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 14 objected to because of the following informalities: recites “and autoencoder” instead of “an autoencoder”. Note that
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.
Claims 1-20 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 1 recites the limitation "at least a portion of the first set of data sources" in line 8 when it is not clear if applicant is referring to the same portion of “at least a portion of the first set of data sources” of line 4. As a result, there is insufficient antecedent basis for this limitation in the claim. For examination, it is interpreted as “at least the portion of the first set of data sources”.
Regarding claims 15 and 18, the claims recite substantially similar limitations as corresponding claim 1 with similar issues of indefiniteness. Thus, the same rationale applies to claims 15 and 18.
Dependent claims 2-14, 16, 17, 19, 20 inherit the deficiencies of their independent claims 1, 15, 18 as described above. As a result, claims 1-20 are rejected under 35 U.S.C 112(b) as being indefinite for failing to particularly point out and distinctly claim the invention.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 8-11, 14, 15, 18 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-6, 8, 11, 12, 17 of Application number 18/443,801 in view of US 20230273908 A1, “Souza Vaz”. Although the claims at issue are not identical, they are not patentably distinct from each other because all of the limitations of the instant application’s claims are contained in co-pending application 18/443,801, with the exception that the reference patent recites “making an identification” without mention the instant applications recitation of using a” second inference model”. This is not considered to be a significant distinction; however, they imply the same meaning as the instant application merely adds an extra step of using a separate model to determine data source unavailability as seen in claim 1. A comparison chart of the claims follows, followed by an analysis.
Instant Application
Co-pending Application 18/443,801
A method of managing an inference model that comprises sub-network units, the method comprising: making an identification, based on an inference generated by a second inference model trained to predict data source unavailability, that at least a portion of a first set of data sources is likely to become unavailable at a future point in time, the first set of the data sources providing first input data for a first sub-network unit of the sub-network units and the inference model being unable to generate an inference model result when at least a portion of the first set of the data sources is unavailable
Examiner notes that a distinction is made between the instant application and application 18/443,801 with the underlined term, see below for an explanation.
A method managing an inference model that comprises sub-network units, the method comprising: making an identification that a portion of the inference model comprising one or more data sources of a first set of data sources is unavailable, the first set of the data sources providing first input data to a first sub-network unit of the sub-network units and when the one or more data sources are unavailable, the inference model is unable to generate an inference model result;
replacing, in response to the identification and at least temporarily, a first portion of the inference model that comprises at least the first sub-network unit with a second portion of the inference model that comprises at least a second sub-network unit to obtain an updated inference model prior to the future point in time, the second portion of the inference model being intended to duplicate operation of the first portion of the inference model within a threshold;
making a first determination, in response to the identification, regarding whether a second sub-network unit duplicates operation of the first sub-network unit within a threshold, the second sub-network unit not being used by the inference model when the identification is made; in a first instance of the first determination in which the second sub-network unit duplicates the operation of the first sub-network unit within the threshold: replacing, at least temporarily, the first sub-network unit with the second sub-network unit to obtain an updated inference model;
and executing the updated inference model to obtain the inference model result.
and executing the updated inference model to obtain the inference model result.
8. The method of claim 1, wherein when a first data source of the first set of the data sources becomes unavailable, a second data source of the first set of the data sources has an increased likelihood of becoming unavailable.
4. The method of claim 1, wherein when a first data source of the first set of the data sources becomes unavailable, a second data source of the first set of the data sources has an increased likelihood of becoming unavailable.
9. The method of claim 1, wherein the first sub-network unit comprises a first set of latent representation generation units and the second sub-network unit comprises a second set of latent representation generation units.
5. The method of claim 3, wherein the first sub-network unit comprises a first set of latent representation generation units and the second sub-network unit comprises a second set of latent representation generation units.
10. The method of claim 9, wherein each latent representation generation unit of the first set of the latent representation generation units is trained to generate a reduced-size representation of the first input data obtained from the first set of the data sources.
