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 responsive to the Applicant’s amendments filed on 11/28/2025. Claims 1-14 and 22-28 remain pending in the application. Claims 1-2, 4-5, 7-9, 11-12, 14, 22-23, 26, and 28 have been amended. Claims 15-21 have been canceled. Any examiner’s note, objection, and rejection not repeated is withdrawn due to Applicant’s amendment.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/01/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner’s Note
The Examiner cites particular columns, paragraphs, figures, and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may also apply. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
The preliminary amendment filed 06/22/2022 is acknowledged and entered by the examiner.
The examiner notes that the remarks dated 11/28/2025 notes “claims 15-18 have been cancelled”. However, the claim set dated 11/28/2025 appears to cancel claims 15-21.
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.
Claim limitations being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph are as follows:
means for extracting static and dynamic data from a packet... (Claim 22; Paragraph 85; “dynamic load balancer 102 includes means for extracting static and dynamic data from a packet. For example, the means for extracting may be implemented by the data extractor circuitry 416”);
first means for causing a first machine learning model to generate... (Claim 22; Paragraph 57; “dynamic load balancer 102 includes means for generating a first plurality of probability distributions. For example, the means for generating may be implemented by probability distribution generator circuitry 404”);
second means for causing a second machine learning model to generate... (Claim 22; Paragraph 57; “dynamic load balancer 102 includes means for generating a first plurality of probability distributions. For example, the means for generating may be implemented by probability distribution generator circuitry 404”);
means for calculating a confidence value... (Claim 22; Paragraph 71; “dynamic load balancer 102 includes means for calculating a confidence value for a first helper compute unit of a plurality of helper compute units. For example, the means for calculating may be implemented by confidence calculator circuitry 410”);
means for assigning the request for service... (Claim 22; Paragraph 78; “dynamic load balancer 102 includes means for assigning a first helper compute unit. For example, the means for assigning may be implemented by the helper selection circuitry 414”);
wherein the means for calculating is to calculate confidence values... (Claim 23; Paragraph 71; “dynamic load balancer 102 includes means for calculating a confidence value for a first helper compute unit of a plurality of helper compute units. For example, the means for calculating may be implemented by confidence calculator circuitry 410”);
wherein the means for calculating is to calculate the confidence value of the first helper compute unit by: determining a first average and a first standard deviation... (Claim 25; Paragraph 71; “dynamic load balancer 102 includes means for calculating a confidence value for a first helper compute unit of a plurality of helper compute units. For example, the means for calculating may be implemented by confidence calculator circuitry 410”);
wherein the means for calculating is to calculate the confidence value of the first helper compute unit by: determining a second average and a second standard deviation... (Claim 25; Paragraph 71; “dynamic load balancer 102 includes means for calculating a confidence value for a first helper compute unit of a plurality of helper compute units. For example, the means for calculating may be implemented by confidence calculator circuitry 410”);
wherein the means for calculating is to calculate the confidence value of the first helper compute unit by: adding the first and second averages (Claim 25; Paragraph 71; “dynamic load balancer 102 includes means for calculating a confidence value for a first helper compute unit of a plurality of helper compute units. For example, the means for calculating may be implemented by confidence calculator circuitry 410”);
wherein the means for calculating is to calculate the confidence value of the first helper compute unit by: subtracting the first and second standard deviations (Claim 25; Paragraph 71; “dynamic load balancer 102 includes means for calculating a confidence value for a first helper compute unit of a plurality of helper compute units. For example, the means for calculating may be implemented by confidence calculator circuitry 410”);
means for logging to: log second static and dynamic data (Claim 26; Paragraph 64; “dynamic load balancer 102 includes means for logging. For example, the means for logging may be implemented by the neural network training circuitry 408”);
means for logging to: log associations between the second static and dynamic data (Claim 26; Paragraph 64; “dynamic load balancer 102 includes means for logging. For example, the means for logging may be implemented by the neural network training circuitry 408”);
means for performing inference with a third machine learning model... (Claim 26; Paragraph 53; “dynamic load balancer 102 includes means for performing inference on static and/or dynamic data. For example, the means for performing inference may be implemented by neural network circuitry 402”);
means for generating a training data set... (Claim 26; Paragraph 62; “The example neural network training circuitry 408 trains the example neural network circuitry 402. To train the neural network circuitry 402, the example neural network training circuitry develops a training data set. In some examples, the training data set is based on output of a preexisting neural network model. For example, a non-bayesian neural network may be in operation prior to implementation of a bayesian neural network such as the deep bayesian neural networks of the example neural network circuitry 402 (e.g., as illustrated in FIG. 5)”);
means for normalizing the confidence values (Claim 28; Paragraph 75; “dynamic load balancer 102 includes means for normalizing a plurality of confidence values. For example, the means for normalizing may be implemented by the example normalization circuitry 412”);
means for generating the probabilistic graph... (Claim 28; Paragraph 60; “dynamic load balancer 102 includes second means for generating a probabilistic graph. For example, the second means for generating may be implemented by the example bayesian network circuitry 406”).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 22-28 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claim purports to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claim.
