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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/15/2025 has been entered.
Applicant' s arguments, filed 12/15/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 12/15/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-7 are the currently pending claims hereby under examination.
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 1-4 and 7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-4 and 7 are directed to a method of processing brainwave physiological signals using a computational algorithm, which is an abstract idea. Claims 1-4 and 7 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is drawn to a process.
Step 2A-Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations:
[A1] “to split the electroencephalogram into a plurality of sub-images based on the time sequence, each of the sub-images being a sub-image in a time period of the time sequence”;
[B1] “identify at least one static feature tag and a plurality of associated dynamic displacement tags based on the time sequence from the plurality of sub-images according to a plurality of static feature tags and a plurality of dynamic displacement tags stored in a brainwave database”;
[C1] “wherein the at least one static feature tag comprises a static background value fixed over time of the electroencephalogram”;
[D1] “each of the plurality of associated dynamic displacement tags comprises a difference value of an associated sub-image at a tagged time period of the electroencephalogram based on the time sequence relative to the at least one static feature tag”;
[E1] “to generate at least one superimposed group tag”;
[F1] “wherein the electroencephalogram is transformed into the compressed brainwave physiological signals comprising the identified at least one static feature tag, the plurality of associated dynamic displacement tags and the at least one superimposed group tag”; and
[G1] “wherein the at least one superimposed group tag is used to integrate the identified at least one static feature tag and the plurality of associated dynamic displacement tags according to the time sequence for reconstructing the electroencephalogram”.
These elements [A1]-[G1] of claim 1 are drawn to an abstract idea because they involve mathematical concepts and data manipulation in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations applied to signal representations (e.g., time-sequenced segmentation of signals, determination of a static background value, calculation of difference values relative to that static value, transformation into a compressed representation, and integration of such values for reconstruction).
Step 2A-Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
[A2] “A method for transmitting compressed brainwave physiological signals in a biological feedback training system”;
[B2] “using a brainwave cap to detect a plurality of brainwave physiological signals of a subject under brain training in the biological feedback training system, and generating an electroencephalogram based on a time sequence of the plurality of brainwave physiological signals using a computing terminal in the biological feedback training system”;
[C2] “using the computing terminal to transmit the identified at least one static feature tag, the plurality of associated dynamic displacement tags and the at least one superimposed group tag instead of the detected plurality of brainwave physiological signals to a remote cloud system for analysis according to the time sequence”;
[D2] “so as to reduce time and data required in transmission to the remote cloud system and enable real-time biological feedback to the subject”; and
[E2] “wherein the method enables the biological feedback training system to provide the real-time biological feedback to the subject and improves efficiency of the brain training by transmitting the compressed brainwave physiological signals”.
These elements [A2]-[E2] of claim 1 do not integrate the exception into a practical application of the exception.
In particular, [A2] is a field-of-use or technological environment limitation (“in a biological feedback training system”) that does not impose meaningful limits on practicing the abstract idea.
[B2] recites detecting brain wave physiological signals using a brainwave cap and generating an electroencephalogram using a computing terminal which merely gathers physiological data for subsequent abstract processing and therefore does not impose any meaningful technological limitation on the judicial exception.
[C2] recites transmitting the output of the abstract processing (compressed tags) to a remote cloud system using a computing terminal, which merely uses generic computing and networking components as tools to communicate information and does not recite any technological improvement to the functioning of the computer, network, or cloud system (see MPEP 2106.04(d) and MPEP 2106.05(f)).
[D2] and [E2] recite intended results and advantages (“reduce time and data required”, “enable real-time biological feedback”, and “improves efficiency of the brain training”) without reciting how such results are technically achieved. These limitations therefore amount to aspirational results rather than a concrete technological implementation.
Looking at the limitations as an ordered combination, they also fail to integrate the judicial exception into a practical application. The combination of elements merely implements the abstract idea using generic computing functions such as detecting signals, performing information encoding and representation, transmitting information, and presenting analysis or feedback results. The combination of elements merely implements the abstract idea using generic computing functions such as detecting signals, performing information encoding/representation, transmitting information, and presenting analysis/feedback results.
Desjardins and Specification-Based Improvement Consideration (MPEP 2106.05(a)):
The specification describes a motivation relating to transmitting large amounts of physiological data and achieving near real-time feedback (e.g., increasing brainwave recording time increases the data and “difficulty in transmission,” and it is “necessary … [to] transmit … brainwave physiological signals … to the remote cloud system in real time”) ([0004]-[0006]). The specification further describes a particular “shape compression technique” that uses “a static base value … and a displacement … of a difference between waveforms,” and describes “shape tags” including a static feature tag (B-Frame), a dynamic displacement tag (M-Frame), and a superimposed group tag (G-Frame), where “the superimposed group tag is a message to deal with the static feature tag and the associated dynamic displacement tag,” and the “background value is fixed over time” ([0008]-[0009]; [0024]-[0025]).
After consulting the specification, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology as an ordered combination and without oversimplification. Here, although claim 1 uses labels such as “static feature tag,” “dynamic displacement tag,” and “superimposed group tag,” and recites that the superimposed group tag “integrate[s]” information for “reconstructing” the electroencephalogram, claim 1 does not recite concrete technical details of the compression/reconstruction implementation (e.g., a particular encoding rule, a defined tag data structure or syntax, specific parameters, or a particular reconstruction rule) that would limit the full scope of the claim to the specific technical solution described in the specification. Instead, claim 1 broadly recites the functional results of identifying a static background value, generating difference values relative to that static value, generating an integration tag, and using the resulting information for reconstruction, with generic transmission to a cloud system. Under BRI, claim 1 therefore reads on generic baseline-and-difference representations and generic integration for reconstruction implemented using generic computing and networking components. Accordingly, the specification’s discussion of the problem and asserted benefit does not resolve the 35 U.S.C. 101 issue because the claim itself is not limited to a particular technical implementation that improves technology or a technical field.
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the operations of signal detection (with a brainwave cap), transmitting the results of abstract processing to a remote cloud system, and providing feedback are recited as generic computing and networking functions used to implement the abstract processing described above. The intended use of the claimed method in a biological feedback training system does not add significantly more to the judicial exception since it does not recite any specific technological improvement. No novel machine, structure, or hardware is introduced, and the method as a whole amounts to using generic computing components to carry out abstract mathematical manipulations and information delivery (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'l, 134 S. Ct. 2347 (2014)).
The “brainwave cap” used for data acquisition is a generic, conventional component that is commercially available, as evidenced by U.S. Patent Application Publication No. US 20140223462 A1 (Aimone), which describes “a wearable sensor for collecting biological data, such as a commercially available consumer grade EEG headset with one or more electrodes for collecting brainwaves from the user” (Aimone, ¶[0389]).
The “computing terminal” is a generic computing component used to perform conventional data processing functions such as signal segmentation identification of tags generation of encoded representations and transmission of information and therefore merely implements the abstract idea on a generic computer without providing a specific technological improvement to computer functionality. As such, the “computing terminal” merely performs instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
The "remote cloud system" used for transmission and storage is a generic and conventional component, as evidenced by U.S. Patent Application Publication No. US 20120271874A1 (Nugent), which describes a system and method for selecting and associating cloud computing services from many cloud vendors. The abstract and paragraphs 0003-0007 of Nugent expressly states that cloud infrastructure is implemented in software form and supports large-scale data processing across various service providers, confirming its well-known and commercial nature.
When the claim is considered as an ordered combination, it still does not amount to significantly more than the abstract idea. The ordered steps of receiving signals, encoding them into tags, and transmitting them represent a generic data processing workflow. There is no recited improvement to the functioning of a computer, network, or cloud platform, and no recited unconventional implementation of computing resources.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 2-4 and 7 depend from claim 1 and recite the same abstract idea as claim 1. These claims merely further limit the types of data or data structures used in the abstract processing method.
Claim 2 defines the nature of the static and dynamic tags.
Claim 3 specifies the physiological signal types.
Claim 4 adds a reconstruction step using the cloud system.
Claim 7 adds a latency requirement by limiting round-trip packet delay to under three seconds.
Each of these limitations continues to focus on abstract data manipulation and transmission without adding a recited technological improvement to the computer, network, or cloud system, an unconventional implementation, or integration into a practical application. Thus, the additional elements in claims 2-4 and 7 do not amount to significantly more than the judicial exception.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 5-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 5-6 are directed to a method of processing brainwave physiological signals using a computational algorithm, which is an abstract idea. Claims 5-6 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 5 is as follows:
Step 1: Claim 5 is drawn to a process.
