Detailed Action
This action is in response to the application filed on 01/13/2025. Claims 11-23 are
pending and have been fully examined.
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 .
Status of the Claims
Claims 11-22 are rejected under 35 U.S.C. 102
Claim 23 is rejected under 35 U.S.C. 103
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.
Claims 11-22 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Kanagovi et al. (U.S. Publication No. 2022/0050738 A1), hereinafter referred to as Kanagovi.
Regarding Claim 11, Kanagovi teaches:
A method for supporting a robustness optimization for a bus-based data processing system, the method comprising:
generating a plurality of message sequences; ([0016]; regarding, “…analyzing error messages obtained from a client(s) (or another entity in the system), deduplicating repetitive error messages, generating message sequences of sequential messages, analyzing the message sequences to obtain a list of high severity message sequences, and servicing the messages.”);
sequentially transmitting the message sequences to a receiving device, which is configured to process the message sequences; ([0037]; regarding, “The error message resolution agent (162) may include functionality for obtaining error messages an error message repository (e.g., 164A) from a client or from another entity”; [0039]; regarding, “The storage (164) may store data structures such as, for example, an error message repository (164A), a sequence frequency track mapping (164B), and an error-resolution mapping (164C).”; [0041]; regarding, “the sequence track mapping (164B) specifies message sequences of error messages obtained from the error message repository”; [0042]; regarding, “The message sequences may be grouped based on a start error message and an end error message in which no intermediate messages are associated with a copy of the error message.”);
capturing, for each message sequence, error alerts which are respectively generated upon processing of the message sequences by the receiving device; ([0049]; regarding, “The sequence frequency track mapping (220) may be an embodiment of the sequence frequency track mapping (164B, FIG. 1B) discussed above. The sequence frequency track mapping (220) may include one or more sequence frequency entries (220A, 220M). Each sequence frequency entry (220A, 220M) may specify one or more error message code frequencies (222, 224, 226). Each sequence frequency entry (220A, 220M) may be associated with a message sequence.”);
and depending upon how many of the error alerts are generated, classifying one of the message sequences as a most problematic message sequence, and outputting the most problematic message sequence as a basis for a corresponding troubleshooting. ([0056]; regarding, “a message sequence frequency algorithm is performed to obtain a high severity message sequence list.”; [0057]; regarding, “The high severity error message sequence list is a list of message sequences determined, based on the message sequence frequency algorithm, to be message sequences that require the most attention by the message resolution manager during an error message resolution.”; [0059]; regarding, “the error message resolution includes identifying the error messages specified in each message sequence in the high severity message sequence list, and using an error-resolution mapping to identify a potential solution to each error in the error messages. The error message resolution may be an action (or a series of actions) performed by the message resolution manager to resolve the error specified in each error message of the message sequences.”).
Regarding Claim 12, Kanagovi teaches the method of claim 11 as referenced above. Kanagovi further teaches:
wherein the message sequences contain same individual messages, but vary with respect to time intervals between each message sequence. (Fig. 4A, [0071]; regarding, “The resulting set of deduplicated error messages are grouped into message sequences based on the points in time in which error messages were deleted.”).
Regarding Claim 13, Kanagovi teaches the method of claim 11 as referenced above. Kanagovi further teaches:
wherein each message sequence is transmitted to the receiving device a plurality of times and, in each case, all of the error alerts are considered for determination of the most problematic message sequence. (Fig. 4A, [0056]; regarding, “a message sequence frequency algorithm is performed to obtain a high severity message sequence list.”; [0057]; regarding, “The high severity error message sequence list is a list of message sequences determined, based on the message sequence frequency algorithm, to be message sequences that require the most attention by the message resolution manager during an error message resolution.”).
Regarding Claim 14, Kanagovi teaches the method of claim 11 as referenced above. Kanagovi fails further teaches:
wherein the message sequences are generated by a predefined genetic algorithm including pairings and mutations across multiple generations. ([0037]; regarding, “The error message resolution agent (162) may include functionality for obtaining error messages an error message repository (e.g., 164A) from a client or from another entity”; [0041]; regarding, “the sequence track mapping (164B) specifies message sequences of error messages obtained from the error message repository”; [0062]; regarding, “The error-resolution mapping may be updated by identifying the error message code and modifying the mapped resolutions to specify the series of actions performed by the client if the series of actions resulted in the error being resolved. Each error message code that may benefit from the update may be updated in accordance with the response from the client.”).
Regarding Claim 15, Kanagovi teaches the method of claim 14 as referenced above. Kanagovi fails further teaches:
wherein, in the genetic algorithm, by way of a respective gene sequence of the message sequences, a respective series of time intervals between individual messages contained in the message sequences is employed. (Fig. 4, [0071]; regarding, “The resulting set of deduplicated error messages are grouped into message sequences based on the points in time in which error messages were deleted. Because the error messages of timestamps T4, T5, T8, T9, and T12 were deleted, there are three periods of times within which no error messages were deleted.”).