Regarding claims 22-23, 25-26, and 28, the specification fails to disclose sufficient corresponding structure for the following limitations:
means for extracting static and dynamic data (Claim 22), means for causing a first machine learning model to generate a first plurality of probability distributions (Claim 22), means for causing a second machine learning model to generate a second plurality of probability distributions (Claim 22), means for calculating a confidence value (Claim 22), means for assigning the request for service to the first helper compute unit (Claim 22), means for calculating (Claims 23, 25), means for logging (Claim 26), means for performing inference with a third machine learning model (Claim 26), means for generating a training data set (Claim 26), means for normalizing the confidence values (Claim 28), means for generating the probabilistic graph (Claim 28).
The specification provides the following statements regarding corresponding structure:
“means for performing inference may be implemented by neural network circuitry 402” (Paragraph 53), “means for generating may be implemented by probability distribution generator circuitry 404” (Paragraph 57), “means for generating may be implemented by the example bayesian network circuitry 406” (Paragraph 60), “means for logging may be implemented by the neural network training circuitry 408” (Paragraph 64), “means for calculating may be implemented by confidence calculator circuitry 410” (Paragraph 71), “means for normalizing may be implemented by the example normalization circuitry 412” (Paragraph 75), “means for assigning may be implemented by the helper selection circuitry 414” (Paragraph 78), “means for extracting may be implemented by the data extractor circuitry 416” (Paragraph 85).
The specification discloses only that generic circuitry performs the various “means for” steps described in the above claims. However, the specification does not describe any algorithm or specific procedure for performing the “means for” steps. The specification merely states the result to be achieved by utilizing the generic description of circuitry without describing the algorithm or method used to accomplish the result. Therefore, the specification fails to provide sufficient description of the specific structure or algorithm used to implement the “means for” steps described in the above claims.
Any claim not specifically mentioned is rejected due to dependency upon a rejected claim.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 and 22-28 are 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.
Regarding claims 1, 8, and 22, the claims recite “the static data to change less frequently than the dynamic data”. It is unclear what threshold determines the difference between static and dynamic data. Paragraph 81 of the instant specification discloses “In some examples, static data does not change throughout the lifetime of the Edge device”, and “In some examples, static data may change over time (e.g., responsive to a device upgrade). Such changes to static variables and/or static data occur less frequently than changes to dynamic data”. Thus, the specification defines “static” in comparative terms relative to “dynamic” data. However, the specification does not provide objective boundaries for determining the threshold that distinguishes “less frequent” from “more frequent” or how to classify data whose rate of change varies on operating conditions. Because the classification of a variable as “static” depends on a relative comparison to “dynamic” data without an articulated standard, a person of ordinary skill in the art would not be able to determine, with reasonable certainty, when a variable that changes over time falls within or outside the scope of “static variable”.
Any claim not specifically mentioned is rejected due to dependency upon a rejected claim.
Regarding claims 22-23, 25-26, and 28, claim limitation “means for extracting static and dynamic data (Claim 22), means for causing a first machine learning model to generate a first plurality of probability distributions (Claim 22), means for causing a second machine learning model to generate a second plurality of probability distributions (Claim 22), means for calculating a confidence value (Claim 22), means for assigning the request for service to the first helper compute unit (Claim 22), means for calculating (Claims 23, 25), means for logging (Claim 26), means for performing inference with a third machine learning model (Claim 26), means for generating a training data set (Claim 26), means for normalizing the confidence values (Claim 28), means for generating the probabilistic graph (Claim 28)” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The specification discloses:
“means for performing inference may be implemented by neural network circuitry 402” (Paragraph 53), “means for generating may be implemented by probability distribution generator circuitry 404” (Paragraph 57), “means for generating may be implemented by the example bayesian network circuitry 406” (Paragraph 60), “means for logging may be implemented by the neural network training circuitry 408” (Paragraph 64), “means for calculating may be implemented by confidence calculator circuitry 410” (Paragraph 71), “means for normalizing may be implemented by the example normalization circuitry 412” (Paragraph 75), “means for assigning may be implemented by the helper selection circuitry 414” (Paragraph 78), “means for extracting may be implemented by the data extractor circuitry 416” (Paragraph 85).
The reference to circuitry for each means for limitation merely identifies a generic computing component and restates the claimed function. For computer-implemented means plus function limitations, disclosure of a general purpose processor or circuitry without an associated algorithm is insufficient to constitute corresponding structure. Because the specification fails to disclose adequate corresponding structure for performing the claimed function, the scope of the claim cannot be determined with reasonable certainty.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Any claim not specifically mentioned is rejected due to dependency upon a rejected claim.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-2, 8-9, and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over O’Donoghue et al. (US 20210357268 A1) hereafter O’Donoghue, in view of He et al. (US 20220237032 A1) hereafter He, further in view of Kumar et al. (US 20070070907 A1) hereafter Kumar, further in view of Dixon et al. (US 11907300 B2) hereafter Dixon.
