DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The disclosure is objected to because of the following informalities:
In paragraphs [0009] and [0095], “preforming” and “preform” should be rewritten as “performing” and “perform”.
Appropriate correction is required.
Claim Objections
Claims 2, 4, 6, and 10 are objected to because of the following informalities:
Regarding claim 2, in line 5, “to determine a sleeping aid value of the each reference audio spectrum”, the word “the” should be removed.
Regarding claim 4, in line 13, “preforming” should be rewritten as “performing”. In line 14, “crossover operation” should be rewritten as “crossover operations”.
Regarding claim 6, in line 4, “comprise” should be rewritten as “comprising”.
Regarding claim 10, in line 14, “preforming” should be rewritten as “performing”.
Regarding claim 16, in line 14, “preforming” should be rewritten as “performing”.
Appropriate correction is required.
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 1-20 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.
In accordance with MPEP 2106.04, claims 1-20 have been analyzed to determine whether it is directed to any judicial exceptions.
Step 1
Claims 1-20 recites a series of steps or acts for generating a sleeping aid audio by obtaining a plurality of sleeping aid audio spectrums with a processor. Thus, the claims are directed to a process, which is one of the statutory categories of invention.
Step 2A, Prong 1
Each of Claims 1-20 recites at least one step or instruction for processing/generating, which is grouped as an abstract idea under the 2019 PEG. The claimed steps of obtaining could be considered data-gathering, which can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
Accordingly, each of Claims 1-20 recites an abstract idea.
Specifically, Claim 1 recites a method for generating a sleeping aid audio, comprising:
Obtaining, by at least one processor, a plurality of sleeping aid audio spectrums;
Processing, by the at least one processor, the plurality of sleeping aid audio spectrums with a genetic algorithm, to obtain a sleeping aid audio spectrum chain; and
Generating, by the at least one processor, at least one sleeping aid audio according to the sleeping aid audio spectrum chain (observation, judgement or evaluation, which is grouped as a mental process under the 2019 PEG);
Further, dependent claims 2-20 merely include limitations that either further define the abstract idea (and thus don't make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they're merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea in each of independent Claim 1 (and their respective dependent Claims 2-20) is not integrated into a practical application under 2019 PEG because the additional elements (identified above in independent Claim 1), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of: obtaining by at least one processing, processing by the at least one processor, and generating by the at least one processor are generically recited computer elements in independent Claim 1 (and its respective dependent claims) which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, affect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claim 1 (and its respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process) using rules (e.g., computer instructions) executed by a computer (e.g., obtaining, processing, and generating by at least one processor as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant's specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. V. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claim 1 (and its respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claim 1 (and its respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: obtaining by at least one processor, a plurality of sleeping aid audio systems; processing, by the at least one processor, the plurality of sleeping aid audio spectrums with a genetic algorithm, to obtain a sleeping aid audio spectrum chain; and generating, by the at least one processor, at least one sleeping aid audio according to the sleeping aid audio spectrum chain.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. V. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant's specification, the processor defined in paragraph [0030], sleeping aid audio spectrums defined in paragraphs [0032] and [0033], and genetic algorithm defined in paragraph [0037]. However, these limitations are generically described without structure or detailed drawings. Such computer components are well understood, routine and conventional.
Accordingly, in light of Applicant's specification, the claimed terms: obtaining by at least one processing, processing by the at least one processor, and generating by the at least one processor are reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant's specification does not describe any special programming or algorithms required for obtaining by at least one processing, processing by the at least one processor, and generating by the at least one processor. This lack of disclosure is acceptable under 35 U.S.C. $112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant's specification demonstrates the well-understood, routine, conventional nature of the above- identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs "well understood, routine, conventional activit[ies]' previously known to the industry" will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claim 1 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs V. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC V. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. V. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC V. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant's specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. V. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the apparatus of Claim 1 is directed to applying an abstract idea as identified above on a general-purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1-20 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claim 1 (and its dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent- eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1-20 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1-20 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1-20 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Van Den Ende (EP 3669922 A1) (hereon referred as Van).
