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 .
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 4-6,8,11 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 8-11,14 of copending Application No. 18/443,771 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims from copending Application No. 18/443,771 anticipates the claims in the instant application (see table below).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application
Co-pending Application No. 18/443,771
1. A method of 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; 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.
Examiner Notes: The instant application is anticipated by the co-pending application with regards to the independent and dependent claims that follow.
1. 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; 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 executing the updated inference model to obtain the inference model result.
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.
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.
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.
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.
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.
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.
8. The method of claim 7, further
comprising: prior to making the
identification: obtaining the inference
model.
11. The method of claim 1, further
comprising: prior to making the
identification: obtaining the inference
model.
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.
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.
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 1-20 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.
Step 1: Claims 1-20 are within the four statutory categories of patent eligible subject matter (a process, machine, manufacture or composition of matter).
Claim 1:
Step 2A Prong 1:
"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..." (This is an abstract idea of a “Mental Process”. The “making an identification” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind, see MPEP 2106.04(a)(2) § III. This limitation encompasses a mental process of evaluation or identification of data unavailability to cluster/group generically recited machine learning models into clusters/groups based on an observed model performance.).
"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..." (This is an abstract idea of a “Mental Process.” The “first determination, in response to the identification” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind, see MPEP 2106.04(a)(2) § III. This limitation encompasses a mental process of evaluation or determination of duplicate sub-network units within a threshold to cluster/group generically recited machine learning models into clusters/groups based on an observed model performance.).
"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..." (This is an abstract idea of a “Mental Process.” The “first determination” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind, see MPEP 2106.04(a)(2) § III.).
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“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” (Mere data gathering recited at a high level of generality, and thus is an insignificant extra-solution activity (MPEP 2106.05(g)). The “first input data” being “unavailable” represents a generic computer function of mere data gathering in conjunction with the abstract idea that is recited at a high generic level.).
“the second sub-network unit not being used by the inference model when the identification is made” (This limitation is merely a post-solution step of storing the data—a nominal addition to the claim that does not meaningfully limit the claim. The method storing is recited at a high level of generality. Simply implementing the abstract idea in a generic method is not a practical application of the abstract idea. Therefore, storing step is an insignificant extra-solution activity. See MPEP 2106.05(g). (Examiner notes that “the second sub-network not being used” means the sub-network is stored somewhere before being called upon.).
“replacing, at least temporarily, the first sub-network unit with the second sub-network unit to obtain an updated inference model;” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The limitation recites “replacing, at least temporarily, the first sub-network unit with the second sub-network unit” represents generic computer component and/or computer instructions. Performing remediation is well-understood, routine, conventional activity -see MPEP 2106.05(d)(II)(vi)).
“and executing the updated inference model to obtain the inference model result.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The limitation recites “executing the updated inference model” represents generic computer component and/or computer instructions. Performing remediation is well-understood, routine, conventional activity -see MPEP 2106.05(d)(II)(vi)).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 2
Step 2A Prong 1: Claim 2, which incorporates the rejection of claim 1, recites further limitation such as:
“in a second instance of the first determination in which the second sub-network unit does not duplicate the operation of the first sub-network unit within the threshold” (This is an abstract idea of a “Mental Process.” The “first determination, in response to the identification” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind, see MPEP 2106.04(a)(2) § III. This limitation encompasses a mental process of evaluation or determination of duplicate sub-network units, or lack of, within a threshold to cluster/group generically recited machine learning models into clusters/groups based on an observed model performance.).
Step 2A Prong 2 and 2B: this claim recites the following additional elements:
“replacing, at least temporarily, the first sub-network unit with the second sub-network unit; replacing, at least temporarily, at least a third sub-network unit of the sub-network units with a fourth sub-network unit to obtain the updated inference model, the third sub-network unit being a portion of the inference model that sources second input data from the first sub-network unit and the fourth sub-network unit being intended to source third input data from the second sub-network unit” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The limitation recites “replacing” both the “first sub-network unit with the second sub-network unit” and the “a third sub-network unit…with a fourth sub-network unit” which represents generic computer component and/or computer instructions. Replacing unavailable sub-network units with available sub-network units is well-understood, routine, conventional activity -see MPEP 2106.05(d)(II)(vi).).
“executing the updated inference model to obtain the inference model result” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The limitation recites “executing the updated inference model” represents generic computer component and/or computer instructions. Performing remediation is well-understood, routine, conventional activity -see MPEP 2106.05(d)(II)(vi)).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 3
Step 2A Prong 1: Claim 3, which incorporates the rejection of claim 2, doesn’t recite an abstract idea.