6. The method of claim 5, wherein each latent representation generation unit of the first set of the latent representation generation units is trained to generate a reduced-size representation of the first input data obtained from the first set of the data sources.
11. The method of claim 1, further comprising: prior to making the identification: obtaining the inference model.
8. The method of claim 7, further comprising: prior to making the identification: obtaining the inference model.
14. The method of claim 11, wherein obtaining the inference model further comprises: grouping data sources based on likelihoods of multiple of the data sources becoming unavailable at same points in time to obtain sets of data sources that comprise portions of the data sources that are likely to become unavailable at the same points in time, and the first set of data sources being one of the sets of the data sources; training, for the first set of sources, and autoencoder; and using a portion of the autoencoder as the first sub-network unit.
11. The method of claim 8, wherein obtaining the inference model further comprises: grouping data sources of the plurality of the data sources based on likelihoods of multiple of the data sources becoming unavailable at same points in time to obtain sets of data sources that comprise portions of the data sources that are likely to become unavailable at the same points in time, and the first set of data sources being one of the sets of the data sources; training, for the first set of the data sources, an autoencoder; and using a portion of the autoencoder as the first sub-network unit.
15. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing an inference model that comprises sub-network units, the operations comprising: making an identification, based on an inference generated by a second inference model trained to predict data source unavailability, that at least a portion of a first set of data sources is likely to become unavailable at a future point in time, the first set of the data sources providing first input data for a first sub-network unit of the sub-network units and the inference model being unable to generate an inference model result when at least a portion of the first set of the data sources is unavailable;
Examiner notes that a distinction is made between the instant application and application 18/443,801 with the underlined term, see below for an explanation.
12. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing an inference model that comprises sub-network units, the operations comprising: making an identification that a portion of the inference model comprising one or more data sources of a first set of data sources is unavailable, the first set of the data sources providing first input data to a first sub-network unit of the sub-network units and when the one or more data sources are unavailable, the inference model is unable to generate an inference model result;
replacing, in response to the identification and at least temporarily, a first portion of the inference model that comprises at least the first sub-network unit with a second portion of the inference model that comprises at least a second sub-network unit to obtain an updated inference model prior to the future point in time, the second portion of the inference model being intended to duplicate operation of the first portion of the inference model within a threshold;
making a first determination, in response to the identification, regarding whether a second sub-network unit duplicates operation of the first sub-network unit within a threshold, the second sub-network unit not being used by the inference model when the identification is made; in a first instance of the first determination in which the second sub-network unit duplicates the operation of the first sub-network unit within the threshold: replacing, at least temporarily, the first sub-network unit with the second sub-network unit to obtain an updated inference model;
and executing the updated inference model to obtain the inference model result.
and executing the updated inference model to obtain the inference model result.
18. A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing an inference model that comprises sub-network units, the operations comprising: making an identification, based on an inference generated by a second inference model trained to predict data source unavailability, that at least a portion of a first set of data sources is likely to become unavailable at a future point in time, the first set of the data sources providing first input data for a first sub-network unit of the sub-network units and the inference model being unable to generate an inference model result when at least a portion of the first set of the data sources is unavailable;
Examiner notes that a distinction is made between the instant application and application 18/443,801 with the underlined term, see below for an explanation.
17. A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing an inference model that comprises sub-network units, the operations comprising: making an identification that a portion of the inference model comprising one or more data sources of a first set of data sources is unavailable, the first set of the data sources providing first input data to a first sub-network unit of the sub-network units and when the one or more data sources are unavailable, the inference model is unable to generate an inference model result;
replacing, in response to the identification and at least temporarily, a first portion of the inference model that comprises at least the first sub-network unit with a second portion of the inference model that comprises at least a second sub-network unit to obtain an updated inference model prior to the future point in time, the second portion of the inference model being intended to duplicate operation of the first portion of the inference model within a threshold;
making a first determination, in response to the identification, regarding whether a second sub-network unit duplicates operation of the first sub-network unit within a threshold, the second sub-network unit not being used by the inference model when the identification is made; in a first instance of the first determination in which the second sub-network unit duplicates the operation of the first sub-network unit within the threshold: replacing, at least temporarily, the first sub-network unit with the second sub-network unit to obtain an updated inference model;
and executing the updated inference model to obtain the inference model result.
and executing the updated inference model to obtain the inference model result.