Regarding claim 1, Van teaches a method for generating a sleeping aid audio (“system and method that adapts how an audio output is generated based on a response of a subject’s sleep parameters, abstract), comprising:
Obtaining, by at least one processor (processing system 5), a plurality of sleeping aid audio spectrums (“system comprises a sleep parameter monitor adapted to obtain values of one or more sleep parameters of a subject”, paragraph 9, page 2);
Processing, by the at least one processor, the plurality of sleeping aid audio spectrums with a genetic algorithm (“machine-learning algorithms (i.e. rule-based machine learning), a rule-based system…set of one or more rules…consist of a model for generating the characteristics of the audio output”, paragraph 5, page 3), to obtain a sleeping aid audio spectrum chain (“a sleep parameter is any parameter or characteristic of the subject that is responsive to changes in the sleep state of the subject or other desired sleep-based characteristics”, paragraph 6, page 3); and
Generating, by the at least one processor, at least one sleeping aid audio according to the sleeping aid audio spectrum chain (“the processing system is adapted to…modify the set of one or more rules based on the determined response of the one or more sleep parameters to the audio output…set of one or more rules can be modified to reflect how the subject responds to a particular audio output…enabling future audio output of the system to be tailored to a particular subject or user”, paragraph 7, page 3).
Regarding claim 2, based on the broadest reasonable interpretation, the “m” reference audio spectrums and the “n” reference audio spectrum will be interpreted as follows for prosecution purposes. “m” reference audio spectrums are data prior to be input into the algorithm to produce an “n” reference audio spectrum as an optimal result for the user to improve sleeping conditions. Van teaches wherein obtaining, by the at least one processor (processing system 5), the plurality of sleeping aid audio spectrums comprises:
Obtaining m reference audio spectrums (“inputs”, paragraph 5, page 3);
Processing, by the at least one processor, each reference audio spectrum with an identification model (“machine-learning algorithms”, paragraph 5, page 3) to determine a sleeping aid value of each reference audio spectrum; and
Determining, by the at least one processor, n reference audio spectrums (“outputs”, paragraph 5, page 3) from the m reference audio spectrums (“inputs…set of one or more rules may therefore comprise or consist of a model for generating the characteristics of the audio output”, paragraph 5, page 3) as the plurality of sleeping aid audio spectrums, wherein each of the n reference audio spectrums corresponds to a sleeping aid value greater than a first threshold, m is greater than n, and n is a natural number greater than 1 (“the second set of values can represent responses of the subject to different characteristics of the audio output…system to learn how a subject responds to particular characteristics…can thereby automatically learn and adapt to an individual subject”, paragraph 2, page 4; set of values indicating more than one input and more than one output).
Regarding claim 3, Van teaches wherein processing, by the at least one processor (processing system 5), the plurality of sleeping aid audio spectrums with the genetic algorithm, to obtain the sleeping aid audio spectrum chain comprises:
Processing, by the at least one processor, the plurality of sleeping aid audio spectrums with the genetic algorithm based on the sleeping aid values corresponding to the plurality of sleeping aid audio spectrums, to obtain the sleeping aid audio spectrum chain (“the processing system may be adapted to: obtain a set of values of the one or more sleep parameters for each iterative modification to the at least one characteristics of the audio output; and modify the set of one or more rules based on the obtained set of values for each iterative modification”, paragraph 3, page 4).