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“wherein the second sub-network unit sources fourth input data from a second set of the data sources, the second set of the data sources being different from the first set of the data sources.” (This limitation recites “second sub-network unit sources fourth input data” which amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II)(i).).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 4
Step 2A Prong 1: Claim 4, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“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.” (This limitation recites a “first data source” and “second data source” which is mere data gathering recited at a high level of generality and thus is an insignificant extra-solution activity (MPEP 2106.05(g)).).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 5
Step 2A Prong 1: Claim 5, which incorporates the rejection of claim 3, doesn’t recite an abstract idea.
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“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.” (This limitation recites a “latent representation generation units” which is mere data gathering recited at a high level of generality and thus is an insignificant extra-solution activity (MPEP 2106.05(g)).).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 6
Step 2A Prong 1: Claim 6, which incorporates the rejection of claim 5, doesn’t recite an abstract idea.
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“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.” (This limitation recites a “latent representation generation units is trained to generate a reduced-size representation” which is mere data gathering recited at a high level of generality and thus is an insignificant extra-solution activity (MPEP 2106.05(g)).).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 7
Step 2A Prong 1: Claim 7, which incorporates the rejection of claim 6, recites further limitation such as:
“comparing the first reduced-size representation of the fourth input data to an expected reduced-size representation of the second input data” and “in an instance of the comparing in which the first reduced-size representation of the fourth input data matches the expected reduced-size representation of the second input data within the threshold” (This is an abstract idea of a “Mental Process.” The “comparing reduced-sized representation” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind, see MPEP 2106.04(a)(2) § III. This limitation encompasses a mental process of evaluation or determination of duplicate sub-network units, or lack of, within a threshold to cluster/group generically recited machine learning models into clusters/groups based on an observed model performance.).
“concluding that the second sub-network unit duplicates the operation of the first sub-network unit within the threshold” (This is an abstract idea of a “Mental Process.” The “concluding that the second sub-network unit duplicates the operation of the first sub-network unit within the threshold” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind, see MPEP 2106.04(a)(2) § III. This limitation encompasses a mental process of evaluation or determination of duplicate sub-network units, or lack of, within a threshold to cluster/group generically recited machine learning models into clusters/groups based on an observed model performance.).
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“obtaining the second sub-network unit from a sub-network unit repository” and “obtaining, using the second sub-network unit and the fourth input data obtained from the second set of the data sources” (This limitation recites “obtaining the second sub-network” and “the fourth input data obtained from the second set of the data source” which amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II)(i).).
“a first reduced-size representation of the fourth input data” and “the expected reduced-size representation of the second input data being generated by the first sub-network unit using the second input data obtained from the first set of the data sources;” (The limitation merely describes the type of data being processed and thus amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)). The high level recitation of “first reduced-size representation” and “expected reduced-size representation” represents computer instructions that are configured to apply the abstract idea. Nothing in the claims provides a technical solution beyond the generic recitation of using computer modules to perform the abstract idea.).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 8
Step 2A Prong 1: Claim 8, which incorporates the rejection of claim 7, doesn’t recite an abstract idea.
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“The method of claim 7, further comprising: prior to making the identification: obtaining the inference model” (This limitation recites “obtaining the inference model” which amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II)(i).).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 9
Step 2A Prong 1: Claim 9, which incorporates the rejection of claim 8, recites further limitation such as:
“making a second determination, based on an intended use of fifth input data supplied by the data source and a quantity of the fifth input data supplied by the data source, regarding whether a latent representation of the fifth input data supplied by the data source is to be used” and “in a first instance of the second determination in which the latent representation of the fifth input data supplied by the data source is to be used:” (This is an abstract idea of a “Mental Process.” The “determination regarding whether a latent representation is to be used” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind, see MPEP 2106.04(a)(2) § III. This limitation encompasses a mental process of evaluation or determination of duplicate sub-network units, or lack of, within a threshold to cluster/group generically recited machine learning models into clusters/groups based on an observed model performance.).
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“wherein obtaining the inference model comprises: obtaining a plurality of data sources; for each data source of the plurality of the data sources” and “obtaining a latent representation generation unit; and obtaining a fifth sub-network unit that comprises the latent representation generation unit.” (This limitation recites “obtaining a plurality of data sources” and “latent representation generation unit" which amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II)(i).).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 10
Step 2A Prong 1: Claim 10, which incorporates the rejection of claim 9, recites further limitation such as:
“in a second instance of the second determination in which the latent representation of the fifth input data supplied by the data source is not to be used” (This is an abstract idea of a “Mental Process.” The “determination regarding whether a latent representation is not to be used” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind, see MPEP 2106.04(a)(2) § III. This limitation encompasses a mental process of evaluation or determination of duplicate sub-network units, or lack of, within a threshold to cluster/group generically recited machine learning models into clusters/groups based on an observed model performance.).
