DETAILED ACTION
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
Claim 16 is objected to because of the following informalities:
Claim 16 recites on the last line: “the verification target [0022]” which should be “the verification target .
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation "the suitable learned model candidate" in Line 3. There is insufficient antecedent basis for this limitation in the claim.
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.
Claims 1-13 and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to evaluating the suitability of specified software with a target device, without significantly more.
The limitation in Independent Claims 1 and 12 of evaluating suitability, and in Independent Claims 6 and 13 of searching for a suitable model, as drafted, are processes that, under their broadest reasonable interpretation, covers steps that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitations which recite, “evaluate suitability of the verification target with the learned model based on the acquired configuration information of the verification target and the learned model” in Claims 1 and 12, and “search for a learned model suitable for verification of the verification target based on the acquired configuration information of the device” in Claims 6 and 13, as drafted, are processes that, under their broadest reasonable interpretation, recite the abstract idea of mental processes. These limitations encompass a human mind carrying out these functions through observation, evaluation judgment and/or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. Claims 1, 6 and 12-13 recite the following additional elements “acquire configuration information of a device including hardware configuration information in a verification target and software configuration information formed by firmware configuration information and protocol processing software configuration information”; wherein Claims 1 and 12 further recite “acquire configuration information of the device in an environment in which a learned model used for verification of the verification target is generated” and “output the evaluated result”; and wherein Claims 6 and 13 further recite “output the searched learned model”, these limitations do nothing more than add insignificant extra solution activity to the judicial exception, such as data gathering and outputting the results of the abstract idea, see MPEP 2106.05(g).
Further, with regard to the “memory storing instructions” and “at least one processor configured to execute the instructions” elements of Claims 1 and 6, these elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, see MPEP 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With regard to the individual dependent claims:
Claims 2 and 7 recite, “the configuration information of the device in the environment in which the verification target and the learned model are generated includes configuration information of a plurality of devices, and… acquire network configuration information including connection information between the plurality of devices as the configuration information.”
Claims 3 and 8 recite, “wherein hardware configuration information in the verification target and the hardware configuration information in which the learned model is generated include chip configuration information for controlling an operation of the device.”
Claims 5 and 10 recite, “store identifier information of the verification target and the configuration information of the verification target in association, and acquire the configuration information stored based on the identifier information of the verification target.”
Claim 9 recites, “receive a suitability result of the learned model for a verification target”.
These limitations of Claims 2-3, 5 and 7-10 do nothing more than add insignificant extra solution activity to the judicial exception, such as data gathering, transmitting and outputting the results of the abstract idea, see MPEP 2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Further, these limitations of Claims 2-3, 5 and 7-10 amount to no more than mere instructions to apply the exception using well-understood, routine and conventional computer components and functions, recited at a high level of generality, i.e. receiving/transmitting data over a network and storing/retrieving information in memory. As such, these additional elements do not amount to an inventive concept and are not by themselves sufficient to transform the judicial exception into a patent eligible invention, see MPEP 2106.05(d).
Claims 2, 4-5, 7, 9-10 and 16 recite, “the at least one processor is further configured to execute the instructions”.
These limitations of Claims 2, 4-5, 7, 9-10 and 16 are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components, see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Further, these limitations of Claims 2, 4-5, 7, 9-10 and 16 amount to no more than mere instructions to apply the exception using well-understood, routine and conventional computer components and functions, recited at a high level of generality, i.e. receiving/transmitting data over a network and storing/retrieving information in memory. As such, these additional elements do not amount to an inventive concept and are not by themselves sufficient to transform the judicial exception into a patent eligible invention, see MPEP 2106.05(d).
Claim 4 further recites, “calculate a suitability degree indicating the degree of suitability based on the acquired configuration information of the verification target and the learned model, and evaluate the suitability based on the calculated degree of suitability.”
Claim 9 further recites, “search for the compatible learned model further using the suitability result.”