Regarding claim 1, O’Donoghue teaches:
An apparatus comprising: at least one memory (Paragraph 40; “Such a non-volatile computer-readable storage medium may also include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory (e.g., Serial, NAND, NOR, and/or the like)”); instructions (Paragraph 37; “A software component comprising assembly language instructions may require conversion into executable machine code by an assembler prior to execution by the hardware architecture and/or platform”); and at least one processor circuit to be programmed by the instructions (Paragraph 50; “whether configured by hardware or computer program products, or by a combination thereof, the processing element 205 may be capable of performing steps or operations according to embodiments of the present invention when configured accordingly”) to:
cause a first machine learning model to generate a first plurality of probability distributions based on the data (Paragraphs 32, 69; “each unit-designation probability distribution that is described by an unit-designation model for the operational system “ and “the unit-designation effectiveness measures described by the unit-designation model may be determined based on at least one of... the output of a machine learning model”. Programming or invoking the unit-designation model to create probability distribution effectiveness measures corresponds to causing a first ML model to generate the probability distributions as claimed.);
cause a second machine learning model to generate a second plurality of probability distributions based on the data (Paragraphs 32, 75; “operator-designation probability distribution that is described by an operator-designation model for the operational system” and “operator-designation effectiveness measures described by the operator-designation model may be determined based on at least one of... the output of a machine learning model”. Programming or invoking the operator-designation model to create probability distribution effectiveness measures corresponds to causing a second ML model to generate the probability distributions as claimed);
calculate a confidence value for the first helper compute unit of a plurality of helper compute units based on at least one of the first plurality of probability distributions and at least one of the second plurality of probability distributions (Paragraphs 25 and 32; the unit designation model describes the effectiveness of a work unit profile corresponding to a helper compute unit for addressing an operational condition designation. The confidence values correspond to the claimed confidence value for a given unit. Examples provide comparative effectiveness measures between different unit-designation pairs allowing selection of the most effective unit, corresponding to a higher likelihood of completing a task/request faster than others. Each unit having its own probability distribution correspond to the plurality of probability distributions whose confidence levels are derived from them. Paragraph 32 further discloses “each arrangement utility measure for an operator-unit mapping arrangement may be a stochastic arrangement utility measure for the operator-unit mapping arrangement, such as a stochastic arrangement utility measure that describes at least one of (e.g., all of) each operator-designation probability distribution”, in which confidence values for a unit can be calculated using multiple probability distributions generated by one or more models, corresponding to being based on the first and second plurality of probability distributions.);
and assign the request for service to the first helper compute unit based on the confidence value (Paragraph 101; the operator profile and work unit profile together identify which unit, corresponding to the helper compute unit, gets which task/request, corresponding to the request for service. Assignment of a subset of work units to a specific operator corresponds to assigning a request to a specific helper unit. Since the optimal mapping depends on prior confidence evaluations, the final assignment is inherently based on those confidence values).
O’Donoghue does not teach extract static and dynamic data from a packet associated with a request for service by an edge device, the static data to change less frequently than the dynamic data; a probabilistic graph of the plurality of helper compute units includes a vertex that represents a first helper compute unit and an edge that represents a confidence corresponding to the first helper compute unit; satisfying the request for service more quickly and with a lower latency.
However, He teaches:
extract static and dynamic data associated with a request for service (Paragraph 43; “Specifically, vision application 150 may use the BWM API to register required bandwidth allocation in a static and/or dynamic manner”, where the registered data corresponding to the bandwidth allocation for a request for service is sent through the API is extracted by the receiver);
and satisfying the request more quickly (Paragraph 23; “Further, based on processing capabilities of various edge devices and required visual resource 230 in network system 110, a suitable edge device located near terminal device 140 can be searched for, so as to deploy visual resources 230 to the edge device based on a time requirement in resource requirement 210”).
O’Donoghue and He are considered to be analogous to the claimed invention because they are in the same field of resource management. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of O’Donoghue with He to have extracted static and dynamic data for use in plurality distributions and utilized request satisfying speed in confidence calculations. A person of ordinary skill in the art would recognize that static data such as infrequently accessed data and dynamic data such as those frequently accessed would need to be extracted to analyze edge resource device information. A person of ordinary skill in the art would have further recognized optimizing execution speed to be a known method in the art predictably yielding optimal execution speed.
O’Donoghue in view of He does not teach a packet associated with a request for service, the static data changing less frequently than the dynamic data; a probabilistic graph of the plurality of helper compute units includes a vertex that represents a first helper compute unit and an edge that represents a confidence corresponding to the first helper compute unit; satisfying the request for service with a lower latency.