Claims 1, 8-11, 14, 15, 18 are essentially identical to their counterparts in the reference application, with the exception of the limitation of “an inference generated by a second inference model”. Souza Vaz teaches making an identification, based on an inference generated by a second inference model trained to predict data source unavailability (in response to training a database failure prediction model using the dataset enhanced using a long short-term memory (LSTM) neural network machine learning technique, storing the database failure prediction model in the machine learning model repository [0018]).
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 reference application to allow for real-time automated actions to address data source availability issues as they arise (Souza Vaz [0105]).
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(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clayton et al., (Pub. No.: US 20170206464 A1) in view of Souza Vaz et al., (Pub. No.: US 20230273908 A1).
Regarding claim 1, Clayton teaches the following:
that at least a portion of a first set of data sources is likely to become unavailable at a future point in time, the first set of the data sources providing first input data for a first sub-network unit of the sub-network units and the inference model being unable to generate an inference model result when at least a portion of the first set of the data sources is unavailable; (Clayton teaches the sensor fusion machine 908 may be particularly advantageous for providing a multi-modal time dependency infused latent distribution that is robust to permanently and/or intermittently unavailable data [0055])
replacing, in response to the identification and at least temporarily, a first portion of the inference model that comprises at least the first sub-network unit with a second portion of the inference model that comprises at least a second sub-network unit to obtain an updated inference model prior to the future point in time, the second portion of the inference model being intended to duplicate operation of the first portion of the inference model within a threshold; and (Clayton teaches the VIM-SDFM pair 902 may receive lidar sensor time series data x.sub.it and output a time dependency infused latent distribution z1.sub.t, the VIM-SDFM pair 904 may receive radar sensor time series data x2.sub.t and output a time dependency infused latent distribution z2.sub.t, the VIM-SDFM pair 906 may receive ultrasound sensor time series data x3.sub.t and output a time dependency infused latent distribution z3.sub.t. [0055] Examiner notes that VIM-SDFM pair 902 can be first portion and VIM-SDFM pair 904 can be a second portion based on the type of data, for example lidar sensor time series or radar sensor time series; see [0029] which describes a scenario where any sensors may become unavailable.)
executing the updated inference model to obtain the inference model result. (Clayton teaches in an example embodiment, a machine learning model is a forecasting model (e.g., autoregressive integrated moving average) or a detection or classification model (e.g., support vector machine, random forest). In an example embodiment, the time dependency infused latent distribution z.sub.t is input into the machine learning model immediately upon being output from the variational inference machine. [0053])
Clayton does not explicitly disclose:
making an identification, based on an inference generated by a second inference model trained to predict data source unavailability.
However, Souza Vaz teaches the limitations:
making an identification, based on an inference generated by a second inference model trained to predict data source unavailability, (Souza Vaz teaches in response to training a database failure prediction model using the dataset enhanced using a long short-term memory (LSTM) neural network machine learning technique, storing the database failure prediction model in the machine learning model repository. [0018]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified a first portion of the inference model that comprises at least the first sub-network unit with a second portion of the inference model that comprises at least a second sub-network unit of Clayton with making an identification, based on an inference generated by a second inference model trained to predict data source unavailability of Souza Vaz with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make sure a combination to allow for real-time automated actions to address data source availability issues as they arise (Souza Vaz [0105]).