Claim 16 further recites, “verify a cyber security of the verification target using the learned model suitable with the verification target.”
These limitations of Claims 4, 9 and 16, as drafted, are processes that, under their broadest reasonable interpretation, recite the abstract idea of a mental process. These limitations encompass a human mind carrying out this function through observation, evaluation judgment and/or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 11 recites, “wherein the learned model is a model that accepts the configuration information as an input and outputs the suitable learned model candidate.”
These limitations of Claim 11 do nothing more than generally link the judicial exception to a particular technological environment, see MPEP 2106.05(h). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
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, 6-9 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US PGPUB 2021/0174137; hereinafter “Kim”) in view of Shetty et al. (US PGPUB 2020/0295991; hereinafter “Shetty”).
Claim 1: (Currently Amended)
Kim teaches a model analysis device comprising:
a memory storing instructions (Fig. 18: Memory 120. [0201] “One or more instructions regarding the electronic device 100 may be stored in the memory 120… In addition, the memory 120 may also store various software programs or applications for operating the electronic device 100 according to diverse embodiments of the disclosure.”); and
at least one processor configured to execute the instructions to (Fig. 18: Processor 130. [0196] “Referring to FIG. 17, the electronic device 100 according to an embodiment of the disclosure includes … a memory 120, and a processor 130.” [0202] “the processor 130 may execute the various software modules stored in the memory 120 to control an operation of the electronic device 100.”):
acquire configuration information of a device including hardware configuration information in a verification target ([0054] “The ‘first device information’ refers to information on the hardware specification of the electronic device 100… the first device information… may include detailed information on the presence, number, type, and performance of each of a plurality of hardware components included in the electronic device 100,” wherein the “first device information” is the “configuration information of a device including hardware configuration information”. [0170] “In response to the request received from the electronic device 100, the first external device 200-1 may transmit the first device information and the first model information to the electronic device 100 at operation S1430-1”);
acquire configuration information of the device in an environment in which a learned model used for verification of the verification target is generated ([0182] “The ‘hardware requirement specification monitor’ refers to a module that monitors a hardware specification required to execute the neural network models included in the electronic device 100. Specifically, the hardware requirement specification monitor may acquire information on the hardware specification required to execute each of the one or more neural network models included in the electronic device 100”);
evaluate suitability of the verification target with the learned model based on the acquired configuration information of the verification target and the learned model ([0010] “identify whether each of the one or more neural network models included in the first external device is suitable for the hardware of the electronic device by inputting the first device information, the second device information, and the first model information into a hardware suitability identifier”); and
output the evaluated result ([0057] “the ‘hardware suitability identification module 1100’ may output information on whether it is suitable to execute a neural network model included in an external device by using hardware of the electronic device 100.” [0215] “the display 151 may display a user interface including information on a neural network model that satisfies the hardware suitability and the model suitability according to the disclosure.”).
With further regard to Claim 1, Kim does not teach the following, however, Shetty teaches:
acquire configuration information of a device including software configuration information formed by firmware configuration information and protocol processing software configuration information ([0045] “the configuration of the network interface of the target IHS may be obtained via a query to the remote access controller of the target IHS.” [0046] “the network interface configurations may be compared by evaluating the attributes of the network interface configurations that specify capabilities supported by each of the network interfaces. A network interface capability attribute may specify whether the interface supports a specific functionality,” wherein the “network interface capability attribute” is the “protocol processing software configuration information”. [0057] “the network migration tool may be tasked with evaluating available target IHSs… . The network migration tool may apply the process of FIG. 3 in evaluating the compatibility of each capability that is included in the source network interface and a candidate network interface. … the network migration tool may further score the suitability of a target network interface based on additional matches with attributes of the source network interface, such as the manufacturer or provider of the network interface, the installed location of the network device configured by the network interface, and the firmware version used by the network device,” wherein the “firmware version” is the “firmware configuration information”.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device as disclosed by Kim with the acquiring of further types of configuration information as taught by Shetty “in order to identify candidates with network configurations that can support the virtualized system” (Shetty [0057]).
