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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
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.
Claim 16 is 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.
The term “improving” in claim 16 is a relative term which renders the claim indefinite. The term “improving” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification, nor the claim, discusses what the metes and bounds of the term means and it is unclear what value is being altered/changed; how that value affects the quality of the communication link; and what amount of improvement is required to be considered successful in “improving quality” versus anomalous behavior that happened to result in improved performance and does one instance of improvement enough or does that improved quality have to persist for some determined amount of time to be considered improved?
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 1-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With regard to claim 1:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
and determines an entity to which the series of calculation is assigned from among the communication terminal, the server, and communication nodes in the communication network on the basis of the information regarding the resources (recites mental process steps of evaluating and judgement steps similar to how someone uses information about potential workers/entities and determining which one should be assigned some task).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
An information processing device (recites generic hardware/computer element at a high-level of generality for performing generic functions, see MPEP 2106.05(f)) that receives information regarding resources of a communication network that relays communication between a communication terminal that transmits an input to a deep neural network or is in charge of at least a part of a series of calculation of the deep neural network (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g)) and transmits a result of the calculation (recites insignificant extrasolution activity of transmitting information over a network, see MPEP 2106.05(g)) and
a server that is able to be in charge of at least a part of the series of calculation (recites generic hardware/computer element at a high-level of generality for performing generic functions, see MPEP 2106.05(f)),
As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recite generic computer functions that are performed by generic computer hardware elements.
Step 2B:
Below is the analysis of the claims:
An information processing device (recites generic hardware/computer element at a high-level of generality for performing generic functions, see MPEP 2106.05(f)) that receives information regarding resources of a communication network that relays communication between a communication terminal that transmits an input to a deep neural network or is in charge of at least a part of a series of calculation of the deep neural network (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d)) and transmits a result of the calculation (recites well-understood, routine, and conventional activity of transmitting information over a network, see MPEP 2106.05(d)) and
a server that is able to be in charge of at least a part of the series of calculation (recites generic hardware/computer element at a high-level of generality for performing generic functions, see MPEP 2106.05(f)),
As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite generic computer functions that are performed by generic computer hardware elements.
With regard to claim 2, this claim recites wherein at least one of the communication nodes is determined as the entity to which the series of calculation is assigned (recites field of use limitations describing intended recipient of the determination, see MPEP 2106.05(h)).
With regard to claim 3, this claim recites wherein a calculation range of which the entity with the series of calculation assigned thereto is in charge is determined on the basis of the information regarding the resources (recites mental process steps of evaluation step for determining a calculation range and judgement step of assigning/associating the range with some entity).
With regard to claim 4, this claim recites wherein at least one of the communication nodes that are present on a communication route between the communication terminal and the server is determined as the entity to which the series of calculation is assigned (recites field of use limitations describing intended network configuration that data/information is to flow from one device to another, see MPEP 2106.05(h)).
With regard to claim 5, this claim recites wherein the resources include communication capacity or communication quality of a communication link in the communication network, and at least one of the communication nodes is determined as the entity to which the series of calculation is assigned on the basis of the communication capacity or the communication quality (recites field of use limitations describing the particular data and its meaning that is being evaluated/utilized to make decisions/judgements, see MPEP 2106.05(h)).
With regard to claim 6, this claim recites wherein a communication time in which the result of the calculation performed by the communication node is transmitted via the communication link is estimated on the basis of the communication capacity or the communication quality (recites mental process steps of performing mental calculations to form an estimate), and at least one of the communication nodes is determined as the entity to which the series of calculation is assigned on the basis of the communication time (recites field of use limitations describing the particular data and its meaning that is being evaluated/utilized to make decisions/judgements, see MPEP 2106.05(h)).
With regard to claim 7, this claim recites wherein the resources include spare calculation capacity of the communication nodes, and at least one of the communication nodes is determined as the entity to which the series of calculation is assigned on the basis of the spare calculation capacity of the communication nodes (recites field of use limitations describing the particular data and its meaning that is being evaluated/utilized to make decisions/judgements, see MPEP 2106.05(h)).
With regard to claim 8, this claim recites wherein calculation times required by the communication nodes to perform the calculation are estimated on the basis of the spare calculation capacity of the communication nodes (recites mental process steps of performing mental calculations to form an estimate), and at least one of the communication nodes is determined as an entity to which the series of calculation is assigned on the basis of the calculation times (recites field of use limitations describing the particular data and its meaning that is being evaluated/utilized to make decisions/judgements, see MPEP 2106.05(h)).
With regard to claim 9, this claim recites wherein the resources include communication capacity or communication quality of a communication link in the communication network and spare calculation capacity of the communication nodes (recites field of use limitations describing the particular data and its meaning that is being evaluated/utilized to make decisions/judgements, see MPEP 2106.05(h)),
communication times in which the result of the calculation performed by the communication nodes are transmitted via the communication link are estimated on the basis of the communication capacity or the communication quality (recites mental process steps of performing mental calculations to form an estimate),
calculation times required by the communication nodes to perform calculation are estimated on the basis of the spare calculation capacity of the communication nodes (recites mental process steps of performing mental calculations to form an estimate),
and at least one of the communication nodes is determined as the entity to which the series of calculation is assigned on the basis of a condition that a sum of the communication time and the calculation time does not exceed a predetermined threshold value (recites mental process steps of performing mental calculations including summation as well as comparison to another value to determine which one is has a greater value).
With regard to claim 10, this claim recites wherein the information processing device further receives information regarding a position of the communication terminal (recites insignificant extrasolution activity of receiving information which amounts to well-understood, routine, and conventional activity of receiving information, see MPEP 2106.05(d)), and the entity to which the series of calculation is assigned is changed in response to a change in the communication route accompanying movement of the communication terminal (recites mental process steps of evaluation and judgement when deciding that some sort of reassignment should occur, e.g. reassigning work from a first worker to another worker).