Step 2A-Prong One: Claim 5 recites an abstract idea. In particular, claim 5 recites the following limitations:
[A1] “generating an electroencephalogram based on a time sequence of the plurality of brainwave physiological signals in the biological feedback training system”;
[B1] “to split the electroencephalogram into a plurality of sub-images based on the time sequence, each of the sub-images being a sub-image in a time period of the time sequence”;
[C1] “to identify a sequence of feature tags from the plurality of sub-images based on the time sequence”;
[D1] “to generate a biological feature sequence comprising a plurality of index patterns based on the identified sequence of feature tags and the time sequence according to brainwave data stored in a brainwave database”;
[E1] “wherein the brainwave data including a plurality of index patterns with each index pattern composed of a plurality of feature tags”;
[F1] “the electroencephalogram is transformed into the compressed brainwave physiological signals comprising the biological feature sequence with each index pattern of the biological feature sequence being identified from the brainwave database based on the identified sequence of feature tags”.
These elements [A1]-[F1] of claim 5 are drawn to an abstract idea because they involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations applied to signal representations (e.g., time-sequenced segmentation of signals, assigning and sequencing feature tags, mapping a sequence of tags to stored index patterns, and transforming an electroencephalogram into a compressed representation comprising a sequence of identified patterns).
Step 2A-Prong Two: Claim 5 recites the following limitations that are beyond the judicial exception:
[A2] “A transmission method for compressed brainwave physiological signals in a biological feedback training system… using a computing terminal”;
[B2] “using a brainwave cap to detect a plurality of brainwave physiological signals of a subject under brain training in the biological feedback training system”;
[C2] “using the computing terminal to transmit the plurality of index patterns comprised in the biological feature sequence instead of the detected plurality of brainwave physiological signals to a remote cloud system for analysis according to the time sequence”;
[D2] “so as to reduce time and data required in transmission to the remote cloud system and enable real-time biological feedback to the subject”; and
[E2] “wherein the transmission method enables the biological feedback training system to provide the real-time biological feedback to the subject and improves efficiency of the brain training by transmitting the compressed brainwave physiological signals”.
These elements [A2]-[E2] of claim 5 do not integrate the exception into a practical application of the exception.
In particular, [A2] is a field-of-use or technological environment limitation (“in a biological feedback training system”) that does not impose meaningful limits on practicing the abstract idea. Additionally, the limitation “using a computing terminal” merely gathers physiological data for subsequent abstract processing and transmitting, and therefore does not impose any meaningful technological limitation on the judicial exception.
[B2] recites the use of a brainwave cap to detect data, which is insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality (see MPEP 2106.04(d) and MPEP 2106.05(g)).
[C2] recites transmitting the output of the abstract processing (index patterns and biological feature sequence) to a remote cloud system “for analysis”, which merely uses generic computing and networking components as tools to communicate information and does not recite any technological improvement to the functioning of the computer, network, or cloud system (see MPEP 2106.04(d) and MPEP 2106.05(f)).
[D2] and [E2] recite intended results and advantages (“reduce time and data required”, “enable real-time biological feedback”, and “improves efficiency of the brain training”) without reciting how such results are technically achieved. These limitations therefore amount to aspirational results rather than a concrete technological implementation.
Looking at the limitations as an ordered combination, they also fail to integrate the judicial exception into a practical application. The combination of elements merely implements the abstract idea using generic computing functions such as detecting signals, performing information encoding and representation (feature tags and index patterns), transmitting information, and presenting analysis or feedback results.
Desjardins and Specification-Based Improvement Consideration (MPEP 2106.05(a)):
The specification describes a motivation relating to transmitting large amounts of physiological data and achieving near real-time feedback (e.g., increasing brainwave recording time increases the data and “difficulty in transmission,” and it is “necessary … [to] transmit … brainwave physiological signals … to the remote cloud system in real time”) (Spec, ¶[0004]-¶[0006]). The specification further describes (in the context of the claim 5-type transmission approach) using “a plurality of feature tags and a plurality of index patterns stored in an brainwave database” to identify a “sequence of feature tags,” generate a “biological feature sequence … composed of a plurality of index patterns,” and transmit the “plurality of index patterns … to a remote cloud system” (Spec, ¶[0007]). The specification also describes generating index patterns by training a neural network using electroencephalograms, and representing each index pattern by a combination of feature tags (Spec, ¶[0011]).
After consulting the specification, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology as an ordered combination and without oversimplification. Here, although claim 5 uses labels such as “feature tags,” “index patterns,” and “biological feature sequence,” claim 5 does not recite concrete technical details of the compression/transmission implementation (e.g., a defined index-pattern data structure, particular neural-network training constraints, concrete feature-tagging rules, specific parameters, or a particular encoding/decoding rule) that would limit the full scope of the claim to the specific technical solution described in the specification. Instead, claim 5 broadly recites the functional results of identifying a sequence of feature tags, mapping that sequence to stored index patterns, and transmitting the resulting pattern identifiers for analysis and feedback. Under BRI, claim 5 therefore reads on generic feature-label sequencing, generic lookup/mapping to stored pattern identifiers, and generic transmission of a reduced representation implemented using generic computing and networking components. Accordingly, the specification’s discussion of the problem and asserted benefit does not resolve the 35 U.S.C. 101 issue because the claim itself is not limited to a particular technical implementation that improves technology or a technical field.
Step 2B: Claim 5 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the operations of signal detection (with a brainwave cap), forming a reduced representation using feature tags and index patterns stored in a database, transmitting the results of abstract processing to a remote cloud system, and providing feedback are recited as generic computing and networking functions used to implement the abstract processing described above. The intended use of the claimed method in a biological feedback training system does not add significantly more to the judicial exception since it does not recite any specific technological improvement. No novel machine, structure, or hardware is introduced, and the method as a whole amounts to using generic computing components to carry out abstract mathematical manipulations and information delivery (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'l, 134 S. Ct. 2347 (2014)).
The “brainwave cap” used for data acquisition is a generic, conventional component that is commercially available, as evidenced by U.S. Patent Application Publication No. US 20140223462 A1 (Aimone), which describes “a wearable sensor for collecting biological data, such as a commercially available consumer grade EEG headset with one or more electrodes for collecting brainwaves from the user” (Aimone, ¶[0389]).
The “computing terminal” is a generic computing component used to perform conventional data processing functions such as signal segmentation identification of tags generation of encoded representations and transmission of information and therefore merely implements the abstract idea on a generic computer without providing a specific technological improvement to computer functionality. As such, the “computing terminal” merely performs instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
The "remote cloud system" used for transmission and storage is a generic and conventional component, as evidenced by U.S. Patent Application Publication No. US 20120271874A1 (Nugent), which describes a system and method for selecting and associating cloud computing services from many cloud vendors. The abstract and paragraphs 0003-0007 of Nugent expressly states that cloud infrastructure is implemented in software form and supports large-scale data processing across various service providers, confirming its well-known and commercial nature.
When the claim is considered as an ordered combination, it still does not amount to significantly more than the abstract idea. The ordered steps of receiving signals, identifying feature tags, identifying index patterns from a database, and transmitting those patterns represent a generic data processing workflow. There is no recited improvement to the functioning of a computer, network, or cloud platform, and no recited unconventional implementation of computing resources.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claim 6 depends from claim 5 and recites the same abstract idea as claim 5. Claim 6 further recites: “using the remote cloud system to determine and analyze behavioral performance or mental process corresponding to the biological feature sequence … by comparing with the brainwave data stored in the brainwave database”. This limitation adds an additional analysis and evaluation of behavioral performance or mental process, which constitutes an abstract mental process and does not recite a specific technical implementation or improvement to computer, network, or cloud technology. Accordingly, claim 6 does not integrate the exception into a practical application or amount to significantly more than the judicial exception.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
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.
Claims 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Hejrati et al. (Hejrati, Behzad, Abdolhossein Fathi, and Fardin Abdali-Mohammadi. “Efficient Lossless Multi-Channel EEG Compression Based on Channel Clustering.” Biomedical signal processing and control 31 (2017): 295–300. Web.), hereto referred as Hejrati, and further in view of Coleman et al. (US 20190113973 A1), hereto referred as Coleman, and Normile et al. (US-5596659-A), hereto referred as Normile.