Claim 2: (Currently Amended)
Kim in view of Shetty teaches the device of claim 1. Kim further teaches wherein
the configuration information of the device in the environment in which the verification target and the learned model are generated includes configuration information of a plurality of devices ([0147] “the electronic device 100 may transmit a first signal for requesting information related to the one or more neural network models included in the one or more first external devices 200-1. As a response to the first signal, the electronic device 100 may receive identification information on the first external device 200-1 and identification information included in each of the first external devices 200-1 from each of the one or more first external devices 200-1.”).
With further regard to Claim 2, Kim does not teach the following, however, Shetty teaches:
the at least one processor is further configured to execute the instructions to:
acquire network configuration information including connection information between the plurality of devices as the configuration information ([0040] “a network migration tool 210 to identify IHSs within the data center that may serve as candidate target IHSs, where the candidates are identified based on utilization of network interface configurations that are compatible with the network interface configuration of the source IHS.” [0046] “the network interface configurations may be compared by evaluating the attributes of the network interface configurations that specify capabilities supported by each of the network interfaces. A network interface capability attribute may specify whether the interface supports a specific functionality, such as support for offloading certain network operations to an offload engine or support for portioning network ports of the network controller configured by the network interface.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device as disclosed by Kim with the acquiring of network configuration information as taught by Shetty “in order to identify candidates with network configurations that can support the virtualized system” (Shetty [0057]).
Claim 3: (Currently Amended)
Kim in view of Shetty teaches the device of claim 1. Kim further teaches
wherein hardware configuration information in the verification target and the hardware configuration information in which the learned model is generated include chip configuration information for controlling an operation of the device ([0055] “the first device information may include information on a specification of a processor included in the electronic device 100,” wherein the “information on a specification of a processor” is the “chip configuration information”. [0070] “the electronic device 100 may perform a hardware suitability identification process for each hardware configuration based on information on the processor specifications… included in each of the first and second device information.”).
Claim 4: (Currently Amended)
Kim in view of Shetty teaches all the limitations of claim 1 as described above. Kim does not teach the following, however, Shetty teaches wherein the at least one processor is further configured to execute the instructions to:
calculate a suitability degree indicating the degree of suitability based on the acquired configuration information of the verification target and the learned model ([0057] “The network migration tool may score the strength of the compatibility of a candidate target network interface based on the number of capabilities of the source network interface that can be supported identically on the candidate target interface.”), and
evaluate the suitability based on the calculated degree of suitability ([0057] “. Based on such scoring, the network migration tool may identify the target IHS with the network interface that is most suitable for migration of a virtualized system.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device as disclosed by Kim with the compatibility scoring as taught by Shetty “in order to identify candidates with network configurations that can support the virtualized system” (Shetty [0057]).
Claim 6: (Currently amended)
Kim teaches a model analysis device comprising:
a memory storing instructions (Fig. 18: Memory 120. [0201] “One or more instructions regarding the electronic device 100 may be stored in the memory 120… In addition, the memory 120 may also store various software programs or applications for operating the electronic device 100 according to diverse embodiments of the disclosure.”); and
at least one processor configured to execute the instructions to (Fig. 18: Processor 130. [0196] “Referring to FIG. 17, the electronic device 100 according to an embodiment of the disclosure includes … a memory 120, and a processor 130.” [0202] “the processor 130 may execute the various software modules stored in the memory 120 to control an operation of the electronic device 100.”):
acquire configuration information of a device including hardware configuration information in a verification target ([0054] “The ‘first device information’ refers to information on the hardware specification of the electronic device 100… the first device information… may include detailed information on the presence, number, type, and performance of each of a plurality of hardware components included in the electronic device 100,” wherein the “first device information” is the “configuration information of a device including hardware configuration information”. [0170] “In response to the request received from the electronic device 100, the first external device 200-1 may transmit the first device information and the first model information to the electronic device 100 at operation S1430-1”);
search for a learned model suitable for verification of the verification target based on the acquired configuration information of the device ([0067] “The electronic device 100 may perform the hardware suitability identification process for all neural network models included in the first external device 200-1.” [0068] “if the specification of each of the plurality of hardware configurations included in the electronic device 100 is greater than or equal to the specifications of the plurality of hardware configurations included in the first external device 200-1 (Y in S140), the electronic device 100 may identify the one or more neural network models included in the first external device 200-1 as suitable for hardware of the electronic device 100 at operation S150-1.”); and
output the searched learned model ([0057] “the ‘hardware suitability identification module 1100’ may output information on whether it is suitable to execute a neural network model included in an external device by using hardware of the electronic device 100.” [0215] “the display 151 may display a user interface including information on a neural network model that satisfies the hardware suitability and the model suitability according to the disclosure.”).