With regard to claim 11, this claim recites wherein the information processing device further receives information regarding a topology of the communication network (recites insignificant extrasolution activity of receiving information which amounts to well-understood, routine, and conventional activity of receiving information, see MPEP 2106.05(d)), and the entity to which the series of calculation is assigned is changed in response to a change in the communication route accompanying a change in the topology (recites mental process steps of evaluation and judgement when deciding that some sort of reassignment should occur, e.g. reassigning work from a first worker to another worker).
With regard to claim 12, this claim recites wherein the calculation range of which the entity with the series of calculation assigned thereto is in charge is determined by selecting one of a plurality of splitting modes on the basis of the resources (recites mental process steps of evaluating and forming a decision including assignment ranges, similar to setting up queues/booths for particular letter ranges for some event registration such as running event, e.g. 1st booth is for last names beginning with A through D; 2nd booth is last names beginning with E through I; et cetera).
With regard to claim 13, this claim recites wherein the resources include a position of the communication terminal (recites field of use limitations describing the particular data and its meaning that is being evaluated/utilized to make decisions/judgements, see MPEP 2106.05(h)), and the splitting modes are recreated when no predetermined communication nodes are present on the communication route changed with movement of the communication terminal (recites mental process steps of readjusting the logical ranges that are to be used based on the availability of resources/entities in the area similar to how organization changes based on location of events such as national marathon race registrations (bib pick-up) can have dozens of booths while a smaller race can have three or less booths being used by workers).
With regard to claim 14, wherein the calculation range of which the entity with the series of calculation assigned thereto is in charge is changed by increasing or decreasing the calculation range of which the entity with the series of calculation assigned thereto is in charge on the basis of variations in the resources (recites mental process steps of changing the range values based on evaluation of the availability of resources, e.g. a group of 5 workers can be assigned work from a larger range than a group of 2 workers).
With regard to claim 15, this claim recites wherein the calculation range is transmitted to the communication node determined as the entity to which the series of calculation is assigned (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d)).
With regard to claim 16, this claim recites wherein a setting value for improving quality of a wireless communication link on the communication route is determined (recites mental process step of evaluating and judgement decisions for particular setting values), and the setting value for improving the quality of the wireless communication link on the communication route is transmitted to the communication nodes that are present on the communication route (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d)).
With regard to claim 17:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
performs calculation of the calculation range (recites mental process steps of evaluating/analysis that can include calculation steps including mathematical calculations).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
An information processing device (recites generic hardware/computer element at a high-level of generality for performing generic functions, see MPEP 2106.05(f)) that receives a part of a series of calculation based on a deep neural network as an assigned calculation range (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g)),
transmits a calculation result of the calculation range to a designated destination (recites insignificant extrasolution activity of transmitting information over a network, see MPEP 2106.05(g)),
acquires information regarding spare calculation capacity or communication capacity or communication quality of a communication link through which the calculation result is transmitted (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g)),
transmits the acquired information to a designation source of the calculation range (recites insignificant extrasolution activity of transmitting information over a network, see MPEP 2106.05(g)),
and receives information regarding a change in the calculation range from the designation source (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g)).
As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recite generic computer functions that are performed by generic computer hardware elements.
Step 2B:
Below is the analysis of the claims:
An information processing device (recites generic hardware/computer element at a high-level of generality for performing generic functions, see MPEP 2106.05(f)) that receives a part of a series of calculation based on a deep neural network as an assigned calculation range (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d)),
transmits a calculation result of the calculation range to a designated destination (recites well-understood, routine, and conventional activity of transmitting information over a network, see MPEP 2106.05(d)),
acquires information regarding spare calculation capacity or communication capacity or communication quality of a communication link through which the calculation result is transmitted (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d)),
transmits the acquired information to a designation source of the calculation range (recites well-understood, routine, and conventional activity of transmitting information over a network, see MPEP 2106.05(d)),
and receives information regarding a change in the calculation range from the designation source (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d)).
As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite generic computer functions that are performed by generic computer hardware elements
With regard to claim 18, this claim recites wherein the information regarding the change in the calculation range is information indicating one of a plurality of splitting modes (recites mental process steps of evaluating and forming a decision including assignment ranges, similar to setting up queues/booths for particular letter ranges for some event registration such as running event, e.g. 1st booth is for last names beginning with A through D; 2nd booth is last names beginning with E through I; et cetera).
With regard to claim 19, this claim recites wherein, in a case where the calculation result satisfies a condition for ending the series of calculation in the middle, the calculation result is transmitted to a final reception destination of the calculation result of the series of calculation rather than the designated destination (recites mental process steps of determining/evaluating how confident is the result and judging step on whether to use the result or keep evaluating/computing information).
With regard to claim 20:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
An information processing method comprising the steps of: determining a plurality of entities to which the series of calculation is assigned from among the communication terminal, the server, and communication nodes in the communication network on the basis of the information regarding the resources (recites mental process steps of evaluating/analyzing data and mapping/identifying which component is meant to perform some task, e.g. similar to knowing what jobs/tasks are assigned to subordinates).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
receiving information regarding resources of a communication network that relays communication between a communication terminal that transmits an input to a deep neural network or is in charge of at least a part of a series of calculation of the deep neural network and transmits a result of the calculation and a server that is able to be in charge of at least a part of the series of calculation (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g)).
As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recite generic computer functions such as receiving or transmitting information that are performed by generic computer hardware elements.
Step 2B:
Below is the analysis of the claims:
receiving information regarding resources of a communication network that relays communication between a communication terminal that transmits an input to a deep neural network or is in charge of at least a part of a series of calculation of the deep neural network and transmits a result of the calculation and a server that is able to be in charge of at least a part of the series of calculation (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d)).
As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite generic computer functions such as receiving/transmitting information that are performed by generic computer hardware elements.
