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 .
Priority
This application is a continuation of a U.S. Utility Patent Application 18/160,938 filed January 27, 2023.
Status of the Claims
Claims 1-20 are pending in the instant patent application.
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.
Regarding Claims 1-7, they are directed to a system, however the claims are rejected because they are directed to a judicial exception without significantly more. Claims 1-7 are directed to the abstract idea of distributing data to geographically distributed users.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites obtain a flow graph of tasks, wherein the flow graph of tasks indicates at least two of: multiple tasks, a user associated with a task in the multiple tasks, a location associated with the user, and an indication of network connectivity associated with the user; obtain a data associated with the flow graph of tasks; obtain metadata associated with the data, wherein the metadata indicates a time associated with the data, a location associated with the data or a purpose associated with the data; determine a particular user to which the data needs to be distributed based on the metadata and the flow graph of tasks; efficiently distribute the data to geographically distributed users by: providing the flow graph of tasks and an indication of the particular user to which the data needs to be distributed; and obtaining an expected time at which the particular user needs the data.
These claim limitations fall within the Certain Methods of Organizing Human Activity due to the managing personal behavior or relationships or interactions between people as it pertains to distributing data between users. Furthermore, the mere recitation of artificial intelligence does not take it out of the abstract idea grouping.
Accordingly, the claim recites an abstract idea and dependent claims 2-7 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of at least one hardware processor, at least one non-transitory memory, a cloud computing pipeline and artificial intelligence. The at least one hardware processor, at least one non-transitory memory, a cloud computing pipeline and artificial intelligence are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 1 includes various elements that are not directed to the abstract idea under 2A. These elements include at least one hardware processor, at least one non-transitory memory, a cloud computing pipeline, artificial intelligence and the generic computing elements described in the Applicant's specification in at least Para 0033-0042. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 1 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 8-13, they are directed to a method, however the claims are rejected because they are directed to a judicial exception without significantly more. Claims 8-13 are directed to the abstract idea of distributing data to geographically distributed users.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 8, claim 8 recites obtaining a flow graph of tasks, wherein the flow graph of tasks indicates at least two of: multiple tasks, a user associated with a task in the multiple tasks, a location associated with the user, and an indication of network connectivity associated with the user; obtaining a data associated with the flow graph of tasks; obtaining metadata associated with the data, wherein the metadata indicates a time associated with the data, a location associated with the data or a purpose associated with the data; determining a particular user to which the data needs to be distributed based on the metadata and the flow graph of tasks; efficiently distributing the data to geographically distributed users by: providing the flow graph of tasks and an indication of the particular user to which the data needs to be distributed; and obtaining an expected time at which the particular user needs the data.
These claim limitations fall within the Certain Methods of Organizing Human Activity due to the managing personal behavior or relationships or interactions between people as it pertains to distributing data between users. Furthermore, the mere recitation of artificial intelligence does not take it out of the abstract idea grouping.
Accordingly, the claim recites an abstract idea and dependent claims 8-13 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a cloud computing pipeline and artificial intelligence. The cloud computing pipeline and artificial intelligence are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 8 includes various elements that are not directed to the abstract idea under 2A. These elements include a cloud computing pipeline, artificial intelligence and the generic computing elements described in the Applicant's specification in at least Para 0033-0042. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 8 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 14-20, they are directed to a non-transitory computer readable medium, however the claims are rejected because they are directed to a judicial exception without significantly more. Claims 14-20 are directed to the abstract idea of distributing data to geographically distributed users.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 14, claim 14 recites obtain a flow graph of tasks, wherein the flow graph of tasks indicates at least two of: multiple tasks, a user associated with a task in the multiple tasks, a location associated with the user, and an indication of network connectivity associated with the user; obtain a data associated with the flow graph of tasks; obtain metadata associated with the data, wherein the metadata indicates a time associated with the data, a location associated with the data or a purpose associated with the data; determine a particular user to which the data needs to be distributed based on the metadata and the flow graph of tasks; efficiently distribute the data to geographically distributed users by: providing the flow graph of tasks and an indication of the particular user to which the data needs to be distributed; and obtaining an expected time at which the particular user needs the data.