With further regard to Claim 6, Kim does not teach the following, however, Shetty teaches:
acquire configuration information of a device including software configuration information formed by firmware configuration information and protocol processing software configuration information ([0045] “the configuration of the network interface of the target IHS may be obtained via a query to the remote access controller of the target IHS.” [0046] “the network interface configurations may be compared by evaluating the attributes of the network interface configurations that specify capabilities supported by each of the network interfaces. A network interface capability attribute may specify whether the interface supports a specific functionality,” wherein the “network interface capability attribute” is the “protocol processing software configuration information”. [0057] “the network migration tool may be tasked with evaluating available target IHSs… . The network migration tool may apply the process of FIG. 3 in evaluating the compatibility of each capability that is included in the source network interface and a candidate network interface. … the network migration tool may further score the suitability of a target network interface based on additional matches with attributes of the source network interface, such as the manufacturer or provider of the network interface, the installed location of the network device configured by the network interface, and the firmware version used by the network device,” wherein the “firmware version” is the “firmware configuration information”.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device as disclosed by Kim with the acquiring of further types of configuration information as taught by Shetty “in order to identify candidates with network configurations that can support the virtualized system” (Shetty [0057]).
Claims 7-8:
With regard to Claims 7-8, these claims are equivalent in scope to Claims 2-3 rejected above, merely having a different independent claim type, and as such Claims 7-8 are rejected under the same grounds and for the same reasons as discussed above with regard to Claims 2-3.
Claim 9: (Currently Amended)
Kim in view of Shetty teaches all the limitations of claim 6 as described above. Kim does not teach the following, however, Shetty teaches wherein the at least one processor is further configured to execute the instructions to:
receive a suitability result of the learned model for a verification target ([0099] “he electronic device 100 may input the first model information and the second model information into the model suitability identification module 1200 at operation S220.” [0065] “the first model information may include… information on a personalization level… of each of the one or more neural network models included in the first external device 200-1,” wherein the “personalization level” is the “suitability result”.), and
search for the compatible learned model further using the suitability result ([0103] “the ‘personalization level suitability identification module 1220’ may identify a neural network model having a high personalization level of among the neural network models having the same service type by comparing the personalization level between the neural network models… based on the information on the personalization level included in each of the first model information and the second model information.” [0112] “if the personalization level of the first neural network model is higher than the personalization level of the second neural network model (Y in operation S240), it may be identified that the first neural network model is suitable to replace the second neural network model at operation S250.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device as disclosed by Kim with the suitability result determination as taught by Shetty so that “the efficiency and reliability of transfer learning may be further improved” (Shetty [0123]).