With regard to claim 21:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
determines a plurality of entities to which the series of calculation is assigned from among the communication terminal, the server, and the communication nodes on the basis of the information regarding the resources (recites mental process steps of evaluating/analyzing data and mapping/identifying which component is meant to perform some task, e.g. similar to knowing what jobs/tasks are assigned to subordinates).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
An information processing system comprising: a plurality of communication nodes (recites generic computer elements to merely apply the judicial exception in a computer, see MPEP 2106.05(f)) that belong to a communication network that relays communication between a communication terminal that transmits an input to a deep neural network or is in charge of at least a part of a series of calculation of the deep neural network and transmits a result of the calculation and a server that is able to be in charge of at least a part of the series of calculation, wherein the plurality of communication nodes transmit information regarding resources of the communication network to a predetermined communication node from among the plurality of communication nodes, and the predetermined communication node receives the information regarding the resources (recites insignificant extrasolution activity of receiving/transmitting information over a network, see MPEP 2106.05(g)).
As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recite generic computer functions such as receiving or transmitting information that are performed by generic computer hardware elements.
Step 2B:
Below is the analysis of the claims:
An information processing system comprising: a plurality of communication nodes (recites generic computer elements to merely apply the judicial exception in a computer, see MPEP 2106.05(f)) that belong to a communication network that relays communication between a communication terminal that transmits an input to a deep neural network or is in charge of at least a part of a series of calculation of the deep neural network and transmits a result of the calculation and a server that is able to be in charge of at least a part of the series of calculation, wherein the plurality of communication nodes transmit information regarding resources of the communication network to a predetermined communication node from among the plurality of communication nodes, and the predetermined communication node receives the information regarding the resources (recites well-understood, routine, and conventional activity of receiving/transmitting information over a network, see MPEP 2106.05(d)).
As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite generic computer functions such as receiving/transmitting information that are performed by generic computer hardware elements.
With regard to claim 22:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
An information processing method comprising the steps of: determining a first assignment range of a series of calculation of a deep neural network (recites mental process steps of evaluating and determining ranges of information to be associated with particular nodes);
executing calculation of the first assignment range (recites mental process step of performing a calculation/computation);
identifying a node to which the output value included in the first information is to be input on the basis of the identification information included in the first information (recites mental process step of evaluating information to determine what do with the result; similar to delegating tasks to subordinates);
and executing remaining calculation of the deep neural network or calculation of a second assignment range by inputting the output value included in the first information to the identified node (recites mental process step of performing a calculation/computation).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
transmitting first information including identification information and an output value of a node included in a final layer in the first assignment range as a result of the calculation of the first assignment range; receiving the first information; (recites insignificant extrasolution activity of receiving/transmitting information over a network, see MPEP 2106.05(g)).
As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recite generic computer functions such as receiving or transmitting information that are performed by generic computer hardware elements.
Step 2B:
Below is the analysis of the claims:
transmitting first information including identification information and an output value of a node included in a final layer in the first assignment range as a result of the calculation of the first assignment range; receiving the first information; (recites well-understood, routine, and conventional activity of receiving/transmitting information over a network, see MPEP 2106.05(d)).
As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite generic computer functions such as receiving/transmitting information that are performed by generic computer hardware elements.
With regard to claim 23, this claim recites replying with a result of the remaining calculation of the deep neural network or the calculation of the second assignment range to a transmission source of the result of the calculation of the first assignment range (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d)).
With regard to claim 24, this claim recites receiving conditions for determining the first assignment range (recites insignificant extrasolution activity of receiving information which amounts to well-understood, routine, and conventional activity of receiving information, see MPEP 2106.05(d)),
wherein the first assignment range is determined on the basis of the conditions (recites mental process steps of forming a determination based on received/monitored observations).
With regard to claim 25, this claim recites wherein the conditions include a condition related to spare calculation capacity of an entity to calculate the first assignment range (recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)).
With regard to claim 26, this claim recites wherein the conditions include a condition related to communication quality between an entity to calculate the first assignment range and a predetermined entity (recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)).
With regard to claim 27, this claim recites wherein the communication quality is calculated on the basis of at least one of a delay time, a data rate, and a channel occupancy ratio (recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)).
With regard to claim 28, wherein an entity to execute the remaining calculation of the deep neural network and the calculation of the second assignment range and an entity to transmit the conditions for determining the first assignment range are different from each other (recites field of use limitations indicating that multiple different computing devices can be used instead of one which adds no meaningful limitation beyond that of the abstract idea as discussed above, see MPEP 2106.05(h)).
With regard to claim 29:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
determines a first assignment range of a series of calculation of the deep neural network on the basis of conditions for determining the first assignment range (recites mental process steps of evaluating and determining ranges of information to be associated with particular nodes);
executes calculation of the first assignment range (recites mental process step of performing a calculation/computation);
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
An information processing device that executes an application using a deep neural network (recites generic computer hardware elements to be used as a tool to implement the abstract idea, see MPEP 2106.05(f)),
and transmits first information including identification information and an output value of a node included in a final layer in the first assignment range as a result of the calculation of the first assignment range (recites insignificant extrasolution activity of receiving/transmitting information over a network, see MPEP 2106.05(g)).
As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recite generic computer functions such as receiving or transmitting information that are performed by generic computer hardware elements.
Step 2B:
Below is the analysis of the claims:
An information processing device that executes an application using a deep neural network (recites generic computer hardware elements to be used as a tool to implement the abstract idea, see MPEP 2106.05(f)),
and transmits first information including identification information and an output value of a node included in a final layer in the first assignment range as a result of the calculation of the first assignment range (recites well-understood, routine, and conventional activity of receiving/transmitting information over a network, see MPEP 2106.05(d)).
As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite generic computer functions such as receiving/transmitting information that are performed by generic computer hardware elements.