Regarding claim 4, Van teaches wherein processing, by the at least one processor, the plurality of sleeping aid audio spectrums with the genetic algorithm (paragraphs 7-9, page 3; the “claimed” inherent steps of genetic algorithm discloses genetic algorithm of iteratively going through sets of data/rules to produce optimal audio output), to obtain the sleeping aid audio spectrum chain comprises:
Selecting one or more target sleeping aid audio spectrums from the plurality of sleeping aid audio spectrums based on sleeping aid values corresponding to the plurality of sleeping aid audio spectrums (“determine the response of the one or more sleep parameters to the audio output using the second values of the one or more sleep parameters”, paragraph 7, page 3);
Processing the one or more target sleeping aid audio spectrums by performing at least one of a crossover operation and a mutation operation, to generate a plurality of first-child-generation sleeping aid audio spectrums (“modify the set of one or more rules based on the determined response of the one or more sleep parameters to the audio output…set of one or more rules can be modified to reflect how the subject responds to a particular audio output”, paragraph 7, page 3);
Selecting one or more target first-child-generation sleeping aid audio spectrums from the plurality of first-child-generation sleeping aid audio spectrums based on sleeping aid values corresponding to the plurality of first-child-generation sleeping aid audio spectrums (“enabling future audio output of the system to be tailored to a particular subject or user…second values are preferably associated with the same sleep parameters used to modify the audio output”, paragraph 7, page 3);
Performing, repeatedly by the at least one processor until a termination condition is satisfied (“the set of rules are modified based on inputs that can be adjusted…for a desired end result”, paragraph 6, page 13), at least one of the crossover operations and the mutation operation based on the one or more target first-child-generation sleeping aid audio spectrums (“system preferably performs iterative adjustments to the characteristics of the audio output, and monitors the subject’s response…to the changes in the characteristics”, paragraph 2, page 10), and
Determining the sleeping aid audio spectrum chain according to the one or more target sleeping aid audio spectrums and generated respective child generations of target sleeping aid audio spectrums (“machine-learning algorithm to determine suitable characteristics for the audio output. A machine-learning algorithm thereby provides a rule (or set of rules) that can be readily modified or trained to adapt to a particular subject’s characteristics or response to an audio output”, paragraph 9, page 3).
Regarding claim 5, Van teaches after generating, by the at least one processor, at least one sleeping aid audio according to the sleeping aid audio spectrum chain:
Obtaining, by the at least one processor, a sleep state of a user while a sleeping aid audio of the at least one sleeping aid audio is being played (“obtain a set of values of the one or more sleep parameters for each iterative modification to the at least one characteristics of the audio output”, paragraph 3, page 4);
Determining, by the at least one processor, a sleeping aid value of the sleeping aid audio according to the sleep state (“specific response of the subject to different audio outputs can be isolated and used to determine which characteristics best suit the individual and/or desired sleep state for that individual”, paragraph 3, page 4); and
Updating, by the at least one processor, according to the sleeping aid value of the sleeping aid audio, the plurality of sleeping aid audio spectrums (“system may iteratively adjust sound intensity, frequency or modify the type of audio output…adjustment is deemed to have an effect on the falling asleep process and the set of rules can then be updated with this knowledge”, paragraph 2, page 13), to obtain a plurality of sleeping aid audio spectrums updated (“updating or modifying the set of rules…algorithm forming a rule or retraining or modifying weights of a machine-learning algorithm”, paragraph 3, page 13).
Regarding claim 6, Van teaches wherein obtaining, by the at least one processor, the sleep state of the user while the sleeping aid audio being played comprises:
Obtaining a plurality of physiological parameters of the user collected by a wearable device (“components of system 1 may be grouped as part of a headband and/or other garment(s) to be worn by the subject 10”, paragraph 4, page 10), wherein the plurality of physiological parameters comprises at least one of: a number of times of turn-overs, a heart rate, a blood pressure, a respiratory rate or a head motion frequency (“sleep parameter monitor may monitor a heart rate, respiration rate, and/or body temperature”, paragraph 2, page 5); and
Determining the sleep state of the user according to the plurality of physiological parameters (“each of these parameters are responsive to a change in sleep state of the subject and can therefore be considered to be sleep parameters of the subject”, paragraph 2, page 5).
Regarding claim 7, Van teaches an apparatus for generating a sleeping aid audio (“system and method that adapts how an audio output is generated based on a response of a subject’s sleep parameters, abstract), comprising:
At least one processor (processing system 5); and
A memory communicatively connected to the at least one processor (“processing system may be associated with one or more storage media such as…computer memory”, paragraph 8, page 14); wherein
The memory stores instructions executable by the at least one processor, and execution of the instructions (“storage media may be encoded with one or more programs that, when executed on one or more processors…perform the required functions”, paragraph 8, page 14) by the at least one processor causes the at least one processor to perform the method of claim 1.