These claim limitations fall within the Certain Methods of Organizing Human Activity due to the managing personal behavior or relationships or interactions between people as it pertains to distributing data between users. Furthermore, the mere recitation of artificial intelligence does not take it out of the abstract idea grouping.
Accordingly, the claim recites an abstract idea and dependent claims 15-20 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of at least one data processor, a cloud computing pipeline and artificial intelligence. The at least one data processor, a cloud computing pipeline and artificial intelligence are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 14 includes various elements that are not directed to the abstract idea under 2A. These elements include at least one data processor, a cloud computing pipeline, artificial intelligence and the generic computing elements described in the Applicant's specification in at least Para 0033-0042. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 14 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 8 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 2008/0222362 A1) in view of Wouhaybi et al. (US 2023/0016946 A1).
Regarding Claim 1, Chang teaches the limitations of Claim 1 which state
at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to (Chang: Para 0053-0054):
obtain a flow graph of tasks (Chang: Para 0023, 0026 via processes automate a flow of tasks and tasks are described as states in a workflow…process descriptions contains tasks and descriptions),
wherein the flow graph of tasks indicates at least two of: multiple tasks, a user associated with a task in the multiple tasks, a location associated with the user, and an indication of network connectivity associated with the user (Chang: Para 0023, 0026 via multiple workflow tasks/states and graph transitions…a user logs in and performs actions though different workflow tasks);
obtain a data associated with the flow graph of tasks (Chang: Para 0023 via information is read from and written to local and remote databases by different workflow tasks);
obtain metadata associated with the data, wherein the metadata indicates a time associated with the data, a location associated with the data or a purpose associated with the data (Chang: Para 0030-0032 via prediction table maintains priority metadata, task timing and query timing)
However, Chang does not explicitly disclose the limitations of Claim 1 which state determine a particular user to which the data needs to be distributed based on the metadata and the flow graph of tasks; efficiently distribute the data associated with a cloud computing pipeline to geographically distributed users by: providing to an artificial intelligence the flow graph of tasks and an indication of the particular user to which the data needs to be distributed; and obtaining from the artificial intelligence an expected time at which the particular user needs the data.
Wouhaybi though, with the teachings of Chang, teaches of
determine a particular user to which the data needs to be distributed based on the metadata and the flow graph of tasks (Wouhaybi: Para 0253, 0332, 0517 via the ML model observes data and metadata flowing through the path and outputs routing patters…ML generates a relevancy score for each particular user from the association between the user and the metadata and the user list is supplied as model input…messages are sent to users associated with the ML-selected destinations nodes);
efficiently distribute the data associated with a cloud computing pipeline to geographically distributed users by (Wouhaybi: Para 0106, 0113-0114, 0258, 0562 via use of cloud computing… Compute, memory, and storage resources which are offered at the edges in the Edge cloud 1210 are critical to providing ultra-low latency response times for services and functions used by the endpoint data sources 1260 as well as reduce network backhaul traffic from the Edge cloud 1210 toward cloud data center 1230 thus improving energy consumption and overall network usages among other benefits…Disclosed systems, methods, apparatus, and articles of manufacture improve the efficiency of using a computing device by routing data, messages, alerts, and the like to pertinent users rather than broadcasting to an entire network, which can reduce the computational, memory, and network resources needed to convey data to the pertinent users):
providing to an artificial intelligence the flow graph of tasks and an indication of the particular user to which the data needs to be distributed (Wouhaybi: Para 0253, 0332, 0458, 0075 via workflow data, graph nodes, data routing paths and metadata are provided to the ML circuitry…the user list and user metadata are supplied to the ML model as inputs…metadata is provided as input to an ML microservice…defines AI as including ML, deep learning and model-based processing of input data); and
obtaining from the artificial intelligence an expected time at which the particular user needs the data (Wouhaybi: Para 0075, 0253, 0332 via the ML model generates outputs from learned patterns and recognitions…the ML system observes past and current data routing behavior and outputs routing patterns…the ML model evaluates the particular user from user and metadata inputs).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chang with the teachings of Wouhaybi in order to have determine a particular user to which the data needs to be distributed based on the metadata and the flow graph of tasks; efficiently distribute the data associated with a cloud computing pipeline to geographically distributed users by: providing to an artificial intelligence the flow graph of tasks and an indication of the particular user to which the data needs to be distributed; and obtaining from the artificial intelligence an expected time at which the particular user needs the data. The motivations behind this being to incorporate the teachings of efficient and proactive data routing using machine learning as taught by Wouhaybi. Furthermore, combining prior art elements according to known methods will yield predictable results.