With regard to claim 30, this claim recites wherein the information processing device transmits the first information to an entity that performs the series of calculation of the deep neural network next, and a result of remaining calculation of the deep neural network or calculation of a second assignment range is received as a reply to the first information (recites insignificant extrasolution activity of receiving/transmitting information which amounts to well-understood, routine, and conventional activity of receiving/transmitting information, see MPEP 2106.05(d)).
With regard to claim 31, this claim recites wherein the conditions include a condition related to spare calculation capacity of the information processing device itself (recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)),
and the first assignment range is determined in accordance with the spare calculation capacity (recites mental process steps of forming a determination based on received/monitored observations).
With regard to claim 32, this claim recites wherein the conditions include a condition related to communication quality between the information processing device itself and a predetermined entity (recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)),
and the first assignment range is determined in accordance with the communication quality (recites mental process steps of forming a determination based on received/monitored observations).
With regard to claim 33, this claim recites wherein the communication quality is calculated on the basis of at least one of a delay time, a data rate, and a channel occupancy ratio (recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)).
With regard to claim 34:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
identifies a node to which the output value included in the first information is to be input on the basis of the identification information included in the first information (recites mental process step of evaluating information to determine what do with the result; similar to delegating tasks to subordinates),
and executes remaining calculation of the deep neural network or calculation of a second assignment range by inputting the output value included in the first information to the identified node (recites mental process step of performing a calculation/computation).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
An information processing device (recites generic hardware element at a high-level of generality to perform the judicial exception, see MPEP 2106.05(f)) that receives first information including identification information and an output value of a node included in a final layer in a first assignment range in a series of calculation of a deep neural network as a result of calculation of the first assignment range (recites insignificant extrasolution activity of receiving/transmitting information over a network, see MPEP 2106.05(g)).
As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recite generic computer functions such as receiving or transmitting information that are performed by generic computer hardware elements.
Step 2B:
Below is the analysis of the claims:
An information processing device (recites generic hardware element at a high-level of generality to perform the judicial exception, see MPEP 2106.05(f)) that receives first information including identification information and an output value of a node included in a final layer in a first assignment range in a series of calculation of a deep neural network as a result of calculation of the first assignment range (recites well-understood, routine, and conventional activity of receiving/transmitting information over a network, see MPEP 2106.05(d)).
As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite generic computer functions such as receiving/transmitting information that are performed by generic computer hardware elements.
With regard to claim 35, this claim recites wherein a result of the remaining calculation of the deep neural network or the calculation of the second assignment range is sent as a reply to a transmission source of the result of the calculation of the first assignment range (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d)).
With regard to claim 36, this claim recites wherein the second assignment range is determined on the basis of conditions for determining the second assignment range (recites mental process steps of forming a determination based on received/monitored observations),
and the conditions include a condition related to spare calculation capacity of the information processing device itself (recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)).
With regard to claim 37, this claim recites wherein the second assignment range is determined on the basis of conditions for determining the second assignment range (recites mental process steps of forming a determination based on received/monitored observations),
and the conditions include a condition related to communication quality between the information processing device itself and a predetermined entity (recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)).
With regard to claim 38, this claim recites wherein the communication quality is calculated on the basis of at least one of a delay time, a data rate, and a channel occupancy ratio (recites field of use limitations describing the intended meaning of the data values that are being gathered/utilized, see MPEP 2106.05(h)).
Claims 1-19, 21, and 29-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to software per se.
With regard to claim 1, this claim recites an information processing device and a server at a high level of generality where the specification at paragraph [0238] indicates that the respective components can be realized by software; therefore, the respective claim elements are rejected for being directed to software per se.
Claims 2-16 depend upon claim 1 and inherit the same deficiencies as claim 1 as discussed above and are rejected for similar reasons as discussed above.
Claim 17 recites an information process device at a high-level of generality where the specification at paragraph [0238] indicates that the respective components can be realized by software; therefore, the respective claim elements are rejected for being directed to software per se.
Claims 18-19 depend upon claim 17 and inherit the same deficiencies as claim 17 as discussed above and are rejected for similar reasons as discussed above.
Claim 21 recites an information process device at a high-level of generality where the specification at paragraph [0238] indicates that the respective components can be realized by software; therefore, the respective claim elements are rejected for being directed to software per se.
Claim 29 recites an information process device at a high-level of generality where the specification at paragraph [0238] indicates that the respective components can be realized by software; therefore, the respective claim elements are rejected for being directed to software per se.
Claims 30-33 depend upon claim 29 and inherit the same deficiencies as claim 29 as discussed above and are rejected for similar reasons as discussed above.
Claim 34 recites an information process device at a high-level of generality where the specification at paragraph [0238] indicates that the respective components can be realized by software; therefore, the respective claim elements are rejected for being directed to software per se.
Claims 35-38 depend upon claim 34 and inherit the same deficiencies as claim 29 as discussed above and are rejected for similar reasons as discussed above.
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 1-8, 11, 12, 14-18, and 20-38 are rejected under 35 U.S.C. 103 as being unpatentable over Sridharan et al [US 2019/0205745 A1] in view of Che et al [US 2020/0175361 A1].
With regard to claim 1, Sridharan teaches an information processing device that receives information regarding resources of a communication network that relays communication between a communication terminal that transmits an input to a deep neural network or is in charge of at least a part of a series of calculation of the deep neural network and transmits a result of the calculation and a server that is able to be in charge of at least a part of the series of calculation (see paragraphs [0191], [0253], and [0216], [0273], [0224]; the system can include device that can receiving information about the resources of the network include various nodes that can perform calculations and transmit results),
and determines an entity from among the communication terminal, the server, and communication nodes in the communication network on the basis of the information regarding the resources (see paragraphs [0223]-[0225]; the system can utilize the information about the resources to make a determination/selection that affects the operations or intended operation of the machine learning process).