Regarding claim 1, Hejrati teaches a method for transmitting compressed brainwave physiological signals (Hejrati, Title: "Efficient lossless multi-channel EEG compression based on channel clustering"; Abstract: "EEG is widely used in telemedicine and neurological research. However, transmitting large EEG data is a challenge, due to high redundancy between channels... Lossless compression methods play an important role when coding medical signals for telemedicine systems since the data remains unchanged", Hejrati discloses a method for transmitting compressed EEG signals for telemedicine applications) comprises: using a brainwave cap to detect a plurality of brainwave physiological signals and generating an electroencephalogram based on a time sequence of the plurality of brainwave physiological signals using a computing terminal (Hejrati, p. 295, 1. Introduction', [1]: "Electroencephalogram (EEG) demonstrates the electrical activities of the brain by one-dimensional signals gathered by electrodes placed on the scalp", Hejrati discloses EEG acquisition with electrodes on the scalp (i.e. brainwave cap) generating an Electroencephalogram which is a time sequence of brainwave physiological signals; p. 298, '3. Experimental results': where the "dataset was collected from five healthy persons while doing a motor imagery task. Signals were recorded by a 64-channel system", disclosing a conventional multi-channel electrode system used to acquire EEG data; Under the broadest reasonable interpretation, a "brainwave cap" is simply a well-known arrangement of multiple electrodes housed in a wearable cap for convenience and subject comfort (see Aimone, [0389]); Thus, Hejrati's disclosure of scalp electrodes and a 64-channel EEG acquisition system corresponds to the claimed use of a brainwave cap to detect brainwave signals and generate an electroencephalogram as a time sequence of those signals; p. 295, 1. Introduction', [1]: “Intelligent systems are usually parts of remote telemedicine, where the data can be remotely transferred from the person’s mobile phone to any destination, such as a remote terminal in a hospital”, demonstrating the use of a computing terminal); using the computing terminal to split the electroencephalogram into a plurality of sub-images based on the time sequence, each of the sub-images being a sub-image in a time period of the time sequence (Hejrati, p. 296, "2. Proposed method": "First, the time-domain data of all channels are divided into N symbol blocks"; Hejrati teaches partitioning the electroencephalogram into discrete, time-ordered representations corresponding to defined temporal intervals. Under the broadest reasonable interpretation, the claimed “sub-images” are not limited to visually rendered images but encompass time-segmented representations of physiological signal data, and Hejrati’s symbol blocks constitute such time-based representations of the electroencephalogram generated and processed by the computing terminal (mobile phone / remote terminal); Because Hejrati expressly divides “time-domain data” into blocks prior to further processing, each symbol block necessarily corresponds to a defined time interval of the electroencephalogram, regardless of whether the blocks are later used for coding efficiency, and thus reads on the claimed sub-images defined by time periods of the time sequence under the broadest reasonable interpretation).
Also regarding claim 1, with respect to the method being directed to use for brain training in a biological feedback training system, Hejrati teaches EEG acquisition and compression for telemedicine and neurological research but does not expressly disclose that the EEG acquisition occurs while the subject is under brain training in a biological feedback training system.
Coleman fills this gap by teaching a biological feedback training system in which EEG and other bio-signal data are used in a meditation training application, and real-time feedback is provided to assist the user in achieving a particular mental state (Coleman, ¶[0052], “User effectors 110 are for providing feedback to the user … user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, explaining that feedback is provided to the user during use of the system; Coleman, ¶[0053], “User effectors 110 can be used, for example, to provide real-time feedback on characteristics related to the user’s current mental state … used to assist a user in achieving a particular mental state, such as, for example, a meditative state … enable a user to interact with a meditation training application”, explaining real-time feedback for a user during a training application).
It would have been prima facie obvious before the effective filing date of the claimed invention to modify Hejrati in view of Coleman such that Hejrati’s EEG acquisition and compression are performed while the subject is under brain training in the biological feedback training system, because Coleman expressly teaches a biological feedback training environment in which a subject is trained and provided real-time feedback based on analyzed signals, and Hejrati teaches a concrete EEG acquisition and compression process that can be executed by a computing terminal (e.g., a mobile phone and or a remote terminal) to generate compressed EEG data for transmission. It would have been possible to combine these teachings by implementing Hejrati’s EEG acquisition and compression steps within Coleman’s biological feedback training system, such that the computing terminal in Coleman’s system acquires EEG during training, applies Hejrati’s compression to produce compressed EEG outputs, and then Coleman’s existing user effectors provide real-time biological feedback to the subject. The benefit of the combination would be enabling efficient compression and transmission of EEG signals while providing real-time biological feedback during training.
Also regarding claim 1, with respect to using the computing terminal to identify at least one static feature tag and a plurality of associated dynamic displacement tags based on the time sequence from the plurality of sub-images according to a plurality of static feature tags and a plurality of dynamic displacement tags stored in a brainwave database, wherein the at least one static feature tag comprises a static background value fixed over time of the electroencephalogram, and each of the plurality of associated dynamic displacement tags comprises a difference value of an associated sub-image at a tagged time period of the electroencephalogram based on the time sequence relative to the at least one static feature tag, the modified Hejrati teaches generating a reference value and generating difference values relative to that reference value, and reconstructing by combining the reference value with the difference values. (Hejrati, p. 296: “the difference between each channel and the corresponding cluster’s centroid is calculated”; “SendData = (ADi, k)”; Hejrati, p. 298: “Each channel is then added to the corresponding cluster’s centroid, and then lossless EEG data is reconstructed”; “DEi = Di + Ck”, showing that a centroid reference value (Ck) and difference values (Di) are used to reconstruct the signal). However, the modified Hejrati does not teach that the reference value is a static background value fixed over time across multiple time periods of the electroencephalogram, because Hejrati’s centroid is expressly generated by clustering channels and is described as “the centroid of each cluster”, i.e., a spatial/channel-cluster reference rather than a reference representation that is reused from one time period of the electroencephalogram to the next. (Hejrati, p. 296: “the well-known KMEANS algorithm is employed to cluster all the channels in different K groups and calculate the centroid of each cluster”).
Normile fills this gap by teaching a stored codebook structure maintained in memory that provides a reusable reference representation across successive frames, specifically teaching reuse “between multiple frames” because “major image features change slowly over time” and “background images tend to change or move slowly”, and that enhanced performance is obtained by “reusing the top layer of the tree from one frame to the next”, with replacement of the reference representation only when change criteria are met (Normile, col. 15, ll. 44-55:“reuse first layer of the codebook tree between multiple frames … the major image features change slowly over time … background images tend to change or move slowly … reusing the top layer of the tree from one frame to the next”, explaining reuse of a stored reference representation across successive time periods rather than recomputation for each period). Under the broadest reasonable interpretation, the claimed “static background value fixed over time” does not require a value that is mathematically invariant for the entire duration of the electroencephalogram, but rather encompasses a background or reference value that is maintained and reused across multiple time periods of the time sequence before being updated. Normile’s disclosure of reusing a stored reference representation across successive frames and updating that reference only when change criteria are satisfied meets this interpretation, because the reference is fixed relative to multiple time periods of the sequence and is not recomputed for each individual time period. Normile further teaches a baseline-removal and residual-coding architecture, in which a baseline value is removed prior to encoding, residual values for each time-period block are encoded, and reconstruction is performed by adding the baseline value back, which corresponds under the broadest reasonable interpretation to dynamic displacement tags comprising difference values relative to a static background value. (Normile, col. 15–16, ll. 56–3: “remove the mean value of the vectors prior to coding”; “this mean is subtracted from all the vectors”; “The residual vectors are encoded”; “At the decoder … reconstructed … by adding the mean values … to the residual codebook”, describing subtraction of a baseline, encoding of difference values, and reconstruction by add-back). Normile also teaches identifying each time-period block by associating the block with a stored codebook vector using an index or identifier, which corresponds under the broadest reasonable interpretation to identifying static feature tags and associated dynamic displacement tags from a database implemented as a stored codebook or codebook tree in memory (Normile, col. 17, ll. 20-35: “Each image vector from the new frame is associated with one of the terminal nodes of the tree (i.e. with a codebook vector)”, showing selection of stored reference entries for each time period). Under the broadest reasonable interpretation, the claimed “brainwave database” is not limited to a database that explicitly stores entries labeled as “static feature tags” or “dynamic displacement tags,” but encompasses any stored collection of reference entries and associated information used by the computing terminal to identify and apply tag-based representations to physiological signal data. Normile’s stored codebook or codebook tree, maintained in memory and accessed using indices to identify reference representations and associated residual information for each time period, constitutes such a stored collection. Accordingly, Normile’s codebook structure reasonably reads on the claimed brainwave database from which the computing terminal identifies static feature tags and associated dynamic displacement tags for time-sequenced sub-images of the electroencephalogram.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Hejrati in view of Normile to identify a static background value fixed over time of the electroencephalogram and associated difference values relative to that static background value for the time-sequenced electroencephalogram sub-images, and to identify such static and dynamic information according to stored entries, because the modified Hejrati already teaches computing and transmitting a reference value (centroid Ck) with associated difference values (Di) and reconstructing by adding the difference values back to the reference value, while Normile teaches maintaining stored reference representations across successive time periods, subtracting a baseline value to encode residuals, and reconstructing by adding the baseline value back. It would have been possible to combine these teachings by implementing Normile’s stored codebook or codebook tree as the brainwave database, populating that database with representative EEG reference values, and using indices or identifiers to select stored reference entries for each EEG sub-image, while using Hejrati’s per-sub-image difference values as the dynamic displacement information transmitted relative to the selected reference entry. The benefit of the combination would be improved compression efficiency for time-sequenced physiological signals by reusing reference information across time and transmitting primarily difference information, thereby reducing transmitted data while retaining reliable reconstructability.