Regarding Claim 16, Kanagovi teaches the method of claim 14 as referenced above. Kanagovi fails further teaches:
wherein, in the genetic algorithm, as a measure of fitness of the message sequences, a number and/or severity and/or type of the error alerts generated in processing of the message sequences is/are employed, wherein a greater number and/or a greater severity of the error alerts and/or a hierarchically higher classification of the error alerts, according to a predefined hierarchy of error alert types, corresponds to a greater fitness. ([0065]; regarding, “a severity score is generated for each message code specified in each message sequence. In one or more embodiments of the invention, the severity score for an error message of a message sequence is calculated using a function (or a combination of functions) that uses inputs that may include, for example, the error message code frequency (or frequencies) of the error message code of the message sequence, the total number of message sequences, and/or the number of unique error codes specified in the message sequence. Other inputs may be used to generate the severity score without departing from the invention.”).
Regarding Claim 17, Kanagovi teaches the method of claim 15 as referenced above. Kanagovi fails further teaches:
wherein, in the genetic algorithm, as a measure of fitness of the message sequences, a number and/or severity and/or type of the error alerts generated in processing of the message sequences is/are employed, wherein a greater number and/or a greater severity of the error alerts and/or a hierarchically higher classification of the error alerts, according to a predefined hierarchy of error alert types, corresponds to a greater fitness. ([0065]; regarding, “a severity score is generated for each message code specified in each message sequence. In one or more embodiments of the invention, the severity score for an error message of a message sequence is calculated using a function (or a combination of functions) that uses inputs that may include, for example, the error message code frequency (or frequencies) of the error message code of the message sequence, the total number of message sequences, and/or the number of unique error codes specified in the message sequence. Other inputs may be used to generate the severity score without departing from the invention.”).
Regarding Claim 18, Kanagovi teaches the method of claim 14 as referenced above. Kanagovi fails further teaches:
wherein production of new message sequences by the genetic algorithm continues until such time as a predefined convergence criterion with respect to the error alerts and/or with respect to a fittest message sequence and/or a predefined interruption criterion is/are fulfilled. ([0066]; regarding, “a cluster evaluation for each message sequence is performed to obtain a set of sequence clusters. In one or more embodiments of the invention, the cluster evaluation is performed using the generated severity scores in step 322. The cluster evaluation includes grouping the message sequences into sequence clusters.”; [0067]; regarding, “The grouping of the sequence clusters may be determined based on the generated severity scores and a clustering mechanism applied to the message sequences. The clustering mechanism may be, for example, a K-means clustering. In one or more embodiments of the invention, the K-means clustering is a mechanism for classifying items based on properties of the items. The items may be the message sequences. The properties may include, for example, the severity scores of each error message code in each message sequence. Other clustering mechanisms may be applied without departing from the invention.”; [0068]; regarding, “the high severity message sequence list is generated by selecting a message sequence from each sequence cluster.”; [0074]; regarding, “After the high severity error message sequence list is generated, an error-resolution mapping is used to identify stored solutions to the errors of the message sequences in the high severity message sequence list.”).
Regarding Claim 19, Kanagovi teaches the method of claim 15 as referenced above. Kanagovi fails further teaches:
wherein production of new message sequences by the genetic algorithm continues until such time as a predefined convergence criterion with respect to the error alerts and/or with respect to a fittest message sequence and/or a predefined interruption criterion is/are fulfilled. ([0066]; regarding, “a cluster evaluation for each message sequence is performed to obtain a set of sequence clusters. In one or more embodiments of the invention, the cluster evaluation is performed using the generated severity scores in step 322. The cluster evaluation includes grouping the message sequences into sequence clusters.”; [0067]; regarding, “The grouping of the sequence clusters may be determined based on the generated severity scores and a clustering mechanism applied to the message sequences. The clustering mechanism may be, for example, a K-means clustering. In one or more embodiments of the invention, the K-means clustering is a mechanism for classifying items based on properties of the items. The items may be the message sequences. The properties may include, for example, the severity scores of each error message code in each message sequence. Other clustering mechanisms may be applied without departing from the invention.”; [0068]; regarding, “the high severity message sequence list is generated by selecting a message sequence from each sequence cluster.”; [0074]; regarding, “After the high severity error message sequence list is generated, an error-resolution mapping is used to identify stored solutions to the errors of the message sequences in the high severity message sequence list.”).