Sridharan does not appear to explicitly teach:
determines an entity
Che teaches an entity to which the series of calculation is assigned from among the communication terminal, the server, and communication nodes in the communication network on the basis of the information regarding the resources (see paragraphs [0034] and [0035]; the system can evaluate device capabilities and determine the respective portion of the calculations will be assigned to the respective node/device).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan by including means to assign particular calculations/operations as taught by Che in order to not overload or overburden particular nodes in the system while also ensuring the system knows which operations are meant to be performed at particular nodes or node groups while maintaining the ability to adjust the assignments later based on observed performance of the devices thereby helping to maximize the efficiency of the system of nodes/devices when performing the various calculations/operations.
Sridharan in view of Che teach determines an entity to which the series of calculation is assigned from among the communication terminal, the server, and communication nodes in the communication network on the basis of the information regarding the resources (see Che, paragraph [0034]; see Sridharan, paragraphs [0223]-[0225]; the system can assign particular calculations or ranges to particular devices).
With regard to claim 2, Sridharan in view of Che teach wherein at least one of the communication nodes is determined as the entity to which the series of calculation is assigned (see Sridharan, paragraph [0226]; see Che, paragraphs [0023] and [0047]; various different types of system nodes can be used in the networked system).
With regard to claim 3, Sridharan in view of Che teach wherein a calculation range of which the entity with the series of calculation assigned thereto is in charge is determined on the basis of the information regarding the resources (see Che, paragraph [0034]; the range of calculations/operations is determined based on the information about the resources).
With regard to claim 4, Sridharan in view of Che teach wherein at least one of the communication nodes that are present on a communication route between the communication terminal and the server is determined as the entity to which the series of calculation is assigned (see Sridharan, paragraphs [0226] and [0254] and [0235]; the system can utilize a network with the communication nodes on a route/path to the server and can select devices on that path for any further configuration/adjustment as needed).
With regard to claim 5, Sridharan in view of Che teach wherein the resources include communication capacity or communication quality of a communication link in the communication network, and at least one of the communication nodes is determined as the entity to which the series of calculation is assigned on the basis of the communication capacity or the communication quality (see Che, paragraph [0034]; the resource can include the communication capacity for the communication link/path).
With regard to claim 6, Sridharan in view of Che teach wherein a communication time in which the result of the calculation performed by the communication node is transmitted via the communication link is estimated on the basis of the communication capacity or the communication quality, and at least one of the communication nodes is determined as the entity to which the series of calculation is assigned on the basis of the communication time (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network).
With regard to claim 7, Sridharan in view of Che teach wherein the resources include spare calculation capacity of the communication nodes, and at least one of the communication nodes is determined as the entity to which the series of calculation is assigned on the basis of the spare calculation capacity of the communication nodes (see Sridharan, paragraph [0216]; the system can determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency)
With regard to claim 8, Sridharan in view of Che teach wherein calculation times required by the communication nodes to perform the calculation are estimated on the basis of the spare calculation capacity of the communication nodes, and at least one of the communication nodes is determined as an entity to which the series of calculation is assigned on the basis of the calculation times (see Sridharan, paragraphs [0216] and [0222]-[0223]; see Che, paragraph [0024]-[0025]; the system can determine the spare capacity of the communication including the latency of the respective nodes which can be utilized as means for the system to make a determination/selection of the node including for adjusting assignments in the system).
With regard to claim 11, Sridharan in view of Che teach wherein the information processing device further receives information regarding a topology of the communication network, and the entity to which the series of calculation is assigned is changed in response to a change in the communication route accompanying a change in the topology (see Sridharan, paragraphs [0222], [0264], and [0224]; the system can receive information about the topology of the network and can change information accordingly based on changes in the topology).
With regard to claim 12, Sridharan in view of Che teach wherein the calculation range of which the entity with the series of calculation assigned thereto is in charge is determined by selecting one of a plurality of splitting modes on the basis of the resources (see Sridharan, paragraphs [0234] and [0216]; see Che, paragraph [0024]; the system can perform various optimizations to determine what type of splitting/grouping or assignments the system would want to do).
With regard to claim 14, Sridharan in view of Che teach wherein the calculation range of which the entity with the series of calculation assigned thereto is in charge is changed by increasing or decreasing the calculation range of which the entity with the series of calculation assigned thereto is in charge on the basis of variations in the resources (see Che, paragraphs [0034] and [0035]; see Sridharan, paragraphs [0216] [0148]; the system can adjust the range accordingly based on the device/node characteristics as well as the respective capabilities of other nodes in the system).
With regard to claim 15, Sridharan in view of Che teach wherein the calculation range is transmitted to the communication node determined as the entity to which the series of calculation is assigned (see Che, paragraph [0034]; the system is able to transmit the respective range of information needed to the respective node).
With regard to claim 16, Sridharan in view of Che teach wherein a setting value for improving quality of a wireless communication link on the communication route is determined, and the setting value for improving the quality of the wireless communication link on the communication route is transmitted to the communication nodes that are present on the communication route (see Sridharan, paragraph [0220] and [0227]; see Che, paragraph [0050]; the system can adjust various settings to help improve the quality of service of the system including its communication quality).
With regard to claim 17, Sridharan teaches an information processing device that receives a part of a series of calculation based on a deep neural network
performs calculation of the calculation range, transmits a calculation result of the calculation range to a designated destination (see paragraphs [0204]-[0205]; the respective device/node can perform computations and transmit the result to a designated or known destination),
acquires information regarding spare calculation capacity or communication capacity or communication quality of a communication link through which the calculation result is transmitted (see paragraphs [0224] and [0222] and [0227] and [0234]; the system can acquire/monitor information about the quality/metrics of the system),
transmits the acquired information to a designation source
Sridharan does not appear to explicitly teach:
an information processing device that receives a part of a series of calculation based on a deep neural network as an assigned calculation range;
transmits the acquired information to a designation source of the calculation range,
and receives information regarding a change in the calculation range from the designation source.