Also regarding claim 1, with respect to using the computing terminal to generate at least one superimposed group tag, wherein the electroencephalogram is transformed into the compressed brainwave physiological signals comprising the identified at least one static feature tag, the plurality of associated dynamic displacement tags and the at least one superimposed group tag, and wherein the at least one superimposed group tag is used to integrate the identified at least one static feature tag and the plurality of associated dynamic displacement tags according to the time sequence for reconstructing the electroencephalogram, Hejrati teaches generating identifiers that associate centroid values with corresponding difference values across time-segmented blocks. (Hejrati, p. 296, Fig. 1–2; Eq. 1–10, explaining that cluster indices identify which centroid applies to each symbol block and its associated differences). However, Hejrati does not explicitly teach a unified tag structure expressly used to integrate static background values and dynamic difference values for reconstruction across a time sequence.
Normile teaches a unified syntax and header structure that is generated by the encoder and transmitted as part of the compressed representation for a time-ordered sequence, which organizes and links (i) identifiers selecting a stored reference representation (codebook indices) with (ii) block type headers indicating how each time-period block is to be interpreted during reconstruction within the time-ordered bitstream, including whether the block is “change” or “no-change”. Specifically, Normile teaches: “the bitstream syntax includes a sequence header 1001, chunk header 1011, frame headers 1021, and codebook headers 1012, 1014. These are followed by the codebook indices, which are delineated by block type headers which indicate what blocktype the following indices refer to" (Normile, col. 20–21, ll. 63–8). Normile further teaches that: “Decoder 351 can then reconstruct the image, knowing which codebook vector to use for each image block and whether or not to upsample” (Normile, col. 20–21, ll. 63–8). Normile also teaches that, “instead of transmitting an index referring to an element in a codebook, a no-change tag is sent … specifying that the block has not changed substantially from a previous frame’s block at the same position” (Normile, col. 5–6, ll. 55–20). Under the broadest reasonable interpretation, Normile’s block type headers and no-change tag collectively correspond to the claimed “at least one superimposed group tag” because they constitute a grouping and control structure included in the compressed output that binds the previously identified reference-selection information and associated per-block difference interpretation information for each time period, thereby enabling reconstruction of the electroencephalogram from the compressed brainwave physiological signals in the proper time order, rather than from the original uncompressed signals.
It would have been prima facie obvious before the effective filing date of the claimed invention to have further modified the modified Hejrati in view of Normile to further generate at least one superimposed group tag that integrates the identified at least one static feature tag and the plurality of associated dynamic displacement tags according to the time sequence for reconstructing the electroencephalogram, because the modified Hejrati already teaches transmitting, for each time-segmented block, both (i) an identifier indicating which reference applies and (ii) corresponding difference data for reconstruction (Hejrati, p. 296, L1–L4: “sent along with the index of the corresponding cluster centroid.”; Hejrati, p. 298, L16–L21: “DEi = Di + Ck … Ei = InvDPCM (DEi)”), and Normile teaches an explicit, unified transmission syntax in which block type headers and codebook indices are delineated and interpreted within a time-sequenced bitstream to guide reconstruction, including a no-change tag that indicates reuse of a reference from a prior time period. It would have been possible to combine these teachings by packaging the modified Hejrati’s centroid identifier and corresponding difference information into Normile’s type-delimited block/header message structure for a time-ordered sequence, so that a decoder uses the group tag structure to determine, for each time period, which stored reference applies and how to apply the associated difference information to reconstruct the electroencephalogram. The benefit of the combination would be improved interoperability and reliable time-sequence reconstruction because the transmitted message structure explicitly integrates reference and difference information for each time period in a standardized, decodable grouping format.
Also regarding claim 1, with respect to using the computing terminal to transmit the identified at least one static feature tag, the plurality of associated dynamic displacement tags and the at least one superimposed group tag instead of the detected plurality of brainwave physiological signals to a remote cloud system for analysis according to the time sequence so as to reduce time and data required in transmission and enable real-time biological feedback to the subject; wherein the method enables the biological feedback training system to provide the real-time biological feedback to the subject and improves efficiency of the brain training by transmitting the compressed brainwave physiological signals, Hejrati teaches transmitting compressed EEG information to address bandwidth constraints in telemedicine environments. (Hejrati, Abstract: "transmitting large EEG data is a challenge, due to high redundancy between channels"). Because Hejrati’s compression is applied to electroencephalogram data that is explicitly a time-ordered signal, transmission of the compressed EEG necessarily preserves the time sequence for subsequent reconstruction and analysis, while reducing the amount of data transmitted relative to transmitting the detected plurality of brainwave physiological signals in uncompressed form. However, Hejrati does not expressly teach transmitting the compressed information to a cloud-based analysis system or providing real-time biological feedback based on cloud analysis.
Coleman teaches transmitting collected bio-signal data to remote or cloud-based servers for processing, analysis, and storage, and providing real-time feedback based on the analyzed data (Coleman, ¶[0054], “the processing and analysis of data can be performed on a client device, a local server, a remotely located server, a cloud-based server, a SAAS platform, or some combination thereof”; Coleman, ¶[0343], “Raw data collected from internal and external sensors may be sent directly to a SAAS platform for processing, analysis, and storage … bandwidth limitations, may require that at least some of the processing and analysis is performed on the client device(s) prior to transmission”; Coleman, ¶[0053], “User effectors 110 can be used … to provide real-time feedback on characteristics related to the user’s current mental state … enable a user to interact with a meditation training application”, explaining real-time biological feedback during training).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the modified Hejrati in view of Coleman to transmit the identified at least one static feature tag, the plurality of associated dynamic displacement tags, and the at least one superimposed group tag to a remote cloud system for analysis in time-sequence order so as to reduce time and data required in transmission and enable real-time biological feedback to the subject, because the modified Hejrati already produces discrete, time-ordered compressed outputs suitable for transmission instead of raw EEG signals, and Coleman teaches cloud-based processing and analysis of transmitted bio-signal data in view of bandwidth limitations, with user effectors providing real-time feedback to a subject during training. It would have been possible to combine these teachings by sending the modified Hejrati compressed outputs from the client computing terminal to Coleman’s cloud-based server or SAAS platform for processing and analysis, and then using Coleman’s user effector mechanisms to provide real-time biological feedback to the subject based on the analyzed, time-sequenced results. The benefit of the combination would be reduced transmission burden, scalable cloud-based analysis, and improved efficiency of providing real-time biological feedback during brain training.
Regarding claim 2, the modified Hejrati teaches that each of the at least one static feature tag is a static base value of a brainwave physiological signal (Hejrati, FIG. 1-2; p. 296, ‘1. Introduction’, ¶[6]: “the centroid of each group is determined and coded by using an entropy reduction technique”, Hejrati discloses centroids which correspond to static base values of EEG signals as shown in figure 2: "cluster centroid value"); and the dynamic displacement tag is a difference value of the brainwave physiological signal of a sub-image relative to the static base value based on the time sequence (Hejrati, FIG. 1-2; p. 296, ‘1. Introduction’, ¶[6]: “the difference between each channel’s data with its corresponding cluster’s centroid is calculated and coded by arithmetic entropy reduction”, Hejrati discloses calculating difference values relative to the centroid, corresponding to dynamic displacement tags as shown in figure 2: "distance value"; these differences are determined for each time-segmented block of EEG data, i.e., sub-images along the time sequence, as figure 2 shows value plotted versus time).