However, Kumar teaches:
a packet associated with a request for service (Paragraphs 62 and 76; “information that is frequently accessed for packet processing (e.g., flow table entries, queue descriptors, packet metadata, etc.) will be stored in SRAM, while bulk packet data (either entire packets or packet payloads) will be stored in DRAM”, and “the WRED data structure includes a static portion and a dynamic portion. The static portion includes WRED drop profile data that is pre-defined and loaded into memory during an initialization operation or the like. The dynamic portion corresponds to data that is periodically updated”, where the access frequency distinction is implied from the memory hierarchy.);
and satisfying a request with a lower latency (Paragraph 89; “In some implementations, the sampling period for the entire set of active flows will be relatively large when compared with the processing latency for a given packet.”).
O’Donoghue, He, and Kumar are considered to be analogous to the claimed invention because they are in the same field of resource management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Donoghue in view of He to incorporate the teachings of Kumar and have utilized packets, and have utilized the idea of lowest latency in confidence calculations. A person of ordinary skill in the art would have recognized the need to perform packet processing, and that a packet having static and dynamic data separately stored would allow a system to optimize resource usage in placing dynamic data in more readily accessible portions of memory in order to access it more quickly than that of static data which may not need to be accessed as often. A person of ordinary skill in the art would have further recognized latency optimization as a known method in the art whose implementation on the confidence calculation of O’Donoghue would yield the predictable result of optimized processing behavior.
O’Donoghue in view of He, further in view of Kumar does not teach a probabilistic graph of the plurality of helper compute units includes a vertex that represents a first unit and an edge that represents a confidence corresponding to the first unit.
However, Dixon teaches:
a probabilistic graph of the plurality of units includes a vertex that represents a first unit and an edge that represents a confidence corresponding to the first unit (Col. 21, lines 20-28; “a graph framework such as the graph framework 840 of FIG. 8 can utilize data as ingested and/or as otherwise accessible to generate a graph that includes vertices and edges where the edges represent relationships between vertices. As explained, data can include and/or be processed to include descriptors. As an example, a graph framework can utilize data and/or data descriptors to deterministically and/or probabilistically determine relationships of a graph.” Edges are determined probabilistically, thereby representing the probabilistic nature of the claimed graph. Vertices correspond to entities, corresponding to the individual units in the claim. The edges encode the strength or confidence of the relationships, corresponding to the claimed confidence value for a unit.).
O’Donoghue, He, Kumar, and Dixon are considered to be analogous to the claimed invention because they are in the same field of probabilistic decision-making. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Donoghue in view of He, further in view of Kumar to incorporate the teachings of Dixon and combine the teachings of a helper compute unit as taught by O’Donoghue to have a probabilistic graph of the plurality of units include a vertex that represents a first unit and an edge that represents a confidence corresponding to the first unit as taught by Dixon. A person of ordinary skill in the art would have recognized representing probabilistic relationships in a graph to be a known method in the art, and the implementation would yield the predictable result of encoding confidence values of helper compute units as edges connecting vertices, consistent with the probabilistic data taught by O’Donoghue in view of He, further in view of Kumar.
Claim 8 recites similar limitations as those of claim 1, additionally reciting a non-transitory computer-readable storage medium. O’Donoghue teaches:
A non-transitory computer-readable storage medium (Paragraph 40; “A non-volatile computer-readable storage medium may also include a punch card, paper tape, optical mark sheet (or any other physical medium with patterns of holes or other optically recognizable indicia), compact disc read only memory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc (DVD), Blu-ray disc (BD), any other non-transitory optical medium, and/or the like”).
Claim 8 is rejected for similar reasons as those of claim 1.
Claim 22 recites similar limitations as those of claim 1, additionally reciting means for. O’Donoghue teaches:
first means for causing a first machine learning model to generate a first plurality of probability distributions (Paragraph 33; the request/command for the optimization-based ensemble machine learning model corresponds to the first means, where it generates multiple stochastic arrangement utility measures, corresponding to the first plurality of probability distributions)
second means for causing a second machine learning model to generate a second plurality of probability distributions (Paragraph 33; the optimization-based ensemble machine learning model corresponds to the second means, where it generates multiple stochastic arrangement utility measures, corresponding to the second plurality of probability distributions);
means for calculating a confidence value (Paragraph 33; the optimization-based ensemble machine learning model corresponds to the means for calculating a confidence value as part of the arrangement utility measures);
and means for assigning the first helper compute unit (Paragraph 101; “operational load balancing computing entity 106” corresponds to the means for assigning the first helper compute unit).
He teaches:
means for extracting static and dynamic data (Paragraph 43; the edge device extracts the required bandwidth allocation information from the static and dynamic data which corresponds to the means for extracting).
Claim 22 is rejected for similar reasons as those of claim 1.