Che teaches an information processing device that receives a part of a series of calculation based on a deep neural network as an assigned calculation range (see paragraphs [0034] and [0035]; the system can evaluate device capabilities and determine the respective portion of the calculations will be assigned to the respective node/device).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan by including means to assign particular calculations/operations as taught by Che in order to not overload or overburden particular nodes in the system while also ensuring the system knows which operations are meant to be performed at particular nodes or node groups while maintaining the ability to adjust the assignments later based on observed performance of the devices thereby helping to maximize the efficiency of the system of nodes/devices when performing the various calculations/operations.
Sridharan in view of Che teach transmits the acquired information to a designation source of the calculation range, and receives information regarding a change in the calculation range from the designation source (see Sridharan, paragraphs [0224] and [0222] and [0227] and [0234]; see Che, paragraphs [0023]-[0025] and [0034]; the system can send information about the various performance counters and utilize that to make any adjustments as needed to the configuration or grouping of the various network components including the respective range for a node/device).
With regard to claim 18, Sridharan in view of Che teach wherein the information regarding the change in the calculation range is information indicating one of a plurality of splitting modes (see Sridharan, paragraphs [0234] and [0216]; see Che, paragraph [0024]; the system can perform various optimizations to determine what type of splitting/grouping or assignments the system would want to do).
With regard to claim 20, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above.
With regard to claim 21, Sridharan teaches an information processing system comprising: a plurality of communication nodes that belong to a communication network that relays communication between a communication terminal that transmits an input to a deep neural network or is in charge of at least a part of a series of calculation of the deep neural network and transmits a result of the calculation and a server that is able to be in charge of at least a part of the series of calculation (see paragraphs [0191], [0253], and [0216], [0273], [0224]; the system can include device that can receiving information about the resources of the network include various nodes that can perform calculations and transmit results),
wherein the plurality of communication nodes transmit information regarding resources of the communication network to a predetermined communication node from among the plurality of communication nodes, the predetermined communication node receives the information regarding the resources, and determines a plurality of entities the server, and the communication nodes on the basis of the information regarding the resources (see paragraphs [0223]-[0225] and [0234]; the system can utilize the information about the resources to make a determination/selection that affects the operations or intended operation of the machine learning process).
Sridharan does not appear to explicitly teach:
the predetermined communication node receives the information regarding the resources, and determines a plurality of entities to which the series of calculation is assigned from among the communication terminal, the server, and the communication nodes on the basis of the information regarding the resources.
Che teaches an entity to which the series of calculation is assigned from among the communication terminal, the server, and communication nodes (see paragraphs [0034] and [0035]; the system can evaluate device capabilities and determine the respective portion of the calculations will be assigned to the respective node/device).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan by including means to assign particular calculations/operations as taught by Che in order to not overload or overburden particular nodes in the system while also ensuring the system knows which operations are meant to be performed at particular nodes or node groups while maintaining the ability to adjust the assignments later based on observed performance of the devices thereby helping to maximize the efficiency of the system of nodes/devices when performing the various calculations/operations.
Sridharan in view of Che teach the predetermined communication node receives the information regarding the resources, and determines a plurality of entities to which the series of calculation is assigned from among the communication terminal, the server, and the communication nodes on the basis of the information regarding the resources (see Che, paragraph [0034]; see Sridharan, paragraphs [0223]-[0225] and [0234]; the system can assign particular calculations or ranges to particular devices based on resource information of the various devices/nodes).
With regard to claim 22, Sridharan teaches an information processing method comprising the steps of: executing calculation
Sridharan does not appear to explicitly teach:
determining a first assignment range of a series of calculation of a deep neural network;
executing calculation of the first assignment range;
and an output value of a node included in a final layer in the first assignment range as a result of the calculation of the first assignment range.
Che teaches determining a first assignment range of a series of calculation of a deep neural network (see paragraphs [0034] and [0035]; the system can evaluate device capabilities and determine the respective portion of the calculations will be assigned to the respective node/device).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan by including means to assign particular calculations/operations as taught by Che in order to not overload or overburden particular nodes in the system while also ensuring the system knows which operations are meant to be performed at particular nodes or node groups while maintaining the ability to adjust the assignments later based on observed performance of the devices thereby helping to maximize the efficiency of the system of nodes/devices when performing the various calculations/operations.
Sridharan in view of Che teach executing calculation of the first assignment range; and an output value of a node included in a final layer in the first assignment range as a result of the calculation of the first assignment range (see Che, paragraph [0034]; see Sridharan, paragraphs [0222]-[0223]; see Figure 22; the system can perform calculations at a first group of nodes and be able to send the information from a particular node to another device that will feed the input into the next layer to continue the processing where the groups can be assigned particular calculations).
With regard to claim 23, Sridharan in view of Che teach the step of: replying with a result of the remaining calculation of the deep neural network or the calculation of the second assignment range to a transmission source of the result of the calculation of the first assignment range (see Sridharan, paragraphs [0223], [0161], and [0181]; see Che, paragraph [0027]; the system has means of replying or transmitting a result of the calculation).
With regard to claim 24, Sridharan in view of Che teach the step of: receiving conditions for determining the first assignment range, wherein the first assignment range is determined on the basis of the conditions (see Che, paragraphs [0023] and [0047]; various different types of system nodes can be used in the networked system and be assigned loads/ranges based on the conditions/capabilities of the device).
With regard to claim 25, Sridharan in view of Che teach wherein the conditions include a condition related to spare calculation capacity of an entity to calculate the first assignment range (see Sridharan, paragraph [0216]; the system can determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency).
