DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to communications filed on 10/03/2025.
Claims 1-42 have been canceled.
Claims 43-67 are pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted was filed on 07/02/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
The use of a trade name or a mark used in commerce (e.g. PERL, JAVASCRIPT, etc.) has been noted in this application. It should be capitalized (each letter) wherever it appears and be accompanied by the generic terminology or, where appropriate, include a proper symbol indicating use in commerce, such as ™, SM, or ® following the word. Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Response to Arguments
With respect to objections to the specification, it is noted that applicant has amended the specification to address some trademarks, but not all, such as PERL, JAVASCRIPT, etc.
Previous objections to the drawings have been withdrawn in view of amendments.
Previous objections to the claims have been withdrawn in view of amendments.
Previous rejections under 35 USC 101 have been withdrawn in view of amendments.
Applicant’s arguments with respect to the prior art have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. See rejections including Zheng et al. (US 20210182922 A1) below for details.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 43-46, 50-54, 58-62, and 65-67 are rejected under 35 U.S.C. 103 as being unpatentable over Priyanto et al. (US 20190332967 A1) in view of Jarvis et al. (US 20210327189 A1) and Zheng et al. (US 20210182922 A1).
As per independent claim 43, Priyanto teaches an apparatus to train a machine learning model, the apparatus comprising:
interface circuitry (e.g. in paragraph 42, “a communication interface or connector 308, configured for setting up a lateral communication with a second device”); machine readable instructions and at least one processor to be programmed by the machine readable instructions (e.g. in paragraph 39, “processing device 304 may include one or more microprocessors, and the data memory 305 may e.g. include a non-volatile memory storage. The processing device 304 is preferably configured to execute the computer program code such that the control unit 303 is configured to control the device to operate as provided in”) to at least:
access training data originating from an edge device, the training data including telemetry information and first information (e.g. in paragraphs 54 and 60, “models may originate from a similar base model, which is trained by adapting to their specific environment. In other words, devices 300, 320 which may be edge-deployed are configured to adapt and improve their estimation model performance by continuous learning from the sensor data they obtain… different weight functions to various parameters… first device 300 has means of distributing its first sensor data to similar devices”); and
train the machine learning model based on the telemetry information and a weighting value (e.g. in paragraphs 54 and 68, “adapt and improve their estimation model performance by continuous learning [i.e. train] from the sensor data they obtain… ability to receive an updated model from a higher network node 330, 350 which have been collecting escalated estimation output data and are capable of re-training [i.e. train] the less complex model of the lower end node 300… applying different weight functions to various parameters in the estimation model… weight factors”),
but does not specifically teach wherein the first information includes attestation information, the attestation information including a collection of claims including probabilistic values; and the weighting value based on the collection of claims including the probabilistic values.
However, Jarvis teaches accessing first information including attestation information and a weighting value based on the attestation information (e.g. in paragraphs 78 and 171, “applying various…models to…the [data] 1404 that are used as input… determining the validity of the data… the number and types of attestors, etc…on device 12a… determine the weight assigned to the [data] 1404 based on a recency of the data used to generate the [data] 1404 (e.g., as the test or other attestation used to generate the [data] 1404 becomes outdated, the weight assigned to the [data] 1404 may be decreased… [models include] a machine learning model”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Priyanto to include the teachings of Jarvis because one of ordinary skill in the art would have recognized the benefit of accounting for validity of data (e.g. also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]; the first information of Priyanto replaced with attestation information of Jarvis and/or the weighting value of Priyanto replaced with weight of Jarvis),
but does not specifically teach the attestation information including a/the collection of claims including probabilistic values.
However, Zheng teaches attestation information including a collection of claims including probabilistic values (e.g. in paragraphs 50, 86, and 92, “a plurality of probabilities of what the item that was placed on the shelf was… weight assigned to each value may be based on a confidence score associated with the sensor corresponding to the value… first computing device 520 may determine [i.e. attest] an object as a bottle of water with 60% [probabilistic] confidence score and a bag of chips with 40% confidence score, while a second computing device 520 observing the same object may determine [i.e. attest] it as a bottle of water with 95% confidence score and a bag of chips with 5% confidence score [i.e. collection of claims]… first computing device 520 may request the related data from the second computing device 520 and re-train its object identification model to improve accuracy”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Zheng because one of ordinary skill in the art would have recognized the benefit of improving model accuracy.