Regarding claim 2, O’Donoghue in view of He, further in view of Kumar, further in view of Dixon teach the apparatus of claim 1. O’Donoghue teaches:
wherein one or more of the at least one processor circuit is to calculate confidence values (Paragraph 25; the unit designation model describes the effectiveness of a work unit profile corresponding to a helper compute unit for addressing an operational condition designation. The confidence values correspond to the claimed confidence value for a given unit. Examples provide comparative effectiveness measures between different unit-designation pairs allowing selection of the most effective unit, corresponding to a higher likelihood of completing a task/request faster than others. Each unit having its own probability distribution correspond to the plurality of probability distributions whose confidence levels are derived from them) for respective ones of the plurality of helper compute units (Paragraph 33; the load balancing assignment corresponds to having assigned the request to a particular helper compute unit after having calculated the confidence for each compute unit), the confidence value of the first helper compute unit greater than the confidence values of the plurality of helper compute units. (Paragraph 101; optimal mapping inherently selects the unit with the highest effectiveness/confidence score for a given task, corresponding to selection of the unit with the greater confidence value among the helper compute units).
Claim 9 recites similar limitations as those of claim 2. Claim 9 is rejected for similar reasons as those of claim 2.
Claim 23 recites similar limitations as those of claim 2, additionally reciting means for.
O’Donoghue teaches:
means for calculating a confidence value (Paragraph 33; the optimization-based ensemble machine learning model corresponds to the means for calculating a confidence value as part of the arrangement utility measures).
Claim 23 is rejected for similar reasons as those of claim 2.
Claims 3, 5-6, 10, 12-13, 24, and 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil et al. (US 20210365279 A1) hereafter Chirakkil.
Regarding claim 3, O’Donoghue in view of He, further in view of Kumar, further in view of Dixon teach the apparatus of claim 2. O’Donoghue in view of He, further in view of Kumar, further in view of Dixon does not teach wherein the first and second machine learning models are Bayesian machine learning models.
However, Chirakkil teaches:
wherein the first and second machine learning models are Bayesian machine learning models (Paragraph 65; “Other directed and undirected model classification approaches include, e.g., naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and/or probabilistic classification models providing different patterns of independence, any of which can be employed”).
O’Donoghue, He, Kumar, Dixon, and Chirakkil are considered to be analogous to the claimed invention because they are in the same field of resource management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Donoghue in view of He further in view of Kumar, further in view of Dixon to incorporate the teachings of Chirakkil and have the first and second ML models be Bayesian ML models. A person of ordinary skill in the art having read the specification of Chirakkil would recognize that any of the listed model classification approaches could have been employed for such a classification task, and would have been motivated to try each and every element in the list.
Claim 10 recites similar limitations as those of claim 3. Claim 10 is rejected for similar reasons as those of claim 3.
Claim 24 recites similar limitations as those of claim 3. Claim 24 is rejected for similar reasons as those of claim 3.
Regarding claim 5, O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil teaches the apparatus of claim 3. Kumar teaches:
static and dynamic data (Paragraph 76; “In the illustrated embodiment, the WRED data structure includes a static portion and a dynamic portion. The static portion includes WRED drop profile data that is pre-defined and loaded into memory during an initialization operation or the like. The dynamic portion corresponds to data that is periodically updated”).
O’Donoghue teaches:
log second static and dynamic data for a threshold period (Paragraph 32; disclosing values and distributions obtained from a plurality of data models and system metrics gathered before processing. Paragraph 27 further discusses a threshold period, “For example, the operator cost-capacity model for a medical provider may describe an hourly cost of utilizing the medical provider as well as the estimated total number of hours of availability of the medical provider during a particular time window”, the time window corresponding to a threshold period).
perform inference with a third machine learning model based on the second static and dynamic data (Paragraph 33; where the optimization-based ensemble machine learning model configured to process values/distributions to generate an arrangement utility measure corresponds to the inference step on collected data. The ensemble machine learning model corresponds to the third machine learning model because it is distinct from the upstream models that generate the probability distributions as disclosed in Paragraph 32 and consumes outputs from those models to perform a higher level inference.);
log associations between the second static and dynamic data and corresponding helper compute units, the corresponding helper compute units selected based on the inference (Paragraph 101; “cause each operator profile to perform… an assigned subset of the work unit profiles”, in which assignments resulting from inference by way of optimal mapping correspond to being logged as associations between collected data and the assigned units. The assignment step produces the association between input data and compute units);
and generate a training data set including the static and dynamic data, indications of the selected helper compute units, and the associations (Paragraph 32-33; to train this ML model, it inherently requires a training data set pairing input conditions with outputs, corresponding to static/dynamic data with selected compute units, which implies the generation of a training data set).
Chirakkil teaches:
explicit generation of a training data set (Paragraph 74; “the computing touchpoint journey component 702 can be considered as a database (e.g., centralized and/or distributed) that can collect computing touchpoint journey data from the interactions of a plurality of clients (not shown) with the set of computing touchpoints 106 and can store that data as training data for the machine learning classifier 502 and/or the machine learning classifier 602”).
A person of ordinary skill in the art would have recognized the need to generate a training data set on a sample of the resource data, and would have recognized the need to collect some operation data and store it as training data for the classifier.