With regard to claim 26, Sridharan in view of Che teach wherein the conditions include a condition related to communication quality between an entity to calculate the first assignment range and a predetermined entity (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network).
With regard to claim 27, Sridharan in view of Che teach wherein the communication quality is calculated on the basis of at least one of a delay time, a data rate, and a channel occupancy ratio (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network).
With regard to claim 28, Sridharan in view of Che teach wherein an entity to execute the remaining calculation of the deep neural network and the calculation of the second assignment range and an entity to transmit the conditions for determining the first assignment range are different from each other (see Sridharan, Figure 22 and Che, Figure 5; different devices can be used in the network for performing different tasks).
With regard to claim 29, Sridharan teaches an information processing device that executes an application using a deep neural network, executes calculation to send the information from a particular node to another device that will feed the input into the next layer to continue the processing).
Sridharan does not appear to explicitly teach:
determines a first assignment range of a series of calculation of the deep neural network on the basis of conditions for determining the first assignment range,
executes calculation of the first assignment range;
and transmits first information including identification information and an output value of a node included in a final layer in the first assignment range as a result of the calculation of the first assignment range.
Che teaches determines a first assignment range of a series of calculation of the deep neural network on the basis of conditions for determining the first assignment range (see paragraphs [0034] and [0035]; the system can evaluate device capabilities and determine the respective portion of the calculations will be assigned to the respective node/device).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan by including means to assign particular calculations/operations as taught by Che in order to not overload or overburden particular nodes in the system while also ensuring the system knows which operations are meant to be performed at particular nodes or node groups while maintaining the ability to adjust the assignments later based on observed performance of the devices thereby helping to maximize the efficiency of the system of nodes/devices when performing the various calculations/operations.
Sridharan in view of Che teach executes calculation of the first assignment range; and transmits first information including identification information and an output value of a node included in a final layer in the first assignment range as a result of the calculation of the first assignment range (see Che, paragraph [0034]; see Sridharan, paragraphs [0222]-[0223]; see Figure 22; the system can perform calculations at a first group of nodes and be able to send the information from a particular node to another device that will feed the input into the next layer to continue the processing where the groups can be assigned particular calculations).
With regard to claim 30, Sridharan in view of Che teach wherein the information processing device transmits the first information to an entity that performs the series of calculation of the deep neural network next, and a result of remaining calculation of the deep neural network or calculation of a second assignment range is received as a reply to the first information (see Sridharan, paragraphs [0222]-[0223]; see Figure 22; the system can perform calculations at a first group of nodes and be able to send the information from a particular node to another device that will feed the input into the next layer to continue the processing).
With regard to claim 31, Sridharan in view of Che teach wherein the conditions include a condition related to spare calculation capacity of the information processing device itself, and the first assignment range is determined in accordance with the spare calculation capacity (see Sridharan, paragraph [0216]; the system can determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency).
With regard to claim 32, Sridharan in view of Che teach wherein the conditions include a condition related to communication quality between the information processing device itself and a predetermined entity, and the first assignment range is determined in accordance with the communication quality (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network).
With regard to claim 33, Sridharan in view of Che teach wherein the communication quality is calculated on the basis of at least one of a delay time, a data rate, and a channel occupancy ratio (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network).
With regard to claim 34, Sridharan teaches an information processing device that receives first information including identification information and an output value of a node included in a final layer
Sridharan does not appear to explicitly teach:
an output value of a node included in a final layer in a first assignment range in a series of calculation of a deep neural network as a result of calculation of the first assignment range,
and executes remaining calculation of the deep neural network or calculation of a second assignment range by inputting the output value included in the first information to the identified node.
Che teaches an output value of a node included in a final layer in a first assignment range in a series of calculation of a deep neural network as a result of calculation of the first assignment range (see paragraphs [0034] and [0035] and Figure 5; the system can evaluate device capabilities and determine the respective portion of the calculations will be assigned to the respective node/device).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan by including means to assign particular calculations/operations as taught by Che in order to not overload or overburden particular nodes in the system while also ensuring the system knows which operations are meant to be performed at particular nodes or node groups while maintaining the ability to adjust the assignments later based on observed performance of the devices thereby helping to maximize the efficiency of the system of nodes/devices when performing the various calculations/operations.
Sridharan in view of Che teach executes remaining calculation of the deep neural network or calculation of a second assignment range by inputting the output value included in the first information to the identified node (see Che, paragraph [0034]; see Sridharan, paragraphs [0222]-[0223]; see Figure 22; the system can perform calculations at a first group of nodes and be able to send the information from a particular node to another device that will feed the input into the next layer to continue the processing where the groups can be assigned particular calculations).
With regard to claim 35, Sridharan in view of Che teach wherein a result of the remaining calculation of the deep neural network or the calculation of the second assignment range is sent as a reply to a transmission source of the result of the calculation of the first assignment range (see Sridharan, paragraphs [0223], [0161], and [0181]; see Che, paragraph [0027]; the system has means of replying or transmitting a result of the calculation).
With regard to claim 36, Sridharan in view of Che teach wherein the second assignment range is determined on the basis of conditions for determining the second assignment range, and the conditions include a condition related to spare calculation capacity of the information processing device itself (see Sridharan, paragraph [0216]; the system can determine that there is spare capacity for the respective communication node and can make adjustments/assignments for that communication node to help improve throughput and latency).
With regard to claim 37, Sridharan in view of Che teach wherein the second assignment range is determined on the basis of conditions for determining the second assignment range, and the conditions include a condition related to communication quality between the information processing device itself and a predetermined entity (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network).
With regard to claim 38, Sridharan in view of Che teach wherein the communication quality is calculated on the basis of at least one of a delay time, a data rate, and a channel occupancy ratio (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Sridharan et al [US 2019/0205745 A1] in view of Che et al [US 2020/0175361 A1] in further view of Zhou et al [US 2018/0276031 A1].