Regarding Claim 2, Clayton in view of Souza Vaz teaches the elements of claim 1 as outlined above, and further teaches:
The method of claim 1, wherein making the identification comprises: obtaining the inference using the second inference model and ingest data, the ingest data indicating a status of the first set of the data sources (Souza Vaz teaches at step 802, the monitoring server 404 trains a database failure prediction model using the time series dataset of FIG. 7. [0088]).
Regarding claim 3, Clayton in view of Souza Vaz teaches the elements of claim 2 as outlined above, and further teaches:
The method of claim 2, wherein the inference indicates a likelihood that the at least the portion of the first set of the data sources will become unavailable at the future point in time. (Souza Vaz teaches real-time performance metrics 900 are additionally fed to a database failure prediction model 911, which outputs a probability value indicating the probability of an imminent failure 912. If the monitoring server 404 determines a failure is imminent based on the probability value, the predicted failure is fed to a database maintenance failure profile model 913, which outputs a suggested recovery profile 914 to address the predicted failure. [0108]).
Regarding claim 4, Clayton in view of Souza Vaz teaches the elements of claim 1 as outlined above, and further teaches:
The method of claim 1, further comprising: prior to making the identification: obtaining the second portion of the inference model; and storing the second portion of the inference model in storage so that at least the sub-network unit is available to replace the first sub-network unit (Clayton teaches the VIM-SDFM pair 902 may receive lidar sensor time series data x.sub.it and output a time dependency infused latent distribution z1.sub.t, the VIM-SDFM pair 904 may receive radar sensor time series data x2.sub.t and output a time dependency infused latent distribution z2.sub.t, the VIM-SDFM pair 906 may receive ultrasound sensor time series data x3.sub.t and output a time dependency infused latent distribution z3.sub.t. [0055] Examiner notes that VIM-SDFM pair 902 can be first portion and VIM-SDFM pair 904 can be a second portion based on the type of data, for example lidar sensor time series or radar sensor time; series see [0029] which describes a scenario where any sensors may become unavailable.).
Clayton does not explicitly disclose:
when the second inference model predicts data source unavailability for the first sub-network unit.
However, Clayton in view of Souza Vaz teaches the limitations:
when the second inference model predicts data source unavailability for the first sub-network unit. (Souza Vaz teaches in response to training a database failure prediction model using the dataset enhanced using a long short-term memory (LSTM) neural network machine learning technique, storing the database failure prediction model in the machine learning model repository [0018]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified obtaining the second portion of the inference model; and storing the second portion of the inference model in storage so that at least the sub-network unit is available to replace the first sub-network unit of Clayton with the second inference model predicts data source unavailability for the first sub-network unit of Souza Vaz with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to allow for real-time automated actions to address data source availability issues as they arise (Souza Vaz [0105]).
Regarding claim 5, Clayton in view of Souza Vaz teaches the elements of claim 4 as outlined above, and further teaches:
The method of claim 4, wherein obtaining the second portion of the inference model comprises: obtaining the second sub-network unit from a sub-network unit repository, the second sub-network unit sourcing input data from a second set of the data sources and the second set of the data sources being different from the first set of the data sources; obtaining, using the second sub-network unit and second input data obtained from the second set of the data sources, (Clayton teaches the VIM-SDFM pair 902 may receive lidar sensor time series data x.sub.it and output a time dependency infused latent distribution z1.sub.t, the VIM-SDFM pair 904 may receive radar sensor time series data x2.sub.t and output a time dependency infused latent distribution z2.sub.t, the VIM-SDFM pair 906 may receive ultrasound sensor time series data x3.sub.t and output a time dependency infused latent distribution z3.sub.t. [0055] Examiner notes that VIM-SDFM pair 902 can be first portion and VIM-SDFM pair 904 can be a second portion based on the type of data, for example lidar sensor time series or radar sensor time.);
a first reduced-size representation of the second input data; comparing the first reduced-size representation of the second input data to an expected reduced-size representation of the first input data, the expected reduced-size representation of the first input data being generated by the first sub-network unit using the first input data obtained from the first set of the data sources; (Clayton teaches the multi-modal time dependency infused latent distribution Z is not necessarily smaller in dimensionality than each time dependency infused latent distribution z that is input into the MMVIM 910, and may typically be larger for systems fusing a large number of time dependency infused latent distributions z (e.g., for 300 time series). Although, typically, a multi-modal time dependency infused latent distribution Z would be smaller than the cumulative dimensionality of all component time dependency infused latent distributions z combined together. [0060]);