Claim 12 recites similar limitations as those of claim 5. Claim 12 is rejected for similar reasons as those of claim 5.
Claim 26 recites similar limitations as those of claim 5, additionally reciting means for. O’Donoghue teaches:
means for logging (Paragraph 90; the optimization-based ensemble machine learning model creates an arrangement utility measure based on a plurality of data models which perform the logging of data);
means for performing inference (Paragraph 33; the optimization-based ensemble machine learning model corresponds to the means for performing inference).
Chirakkil teaches:
third means for generating a training data set (Paragraph 74; the computing touchpoint journey component 702 corresponds to the third means for generating a training data set).
Claim 26 is rejected for similar reasons as those of claim 5.
Regarding claim 6, O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil teaches the apparatus of claim 5. Chirakkil teaches:
wherein the third machine learning model is a non-Bayesian machine learning model (Paragraph 65; “Other directed and undirected model classification approaches include, e.g., naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and/or probabilistic classification models providing different patterns of independence, any of which can be employed. Classification as used herein also is inclusive of statistical regression that is utilized to develop models of priority”, where any of the list not including Bayesian networks are options for non-Bayesian machine learning models for classification);
and the threshold period is either a sample quantity or a time period (Paragraph 27; “For example, the operator cost-capacity model for a medical provider may describe an hourly cost of utilizing the medical provider as well as the estimated total number of hours of availability of the medical provider during a particular time window”, the time window corresponding to a threshold period).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have utilized the time window teaching of O’Donoghue and applied the idea to a threshold period. A person of ordinary skill in the art would recognize the need to set a threshold limit on the recorded period of time representing a sample.
Claim 13 recites similar limitations as those of claim 6. Claim 13 is rejected for similar reasons as those of claim 6.
Claim 27 recites similar limitations as those of claim 6. Claim 27 is rejected for similar reasons as those of claim 6.
Claims 4, 11, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil, further in view of Xu (US 20230244970 A1), further in view of Cao et al. (US 20240387060 A1) hereafter Cao.
Regarding claim 4, O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil teach the apparatus of claim 2. O’Donoghue teaches:
a first plurality of probability distributions (Paragraphs 32-33; “the optimization-based ensemble machine learning model is a stochastic optimization model, e.g., a stochastic optimization model that is configured to generate stochastic arrangement utility measures for operator-unit mapping arrangements and process the stochastic arrangement utility measures in order to determine an optimal operator-unit mapping arrangement”, where the stochastic arrangement utility measure refers to measures that are represented by a probability distribution);
and a second plurality of probability distributions (Paragraphs 32-33; “the optimization-based ensemble machine learning model is a stochastic optimization model, e.g., a stochastic optimization model that is configured to generate stochastic arrangement utility measures for operator-unit mapping arrangements and process the stochastic arrangement utility measures in order to determine an optimal operator-unit mapping arrangement”, where the stochastic arrangement utility measure refers to measures that are represented by a probability distribution. The generation of multiple probability distributions correspond to the second plurality).
O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil does not teach determine a first average and a first standard deviation of a first probability distribution; and determine a second average and a second standard deviation of a first probability distribution.
However, Xu teaches:
determine a first average and a first standard deviation of a first probability distribution (Paragraph 28; “For example, exemplary embodiments can provide optimal values (e.g., mean/average) and confidence intervals (e.g., standard deviation (std)) of the resource demands of each vDU”, where embodiments determine and provide the average and standard deviations of the resource demands of each resource for each vDU, corresponding to the first average and first standard deviation);
and determine a second average and a second standard deviation of a first probability distribution (Paragraph 28; “For example, exemplary embodiments can provide optimal values (e.g., mean/average) and confidence intervals (e.g., standard deviation (std)) of the resource demands of each vDU”, where embodiments determine and provide the average and standard deviations of the resource demands of each resource for each vDU, corresponding to the second average and second standard deviation).
O’Donoghue, He, Kumar, Dixon, Chirakkil, and Xu are considered to be analogous to the claimed invention because they are in the same field of resource management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Donoghue in view of He further in view of Kumar further in view of Chirakkil to incorporate the teachings of Xu and have determined averages and standard deviations of each probability distribution. A person of ordinary skill in the art would have been motivated by the need to perform statistical analysis on datasets to optimize resource selection.
O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil, further in view of Xu does not teach add the first and second averages; and subtract the first and second standard deviations.
However, Cao teaches:
add the first and second averages (Paragraph 111; “In an exemplary implementation, in act A13, the controlling the encoder to add a part of the normalized expression average values or to subtract the normalized expression standard deviations corresponding to the normalized expression average values”, where the part of the normalized averages correspond to the first and second averages);
and subtract the first and second standard deviations (Paragraph 111; “In an exemplary implementation, in act A13, the controlling the encoder to add a part of the normalized expression average values or to subtract the normalized expression standard deviations corresponding to the normalized expression average values”, where the normalized expression standard deviation correspond to the first and second standard deviations).