Regarding Claims 8 and 14, they are analogous in nature to Claim 1 and are rejected for the same reasons.
Claim(s) 2, 9 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 2008/0222362 A1) in view of Wouhaybi et al. (US 2023/0016946 A1) in view of Lussier et al. (US 2011/0029883 A1) further in view of Galant et al. (US 2016/0365114 A1).
Regarding Claim 2, while Chang/Wouhaybi teaches the limitations of Claim 1 and the limitations of Claim 2 which state
upon determining that the data cannot reach the particular user before the expected time, generate a reduced-fidelity version associated with the data, wherein the reduced-fidelity version associated with the data occupies less memory than the data (Chang: Para 0023, 0045, 0050 via situations are addressed in which unreliable or slow connections interrupt timely access to remote data… T(q) indicates expected workflow-task time and communication time…expected need time, probability of use and query information determine delivery priority…Wouhaybi: Para 0570, 562 via after determining that a destination node has bandwidth below a threshold, the system generates the transmitted message to contain audio or text rather than bandwidth heavy video…this selective routing reduces memory and network resource usage).
It does not explicitly teach the limitations of Claim 2 which state obtain, from the particular user, a modification to the reduced-fidelity version associated with the data; provide to the artificial intelligence the data and the modification to the reduced-fidelity version associated with the data.
Lussier though, with the teachings of Chang/Wouhaybi, teaches of
obtain, from the particular user, a modification to the reduced-fidelity version associated with the data (Lussier: Para 0083, 0116 via user accesses a proxy copy and selections portions of the low resolution video…user edits displayed low resolution media, rearranges clips and creates an edit decision list as metadata…);
provide to the artificial intelligence the data and the modification to the reduced-fidelity version associated with the data (Lussier: Para 0116, 0157-0160 via edits made using the low resolution media are stored in metadata associated with the original high resolution file…original high resolution footage and editing modifications received from the proxy editing user are provided to the editing/generation system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chang/Wouhaybi with the teachings of Lussier in order to have obtain, from the particular user, a modification to the reduced-fidelity version associated with the data; provide to the artificial intelligence the data and the modification to the reduced-fidelity version associated with the data. The motivations behind this being to incorporate the teachings of content aggregation, editing and delivery as taught by Lussier. Furthermore, simple substitution of one known element for another to obtain predictable results (editing/generation system of Lussier with the ML in Wouhaybi). Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
In addition, Chang/Wouhaybi does not teach the limitations of Claim 2 which state cause the artificial intelligence to apply the modification to the data.
Galant though, with the teaching of Chang/Wouhaybi/Lussier, teaches of
cause the artificial intelligence to apply the modification to the data (Galant: Para 0143, 0150, 0193 via generating editing control settings based on user edits… edit parameters control transformation of raw video into the final cut).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chang/Wouhaybi/Lussier with the teachings of Galant in order to have cause the artificial intelligence to apply the modification to the data. The motivations behind this being to incorporate the teachings of utilizing machine learning to control an editing process as taught by Galant. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claims 9 and 15, they are analogous to Claim 2 and are rejected for the same reasons.
Claim(s) 3, 6, 7, 10, 13, 16, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 2008/0222362 A1) in view of Wouhaybi et al. (US 2023/0016946 A1) further in view of Visharam et al. (US 2012/0254456 A1) .