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“wherein obtaining the inference model further comprises” (This limitation recites “obtaining the inference model” which amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II)(i).).
“treating the fifth input data supplied by the data source as ingest for a sixth sub-network unit of the inference model, the fifth input data supplied by the data source not being fed into a latent representation generation unit prior to being used by the sixth sub-network unit.” (This limitation recites “fifth input data supplied by the data source as ingest for a sixth sub-network unit” which amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II)(i).).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 11
Step 2A Prong 1: Claim 11, which incorporates the rejection of claim 8, recites further limitation such as
“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” (This is an abstract idea of a “Mental Process.” The “grouping data sources” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind, see MPEP 2106.04(a)(2) § III. This limitation encompasses a mental process of evaluation or determination of duplicate sub-network units, or lack of, within a threshold to cluster/group generically recited machine learning models into clusters/groups based on an observed model performance.).
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“wherein obtaining the inference model further comprises” (This limitation recites “obtaining the inference model” which amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II)(i).).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 12
Step 2A Prong 1: The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (the abstract idea of “a mental process”) that was utilized under step 2A prong 1 for claim 1, as described above is equally applicable to claim 12.
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“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” (The claimed additional elements represent generic computer components and computer instructions to apply the abstract idea. Therefore, these elements are sufficient to integrate the judicial exception into a practical application, see MPEP 2106.05(f).).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 13
The claim recites similar limitations as corresponding claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 13. Therefore, the claim is ineligible.
Claim 14
The claim recites similar limitations as corresponding claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 14. Therefore, the claim is ineligible.
Claim 15
The claim recites similar limitations as corresponding claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 15. Therefore, the claim is ineligible.
Claim 16
The claim recites similar limitations as corresponding claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 16. Therefore, the claim is ineligible.
Claim 17
Step 2A Prong 1: The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (the abstract idea of “a mental process”) that was utilized under step 2A prong 1 for claim 1, as described above is equally applicable to claim 17.
Step 2A Prong 2 and 2B: This claim recites the following additional elements:
“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” (The claimed additional elements represent generic computer components and computer instructions to apply the abstract idea. Therefore, these elements are sufficient to integrate the judicial exception into a practical application, see MPEP 2106.05(f).).
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 18
The claim recites similar limitations as corresponding claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 18. Therefore, the claim is ineligible.
Claim 19
The claim recites similar limitations as corresponding claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 19. Therefore, the claim is ineligible.
Claim 20
The claim recites similar limitations as corresponding claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 20. Therefore, the claim is ineligible.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Clayton et al., (Pub. No.: US 20170206464 A1) in view of Lee et al., (Pub. No.: US 20240119282 A1).
Regarding Claim 1, Clayton teaches the following:
A method of 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, (Clayton teaches “accordingly, as shown above, many time intervals will include one or more unavailable time series (e.g., x2 or x3). However, the multi-modal time dependency infused latent distribution Z will be robust to this missing sensor input data…” [0065]).
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; (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]).
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; (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.”).
and 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 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.
However, Lee teaches the limitation:
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; (Lee teaches “the original neural network model (N), this may mean that the input and output (i.e., an input tensor and an output tensor) of the subnetwork (S.sub.j) and the input and output (i.e., an input tensor and an output tensor) of the alternative block B.sub.k, which have the same number of dimensions and the same number of channels, may correspond to each other and a change in the spatial dimension of data when the data passes through the two blocks (i.e., the alternative block and the subnetwork) is the same.” [0059] and “Since the inputs and outputs of the two blocks (i.e., the alternative block (A.sub.i) and the subnetwork (O.sub.i)) are mapped in a one-to-one manner, the apparatus 400 for searching for a light-weight model according to an embodiment of the present disclosure may perform the re-routing task based on a corresponding relation between the inputs and outputs of the two blocks O.sub.i and A.sub.i. The apparatus 400 for searching for a light-weight model according to an embodiment of the present disclosure generates a candidate neural network model (N.sub.cand) 96 through the process 95 of replacing a subnetwork.” [0065]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date 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 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 of Lee with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to combine to improve inference time and memory usage of the subnetwork or any combination of the inference time, the memory usage and the accuracy (Lee [0014]).
Regarding Claim 2, Clayton in view of Lee, teaches the elements of claim 1 as outlined above, and further teaches:
The method of claim 1, further comprising: in a second instance of the first determination in which the second sub-network unit does not duplicate the operation of the first sub-network unit 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.).
replacing, at least temporarily, the first sub-network unit with the second 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.).
replacing, at least temporarily, at least a third sub-network unit of the sub-network units with a fourth sub-network unit to obtain the updated inference model, the third sub-network unit being a portion of the inference model that sources second input data from the first sub-network unit and the fourth sub-network unit being intended to source third input data from the second 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.).
and 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]).