and in an instance of the comparing in which the first reduced-size representation of the second input data matches the expected reduced-size representation of the first input data within the threshold: concluding that the second sub-network unit duplicates the operation of the first sub-network unit within the threshold; (Clayton teaches the multi-modal time dependency infused latent distribution Z.sub.t may be provided as an input to a machine learning model 206 (e.g., a collision avoidance model). If any of the sensors (e.g., ultrasound, radar, lidar, video) used to generate any of the time dependency infused latent distributions z1.sub.t, z2.sub.t, z3.sub.t, becomes intermittently unavailable, the multi-modal time dependency infused latent distribution Z.sub.t may advantageously be robust to the unavailable sensor data based on multi-modal time dependencies maintained in the multi-modal hidden state H.sub.t−1.. [0058]);
and adding the second sub-network unit to the second portion of the inference model. (Clayton teaches the updated multi-modal hidden state H.sub.t is then exported to the input layer of the MMVIM 910 at the next time interval, and the process may continue iteratively updating the multi-modal hidden state H and the multi-modal time dependency infused latent distribution Z, which is provided to the machine learning model during each time interval. [0059]).
Regarding claim 6, Clayton in view of Souza Vaz teaches the elements of claim 5 as outlined above, and further teaches:
The method of claim 5, wherein obtaining the second portion of the inference model further comprises: in a second instance of the comparing in which the first reduced-size representation of the second input data does not match the expected reduced-size representation of the first input data within the threshold: ( Clayton teaches the machine learning module 108 executes the machine learning model using the collected and/or adapted data to make a forecast, a prediction, a classification, a clustering, an anomaly detection, and/or a recognition, which is then output as a result [0027]. Examiner notes that “collected” data refers to data not having the reduced size representation applied onto the input data.);
adding the second sub-network unit to the second portion of the inference model; and obtaining a fourth sub-network unit from the sub-network unit repository, the fourth sub-network unit being trained to ingest an output of the second sub-network unit and the fourth sub-network being intended to duplicate operation of a third sub-network unit of the inference model within a second threshold, the third sub-network unit being part of the first portion of the inference model; and adding the fourth sub-network unit to the second portion of the inference model. (Clayton teaches a VIM-SDFM pair 902 may have a threshold for a specific quantity of time intervals with time series data x1 being unavailable before the output of time dependency infused latent distribution z1 is stopped [0077] and the hidden states h2.sub.t−1 and h3.sub.t−1 may be received from the RNNs 204 in the VIM-SDFM pairs 904 and 906 at the beginning of the time interval. Each of the VIM-SDFM pairs 904 and 906 may be structured similarly to the VIM-SDFM pair 902 illustrated in FIG. 15, such that all three VIM-SDFM pairs 902, 904s and 906 are providing their respective hidden states h1, h2, and h3 to each other. [0078]);
Regarding claim 7, Clayton in view of Souza Vaz teaches the elements of claim 6 as outlined above, and further teaches:
The method of claim 6, wherein replacing the first portion of the inference model with the second portion of the inference model comprises: replacing the first sub-network unit with the second sub-network unit; and replacing the third sub-network unit with the fourth sub-network unit. (Clayton teaches the VIM-SDFM pair 902 may receive lidar sensor time series data x.sub.it and output a time dependency infused latent distribution z1.sub.t, the VIM-SDFM pair 904 may receive radar sensor time series data x2.sub.t and output a time dependency infused latent distribution z2.sub.t, the VIM-SDFM pair 906 may receive ultrasound sensor time series data x3.sub.t and output a time dependency infused latent distribution z3.sub.t, the VIM-SDFM pair 906 may receive ultrasound sensor time series data x3.sub.t and output a time dependency infused latent distribution z3.sub.t. The sensor fusion machine 908 includes a multi-modal variational inference machine 910 and a multi-modal sequential data forecast machine. [0055] Examiner notes that VIM-SDFM pair 902 can be first portion and VIM-SDFM pair 904 can be a second portion based on the type of data and VIM-SDFM pair 906 can be a third portion and many more portions may be added to the multimodal variational inference machine 910, for example lidar sensor time series, radar sensor time series, or ultrasound sensor time series; see [0029] which describes a scenario where any sensors may become unavailable.).