Regarding Claim 3, while Chang/Wouhaybi teaches the limitations of Claim 1 and the limitations of Claim 3 which state
upon determining that the data cannot reach the particular user before the expected time, generate a reduced-fidelity version associated with the data, wherein the reduced-fidelity version associated with the data occupies less memory than the data (Chang: Para 0023, 0045, 0050 via situations are addressed in which unreliable or slow connections interrupt timely access to remote data… T(q) indicates expected workflow-task time and communication time…expected need time, probability of use and query information determine delivery priority…Wouhaybi: Para 0570, 562 via after determining that a destination node has bandwidth below a threshold, the system generates the transmitted message to contain audio or text rather than bandwidth heavy video…this selective routing reduces memory and network resource usage).
It does not explicitly disclose the limitations of Claim 3 which state obtain a current time, a size associated with the data, and network bandwidth between the location associated with the data and the location associated with the particular user; based on the current time, the size associated with the data, the network bandwidth, and the expected time, determine whether the data can reach the particular user before the expected time; based on the current time, the size associated with the data, the network bandwidth, and the expected time, determining an upper reduced- fidelity size associated with the reduced-fidelity version associated with the data, wherein a network providing the network bandwidth is configured to deliver the reduced-fidelity version having the upper reduced- fidelity size before the expected time; and based on the upper reduced-fidelity size, generating the reduced-fidelity version associated with the data according to a criterion stored in the flow graph of tasks, wherein the reduced-fidelity version associated with the data occupies a matching amount of memory or less memory than the memory indicated by the upper reduced-fidelity size.
Visharam though, with the teachings of Chang/Wouhaybi, teaches of
obtain a current time, a size associated with the data, and network bandwidth between the location associated with the data and the location associated with the particular user (Visharam: Para 0067-0069 via obtaining effective client bandwidth…records elapsed time and amount of data sent…knows total file size, remaining data and remaining delivery time…);
based on the current time, the size associated with the data, the network bandwidth, and the expected time, determine whether the data can reach the particular user before the expected time (Visharam: Para 0068-0071 via the server determines whether delivery has fallen behind, calculates the time remaining, establishes a deadline and calculates the assured flow rate…);
based on the current time, the size associated with the data, the network bandwidth, and the expected time, determining an upper reduced- fidelity size associated with the reduced-fidelity version associated with the data (Visharam: Para 0056, 0068-0071 via mapping available bandwidth to an appropriate bitrate…maximum deliverable size is the done using the equation Smax=Bavailablex(Tdeadline-Tcurrent)…,
wherein a network providing the network bandwidth is configured to deliver the reduced-fidelity version having the upper reduced- fidelity size before the expected time (Visharam: Para 0069-0071 via calculating the amount deliverable in the time window…selecting the transmission rate required to prevent stalling and sets deadlines to that rate is met…); and
based on the upper reduced-fidelity size, generating the reduced-fidelity version associated with the data according to a criterion stored in the flow graph of tasks, wherein the reduced-fidelity version associated with the data occupies a matching amount of memory or less memory than the memory indicated by the upper reduced-fidelity size (Visharam: Para 0056, 0071-0072 via selecting/transcoding to a bitrate compatible with the available capacity…).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chang/Wouhaybi with the teachings of Visharam in order to have obtain a current time, a size associated with the data, and network bandwidth between the location associated with the data and the location associated with the particular user; based on the current time, the size associated with the data, the network bandwidth, and the expected time, determine whether the data can reach the particular user before the expected time; based on the current time, the size associated with the data, the network bandwidth, and the expected time, determining an upper reduced- fidelity size associated with the reduced-fidelity version associated with the data, wherein a network providing the network bandwidth is configured to deliver the reduced-fidelity version having the upper reduced- fidelity size before the expected time; and based on the upper reduced-fidelity size, generating the reduced-fidelity version associated with the data according to a criterion stored in the flow graph of tasks, wherein the reduced-fidelity version associated with the data occupies a matching amount of memory or less memory than the memory indicated by the upper reduced-fidelity size. The motivations behind this being to incorporate the teachings of media content delivery as taught by Visharam. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claims 10 and 16, they are analogous to Claim 3 and are rejected for the same reasons.