As per claim 44, the rejection of claim 43 is incorporated and the combination further teaches access second data collected by a second edge device, the second data including second attestation information associated with the second edge device (e.g. Priyanto, in paragraphs 54 and 60, “continuous learning from the…data they obtain… different weight functions to various parameters… devices 300, 320, 340 in the close vicinity may have knowledge of, and are capable of communicating with, each other… device…has means of distributing its first sensor data to similar devices”; Jarvis, in paragraphs 37, 78, and 171, “devices 12a, 12b… applying various…models to…the [data] 1404 that are used as input… determine the weight assigned to the [data] 1404 based on a recency of the data used to generate the [data] 1404 (e.g., as the test or other attestation used to generate the [data] 1404 becomes outdated, the weight assigned to the [data] 1404 may be decreased… [models include] a machine learning model”); and execute the machine learning model based at least in part on the second attestation information (e.g. Priyanto, in paragraphs 54 and 60, “model performance by continuous learning from the…data they obtain… different weight functions to various parameters”; Jarvis, in paragraphs 37, 78, and 171, “devices 12a, 12b… applying various…models to…the [data] 1404 that are used as input… determining the validity of the data… the number and types of attestors, etc…on device… determine the weight assigned to the [data] 1404 based on a recency of the data used to generate the [data] 1404 (e.g., as the test or other attestation used to generate the [data] 1404 becomes outdated, the weight assigned to the [data] 1404 may be decreased… [models include] a machine learning model”).
As per claim 45, the rejection of claim 44 is incorporated and the combination further teaches wherein the second edge device is the first edge device (e.g. Priyanto, in paragraphs 54 and 60, “continuous learning from the sensor data they obtain… different weight functions to various parameters… first device 300 has means of distributing its first sensor data to similar devices”; Jarvis, in paragraphs 37, 78, and 171, “applying various…models to…the [data] 1404 that are used as input… determine the weight assigned to the [data] 1404 based on a recency of the data used to generate the [data] 1404 (e.g., as the test or other attestation used to generate the [data] 1404 becomes outdated, the weight assigned to the [data] 1404 may be decreased… [models include] a machine learning model”).
As per claim 46, the rejection of claim 43 is incorporated and the combination further teaches wherein the machine learning model is to accept attestation information as an input (e.g. Priyanto, in paragraphs 54 and 60, “model performance by continuous learning from the…data they obtain… different weight functions to various parameters”; Jarvis, in paragraphs 78, and 171, “applying various…models to…the [data] 1404 that are used as input… determine the weight assigned to the [data] 1404 based on a recency of the data used to generate the [data] 1404 (e.g., as the test or other attestation used to generate the [data] 1404 becomes outdated, the weight assigned to the [data] 1404 may be decreased… [models include] a machine learning model”).
As per claim 50, the rejection of claim 43 is incorporated and the combination further teaches wherein one or more of the at least one processor is to distribute the machine learning model to a second edge device (e.g. Priyanto, in paragraph 54, “ability to receive an updated model from a higher network node 330, 350 which have been collecting escalated estimation output data and are capable of re-training the less complex model of the lower end node 300”).
Claims 51-54 and 58 are the medium claims corresponding to apparatus claims 43-46 and 50, and are rejected under the same reasons set forth and the combination further teaches at least one non-transitory computer readable medium comprising instructions to cause at least one processor (e.g. Priyanto, in paragraph 39, “processing device 304 may include one or more microprocessors, and the data memory 305 may e.g. include a non-volatile memory storage. The processing device 304 is preferably configured to execute the computer program code such that the control unit 303 is configured to control the device to operate as provided in”).
Claims 59-62 are the method claims corresponding to apparatus claims 43-46, and are rejected under the same reasons set forth.
Claims 65-67 correspond to apparatus claims 43-45, and are rejected under the same reasons set forth.
Claims 47, 55, and 63 are rejected under 35 U.S.C. 103 as being unpatentable over Priyanto et al. (US 20190332967 A1) in view of Jarvis et al. (US 20210327189 A1) and Zheng et al. (US 20210182922 A1) and further in view of Wong et al. (US 5809499).
As per claim 47, the rejection of claim 43 is incorporated, but the combination does not specifically teach wherein one or more of the at least one processor is to determine the weighting value based on domain knowledge. However, Wong teaches determining a weighting value based on domain knowledge (e.g. in column 13 lines 63-67, “If such domain knowledge is available, we can always take into account in the process of calculating the weight”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Wong because one of ordinary skill in the art would have recognized the benefit of utilizing available relevant information.
Claim 55 is the medium claim corresponding to apparatus claim 47, and is rejected under the same reasons set forth.
Claim 63 is the method claim corresponding to apparatus claim 47, and is rejected under the same reasons set forth.