Regarding claim 8, Clayton in view of Souza Vaz teaches the elements of claim 1 as outlined above, and further teaches:
The method of claim 1, wherein when a first data source of the first set of the data sources becomes unavailable, a second data source of the first set of the data sources has an increased likelihood of becoming unavailable. (Souza Vaz teaches real-time performance metrics 900 are additionally fed to a database failure prediction model 911, which outputs a probability value indicating the probability of an imminent failure 912. If the monitoring server 404 determines a failure is imminent based on the probability value, the predicted failure is fed to a database maintenance failure profile model 913, which outputs a suggested recovery profile 914 to address the predicted failure. [0108]).
Regarding claim 9, Clayton in view of Souza Vaz teaches the elements of claim 1 as outlined above, and further teaches:
The method of claim 1, wherein the first sub-network unit comprises a first set of latent representation generation units and the second sub-network unit comprises a second set of latent representation generation units. (Clayton teaches the predefined type of input for the machine learning model may be a time series from a sensor, multiple time series from different sensors, a distributed representation of a time series from a sensor, or a distributed representation of multiple time series from different sensors. [0028]).
Regarding claim 10, Clayton in view of Souza Vaz teaches the elements of claim 9 as outlined above, and further teaches:
The method of claim 9, wherein each latent representation generation unit of the first set of the latent representation generation units is trained to generate a reduced-size representation of the first input data obtained from the first set of the data sources. (Clayton teaches typically, a multi-modal time dependency infused latent distribution Z would be smaller than the cumulative dimensionality of all component time dependency infused latent distributions z combined together. [0060]).
Regarding claim 11, Clayton in view of Souza Vaz teaches the elements of claim 1 as outlined above, and further teaches:
The method of claim 1, further comprising: prior to making the identification: obtaining the inference model. (Clayton teaches the time series data adaptation module 110 includes a variational inference machine 202 and a sequential data forecast machine 204. Although only one pair of the variational inference machine 202 and the sequential data forecast machine 204 are illustrated in FIG. 2. [0032]).
Regarding claim 12, Clayton in view of Souza Vaz teaches the elements of claim 11 as outlined above, and further teaches:
The method of claim 11, wherein obtaining the inference model comprises: obtaining a plurality of data sources; for each data source of the plurality of the data sources: making a second determination, based on an intended use of the data source and a quantity of data supplied by the data source, regarding whether a latent representation of the data supplied by the data source is to be used; (Clayton teaches the sensor fusion machine 908 may receive the time dependency infused latent distributions z1.sub.t, z2.sub.t, and z3.sub.t, for example, from the VIM-SDFM pairs 902, 904, 906…an input layer to any variational inference machine 202, 910 may include sublayers for each different type of data that are input in parallel (e.g., H.sub.t−1, z1.sub.t, z2.sub.t, and z3.sub.t). The output layer of the MMVIM 910 outputs a multi-modal time dependency infused latent distribution Z.sub.t.. [0058]).