Regarding claim 3, the modified Hejrati teaches EEG acquisition, as shown above in claim 1, but does not explicitly disclose that the plurality of brainwave physiological signals include power, frequency, current, current source density, asymmetry, coherence or phase lag. However, Coleman teaches EEG band power and power spectrum measures (¶[0081], ¶[0146]), frequency features and phase differences (¶[0146]), and hemispheric asymmetry analysis (¶[0070], ¶[0151]). Coleman further describes coherence-related measures in the context of phase and asymmetry (¶[0151]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Hejrati in view of Coleman to include these recognized EEG feature measures as part of the compressed and transmitted physiological signals. Hejrati already provides the acquisition and compression framework, and Coleman provides explicit disclosure of the feature measures used in EEG analysis. The combination is feasible because EEG feature extraction and compression are complementary steps routinely performed in telemedicine and cloud-based analysis systems. One of ordinary skill in the art would have been motivated to combine these teachings in order to ensure that compressed EEG data retains physiologically meaningful features such as power, frequency, coherence, and asymmetry for clinical or training analysis. The benefit of this combination would be to reduce the transmission load through compression while preserving critical EEG features for accurate remote analysis and real-time feedback to the subject.
Regarding claim 4, with respect to using the remote cloud system to integrate the identified at least one static feature tag and the associated dynamic displacement tag according to the time sequence and the superimposed group tag to restore a plurality of sub-images and combining the restored sub-images to obtain the electroencephalogram according to the time sequence; wherein the superimposed group tag is a message for integrating the static feature tag and the associated dynamic displacement tag to restore a plurality of sub-images according to the time sequence, the modified Hejrati (as applied to claim 1) teaches transmitting a compressed, time-ordered representation including (i) reference-selection information and associated difference information and (ii) a transmitted grouping and control message structure (the superimposed group tag) that delineates and controls how time-period blocks are interpreted for reconstruction across the time sequence. Further, Hejrati teaches restoring time-ordered EEG portions from transmitted reference and difference information by decoding the transmitted data and adding the difference information back to the corresponding reference value to reconstruct the EEG signal (Hejrati, p. 296-298; FIG. 1-2; Eq. 1-11; p. 296: “SendData = (ADi, k)”; p. 298: “Each channel is then added to the corresponding cluster’s centroid, and then lossless EEG data is reconstructed by applying the inverse DPCM method”; Hejrati, p. 298: “DEi = Di + Ck”). Under the broadest reasonable interpretation, reconstructing the EEG from reconstructed time-domain symbol blocks corresponds to restoring a plurality of time-ordered sub-images and combining the restored sub-images to obtain the electroencephalogram according to the time sequence. However, the modified Hejrati does not expressly teach that the remote cloud system performs the integration and restoration operations to restore the plurality of sub-images and combine the restored sub-images to obtain the electroencephalogram, as opposed to performing cloud analysis after receipt of compressed data.
Coleman fills this remaining cloud-processing gap by teaching that processing and analysis of biosignal data can be performed on a remotely located server, a cloud-based server, or a SAAS platform, including receiving transmitted data for server-side processing (Coleman, ¶[0054], “the processing and analysis of data can be performed on a client device, a local server, a remotely located server, a cloud-based server, a SAAS platform, or some combination thereof”; Coleman, ¶[0343], “Raw data collected from internal and external sensors may be sent directly to a SAAS platform for processing, analysis, and storage”).
It would have been prima facie obvious before the effective filing date of the claimed invention to have further modified the modified Hejrati in view of Coleman to perform the integration and restoration of the transmitted compressed EEG information in the remote cloud system, because Hejrati teaches reconstructing EEG by integrating reference and difference information to restore time-ordered blocks, Normile teaches a transmitted message syntax (superimposed group tag) that delineates and controls how time-period blocks are interpreted for time-sequenced reconstruction, and Coleman teaches performing biosignal processing on a remote cloud-based server or SAAS platform. It would have been possible to combine these teachings by configuring Coleman’s cloud-based server to receive the transmitted compressed representation and execute the reconstruction operations using Hejrati’s add-back reconstruction while applying Normile’s transmitted message structure to interpret the time-ordered blocks, thereby restoring the plurality of sub-images and combining them to obtain the electroencephalogram according to the time sequence. The benefit of the combination would be scalable, centralized reconstruction and processing while retaining a reliable, time-sequenced message structure for reconstruction across the time sequence.
Regarding claim 5, Hejrati teaches a transmission method for compressed brainwave physiological signals (Hejrati, Title: "Efficient lossless multi-channel EEG compression based on channel clustering"; Abstract: "EEG is widely used in telemedicine and neurological research. However, transmitting large EEG data is a challenge, due to high redundancy between channels... Lossless compression methods play an important role when coding medical signals for telemedicine systems since the data remains unchanged", Hejrati discloses transmitting compressed EEG signals in view of transmission constraints) comprising: using a brainwave cap to detect a plurality of brainwave physiological signals of a subject and generating an electroencephalogram based on a time sequence of the plurality of brainwave physiological signals using a computing terminal (Hejrati, p. 295, 1. Introduction, [1]: "Electroencephalogram (EEG) demonstrates the electrical activities of the brain by one-dimensional signals gathered by electrodes placed on the scalp", Hejrati discloses EEG acquisition with electrodes on the scalp generating an electroencephalogram that is a time sequence of brainwave physiological signals; p. 298, 3. Experimental results: "Signals were recorded by a 64-channel system", disclosing a conventional multi-channel electrode system used to acquire EEG data; Under the broadest reasonable interpretation, a "brainwave cap" is simply a well-known arrangement of multiple electrodes housed in a wearable cap for convenience and subject comfort (see Aimone, [0389]); Thus, Hejrati’s disclosure of scalp electrodes and a multi-channel EEG acquisition system corresponds to using a brainwave cap to detect brainwave signals and generate an electroencephalogram as a time sequence of those signals; p. 295, 1. Introduction, [1]: "Intelligent systems are usually parts of remote telemedicine, where the data can be remotely transferred from the person’s mobile phone to any destination, such as a remote terminal in a hospital", demonstrating use of a computing terminal); and using the computing terminal to split the electroencephalogram into a plurality of sub-images based on the time sequence, each of the sub-images being a sub-image in a time period of the time sequence (Hejrati, p. 296, 2. Proposed method: "First, the time-domain data of all channels are divided into N symbol blocks"; Under the broadest reasonable interpretation, the claimed “sub-images” are not limited to visually rendered images but encompass time-segmented representations of physiological signal data, and Hejrati’s symbol blocks constitute such time-based representations of the electroencephalogram generated and processed by the computing terminal (mobile phone or remote terminal); Because Hejrati expressly divides “time-domain data” into blocks prior to further processing, each symbol block necessarily corresponds to a defined time interval of the electroencephalogram, regardless of whether the blocks are later used for coding efficiency, and thus reads on the claimed sub-images defined by time periods of the time sequence under the broadest reasonable interpretation).
Also regarding claim 5, with respect to the method being directed to brain training in a biological feedback training system, Hejrati teaches EEG acquisition and compression for telemedicine and neurological research but does not expressly disclose that the EEG acquisition occurs while the subject is under brain training in a biological feedback training system.
Coleman fills this gap by teaching a biological feedback training system in which EEG and other bio-signal data are used in a meditation training application, and real-time feedback is provided to assist the user in achieving a particular mental state (Coleman, ¶[0052], “User effectors 110 are for providing feedback to the user … user effector 110 could be a vibration, sound, visual indication on a display or some other way of having an effect on the user”, explaining feedback to the user during use of the system; Coleman, ¶[0053], “User effectors 110 can be used, for example, to provide real-time feedback on characteristics related to the user’s current mental state … used to assist a user in achieving a particular mental state, such as, for example, a meditative state … enable a user to interact with a meditation training application”, explaining real-time feedback during a training application).
It would have been prima facie obvious before the effective filing date of the claimed invention to modify Hejrati in view of Coleman such that Hejrati’s EEG acquisition and compression are performed while the subject is under brain training in the biological feedback training system, because Coleman expressly teaches a biological feedback training environment in which a subject is trained and provided real-time feedback based on analyzed signals, and Hejrati teaches a concrete EEG acquisition and compression process that can be executed by a computing terminal (e.g., a mobile phone and or a remote terminal) to generate compressed EEG data for transmission. It would have been possible to combine these teachings by implementing Hejrati’s EEG acquisition and compression steps within Coleman’s biological feedback training system, such that the computing terminal in Coleman’s system acquires EEG during training, applies Hejrati’s compression to produce compressed EEG outputs, and then Coleman’s existing user effectors provide real-time biological feedback to the subject. The benefit of the combination would be enabling efficient compression and transmission of EEG signals while providing real-time biological feedback during training.