Claim 11: (Currently Amended)
Kim in view of Shetty teaches all the limitations of claim 6 as described above. Kim further teaches:
wherein the learned model is a model that accepts the configuration information as an input and outputs the suitable learned model candidate ([0189] “the neural network model installation information management module 1620 may transmit the installation data of the one or more neural network models to the electronic device 100… the installation data may include identification information for each of the one or more neural network models and configuration information necessary to install the one or more identified neural network models. The ‘configuration information’ may include the structure and type of the neural networks included in the neural network model, the number of layers included in the neural network, the number of nodes for each layer, a weight value of each node, and a connection relationship between a plurality of nodes.” [0190] “the neural network model installation information management module 1620 may transmit personalization data for the one or more neural network models to the electronic device 100 together with the installation data”).
Claims 12-13:
With regard to Claims 12-13, these claims are equivalent in scope to Claims 1 and 6 rejected above, merely having a different independent claim type, and as such Claims 12-13 are rejected under the same grounds and for the same reasons as discussed above with regard to Claims 1 and 6.
Claims 5 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Shetty as applied to Claims 1 and 6 above, and further in view of Taylor et al. (US PGPUB 2022/0100262; hereinafter “Taylor”).
Claim 5: (Currently Amended)
Kim in view of Shetty teaches all the limitations of claim 1 as described above. Kim in view of Shetty does not teach the following, however, Taylor teaches further comprising:
wherein the at least one processor is further configured to execute the instructions to:
store identifier information of the verification target and the configuration information of the verification target in association ([0027] ” a look-up table to determine configurations that are associated with a particular device identifier,” wherein the “configuration information” and associated “device identifier” must necessarily have been stored in the “look-up table” prior to it being accessed in a later look-up operation.), and
acquire the configuration information stored based on the identifier information of the verification target ([0027] “the network device 102 may use the device identifier to identify a device type for the virtual reality device 104 and/or to identify the capabilities of the virtual reality device 104. The network device 102 may determine a hardware configuration, a firmware configuration, and/or a software configuration for the virtual reality device 104 based on the device identifier. For instance, the network device 102 may use a look-up table to determine configurations that are associated with a particular device identifier.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device as disclosed by Kim in view of Shetty with the device identifier usage as taught by Taylor as this “prevents the network device 102 from providing virtual simulations 120 that are not compatible with the virtual reality device” (Taylor [0027]).
Claim 10:
With regard to Claim 10, this claim is equivalent in scope to Claim 5 rejected above, merely having a different independent claim type, and as such Claim 10 is rejected under the same grounds and for the same reasons as discussed above with regard to Claim 5.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Shetty as applied to Claim 1 above, and further in view of El-Moussa (US PGPUB 2021/0004468; hereinafter “El-Moussa”).
Claim 16: (New)
Kim in view of Shetty teaches all the limitations of claim 1 as described above. Kim in view of Shetty does not teach the following, however, El-Moussa teaches
wherein the at least one processor is further configured to execute the instructions to:
verify a cyber security of the verification target using the learned model suitable with the verification target ([0028] “The configuration information 200 is received by a risk evaluator 218 as a hardware, software, firmware or combination component for evaluating a risk score for a risk of occurrence of a security attack 214 in, with or for an application deployed in accordance with the application configuration information 200.” [0044] “At 306 the risk evaluator 218 evaluates a risk score for a risk of occurrence of the security attack 214 were the application to be deployed according to the configuration information 200.” [0048] “an adjusted application configuration information 202 is generated having a lower risk of attack by security attack 214 than the original application configuration information 200.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device as disclosed by Kim in view of Shetty with the security verification as taught by El-Moussa since “ It is desirable to mitigate such attacks in virtualized computing environments” (El-Moussa [0005]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is as follows:
Sawal et al. (US PGPUB 2019/0042349) discloses a system wherein a module is tested for compatibility with a server rack chassis without being inserted into the chassis, wherein information about the module is analyzed against the platform specification and chassis configuration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joanne G. Macasiano whose telephone number is (571)270-7749. The examiner can normally be reached Monday to Thursday, 10:30 AM to 6:00 PM Eastern Standard Time.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bradley Teets can be reached at (571) 272-3338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOANNE G MACASIANO/Examiner, Art Unit 2197