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 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 – 6 and 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1 – 6 and 8 directed to an abstract idea.
Regarding claim 1 and analogous claims 6 and 8:
Per step 1 of the Subject Matter Eligibility Test for Products and Processes, “Is the claim to a process, machine, manufacture, or composition of matter?”, claim 1 is directed to a machine, claim 6 is a method, and claim 8 is a manufacture.
Step 1: yes.
Per step 2A prong 1, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?”,
a generator that generates pseudo-observable data using the causal structure matrix and a discriminator that identifies whether the pseudo-observable data is false or not, wherein the causal structure matrix represents a causal structure between the combined observable data; and
claim 1 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A: prong 1: yes.
Per step 2A prong 2, “Does the claim recite additional elements that integrate the judicial exception into a practical application?”, the following elements of claim 1 are directed to additional elements:
An information processing device, comprising one or more processors configured to perform operations comprising: (well-understood, routine, and conventional generic computer, see MPEP 2106.05(f))
collecting and combining various observable data acquired from a managed target at predetermined time intervals; … inputting the combined observable data (insignificant extra-solution activity of mere data gathering, see MPEP 2106.05(g))
updating a causal structure matrix by repeatedly learning with (adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, see MPEP 2106.05(f))
outputting the causal structure between the observable data based on the causal structure matrix. (insignificant extra-solution activity of mere data output, see MPEP 2106.05(g))
Step 2A prong 2: no.
Per step 2B, “Does the claim recite additional elements that amount to significantly more than the judicial exception?”, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the claimed inventions simply append well-understood, routine and conventional activities previously known to the industry (see analysis under prong 2), both when viewed independently and as an ordered combination, specified at a high level of generality, to the judicial exception, (e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry).
Step 2B: no.
Regarding claim 2:
Per step 2A prong 1, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?”,
converting timestamp information of the observable data into time information indicating the predetermined time interval, and combining the observable data having the same time information. Claim 2 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A: prong 1: yes.
Claim 2 does not add any additional elements (step 2A: prong 2) or significantly more (step 2B)
Regarding claim 3:
Per step 2A prong 1, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?”,
wherein the operations further comprise converting the observable data which is a character string into a numerical value. Claim 3 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A: prong 1: yes.
Claim 3 does not add any additional elements (step 2A: prong 2) or significantly more (step 2B)
Regarding claim 4:
Per step 2A prong 1, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?”,
handling a missing value of the observable data combined at the predetermined time intervals. Claim 4 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A: prong 1: yes.
Claim 4 does not add any additional elements (step 2A: prong 2) or significantly more (step 2B)
Regarding claim 5:
Per step 2A prong 1, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?”,
elements of the causal structure matrix are numerical values of the causal structure between observable data in rows of the elements and observable data in columns of the elements, (i.e. further modifies the mental process).
in which row observable data and column observable data of the elements of the causal structure matrix are equal to or greater than a threshold and are regarded as nodes, and the nodes are connected by edges (i.e. mental process e.g. judgment or evaluation).
Step 2A: prong 1: yes.
Per step 2A prong 2, “Does the claim recite additional elements that integrate the judicial exception into a practical application?”, the following elements of claim 1 are directed to additional elements: outputting a directed acyclic graph (insignificant extra-solution activity of mere data output, see MPEP 2106.05(g))
Step 2A prong 2: no.
Per step 2B, “Does the claim recite additional elements that amount to significantly more than the judicial exception?”, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the claimed inventions simply append well-understood, routine and conventional activities previously known to the industry (see analysis under prong 2), both when viewed independently and as an ordered combination, specified at a high level of generality, to the judicial exception, (e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry).
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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1, 2, 4 – 6, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Jung, Pre-Grant Publication No. US 2020/0401470 (“Jung”) in view of Afo Guard, “‘Statistical Causal Search’ Model Using GANs: Applying SAM (Structural Agnostic Modelling) to Titanic Datasets”, 30 December 2020 (Accessed from <<https://qiita.com/Afo_guard_enthusiast/items/0470b030a6a52d24fc67>> with machine translation by Edge browser). (“AFO”).
Regarding claim 1 and analogous claims 6 and 8, Jung teaches An information processing device, comprising one or more processors configured to perform operations comprising: collecting and combining various observable data acquired from a managed target at predetermined time intervals; (Jung, paragraphs 0036 and 0067 - 0068, “[0036] In one embodiment of the present invention, a user can set two timestamps, i.e., Ts to Te, indicating a time interval covering all observations. For example, the time interval could be set to one year. This time interval can be referred to as an analysis period. The analysis period can be represented as a pair of (Ts, Te), and the system can automatically set the analysis period to support periodical update, unless explicitly specified by users. [0067] FIG. 8 presents a flowchart illustrating an exemplary process for performing root cause analysis of anomaly events, according to one embodiment of the present invention. During operation, the system may obtain sensor data stored in a sensor database (operation 802). The sensor data is associated with a plurality of sensors embedded in one or more machines in a factory layout. [0068] In industrial IoT applications, due to the increasing number of sensors, the amount of sensor data collected can be large. Furthermore, the sensor data usually contain a large number of ill-conditioned data that include missing, corrupted, noisy, and highly correlate values. When such poor quality sensor data are used for performing root cause analysis, the results can be incorrect and hence unreliable. Furthermore, the computational complexity of root cause analysis can increase significantly due to the large number of sensor data. In order to provide a compact and informative representation of sensor data, the system converts the sensor data into a set of sensor states (operation 804).”).