Also regarding claim 5, with respect to using the computing terminal to identify a sequence of feature tags from the plurality of sub-images based on the time sequence, and using the computing terminal to generate a biological feature sequence comprising a plurality of index patterns based on the identified sequence of feature tags and the time sequence according to brainwave data stored in a brainwave database, wherein the brainwave data includes a plurality of index patterns with each index pattern composed of a plurality of feature tags and the electroencephalogram is transformed into the compressed brainwave physiological signals comprising the biological feature sequence with each index pattern of the biological feature sequence being identified from the brainwave database based on the identified sequence of feature tags, the modified Hejrati teaches generating identifiers associated with time-segmented blocks and transmitting those identifiers together with associated coded information for reconstruction (Hejrati, p. 296: “SendData = (ADi, k)”, where k is a cluster index identifier sent with encoded information; Fig. 1–2 and Eq. 1–10, explaining that k identifies which centroid applies for a given block and that difference information is encoded for that block). Under the broadest reasonable interpretation, Hejrati’s per-block index k constitutes a feature tag associated with each time-period block, and a sequence of such per-block identifiers across the time sequence constitutes a sequence of feature tags. However, Hejrati does not expressly teach generating a biological feature sequence comprising a plurality of index patterns according to brainwave data stored in a brainwave database, where the brainwave data includes a plurality of index patterns each composed of a plurality of feature tags, nor does Hejrati teach that each index pattern of the biological feature sequence is identified from the brainwave database based on the identified sequence of feature tags. Rather, Hejrati teaches transmitting a per-block cluster index k together with encoded difference information, but does not teach a database of reusable index patterns composed of multiple feature tags or a pattern-identification (lookup/matching) step that selects an index pattern entry from such a database based on a sequence of feature tags.
Normile fills the remaining aspects of the “brainwave database” and “index pattern” structure by teaching a stored codebook structure maintained in memory and accessed via indices, and by teaching a time-ordered bitstream syntax that delineates and groups indices within a sequence, chunk, and frame structure (Normile, col. 17, ll. 20–35: “Each image vector from the new frame is associated with one of the terminal nodes of the tree (i.e. with a codebook vector)”, showing identifying blocks using codebook indices; Normile, col. 20–21, ll. 63–8: “the bitstream syntax includes a sequence header 1001, chunk header 1011, frame headers 1021, and codebook headers 1012, 1014. These are followed by the codebook indices, which are delineated by block type headers which indicate what blocktype the following indices refer to”, showing a transmitted, time-ordered structure that groups indices for reconstruction). Under the broadest reasonable interpretation, an “index pattern” is a structured grouping of multiple feature tags used to represent information for a given time period or set of time periods, and Normile’s grouped codebook indices within the time-ordered syntax constitute such index patterns composed of a plurality of feature tags. In particular, Normile’s syntax organizes multiple codebook indices within the time-ordered sequence, chunk, and frame structure, such that for a given coded unit (e.g., a frame or a portion delineated by the headers) the grouped set of multiple indices forms a repeatable pattern of feature tags that represents the encoded content for that time period or defined portion of the time sequence. Under the broadest reasonable interpretation, the claimed “brainwave database” is not limited to a database that explicitly stores entries labeled as “feature tags” or “index patterns,” but encompasses any stored collection of reference entries and associated information used by the computing terminal to identify and apply tag-based representations to physiological signal data. Normile’s stored codebook or codebook tree, maintained in memory and accessed using indices for time-ordered blocks, constitutes such a stored collection from which feature tags (indices) and grouped index patterns (structured sets of indices) are identified and used to form the compressed representation. Further, Normile teaches that the encoder associates each new time-period block (image vector/feature representation) with a stored codebook vector and outputs the corresponding index, such that the sequence of extracted feature representations across the time sequence drives selection of the indices from the stored codebook, thereby identifying each grouped index pattern from the database based on the identified sequence of feature tags under BRI.
It would have been prima facie obvious before the effective filing date of the claimed invention to have further modified the modified Hejrati in view of Normile to identify a sequence of feature tags and generate a biological feature sequence comprising a plurality of index patterns according to brainwave data stored in a brainwave database, because the modified Hejrati already teaches generating per-block identifiers (k) associated with time-segmented EEG blocks and transmitting those identifiers with encoded information, while Normile teaches maintaining a stored codebook structure and generating a time-ordered, delineated index syntax that groups indices into structured patterns for reconstruction. It would have been possible to combine these teachings by implementing Normile’s stored codebook or codebook tree as the brainwave database and using Hejrati’s per-block identifiers and associated coded information within Normile’s time-ordered indexing and grouping framework, such that the computing terminal identifies sequences of feature tags (indices) and forms grouped index patterns constituting the biological feature sequence. The benefit of the combination would be improved compression and interpretability of time-sequenced physiological signals by using stored reference entries and structured index patterns for efficient reconstruction.
Also regarding claim 5, with respect to using the computing terminal to transmit the plurality of index patterns comprised in the biological feature sequence instead of the detected plurality of brainwave physiological signals to a remote cloud system for analysis according to the time sequence so as to reduce time and data required in transmission to the remote cloud system and enable real-time biological feedback to the subject; wherein the transmission method enables the biological feedback training system to provide the real-time biological feedback to the subject and improves efficiency of the brain training by transmitting the compressed brainwave physiological signals, Hejrati teaches transmitting compressed EEG information to address bandwidth constraints in telemedicine environments (Hejrati, Abstract: “transmitting large EEG data is a challenge, due to high redundancy between channels”). Because Hejrati’s compression is applied to electroencephalogram data that is explicitly a time-ordered signal, transmission of the compressed EEG necessarily preserves the time sequence for subsequent reconstruction and analysis, while reducing the amount of data transmitted relative to transmitting the detected plurality of brainwave physiological signals in uncompressed form. However, Hejrati does not expressly teach transmitting the compressed information to a cloud-based analysis system or providing real-time biological feedback based on cloud analysis.
Coleman fills this gap by teaching transmitting collected bio-signal data to remote or cloud-based servers for processing, analysis, and storage, and providing real-time feedback based on the analyzed data (Coleman, ¶[0054], “the processing and analysis of data can be performed on a client device, a local server, a remotely located server, a cloud-based server, a SAAS platform, or some combination thereof”; Coleman, ¶[0343], “Raw data collected from internal and external sensors may be sent directly to a SAAS platform for processing, analysis, and storage … bandwidth limitations, may require that at least some of the processing and analysis is performed on the client device(s) prior to transmission”; Coleman, ¶[0053], “User effectors 110 can be used … to provide real-time feedback on characteristics related to the user’s current mental state … enable a user to interact with a meditation training application”, explaining real-time biological feedback during training).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the modified Hejrati in view of Coleman to transmit the plurality of index patterns comprised in the biological feature sequence instead of the detected plurality of brainwave physiological signals to a remote cloud system for analysis in time-sequence order so as to reduce time and data required in transmission and enable real-time biological feedback to the subject, because the modified Hejrati already produces discrete, time-ordered compressed outputs suitable for transmission instead of raw EEG signals, and Coleman teaches cloud-based processing and analysis of transmitted bio-signal data in view of bandwidth limitations, with user effectors providing real-time feedback to a subject during training. It would have been possible to combine these teachings by sending the modified Hejrati compressed outputs, in the form of grouped index patterns representing the compressed EEG, from the client computing terminal to Coleman’s cloud-based server or SAAS platform for processing and analysis, and then using Coleman’s user effector mechanisms to provide real-time biological feedback to the subject based on the analyzed, time-sequenced results. The benefit of the combination would be reduced transmission burden, scalable cloud-based analysis, and improved efficiency of providing real-time biological feedback during brain training.