Regarding Claim 3, Clayton in view of Lee, teaches the elements of claim 2 as outlined above, and further teaches:
The method of claim 2, wherein the second sub-network unit sources fourth input data from a second set of the data sources, the second set of the data sources being different from the first set of the data sources. (Clayton teaches “the time series data adaptation module 110 may include multiple variational inference machines 202 and sequential data forecast machines 204 of different types and configurations, and as discussed in greater detail below, the variational inference machines 202 and sequential data forecast machines 204 may process various different types of input data.” [0032]).
Regarding Claim 4, Clayton in view of Lee, 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. (Clayton teaches “using the sensor fusion machine 908 to fuse different types of data may provide an information rich multi-modal time dependency infused latent distribution that may be particularly robust to unavailable sensor data.” [0056]).
Regarding Claim 5, Clayton in view of Lee, teaches the elements of claim 3 as outlined above, and further teaches:
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. (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]).
Regarding Claim 6, Clayton in view of Lee, teaches the elements of claim 5 as outlined above, and further teaches:
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. (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 7, Clayton in view of Lee, teaches the elements of claim 6 as outlined above, and further teaches:
The method of claim 6, wherein making the first determination comprises: obtaining the second sub-network unit from a sub-network unit repository; (Clayton teaches “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]).
obtaining, using the second sub-network unit and the fourth input data obtained from the second set of the data sources, a first reduced-size representation of the fourth input data; (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]).
comparing the first reduced-size representation of the fourth input data to an expected reduced-size representation of the second input data, the expected reduced-size representation of the second input data being generated by the first sub-network unit using the second 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 fourth input data matches the expected reduced-size representation of the second input data 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]).
concluding that the second sub-network unit duplicates the operation of the first sub-network unit within the threshold. (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 8, Clayton in view of Lee, teaches the elements of claim 7 as outlined above, and further teaches:
The method of claim 7, 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 9, Clayton in view of Lee, teaches the elements of claim 8 as outlined above, and further teaches:
The method of claim 8, wherein obtaining the inference model comprises: obtaining a plurality of data sources; for each data source of the plurality of the data sources: (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]).
making a second determination, based on an intended use of fifth input data supplied by the data source and a quantity of the fifth input data supplied by the data source, regarding whether a latent representation of the fifth input 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 fifth input data supplied by the data source is to be used: obtaining a latent representation generation unit; and obtaining a fifth 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 10, Clayton in view of Lee, teaches the elements of claim 9 as outlined above, and further teaches:
The method of claim 9, wherein obtaining the inference model further comprises: in a second instance of the second determination in which the latent representation of the fifth input 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 fifth input data supplied by the data source as ingest for a sixth sub-network unit of the inference model, the fifth input data supplied by the data source not being fed into a latent representation generation unit prior to being used by the sixth 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 11, Clayton in view of Lee, teaches the elements of claim 8 as outlined above, and further teaches:
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; (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]).
training, for the first set of the data sources, an autoencoder; (Clayton teaches “a variational autoencoder is used to train a variational inference machine 202.” [0036]).
and using a portion of the autoencoder as the first sub-network unit. (Clayton teaches “both of the variational inference machine 202 and the variational generation machine 400 may be simultaneously trained as an autoencoder … and updating weights in the hidden layers, for example using log likelihood techniques as described in more detail below. A variational inference machine 202 for a data type X may be trained using a set of time series {x.sub.1, x.sub.2, . . . x.sub.n}, all of data type X, so that once trained, the variational inference machine 202 will optimally encode an input variable x.” [0036]).
Regarding Claim 12, 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 12 is directed to a non-transitory machine-readable medium. Clayton in view of Lee, teaches the elements of claim 12, 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 13, the claim recites similar limitation as claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding Claim 14, the claim recites similar limitation as claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding Claim 15, the claim recites similar limitation as claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Regarding Claim 16, the claim recites similar limitation as claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding claim 17, 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 17 is directed to data processing system. Clayton in view of Lee teaches the elements of claim 17, 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.” [0031]).
Regarding Claim 18, the claim recites similar limitation as claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding Claim 19, the claim recites similar limitation as claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding Claim 20, the claim recites similar limitation as claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nadeem A Abdulmelik whose telephone number is (571)270-3584. The examiner can normally be reached Monday-Friday (8:00 am - 5:00 pm) EST Off every other Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NADEEM AMIR ABDULMELIK/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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