Regarding Claim 20, Kanagovi teaches the method of claim 16 as referenced above. Kanagovi fails further teaches:
wherein production of new message sequences by the genetic algorithm continues until such time as a predefined convergence criterion with respect to the error alerts and/or with respect to a fittest message sequence and/or a predefined interruption criterion is/are fulfilled. ([0066]; regarding, “a cluster evaluation for each message sequence is performed to obtain a set of sequence clusters. In one or more embodiments of the invention, the cluster evaluation is performed using the generated severity scores in step 322. The cluster evaluation includes grouping the message sequences into sequence clusters.”; [0067]; regarding, “The grouping of the sequence clusters may be determined based on the generated severity scores and a clustering mechanism applied to the message sequences. The clustering mechanism may be, for example, a K-means clustering. In one or more embodiments of the invention, the K-means clustering is a mechanism for classifying items based on properties of the items. The items may be the message sequences. The properties may include, for example, the severity scores of each error message code in each message sequence. Other clustering mechanisms may be applied without departing from the invention.”; [0068]; regarding, “the high severity message sequence list is generated by selecting a message sequence from each sequence cluster.”; [0074]; regarding, “After the high severity error message sequence list is generated, an error-resolution mapping is used to identify stored solutions to the errors of the message sequences in the high severity message sequence list.”).
Regarding Claim 21, Kanagovi teaches the method of claim 11 as referenced above. Kanagovi fails further teaches:
wherein a type and/or severity of the error alerts is also captured and considered in combination with a number of the error alerts for determination of the most problematic message sequence. ([0067]; regarding, “The clustering mechanism may be, for example, a K-means clustering. In one or more embodiments of the invention, the K-means clustering is a mechanism for classifying items based on properties of the items. The items may be the message sequences. The properties may include, for example, the severity scores of each error message code in each message sequence. Other clustering mechanisms may be applied without departing from the invention.”).
Regarding Claim 22, Kanagovi teaches the method of claim 11 as referenced above. Kanagovi fails further teaches:
A support device for supporting a robustness optimization for a data processing system, comprising a processor device and a non-transitory computer-readable data memory which is connected thereto, and at least one interface for transmitting message sequences to a receiving device and for receiving error alerts from the receiving device, wherein the support device is configured to execute a method according to claim 11. ([0077]; regarding, “The computing device (500) may include one or more computer processors (502), non-persistent storage (504) (e.g., volatile memory, such as random access memory (RAM), cache memory), persistent storage (506) (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory, etc.), a communication interface (512) (e.g., Bluetooth interface, infrared interface, network interface, optical interface, etc.), input devices (510), output devices (508), and numerous other elements (not shown) and functionalities”).
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.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Kanagovi et al. (U.S. Publication No. 2022/0050738 A1), hereinafter referred to as Kanagovi, in view of Erdem et al. (U.S. Patent No. 9,566,966 B2), hereinafter referred to as Erdem.
Regarding Claim 23, Kanagovi teaches the method of claim 22 as referenced above. Kanagovi further teaches:
and to automatically generate and output a corresponding report, which also includes a most problematic message sequence identified by the support device. ([0057]; regarding, “The high severity error message sequence list is a list of message sequences determined, based on the message sequence frequency algorithm, to be message sequences that require the most attention by the message resolution manager during an error message resolution.”; [0059]; regarding, “the error message resolution includes identifying the error messages specified in each message sequence in the high severity message sequence list, and using an error-resolution mapping to identify a potential solution to each error in the error messages. The error message resolution may be an action (or a series of actions) performed by the message resolution manager to resolve the error specified in each error message of the message sequences.”).
Kanagovi fails to explicitly disclose but Erdem teaches:
automatically check, by the support device, introduced software components for susceptibility to errors, (Col. 11, lines 5-15; regarding, “The total system 1 is also configured to carry out diagnostic tests repeatedly at time intervals for checking whether, in one or more of the electrical, electronic and/or programmable systems, that is to say in one of the safety-relevant E/E/PE systems of the total system 1, there is a fault or a failure has occurred that can adversely affect the carrying out of the safety function. In this example there is provision that testing can be carried out automatically by the E/E/PE systems of the total system 1 themselves (self-tests of these systems).”);
in an event that no error alert is captured during checking, release a respective software component for integration; (Col. 12 lines 60-67; Col. 13, lines 1-10; regarding, “The control unit 4 checks, as a function of the reliability value of the data which is obtained as a product of the values of these diagnostic coverages DC.sub.RF, whether the transmitted data is sufficiently reliable for carrying out the safety function by comparing the reliability value of the data with a predefined threshold value. The data is then evaluated as being sufficiently secure if the reliability value is greater (or alternatively smaller) than this threshold value.”; Col. 14, lines 21-28; regarding, “data is transmitted to the control unit 4 again after a predefined waiting time period if the data, necessary for carrying out the safety function, in the control unit 4 is not completely present or not sufficiently reliable, wherein the data is in this way transmitted repeatedly to the control unit 4 until the data is completely present and sufficiently reliable.”; )
in an event that at least one error alert is captured during checking, reject the respective software component (Col. 13, lines 10-20; regarding, “If the check reveals that the data necessary for carrying out the safety function is not completely present or not sufficient reliable…a deactivation signal is sent to the functional unit 5, wherein, after reception of this deactivation signal, the functional unit 5 automatically goes into a safety mode in which the safety function cannot be carried out.”);
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Kanagovi with the teachings of Erdem. Doing so could increase the availability of the total system (Erdem, Col. 16, lines 49-59).
Conclusion
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/M.D.G./Examiner, Art Unit 2113
/BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113