Regarding claim 6, the modified Hejrati does not disclose using the remote cloud system to determine and analyze behavioral performance or mental process corresponding to the biological feature sequence according to the plurality of index patterns received by comparing with brainwave data stored in the brainwave database. Rather, the modified Hejrati provides compression and transmission of EEG data and transmission of time-sequenced index patterns identified from stored entries, but does not disclose using the transmitted plurality of index patterns for behavioral or mental process analysis by comparison with stored brain wave data patterns in the database. Coleman further teaches prediction and classification from stored EEG feature patterns: “prediction methods may provide for: estimation of hemispheric asymmetries” and are “important for analyzing cognitive tasks such as memory, learning, and perception” (Coleman, ¶[0151]); the platform “may maintain a database store of all of the combination of feature events,” it “finds matching patterns and uses the highest order feature events first to do the classification,” and “the feature event data may be time-coded” (Coleman, ¶[0219]); higher-order patterns are explicit, e.g., “an example of a 4th order feature-event would be… 1) moderately relaxed 2) heart rate variability is low, 3) alpha power is high and 4) alpha variability is medium” (Coleman, ¶[0222]); and “a database of significant patterns (or user-response classifications) is accumulated in a pattern database in the Machine Learning module of the system platform” (Coleman, ¶[0232]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Hejrati in view of Coleman to analyze received EEG biological feature sequences comprising index patterns in a cloud system by comparing them to stored brainwave database patterns to determine behavioral performance or mental processes. Hejrati contributes the mechanism of producing and transmitting compact symbolic units of EEG data, Normile contributes identifying and grouping such units into time sequenced index patterns selected from stored entries, and Coleman contributes the framework for interpreting such patterns as feature events combining them into higher-order patterns, and analyzing behavioral or mental processes. One of ordinary skill in the art would have been motivated to combine these teachings in order to extend compression and transmission of EEG data into meaningful real-time analysis of cognitive and behavioral states. The benefit of this combination would be to enhance telemedicine and training systems by enabling efficient transmission (Hejrati) alongside cloud-based behavioral analysis and feedback (Coleman), thus improving usability and clinical/training effectiveness.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Hejrati et al. (Hejrati, Behzad, Abdolhossein Fathi, and Fardin Abdali-Mohammadi. “Efficient Lossless Multi-Channel EEG Compression Based on Channel Clustering.” Biomedical signal processing and control 31 (2017): 295–300. Web.), hereto referred as Hejrati, and further in view of Coleman et al. (US 20190113973 A1), hereto referred as Coleman, and Normile et al. (US-5596659-A), hereto referred as Normile, and further in view of FCC (FCC, Measuring Broadband America Fixed Broadband Report, www.fcc.gov/reports-research/reports/measuring-broadband-america/measuring-fixed-broadband-report-2016, December 1, 2016, accessed 8/29/2025, Web.), hereto referred as FCC.
Regarding claim 7, the modified Hejrati do not explicitly disclose that the method maintains a round-trip packet latency of less than three 3 seconds, thereby providing reduced end-to-end communication delay. Rather, the modified Hejrati teaches compression of EEG data into compact symbolic representations for telemedicine applications, and Coleman builds on this by teaching a packetized telemetry scheme that continuously transmits EEG samples over Bluetooth RFCOMM using small packets with headers, data payloads, and tails (Coleman, ¶[0123], ¶[0125]). Coleman further acknowledges limited bandwidth of approximately 6–10 kbit/s (Coleman, ¶[0104]) and discloses timestamp assignment, time-indexed cloud storage, and clock synchronization using NTP or RTT delay estimation (Coleman, ¶[0099]–[0100], ¶[0200]). However, neither Hejrati nor Coleman disclose explicitly maintaining round-trip packet latency under 3 seconds. FCC fills this gap by teaching a conventional small-UDP RTT testing methodology in which latency is measured using minimal-payload packets (16 bytes) and any packet not returned within three seconds is deemed lost (FCC, Technical Appendix, p. 31, 34). This shows that a 3-second RTT threshold was a recognized industry convention for acceptable small-packet transmission performance, rather than a guarantee of inherent packet timing. It would have been prima facie obvious before the effective filing date of the claimed invention to have further modified the modified Hejrati system in view of FCC to configure the EEG telemetry loop to maintain a round-trip packet latency of less than 3 seconds. The combination is feasible because Coleman already transmits ~52-byte packets over Bluetooth RFCOMM at 6–10 kbit/s, which corresponds to a serialization time of roughly 42–69 ms per packet (52 × 8 ÷ 6,000–10,000 bits/s). Even accounting for return transmission, this adds only ~84–138 ms of serialization overhead to the RTT, well below the 3-second threshold. Thus, the RTT budget would primarily be dominated by propagation and processing delay, not serialization. FCC demonstrates the accepted practice of using a 3-second RTT cutoff for determining successful small-packet delivery. One of ordinary skill in the art would have been motivated to combine these teachings to ensure timely end-to-end EEG data delivery and feedback in Coleman’s real-time training context. The benefit of this combination would be predictable and reliable low-latency operation, enabling effective biological feedback training while conforming to conventional networking practice.
Response to Arguments
35 U.S.C. §101
Applicant's arguments filed 12/15/2025, pages 8-12, regarding the previous 101 Rejections of claims 1-6 have been fully considered and are not persuasive. Specifically:
Argument: Applicant argues that amending claim 1 to recite “A method for transmitting compressed brainwave physiological signals in a biological feedback training system” (including removing the word “used”) means that the method is executed and integrated in a biological feedback training system rather than reciting an intended use.
Response: Applicant’s argument is not persuasive. The recitation “in a biological feedback training system” (or similar language) remains a field-of-use/technological environment limitation because it does not add a particular technological implementation that improves computer functionality, network communications, or another technical field. The claim still broadly recites performing the abstract processing and then using generic components (brainwave cap, computing terminal, remote cloud system) to carry it out. Therefore, the amendment does not, by itself, integrate the judicial exception into a practical application.
Argument: Applicant argues that the amended claim 1 recites an “innovative method and improvement in the brainwave physiological data compression technology” by converting brainwave physiological signals into an electroencephalogram and transforming the electroencephalogram into “compressed brainwave physiological signals” using “static feature tag[s]”, “dynamic displacement tag[s]”, and a “superimposed group tag”, and that this is “significantly more” than the judicial exception.
Response: Applicant’s argument is not persuasive. The claim’s “static background value” and “difference value … relative to” that static value recite mathematical relationships applied to signal representations, and the generation of the “superimposed group tag” to “integrate” information for “reconstructing” the electroencephalogram is likewise abstract information processing. While Applicant characterizes the approach as “innovative” and “unique”, these are conclusory assertions and do not identify a particular, claimed technological mechanism that confines the full scope of the claim (under BRI) to a specific technical solution. The claim does not recite a particular encoding rule, a specific tag data structure, specific parameters, or a specific reconstruction procedure beyond the high-level functional results of identifying tags, generating an integration tag, transforming the electroencephalogram into a compressed representation, and reconstructing. Although the specification describes motivations and examples of “real time” transmission and a “shape compression technique” using a “static base value” and “a displacement … of a difference between waveforms” (Spec, ¶[0004]-¶[0006]; Spec, ¶[0008]-¶[0009]), claim 1 itself does not recite the concrete technical implementation details described in the specification with sufficient specificity to limit the claim scope to that asserted technological solution (see MPEP § 2106.05(a) and the Desjardins analysis framework).
Argument: Applicant further argues that transmitting the identified tags “instead of” transmitting the detected plurality of brainwave physiological signals “so as to reduce time and data required in transmission … and enable real-time biological feedback” clearly integrates the judicial exception into a practical application and reflects an improvement in the technical field of brain training.
Response: Applicant’s argument is not persuasive. Claim 1 does recite transmitting the tags “instead of” the detected plurality of brainwave physiological signals and recites the intended advantages (“reduce time and data required”, “enable real-time biological feedback”, and “improves efficiency”). However, these recitations do not specify how the claimed method achieves a technological improvement to the computer, network, transmission protocol, or cloud system. Rather, the claim broadly recites generating a reduced representation and sending it to a remote cloud system using generic computing and networking components. The recited results, without additional concrete technical limitations, do not integrate the abstract idea into a practical application.
Accordingly, Applicant’s arguments are not persuasive. Although the specification discusses a motivation relating to transmission of large amounts of physiological data and achieving near real-time feedback (Spec, ¶[0004]-¶[0006]) and describes a “shape compression technique” using a “static base value” and “a displacement … of a difference between waveforms” and describes background and movement frames and an integration message concept (Spec, ¶[0008]-¶[0009]), claim 1 must itself reflect a particular technological improvement to computer functionality or another technical field under MPEP § 2106.05(a) and the guidance discussed in Ex Parte Desjardins. Here, claim 1 broadly covers abstract signal representation and mathematical encoding concepts implemented on generic computing and networking components, and it recites the transmission and “real-time” benefits primarily as intended results.
In general, to overcome the 35 U.S.C. 101 rejection, the claims would need to recite additional elements that meaningfully limit the claim to a particular technical solution to a technological problem (for example, a specific encoding and reconstruction procedure with concrete rules or parameters, a particular data structure or packet format, or a particular improvement to transmission or computer/network operation), rather than broadly claiming the idea of compressing EEG-related data and transmitting a reduced representation for analysis and feedback.
