionally teaches:
wherein the device resource availability levels including one or more of a device power availability level, a device operations per second (OPS) level, and a cache availability level (In addition, each device 102 may also have respective computing resource availability information, which the device may store in its data storage. This information may include an indication of the availability of one or more computing resources of the device 102. For example, this information may include information relating to the theoretical maximum availability and/or current availability of resources such as processing power, memory, battery life, among various other examples – [0047]).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 3 and is/are rejected under 35 U.S.C. 103 as being unpatentable over Maker in view of US 20180183855 A1 (hereinafter referred to as Sabella).
As per claim 3 – Maker teaches the method of claim 1. Although Maker teaches delegation of resource-intensive AI tasks to selected proximate devices, it does not teach the nuance of only delegating the task after receiving an acknowledgement from the selected proximate device. Consequently, it does not teach the following limitation:
further comprising: receiving an acknowledgement from the selected proximate device in response to the task delegation request; and in response to receiving the acknowledgement, delegating the resource-intensive AI task to the selected proximate device.
However, Sabella, in an analogous art of computational offloading, teaches them as can be seen in the in-line citations below:
further comprising: receiving an acknowledgement from the selected proximate device in response to the task delegation request; and in response to receiving the acknowledgement, delegating the resource-intensive AI task to the selected proximate device (In some embodiments, before sending the applications tasks, etc., the offloader 732 of the UE 101 may control transmission of an offloading request to the indicated MEC host (e.g., MEH 200-3), and the MEH 200-3 may respond with an application offloading acknowledgement (ACK) or negative ACK (NACK) to indicate acceptance or non-acceptance of the application offloading request, respectively – [0156]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to incorporate the acknowledgment process of Sabella to the overall AI proximate network task offloading system of Maker. This combination would have been obvious to one of ordinary skill in the art because requiring acknowledgments when attempting to offload computing tasks to separate devices, whether on a proximate network or not, is a safe practice that enables secure and efficient communication.
Claim(s) 5-6 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maker in view of “Dynamic Trust-Based Device Legitimacy Assessment Towards Secure IoT Interactions” (pub. 2022, hereinafter referred to as Garagad).
As per claim 5 – Maker teaches the method of claim 4. It does not teach the limitations of claim 5:
further comprising evaluating the device identifiers of a proximate device to determine that the proximate device is a trusted device.
However, Garagad, in an analogous art, teaches them as can be seen in the in-line citations below:
further comprising evaluating the device identifiers of a proximate device to determine that the proximate device is a trusted device (Each device maintains a trust table in the network for its neighbouring devices – [pg. 273]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to include the neighbor trust table of Garagad to the overall AI proximate network task offloading system of Maker. This combination would have been obvious to one of ordinary skill in the art because of the explicitly stated need in Garagad for network communication devices to have the ability to determine if another device is trustworthy or not. Garagad notes “With the advent of such threat, models arise the need for a mechanism that can dynamically evaluate the credibility and legitimacy of nodes and manage the access control rights of the nodes [5]. Data security is ensured by estimating the peer device’s trustworthiness before interaction [6]. The model, governed by policies to evaluate the trustworthiness of the device and yet feasible enough to be deployed on the resource constrained edge device [9]” [269].
As per claim 6 - Maker teaches the method of claim 4. It does not teach the limitations of claim 6:
further comprising: evaluating the device identifiers of a proximate device of the one or more proximate devices to determine that the proximate device is not a trusted device; and in response to determining that the proximate device is not a trusted device and that the proximate device belongs to a user network of the primary device, communicating a request to the proximate device to become one of a trusted device and a transiently trusted device.
However, Garagad, in an analogous art of secure networking, teaches them as can be seen in the in-line citations below:
further comprising: evaluating the device identifiers of a proximate device of the one or more proximate devices to determine that the proximate device is not a trusted device; and in response to determining that the proximate device is not a trusted device and that the proximate device belongs to a user network of the primary device, communicating a request to the proximate device to become one of a trusted device and a transiently trusted device (The primary phase in any network based model is to discover the neighbour participants in the network. This phase is termed as neighbour discovery in the current work. In the proposed technique of neighbour discovery every device identifies its neighbours based on the single hop wireless communication range of the discovering device. The discovering device broadcasts a HELLO message in the network. The neighbour devices that receive the HELLO message acknowledge the message to register itself in the neighbour table or database of the broadcasting device. The process of neighbour discovery is scheduled at periodic intervals to list and de-list the dynamic devices in the network. Newly joined device is registered and listed in the neighbour table and device that fails to acknowledge is de-listed from the neighbour table – [pg. 272]; This process teaches “communicating a request to the proximate device to become one of a trusted device and a transiently trusted device”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to include the process of adding a device to a database of trusted devices of Garagad to the overall AI proximate network task offloading system of Maker. This combination would have been obvious to one of ordinary skill in the art because of the explicitly stated need in Garagad for network communication devices to have the ability to determine if another device is trustworthy or not. Garagad notes “With the advent of such threat, models arise the need for a mechanism that can dynamically evaluate the credibility and legitimacy of nodes and manage the access control rights of the nodes [5]. Data security is ensured by estimating the peer device’s trustworthiness before interaction [6]. The model, governed by policies to evaluate the trustworthiness of the device and yet feasible enough to be deployed on the resource constrained edge device [9]” [269].