Jung does not explicitly teach:
inputting the combined observable data and updating a causal structure matrix by repeatedly learning with a generator that generates pseudo-observable data using the causal structure matrix and a discriminator that identifies whether the pseudo-observable data is false or not,
wherein the causal structure matrix represents a causal structure between the combined observable data; and
outputting the causal structure between the observable data based on the causal structure matrix..
AFO teaches:
inputting the combined observable data and updating a causal structure matrix by repeatedly learning with a generator that generates pseudo-observable data using the causal structure matrix and a discriminator that identifies whether the pseudo-observable data is false or not, (AFO, p. 7).
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wherein the causal structure matrix represents a causal structure between the combined observable data; and (AFO, p. 8).
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outputting the causal structure between the observable data based on the causal structure matrix. (AFO, p. 4).
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In view of the teachings of AFO it would have been obvious for a person of ordinary skill in the art to apply the teachings of AFO to Jung before the effective filing date of the claimed invention in order to combine the teachings of Jung (i.e. a Directed Acyclic Graph with relevant time stamped data) with the teachings of AFO (i.e. DAG that interpolates data in a limited dataset), with the combination of these familiar elements functioning together according to known methods yielding predictable results (i.e. a DAG with limited time stamped data that is interpolated).
Regarding claim 2, Jung, as modified by AFO, teaches the information processing device according to claim 1.
Jung further teaches wherein the operations further comprise converting timestamp information of the observable data into time information indicating the predetermined time interval, and combining the observable data having the same time information. (Jung, paragraph 0042, “[0042] Data interpolation module 212 can aggregate all samples with timestamps within the time interval to calculate a mean and a variance associated with a specific attribute containing numerical values, so that there is at least one valid sample for the time interval. Otherwise, data interpolation module 212 can run an interpolation algorithm based on aggregation results of neighboring intervals. If the attribute contains categorical values, e.g., [high, median, low], data interpolation module 212 can apply random hashing to map concrete values to an integer domain, and processes the attribute after mapping as continuous numerical values. For example, data interpolation module 212 can replace missed sensor readings by using linear interpolation for continuous values types, and nearest-neighbor interpolation for discrete value types.”).
Regarding claim 4, Jung, as modified by AFO, teaches the information processing device according to claim 1.
Jung further teaches wherein the operations further comprise handling a missing value of the observable data combined at the predetermined time intervals. (Jung, paragraph 0042, “[0042] Data interpolation module 212 can aggregate all samples with timestamps within the time interval to calculate a mean and a variance associated with a specific attribute containing numerical values, so that there is at least one valid sample for the time interval. Otherwise, data interpolation module 212 can run an interpolation algorithm based on aggregation results of neighboring intervals. If the attribute contains categorical values, e.g., [high, median, low], data interpolation module 212 can apply random hashing to map concrete values to an integer domain, and processes the attribute after mapping as continuous numerical values. For example, data interpolation module 212 can replace missed sensor readings by using linear interpolation for continuous values types, and nearest-neighbor interpolation for discrete value types.”).
Regarding claim 5, Jung, as modified by AFO, teaches the information processing device according to claim 1.
AFO further teaches wherein elements of the causal structure matrix are numerical values of the causal structure between observable data in rows of the elements and observable data in columns of the elements, and the operations further comprise outputting a directed acyclic graph in which row observable data and column observable data of the elements of the causal structure matrix are equal to or greater than a threshold and are regarded as nodes, and the nodes are connected by edges. (AFO, pp. 4 - 5; The Examiner notes that relationships (i.e. edges) between matrix items (i.e. nodes) are denoted as causal relationships, denoted as 1’s in the second array, between data in various columns and rows).
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Jung and AFO are combinable for the same rationale as set forth above with respect to claim 1 and analogous claims 6 and 8.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Jung, as modified by AFO, in view of Microsoft, “Type Conversion Functions”, accessed via WayBack Machine for webpage captured on 12 August 2020 (accessed on 5 March 2026 from <<https://web.archive.org/web/20200812011839/ support.microsoft.com/en-us/office/type-conversionfunctions- 8ebb0e94-2d43-4975-bb13-87ac8d1a2202>> “Microsoft”).
Jung, as modified by AFO, teaches the information processing device according to claim 1.
Jung and AFO are combinable for the same rationale as set forth above with respect to claim 1 and analogous claims 6 and 8.
Jung, as modified by AFO, does not explicitly teach wherein the operations further comprise converting the observable data which is a character string into a numerical value..
Microsoft teaches wherein the operations further comprise converting the observable data which is a character string into a numerical value. (Microsoft, pp. 1ff., “Each function coerces an expression to a specific data type”; converting between various data types (e.g. strings (i.e. character string) and date/numeric values (i.e. numerical values) is well known in computer programming).
In view of the teachings of Microsoft it would have been obvious for a person of ordinary skill in the art to apply the teachings of Microsoft to Jung before the effective filing date of the claimed invention in order to convert between various data types.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J. HUNTLEY whose telephone number is (303) 297-4307 and email address is michael.huntley@uspto.gov. The examiner can normally be reached on Monday – Friday, 8:00 am – 5:00 pm MT.
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/MICHAEL J HUNTLEY/
Supervisory Patent Examiner, Art Unit 2129