Here, although the specification describes a compression approach conceptually using a static background value, displacement values, and an integration or group tag (Spec, ¶[0008]-¶[0009]), the claims do not recite this approach with sufficient technical specificity. In particular, the claims do not define how the static background value is selected or computed, how the displacement values are generated, bounded, or encoded, what information is structurally contained in the superimposed group tag, or what specific reconstruction rule is applied to reconstitute the electroencephalogram. As a result, under a broadest reasonable interpretation, the claims encompass any baseline-and-difference style encoding applied to brainwave data, implemented using generic computing and networking components, with the asserted real-time and efficiency benefits recited as intended results. As such, the procedure as described in the specification, and as broadly claimed, does not rise to the level of a particular technical implementation sufficient to overcome the judicial exception.
Accordingly, Applicant’s arguments are not persuasive, and the rejection under 35 U.S.C. 101 is maintained.
Additional argument (claim 5 / claims 5-6): Applicant makes parallel arguments for claim 5 based on “feature tags,” “index patterns,” and a “biological feature sequence.”
Response: Applicant’s arguments are not persuasive for the same reasons above. Although the specification describes using “feature tags” and “index patterns” stored in a brainwave database and transmitting index patterns to a remote cloud system (Spec, ¶[0007]), and also describes generating index patterns by training a neural network using electroencephalograms (Spec, ¶[0011]), claim 5 itself does not recite concrete technical implementation details (e.g., defined index-pattern structure, specific tagging rules, specific neural-network constraints, or a specific encoding/decoding procedure) that would limit the claim scope to that asserted technical solution. Instead, claim 5 broadly recites the functional results of identifying a sequence of feature tags, mapping that sequence to stored index patterns, and transmitting the resulting information, which under BRI remains abstract information processing implemented using generic computing and networking components.
35 U.S.C. §103
Applicant's arguments filed 12/15/2025, pages 13-22, regarding the previous 103 Rejections of claims 1-6 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. That is, there are new grounds of rejection. Additionally:
Argument: Applicant argues that Hejrati’s disclosure that “the time-domain data of all channels are divided into N symbol blocks” is not equivalent to “splitting the electroencephalogram into a plurality of sub-images based on the time sequence,” because Hejrati does not define “symbol blocks,” the term “symbol” is not widely used as “time-segment,” and Hejrati’s DPCM approach “does not divide or segment the time-domain data into time-segment blocks or sub-images.”
Response: Applicant’s argument is not persuasive. Under the broadest reasonable interpretation, the claimed “sub-images” are not limited to visually rendered images, but encompass any discrete, time-ordered partitioning of the electroencephalogram into representations corresponding to time periods of the time sequence. The claim itself defines each “sub-image” as being “in a time period of the time sequence,” and therefore the term “sub-image” does not require a two-dimensional pixel-based image representation under BRI. Hejrati expressly divides the EEG time-domain data into “N … blocks,” which are discrete units derived from the time-domain signal and necessarily correspond to defined temporal extents of the time sequence. Further, the claim does not require that the prior art use the same terminology (“sub-image”) or explicitly label the blocks as “time-segments,” so long as the blocks represent subdivisions of the time-sequenced electroencephalogram. Accordingly, Hejrati’s division of time-domain EEG data into blocks reasonably reads on “splitting … into a plurality of sub-images … each … in a time period of the time sequence” under BRI.
Argument: Applicant argues that Hejrati’s “symbol blocks” are for arithmetic coding efficiency and are “not time-based,” and therefore it is “against widely accepted knowledge” to treat Hejrati’s symbol blocks as time-segmented blocks or sub-images.
Response: Applicant’s argument is not persuasive. Even if “symbol blocks” are used to support efficient coding, the blocks are still formed from the time-domain EEG data. The claim does not require the purpose of the partitioning to be “based on time” in the sense Applicant asserts; rather, the claim requires that the electroencephalogram (a time-sequenced signal) be split into multiple units, each corresponding to a time period of the time sequence. A block of time-domain samples (regardless of whether it is later coded using arithmetic coding symbols) remains a time-ordered representation derived from the time sequence.
Argument: Applicant argues that Hejrati’s “centroids” and “difference between each channel’s data with its corresponding cluster’s centroid” have “absolutely no resemblance” to the claimed “static feature tags” and “dynamic displacement tags,” because Hejrati’s centroid/difference are “spatially based” across channels rather than “based on time sequence,” and the centroid “cannot be a static value over time.”
Response: Applicant’s argument is not persuasive because it does not address the rejection as currently maintained. The rejection does not rely on Hejrati alone to teach “a static background value fixed over time of the electroencephalogram.” Rather, the rejection relies on the combined teachings of Hejrati with Normile for the “static background value fixed over time” concept and for maintaining/reusing a reference representation across successive time periods/frames (i.e., reuse “between multiple frames” based on slowly changing “background” content), and for subtracting a baseline/mean and coding residuals with add-back reconstruction. In the rejection, Hejrati is applied for its disclosure of using a reference representation with associated difference information and reconstruction via combining reference + differences, while Normile supplies the time-persistent “background/static” aspect and the time-sequence framing/bitstream structure used to integrate the reference and difference information across a sequence. Accordingly, even if Applicant contends Hejrati’s centroid is not itself “fixed over time,” the rejection’s mapping is satisfied by the combination, where Normile teaches reuse of the background/reference representation across time and Hejrati teaches transmitting/using differences relative to a reference and reconstructing via add-back. To the extent Applicant argues that Hejrati’s centroid is channel-cluster based rather than time-persistent, the rejection agrees that Hejrati alone does not expressly teach a value “fixed over time,” and therefore relies on Normile for that feature.
Argument: Applicant argues that Hejrati is “lossless EEG signal compression,” whereas the claimed “static feature tags” and “dynamic displacement tags” are “the result of a lossy compression method,” and thus Hejrati fails to disclose the claimed limitations.
Response: Applicant’s argument is not persuasive. The claims do not recite “lossy compression,” do not require discarding information, and do not recite any limitation that excludes lossless approaches. Applicant’s characterization of the claims as requiring lossy compression improperly imports a limitation that is not recited in the claims. The claims recite transforming the electroencephalogram into a compressed representation comprising a static/background value and associated difference values, and reconstructing/restoring based on those values; such a framework encompasses both lossless and lossy implementations under BRI. Therefore, the fact that Hejrati characterizes its method as “lossless” does not, by itself, negate its applicability to the claimed compression/tagging framework.
Argument: Applicant argues that neither Coleman nor Aimone discloses or suggests compressing brainwave physiological signals into “at least one static feature tag, … dynamic displacement tags … and … superimposed group tag,” and therefore the rejection is “baseless/unfounded.”
Response: Applicant’s argument is not persuasive because it does not address the rejection as currently maintained. The rejection relies on Coleman for the biological feedback training system context (training/feedback environment and cloud/server-side processing for biosignals) and relies on Normile for (i) maintaining/reusing a reference/background representation across time periods/frames, (ii) baseline removal and residual coding with add-back reconstruction, and (iii) an explicit bitstream/header/tag structure for time-sequenced reconstruction (i.e., integrating reference selection information with per-block interpretation across the time sequence). Aimone is applied as evidence of conventionality for the brainwave cap, not as the source of the tag-based compression structure. Accordingly, the combination as applied in the rejection provides the claimed static/dynamic/group-tag concepts via the respective teachings of Hejrati and Normile, with Coleman supplying the biological feedback training and cloud-processing context.
Argument: Applicant argues that, because claim 1 is amended to recite that “the … static feature tag comprises a static background value fixed over time of the electroencephalogram” and each dynamic displacement tag comprises a “difference value … relative to” that static feature tag, and because Coleman and Aimone also do not teach this, amended claim 1 “should be allowable” over the cited art.
Response: Applicant’s argument is not persuasive. The rejection addresses the newly added “static background value fixed over time” limitation by applying Normile’s teaching of reusing a background/reference representation across multiple frames/time periods, as well as Normile’s teaching of subtracting a baseline/mean prior to coding residuals and reconstructing by adding the baseline back. In combination with Hejrati’s disclosure of forming and using difference information relative to a reference representation and reconstructing by combining reference + difference information, the rejection teaches (under BRI) the claimed static background value fixed over time and the associated difference values relative to that static value for time-ordered segments. Therefore, the amended limitations do not distinguish over the rejection’s combination as applied.
Accordingly, Applicant’s arguments are not persuasive, and the rejection under 35 U.S.C. §103 is maintained.
Conclusion
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/AARON MERRIAM/Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791