in a first instance of the second determination in which the latent representation of the data supplied by the data source is to be used: obtaining a latent representation generation unit; and obtaining a third sub-network unit that comprises the latent representation generation unit. (Clayton teaches If any of the sensors (e.g., ultrasound, radar, lidar, video) used to generate any of the time dependency infused latent distributions z1.sub.t, z2.sub.t, z3.sub.t, becomes intermittently unavailable, the multi-modal time dependency infused latent distribution Z.sub.t may advantageously be robust to the unavailable sensor data based on multi-modal time dependencies maintained in the multi-modal hidden state H.sub.t−1 [0058]. Examiner notes that for example any latent distributed lidar sensor time series, radar sensor time series, or ultrasound sensor time series can be obtained if another sensor input data is unavailable.).
Regarding claim 13, Clayton in view of Souza Vaz teaches the elements of claim 12 as outlined above, and further teaches:
The method of claim 12, wherein obtaining the inference model further comprises: in a second instance of the second determination in which the latent representation of the data supplied by the data source is not to be used: (Clayton teaches machine learning module 108 may receive collected time series data as inputs and/or may receive adapted data that is representative of collected time series data (e.g., sensor fusion data) as inputs. [0027]).
treating the input data supplied by the data source as ingest for a fourth sub-network unit of the inference model, the input data supplied by the data source not being fed into a latent representation generation unit prior to being used by the fourth sub-network unit. (Clayton teaches the machine learning module 108 executes the machine learning model using the collected and/or adapted data to make a forecast, a prediction, a classification, a clustering, an anomaly detection, and/or a recognition, which is then output as a result. The machine learning model may iteratively update the result. For example, the machine learning model may continuously execute using all available “collected” data stored in memory or using adapted data representative of collected time series data, and may produce a continuous result or a periodic result. [0027]).
Regarding claim 14, Clayton in view of Souza Vaz teaches the elements of claim 11 as outlined above, and further teaches:
The method of claim 11, wherein obtaining the inference model further comprises: grouping data sources based on likelihoods of multiple of the data sources becoming unavailable at same points in time to obtain sets of data sources that comprise portions of the data sources that are likely to become unavailable at the same points in time, (Clayton teaches the sensor fusion machine 908 fuses data of different data types to form more robust, information-rich data types that are better suited for producing reliable, accurate, precise and/or quick recognition results in machine learning analysis or the like. The sensor fusion machine 908 may be particularly advantageous for providing a multi-modal time dependency infused latent distribution that is robust to permanently and/or intermittently unavailable data. [0055]).
and the first set of data sources being one of the sets of the data sources; training, for the first set of sources, and autoencoder; and using a portion of the autoencoder as the first sub-network unit. (Clayton teaches a variational autoencoder is used to train a variational inference machine 202. [0036]).
Regarding claim 15, the claim recites similar limitation as claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Claim 1 is directed to a method, and claim 15 is directed to a non-transitory machine-readable medium. Clayton in view of Souza Vaz teaches the elements of claim 15, and further teaches:
A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing an inference model that comprises sub-network units, the operations comprising: (Clayton teaches an edge device 100 may have a central processing unit, and may also have one or more additional processors dedicated to various specific tasks. [0031]).
Regarding claim 16, the claim recites similar limitation as claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding claim 17, the claim recites similar limitation as claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding claim 18, the claim recites similar limitation as claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Claim 1 is directed to a method and claim 18 is directed to data processing system. Clayton in view of Souza Vaz teaches the elements of claim 18, and further teaches:
A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing an inference model that comprises sub-network units, the operations comprising: (Clayton teaches an edge device 100 may have a central processing unit, and may also have one or more additional processors dedicated to various specific tasks. Each edge device 100 may use one or more processors, memories, buses, and the like. [0031]).
Regarding claim 19, the claim recites similar limitation as claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding claim 20, the claim recites similar limitation as claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Conclusion
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/NADEEM AMIR ABDULMELIK/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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