As per claim 12 – Maker teaches the system of claim 10. Maker additionally teaches:
wherein the device advertisement packets comprising one or more of device identifiers, device configuration specification, and device resource availability levels (((a) wherein the separate set of devices are configured to perform an arbitration process to select a second device from among the separate set of devices based at least on the second device having favorable computing resource availability as compared to respective computing resource availability of any other devices of the separate set of devices – [0002]).
Maker does not teach the following limitations:
and wherein the computer process further comprising: evaluating the device identifiers of a proximate device to determine that the proximate device is not a trusted device; and in response to determining that a proximate device is not a trusted device and that the proximate device belongs to a user network of the primary device, communicating a request to the proximate device to become one of a trusted device and a transiently trusted device.
However, Garagad, in an analogous art of secure networking, teaches them as can be seen in the in-line citations below:
and wherein the computer process further comprising: evaluating the device identifiers of a proximate device to determine that the proximate device is not a trusted device; and in response to determining that a proximate device is not a trusted device and that the proximate device belongs to a user network of the primary device, communicating a request to the proximate device to become one of a trusted device and a transiently trusted device (The primary phase in any network based model is to discover the neighbour participants in the network. This phase is termed as neighbour discovery in the current work. In the proposed technique of neighbour discovery every device identifies its neighbours based on the single hop wireless communication range of the discovering device. The discovering device broadcasts a HELLO message in the network. The neighbour devices that receive the HELLO message acknowledge the message to register itself in the neighbour table or database of the broadcasting device. The process of neighbour discovery is scheduled at periodic intervals to list and de-list the dynamic devices in the network. Newly joined device is registered and listed in the neighbour table and device that fails to acknowledge is de-listed from the neighbour table – [pg. 272]; This process teaches “communicating a request to the proximate device to become one of a trusted device and a transiently trusted device”).
Claim(s) 8, 15, and 16 and is/are rejected under 35 U.S.C. 103 as being unpatentable over Maker in view of US 20200195731 A1 (hereinafter referred to as Guo).
As per claim 8 – Maker teaches the method of claim 4. It does not teach the following limitations of claim 8:
wherein the device advertisement packets received from the one or more proximate devices further comprising performance levels for previously executed tasks delegated to the one or more proximate devices.
However, Guo, in an analogous art of computational offloading, teaches them as can be seen in the in-line citations below:
wherein the device advertisement packets received from the one or more proximate devices further comprising performance levels for previously executed tasks delegated to the one or more proximate devices (The offload controller 22 is the unit to determine whether the code of compute-intensive tasks should be offloaded based on device information collected by the device profiler 23, e.g. code scale and network environment. Besides, the device profiler 23 also monitors the execution and generates history records both in remote mode and local mode to help the offload controller 22 make decisions – [0045]; The offload controller performs the same general function of determining where to offload computing tasks, making them equivalents in this context. The offload controller of Guo additionally teaches accounting for historical execution records, which corresponds to “performance levels for previously executed tasks delegated to the one or more proximate devices”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to incorporate the history execution records of Guo to the overall AI proximate network task offloading system of Maker. This combination would have been obvious to one of ordinary skill in the art because Guo explicitly states that history records “help the offload controller make decisions”.
As per claim 15 – Maker teaches the system of claim 13. It does not teach:
wherein the device advertisement packets received from the one or more proximate devices further comprising performance levels for previously executed tasks delegated to the one or more proximate devices.
However, Guo, in an analogous art of computational offloading, teaches them as can be seen in the in-line citations below:
wherein the device advertisement packets received from the one or more proximate devices further comprising performance levels for previously executed tasks delegated to the one or more proximate devices (The offload controller 22 is the unit to determine whether the code of compute-intensive tasks should be offloaded based on device information collected by the device profiler 23, e.g. code scale and network environment. Besides, the device profiler 23 also monitors the execution and generates history records both in remote mode and local mode to help the offload controller 22 make decisions – [0045]; The offload controller performs the same general function of determining where to offload computing tasks, making them equivalents in this context. The offload controller of Guo additionally teaches accounting for historical execution records, which corresponds to “performance levels for previously executed tasks delegated to the one or more proximate devices”).
As per claim 16 – Maker teaches the system of claim 15. It does not teach the following limitations of claim 16:
wherein determining weighted scores for the one or more proximate devices further comprising determining weighted scores for the one or more proximate devices based on the performance levels for previously executed tasks delegated to the one or more proximate devices.
However, Guo, in an analogous art of computational offloading, teaches them as can be seen in the in-line citations below:
wherein determining weighted scores for the one or more proximate devices further comprising determining weighted scores for the one or more proximate devices based on the performance levels for previously executed tasks delegated to the one or more proximate devices (The offload controller 22 is the unit to determine whether the code of compute-intensive tasks should be offloaded based on device information collected by the device profiler 23, e.g. code scale and network environment. Besides, the device profiler 23 also monitors the execution and generates history records both in remote mode and local mode to help the offload controller 22 make decisions – [0045]; The offloading controller takes into account device information when making offloading decisions, therefore, there it must have some way of valuing certain device statistics over others. This corresponds to the “weighted scores”. It is also mentioned that the offload controller takes execution history records into account when making decisions, which corresponds to “the weighted scores” being “based on the performance levels for previously executed tasks”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO 2009016371 A1 discusses a process of distributing keys to trusted devices on a network.
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/JOSEPH MAXEN LANE/Examiner, Art Unit 2196
/APRIL Y BLAIR/Supervisory Patent Examiner, Art Unit 2196
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