Claims 48-49, 56-57, and 64 are rejected under 35 U.S.C. 103 as being unpatentable over Priyanto et al. (US 20190332967 A1) in view of Jarvis et al. (US 20210327189 A1) and Zheng et al. (US 20210182922 A1) and further in view of Solomon et al. (US 20180233141 A1).
As per claim 48, the rejection of claim 43 is incorporated and the combination further teaches wherein one or more of the at least one processor is to determine the weighting value based on information of the edge device (e.g. Priyanto, in paragraphs 39, 54 and 60, “the device 300 may include said one or more sensors 301… devices 300, 320 which may be edge-deployed are configured to adapt and improve their estimation model performance… model performance by continuous learning from the…data they obtain… different weight functions to various parameters”; Jarvis, in paragraphs 37, 78, and 171, “applying various…models to…the [data] 1404 that are used as input… determining the validity of the data… the number and types of attestors, etc…on device… determine the weight assigned to the [data] 1404 based on a recency of the data used to generate the [data] 1404 (e.g., as the test or other attestation used to generate the [data] 1404 becomes outdated, the weight assigned to the [data] 1404 may be decreased… [models include] a machine learning model”),
but does not specifically teach wherein the information includes a reliability.
However, Solomon teaches determining a weighting value based on a reliability of a device (e.g. in paragraphs 163-164, “data received from various sensors may be weighted differently depending upon a reliability of the sensor data. This can be especially relevant in situations where multiple sensors are outputting seemingly inconsistent data… reliability of a sensor's data”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Solomon because one of ordinary skill in the art would have recognized the benefit of accounting for devices with inconsistent data (e.g. also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]; the information of the combination replaced with the reliability of Solomon).
As per claim 49, the rejection of claim 43 is incorporated and the combination further teaches wherein the edge device includes a sensor (e.g. Priyanto, in paragraph 39, “the device 300 may include said one or more sensors 301”) and the attestation information represents information of the sensor (e.g. Priyanto, in paragraphs 39, 54, and 60, “the device 300 may include said one or more sensors… devices 300, 320 which may be edge-deployed are configured to adapt and improve their estimation model performance… model performance by continuous learning from the sensor data they obtain… their models will develop slightly specialized properties, such as by applying different weight functions to various parameters”; Jarvis, in paragraphs 37, 78, and 171, “applying various…models to…the [data] 1404 that are used as input… determining the validity of the data… the number and types of attestors, etc…on device… determine the weight assigned to the [data] 1404 based on a recency of the data used to generate the [data] 1404 (e.g., as the test or other attestation used to generate the [data] 1404 becomes outdated, the weight assigned to the [data] 1404 may be decreased… [models include] a machine learning model”),
but does not specifically teach the attestation information represents a reliability of the sensor.
However, Solomon teaches attestation information representing a reliability of a sensor (e.g. in paragraphs 163-164, “data received from various sensors may be weighted differently depending upon a reliability of the sensor data. This can be especially relevant in situations where multiple sensors are outputting seemingly inconsistent data… reliability of a sensor's data”, i.e. attestation). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Solomon because one of ordinary skill in the art would have recognized the benefit of accounting for devices with inconsistent data (e.g. also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]; the attestation information of the combination replaced with the reliability of Solomon).
Claims 56-57 are the medium claims corresponding to apparatus claims 48-49 and are rejected under the same reasons set forth.
Claim 64 is the method claim corresponding to apparatus claim 48, and is rejected under the same reasons set forth.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Baker et al. (US 20200334541 A1) teaches attestation information including a collection of claims including probabilistic values and a weighting value based on the collection of claims including the probabilistic values (e.g. in paragraph 247, “assesses whether the presence of example i in the training set makes the performance on validation data better or worse… regression coefficient… confidence interval… In one example embodiment, each data example has a weight, e.g., between zero and one. In this embodiment, in box 1105, the computer system merely reduces the weight of example i. When a data example is weighted, the update computed for each mini-batch (for example, box 207 of FIG. 2) multiples the gradient estimate for each data example by its weight, sums the weighted estimates, and divides by the sum of the data weights for the mini-batch. In one embodiment, the procedure may iteratively update each data weight by looping back to box 1102. In another embodiment, the iterative updating of data weights may be done over the course of multiple training set and validation set pairs”).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM WONG whose telephone number is (571)270-1399. The examiner can normally be reached Monday-Friday 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TAMARA KYLE can be reached at (571)272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/W.W/Examiner, Art Unit 2144 11/01/2025
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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