Regarding Claim 6 while Chang/Wouhaybi teaches the limitations of Claim 1, they do not explicitly disclose the limitations of Claim 6 which state wherein the data includes a video, a scan of geometry, audio, or motion capture data.
Visharam though, with the teachings of Chang/Wouhaybi, teaches of
wherein the data includes a video, a scan of geometry, audio, or motion capture data (Visharam: Para 0055 via video content and video data are used as examples of media content and media data, but the examples are not limited to video content alone and may include other types of media content and media data, e.g., audio, multimedia presentations, slide shows, etc).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chang/Wouhaybi with the teachings of Visharam in order to have wherein the data includes a video, a scan of geometry, audio, or motion capture data. The motivations behind this being to specifically note that the data pertains to multi-media. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 19, it is analogous to Claim 6 and is rejected for the same reasons.
Regarding Claim 7, while Chang/Wouhaybi teaches the limitation of Claim 7 which states
upon determining that the data cannot reach the particular user before the expected time, generate a reduced-fidelity version associated with the data, wherein the reduced-fidelity version associated with the data occupies less memory than the data (Chang: Para 0023, 0045, 0050 via situations are addressed in which unreliable or slow connections interrupt timely access to remote data… T(q) indicates expected workflow-task time and communication time…expected need time, probability of use and query information determine delivery priority…Wouhaybi: Para 0570, 562 via after determining that a destination node has bandwidth below a threshold, the system generates the transmitted message to contain audio or text rather than bandwidth heavy video…this selective routing reduces memory and network resource usage).
It does not explicitly disclose the limitations of Claim 7 which state send at least a portion of the reduced-fidelity version associated with the data to the particular user; and upon determining that the data can reach the particular user before the expected time, send at least a portion of the data to the particular user.
Visharam though, with the teachings of Chang/Wouhaybi, teaches of
send at least a portion of the reduced-fidelity version associated with the data to the particular user (Visharam: Para 0055-0060, 0067-0072 via delivering the bandwidth selected media stream to the client…); and
upon determining that the data can reach the particular user before the expected time, send at least a portion of the data to the particular user (Visharam: Para 0056-0057, 0067-0072 via selecting the quality/bitrate sustainable by the measured bandwidth and switch upwards when bandwidth permits…).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chang/Wouhaybi with the teachings of Visharam in order to have send at least a portion of the reduced-fidelity version associated with the data to the particular user; and upon determining that the data can reach the particular user before the expected time, send at least a portion of the data to the particular user. The motivations behind this being to incorporate the teachings of media content delivery as taught by Visharam. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claims 13 and 20, they are analogous to Claim 7 and are rejected for the same reasons.
Claim(s) 4, 11 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 2008/0222362 A1) in view of Wouhaybi et al. (US 2023/0016946 A1) further in view of Strobl et al. (US 2014/0328569 A1).
Regarding Claim 4, while Chang/Wouhaybi teaches the limitations of Claim 1, they do not explicitly disclose the limitations of Claim 4 which state generate a reduced-fidelity version associated with the data, wherein the reduced-fidelity version associated with the data occupies less memory than the data; obtain a first identifier (ID) associated with the reduced-fidelity version associated with the data, wherein the first ID uniquely identifies the reduced-fidelity version associated with the data; obtain second data and a second ID associated with the second data; determine whether at least a portion of the second ID is the same as a portion of the first ID; and upon determining that the at least a portion of the second ID is the same as the portion of the first ID, determine that the reduced-fidelity version associated with the data and the second data represent matching information with differing fidelity.