O’Donoghue, He, Kumar, Dixon, Chirakkil, Xu, and Cao are considered to be analogous to the claimed invention because they are in the same field of resource management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Donoghue in view of He further in view of Kumar further in view of Chirakkil further in view of Xu to incorporate the teachings of Cao and utilized a custom parameter formed using the difference between the sum of the averages subtracted by the first and second standard deviations. A person of ordinary skill in the art would recognize the combination as a straightforward feature transformation that captures both average and stability in a single dimension, thus reducing system complexity due to the simple arithmetic calculation being computationally light and easily interpretable.
Claim 11 recites similar limitations as those of claim 4. Claim 11 is rejected for similar reasons as those of claim 4.
Claim 25 recites similar limitations as those of claim 4, additionally reciting means for. O’Donoghue teaches:
means for calculating a confidence value (Paragraph 33; the optimization-based ensemble machine learning model corresponds to the means for calculating a confidence value as part of the arrangement utility measures).
Claim 25 is rejected for similar reasons as those of claim 4.
Claims 7, 14, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil, further in view of Xu, further in view of Therani et al. (US 20210258400 A1) hereafter Therani.
Regarding claim 7, O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil teaches the apparatus of claim 3. Kumar teaches:
static and dynamic data (Paragraph 76; “In the illustrated embodiment, the WRED data structure includes a static portion and a dynamic portion. The static portion includes WRED drop profile data that is pre-defined and loaded into memory during an initialization operation or the like. The dynamic portion corresponds to data that is periodically updated”).
O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil does not teach normalize the confidence values; the static data including a device identifier, a device type, and application type; and the dynamic data including a geographic location and time data.
However, Xu teaches:
normalize the confidence values (Paragraph 50; “normalized correlation is defined with a sub-graph”).
O’Donoghue, He, Kumar, Dixon, Chirakkil, and Xu are considered to be analogous to the claimed invention because they are in the same field of resource management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Donoghue in view of He further in view of Kumar further in view of Dixon further in view of Chirakkil to incorporate the teachings of Xu and normalize the confidence values. A person of ordinary skill in the art would have been motivated by the need to dampen the effects of outliers in confidence values and would have found normalization to be an effective solution on statistical yielding the predictable result of dampening the effect of outliers.
O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil, further in view of Xu does not teach the static data including a device identifier, a device type, and application type; and the dynamic data including a geographic location and time data.
However, Therani teaches:
wherein the static data includes a device identifier, a device type, and application type (Paragraph 37; “entity identifier” corresponds to device identifier, “particular type of identifier” corresponds to device type, “application” corresponds to application type);
wherein the dynamic data includes a geographic location and time data (Paragraph 38; “spatial… status of the entity” corresponds to geographic location, “temporal… status of the entity” corresponds to time data).
O’Donoghue, He, Kumar, Dixon, Chirakkil, Xu, and Therani are considered to be analogous to the claimed invention because they are in the same field of resource management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Donoghue in view of He further in view of Kumar further in view of Dixon further in view of Chirakkil to incorporate the teachings of Therani and have the static data comprise a device identifier, device type, and application type, and further have the dynamic data include geographic location and time data. A person of ordinary skill in the art would have recognized that static data would typically comprise data that is not often modified, and would have been motivated to have included device ID, type, and application type in static data due to that data being rarely updated. Similarly, a person of ordinary skill in the art would have recognized that dynamic data would typically comprise data that is often modified, such as geographic data and time data, and would have been motivated to include that information in dynamic data.
Claim 14 recites similar limitations as those of claim 7. Claim 14 is rejected for similar reasons as those of claim 7.
Regarding claim 28, O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil teaches the apparatus of claim 26.
Claim 28 recites similar limitations as those of claim 7, additionally reciting means for.
Kumar teaches:
static and dynamic data (Paragraph 76; “In the illustrated embodiment, the WRED data structure includes a static portion and a dynamic portion. The static portion includes WRED drop profile data that is pre-defined and loaded into memory during an initialization operation or the like. The dynamic portion corresponds to data that is periodically updated”).
O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil does not teach means for normalizing the confidence values; fourth means for generating a probabilistic graph; or normalize the confidence values.
However, Xu teaches:
means for normalizing the confidence values (Paragraph 50; where the “Gaussian process based propagation mechanism” performs the normalization, corresponding to the means for normalizing the confidence values);
and fourth means for generating a probabilistic graph (Paragraph 42; the graph-based system embodied by the fourteenth example which is configured to perform the steps for execution of graph generation using one or more hardware processors corresponds to the fourth means for generating a probabilistic graph. See Paragraph 51 for disclosure of the graph representing confidence intervals and integrated into neural networks via the constructed graph, corresponding to a probabilistic graph).
normalize the confidence values (Paragraph 50; “normalized correlation is defined with a sub-graph”).