With regard to claim 9, Sridharan in view of Che teach all the claim limitations of claims 1 and 2 as discussed above.
Sridharan in view of Che teach wherein the resources include communication capacity or communication quality of a communication link in the communication network and spare calculation capacity of the communication nodes (see Che, paragraph [0034]; the resource can include the communication capacity for the communication link/path), communication times in which the result of the calculation performed by the communication nodes are transmitted via the communication link are estimated on the basis of the communication capacity or the communication quality, calculation times required by the communication nodes to perform calculation are estimated on the basis of the spare calculation capacity of the communication nodes (see Sridharan, paragraphs [0216] and [0230]; the system can also take into account the network/communication latency or time when analyzing/selecting particular devices in the network),
Sridharan in view of Che do not appear to explicitly teach: at least one of the communication nodes is determined as the entity to which the series of calculation is assigned on the basis of a condition that a sum of the communication time and the calculation time does not exceed a predetermined threshold value.
Zhou teaches at least one of the communication nodes is determined as the entity to which the series of calculation is assigned on the basis of a condition that a sum of the communication time and the calculation time does not exceed a predetermined threshold value (see paragraphs [0093]-[0095] and [0042], [0054], and [0081]; various metrics can be analyzed together to determine how well the node is expected to perform or is performing and be able to make a determination/selection of a node based on that information).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan in view of Che by analyzing/utilizing performance metrics that combine different metrics such as network metrics and computation/processor metrics as taught by Zhou in order to be able to make determinations/selections of nodes based on their capabilities and whether they meet expected performance constraints for respective tasks/operations while considering all aspects of how a networked system interacts rather than just one component (e.g. I/O cost) thus allowing the system to make more informed decisions to help better optimize the networked system to perform the operations.
Claims 10 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sridharan et al [US 2019/0205745 A1] in view of Che et al [US 2020/0175361 A1] in further view of Nolan et al [US 2019/0349733 A1].
With regard to claim 10, Sridharan in view of Che teach all the claim limitations of claims 1, 2, and 4 as discussed above.
Sridharan in view of Che do not appear to explicitly teach wherein the information processing device further receives information regarding a position of the communication terminal, and the entity to which the series of calculation is assigned is changed in response to a change in the communication route accompanying movement of the communication terminal.
Nolan teaches a change in the communication route accompanying movement of the communication terminal (see Nolan, paragraph [0151] and [0222]; detects that other devices are now in the network, i.e. moved).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan in view of Che by including means to identify local devices and try to preserve the locality of calculations as taught by Nolan in order to be able to detect when changes occur and respective nodes are now local to other sets of nodes so that the system can dynamically adjust its groupings to help ensure overall quality of service by maintaining low latency and high-throughput and not degrading the quality by having nodes communicate with each other that are at large distances away from each other.
Sridharan in view of Che in further view of Nolan teach wherein the information processing device further receives information regarding a position of the communication terminal, and the entity to which the series of calculation is assigned is changed in response to a change in the communication route accompanying movement of the communication terminal (see Sridharan, paragraphs [0225]-[0226]; see Che, paragraph [0035]; see Nolan, paragraph [0151] and [0222]; changes in the network including movement of devices to determine which sets of devices are local to each other and can adjust the grouping of devices accordingly).
With regard to claim 13, Sridharan in view of Che teach all the claim limitations of claims 1, 3, and 12 as discussed above.
Sridharan in view of Che teach the splitting modes are recreated when no predetermined communication nodes are present on the communication route changed with movement of the communication terminal (see Sridharan, paragraphs [0226] and [0264]; the system can determine when no nodes are present/available on a route or path and can do adjustments as needed including adjusting the paths as necessary).
Sridharan in view of Che do not appear to explicitly teach wherein the resources include a position of the communication terminal.
Nolan teaches a change in the communication route accompanying movement of the communication terminal (see Nolan, paragraphs [0221]-[0222] and [0151]; the devices can be associated with locations where the devices can be small enough to be moved).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan in view of Che by including means to identify local devices and try to preserve the locality of calculations as taught by Nolan in order to be able to detect when changes occur and respective nodes are now local to other sets of nodes so that the system can dynamically adjust its groupings to help ensure overall quality of service by maintaining low latency and high-throughput and not degrading the quality by having nodes communicate with each other that are at large distances away from each other.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Sridharan et al [US 2019/0205745 A1] in view of Che et al [US 2020/0175361 A1] in further view of Grokop et al [US 2014/0143579 A1].
With regard to claim 19, Sridharan in view of Che teach all the claim limitations of claim 17 as discussed above.
Sridharan in view of Che do not appear to explicitly teach wherein, in a case where the calculation result satisfies a condition for ending the series of calculation in the middle, the calculation result is transmitted to a final reception destination of the calculation result of the series of calculation rather than the designated destination.
Grokop teaches wherein, in a case where the calculation result satisfies a condition for ending the series of calculation in the middle (see paragraphs [0032] and [0035]; the system can perform a series of calculations and be able to, upon determining high confidence, end/skip the other calculations in the middle).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the distributed machine learning system of Sridharan in view of Che by including means to determine when the evaluation/computations are above a threshold level of confidence even when in the middle of the computations as taught by Grokop in order to save processing time and network communication costs by not having to continue various calculations/computations when the confidence of the output can already be determined with a high-level of confidence.
Sridharan in view of Che in further view of Grokop teach the calculation result is transmitted to a final reception destination of the calculation result of the series of calculation rather than the designated destination (see Grokop, paragraphs [0032] and [0035]; see Che, paragraph [0023]; see Sridharan, Figures 19, 20E, and 22; the system can end the series of calculations/computations even before reaching the end stage/layer and be able to output the result).
Conclusion
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/MARC S SOMERS/Primary Examiner, Art Unit 2159 2/4/2026