Strobl though, with the teachings of Chang/Wouhaybi, teaches of
generate a reduced-fidelity version associated with the data, wherein the reduced-fidelity version associated with the data occupies less memory than the data (Strobl: Para 0009, 0048 via high resolution media is transcoded into a low res proxy…only a small amount of data must be transmitted and the amount is significantly reduced…);
obtain a first identifier (ID) associated with the reduced-fidelity version associated with the data, wherein the first ID uniquely identifies the reduced-fidelity version associated with the data (Strobl: Para 0040 via the media stream box provides the two media streams with an unique media stream identifier (e.g. a generated unique identifier--UID), by means of which media streams of the same content belonging to each other can be found in the media database…);
obtain second data and a second ID associated with the second data (Strobl: Para 0019, 0026, 0040 via second data is corresponding hi res stream which has the matching stream identifier and corresponding image codes…);
determine whether at least a portion of the second ID is the same as a portion of the first ID (Strobl: Para 0019, 0026, 0040 via the stream identifiers are identical and corresponding images in both streams are assigned identical identification codes…); and
upon determining that the at least a portion of the second ID is the same as the portion of the first ID, determine that the reduced-fidelity version associated with the data and the second data represent matching information with differing fidelity (Strobl: Para 0040, 0051, 0076 via common identifiers are used to find low res and hi res content and corresponding images/sequences…).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chang/Wouhaybi with the teachings of Strobl in order to have generate a reduced-fidelity version associated with the data, wherein the reduced-fidelity version associated with the data occupies less memory than the data; obtain a first identifier (ID) associated with the reduced-fidelity version associated with the data, wherein the first ID uniquely identifies the reduced-fidelity version associated with the data; obtain second data and a second ID associated with the second data; determine whether at least a portion of the second ID is the same as a portion of the first ID; and upon determining that the at least a portion of the second ID is the same as the portion of the first ID, determine that the reduced-fidelity version associated with the data and the second data represent matching information with differing fidelity. The motivations behind this being to incorporate the teachings of processing or editing media content and tagging, storing and for the cutting and distribution of media data as taught by Strobl. Furthermore, combining prior art elements according to known methods will yield predictable results.
Regarding Claims 11 and 17, they are analogous to Claim 4 and is rejected for the same reasons.
Claim(s) 5, 12 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 2008/0222362 A1) in view of Wouhaybi et al. (US 2023/0016946 A1) further in view of Zhang (US 2016/0381508 A1).
Regarding Claim 5, while the combination of Chang/Wouhaybi teaches Claim 1 and the limitation of Claim 5 which states based on the indication of the particular user's requirement time determine the expected time (Chang: Para 0042-0050 via calculating the expected time until a user’s predicted data/query need…).
It does not explicitly disclose the limitations of Claim 5 which state obtain a schedule associated with the particular user, wherein the schedule indicates availability of the particular user; based on the schedule determine a particular user's requirement time associated with the data, wherein the particular user's requirement time indicates anticipated utilization time associated with the data.
Zhang though, with the teachings of Chang/Wouhaybi, teaches of
obtain a schedule associated with the particular user, wherein the schedule indicates availability of the particular user (Zhang: Para 0019, 0041-0042 via obtaining scheduled future events from the user’s calendar app….event times and time gaps indicate when the user is available or occupied…);
based on the schedule determine a particular user's requirement time associated with the data (Zhang: Para 0039-0047 via a predicted future event includes time, location, participant and action…the prefetch server selects and sends information associated with that event. The event time supplies the time in which the corresponding information is required…),
wherein the particular user's requirement time indicates anticipated utilization time associated with the data (Zhang: Para 0038-0047 via seeking a high probability that prefetched information will be accessed by the user, predicts the future event/action and retrieves event specific information for that anticipated use…).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chang/Wouhaybi with the teachings of Zhang in order to have obtain a schedule associated with the particular user, wherein the schedule indicates availability of the particular user; based on the schedule determine a particular user's requirement time associated with the data, wherein the particular user's requirement time indicates anticipated utilization time associated with the data. The motivations behind this being to incorporate the teachings of determining the most effective time to deliver data to a user based on the users needs and availability as taught by Zhang. Furthermore, combining prior art elements according to known methods will yield predictable results.
Regarding Claims 12 and 18, they are analogous to Claim 5 and are rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tallamy et al. (US 2021/0337042 A1)
Claman et al. (US 2014/0289472 A1)
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/T.E.S./Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