O’Donoghue, He, Kumar, Dixon, Chirakkil, and Xu are considered to be analogous to the claimed invention because they are in the same field of resource management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Donoghue in view of He further in view of Kumar further in view of Dixon further in view of Chirakkil to incorporate the teachings of Xu and normalize the confidence values. A person of ordinary skill in the art would have been motivated by the need to dampen the effects of outliers in predicted confidence values and would have found normalization and graphical representation to be an effective solution on statistical yielding the predictable result of dampening the effect of outliers and providing a visual representation for analysis thereof.
O’Donoghue in view of He, further in view of Kumar, further in view of Dixon, further in view of Chirakkil, further in view of Xu does not teach the static data including a device identifier, a device type, and application type; and the dynamic data including a geographic location and time data.
However, Therani teaches:
wherein the static data includes a device identifier, a device type, and application type (Paragraph 37; “entity identifier” corresponds to device identifier, “particular type of identifier” corresponds to device type, “application” corresponds to application type);
wherein the dynamic data includes a geographic location and time data (Paragraph 38; “spatial… status of the entity” corresponds to geographic location, “temporal… status of the entity” corresponds to time data).
O’Donoghue, He, Kumar, Dixon, Chirakkil, Xu, and Therani are considered to be analogous to the claimed invention because they are in the same field of resource management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Donoghue in view of He further in view of Kumar, further in view of Dixon, further in view of Chirakkil, further in view of Xu to incorporate the teachings of Therani and have the static data comprise a device identifier, device type, and application type, and further have the dynamic data include geographic location and time data. A person of ordinary skill in the art would have recognized that static data would typically comprise data that is not often modified, and would have been motivated to have included device ID, type, and application type in static data due to that data being rarely updated. Similarly, a person of ordinary skill in the art would have recognized that dynamic data would typically comprise data that is often modified, such as geographic data and time data, and would have been motivated to include that information in dynamic data.
Response to Arguments
Applicant's arguments filed 11/28/2025 have been fully considered but some are not persuasive. Applicant’s arguments are summarized below:
Static and dynamic data are known terms of art being used consistently with the specification. Therefore, the claims meet the requirements of 35 U.S.C. 112(b).
The independent claims do not recite mathematical concepts. Therefore, the claims are eligible under 35 U.S.C. 101.
The prior art references do not teach or suggest the limitation of a probabilistic graph having vertices and edges as set forth in newly amended independent claims 1, 8, and 22. Therefore, the rejections of claims 1-14 and 22-28 under 35 U.S.C. 103 should be withdrawn.
The Examiner respectfully disagrees with point A.
Applicant’s cited definition characterizes static data as data that “stays the same over time” (Remarks, Page 1). In contrast, the instant specification states that static data “may change over time” and distinguishes static from dynamic data based on change frequency, i.e. that changes to static data “occur less frequently than changes to dynamic data” (Paragraph 81). Thus, the specification appears to define the distinction using a relative comparison based on the frequency of change. Because the specification discloses a distinction between static and dynamic data as based on whether one changes “less frequently” than the other, the scope of static data depends on an unspecified threshold for determining what qualifies as “less frequent”. The specification does not provide objective boundaries for this comparison. As a result, whether particular data qualifies as “static” depends on a relative and undefined comparison rather than a fixed property. Even if the terms are known in the abstract, when claim scope in light of the specification depends on a comparative standard without articulated boundaries, the claims must still inform a person of ordinary skill in the art with reasonable certainty to reliably determine infringement. The absence of a defined threshold for “less frequent” renders the boundary between static and dynamic variables unclear. Therefore, contrary to Applicant’s arguments, the distinction between “static” and “dynamic” data lacks clear objective boundaries.
Upon reanalysis of independent claims 1, 8, and 22, the examiner has determined that the step of extracting static and data from a packet and causing machine learning models to generate probability distributions is directed toward an apparatus, and therefore not a mathematical process nor able to be executed in the mind. No other claim covers further abstract ideas. Therefore, the analysis of claims 1-14 and 22-28 under 35 U.S.C. 101 ends at step 2A, prong one, with a conclusion of eligibility.
The examiner agrees that the prior art of record does not teach or suggest the amended limitation in claim 1 of a probabilistic graph having vertices and edges. Accordingly, the previous rejections of claims 1-14 and 22-28 under 35 U.S.C. 103 are withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of O’Donoghue, He, Kumar, and Dixon under 35 U.S.C. 103.
Accordingly, the rejections of claims 1-14 and 22-28 under 35 U.S.C. 112(b) are maintained.
Accordingly, the rejections of claims 1-14 and 22-28 under 35 U.S.C. 101 are withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ben-Itzhak et al. (US 20220012550 A1) discusses implementing a tree-based ensemble classifier that selects an optimal resource classification for deployment with a sufficiently high confidence level.
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.
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/KENNETH P TRAN/ Examiner, Art Unit 2196
/APRIL Y BLAIR/ Supervisory Patent Examiner, Art Unit 2196