Regarding claim 8, Van teaches wherein obtaining, by the at least one processor (processing system 5), the plurality of sleeping aid audio spectrums comprises:
Obtaining m reference audio spectrums (“inputs”, paragraph 5, page 3);
Processing, by the at least one processor, each reference audio spectrum with an identification model generated by training (“machine-learning algorithms”, paragraph 5, page 3), to determine a sleeping aid value of each reference audio spectrum; and
Determining, by the at least one processor, n reference audio spectrums (“outputs”, paragraph 5, page 3) from the m reference audio spectrums (“inputs…set of one or more rules may therefore comprise or consist of a model for generating the characteristics of the audio output”, paragraph 5, page 3) as the plurality of sleeping aid audio spectrums, wherein each of the n reference audio spectrums corresponds to a sleeping aid value greater than a first threshold, m is greater than n, and n is a natural number greater than 1 (“the second set of values can represent responses of the subject to different characteristics of the audio output…system to learn how a subject responds to particular characteristics…can thereby automatically learn and adapt to an individual subject”, paragraph 2, page 4; set of values indicating more than one input and more than one output).
Regarding claim 9, Van teaches wherein processing, by the at least one processor (processing system 5), the plurality of sleeping aid audio spectrums with the genetic algorithm, to obtain the sleeping aid audio spectrum chain comprises:
Processing, by the at least one processor, the plurality of sleeping aid audio spectrums with the genetic algorithm based on the sleeping aid values corresponding to the plurality of sleeping aid audio spectrums, to obtain the sleeping aid audio spectrum chain (“the processing system may be adapted to: obtain a set of values of the one or more sleep parameters for each iterative modification to the at least one characteristics of the audio output; and modify the set of one or more rules based on the obtained set of values for each iterative modification”, paragraph 3, page 4).
Regarding claim 10, Van teaches wherein processing, by the at least one processor, the plurality of sleeping aid audio spectrums with the genetic algorithm based on the sleeping aid values corresponding to the plurality of sleeping aid audio spectrums (paragraphs 7-9, page 3; the “claimed” inherent steps of genetic algorithm discloses genetic algorithm of iteratively going through sets of data/rules to produce optimal audio output), to obtain the sleeping aid audio spectrum chain comprises:
Selecting one or more target sleeping aid audio spectrums to be processed from the plurality of sleeping aid audio spectrums based on sleeping aid values corresponding to the plurality of sleeping aid audio spectrums (“determine the response of the one or more sleep parameters to the audio output using the second values of the one or more sleep parameters”, paragraph 7, page 3);
Processing the one or more target sleeping aid audio spectrums by performing at least one of a crossover operation and a mutation operation, to generate a plurality of first-child-generation sleeping aid audio spectrums (“modify the set of one or more rules based on the determined response of the one or more sleep parameters to the audio output…set of one or more rules can be modified to reflect how the subject responds to a particular audio output”, paragraph 7, page 3);
Selecting one or more target first-child-generation sleeping aid audio spectrums from the plurality of first-child-generation sleeping aid audio spectrums based on sleeping aid values corresponding to the plurality of first-child-generation sleeping aid audio spectrums (“enabling future audio output of the system to be tailored to a particular subject or user…second values are preferably associated with the same sleep parameters used to modify the audio output”, paragraph 7, page 3); and
Performing, repeatedly by the at least one processor until a termination condition is satisfied (“the set of rules are modified based on inputs that can be adjusted…for a desired end result”, paragraph 6, page 13), at least one of the crossover operations and the mutation operation based on the one or more target first-child-generation sleeping aid audio spectrums (“system preferably performs iterative adjustments to the characteristics of the audio output, and monitors the subject’s response…to the changes in the characteristics”, paragraph 2, page 10), and
Determining the sleeping aid audio spectrum chain according to the one or more target sleeping aid audio spectrums and generated respective child generations of target sleeping aid audio spectrums (“machine-learning algorithm to determine suitable characteristics for the audio output. A machine-learning algorithm thereby provides a rule (or set of rules) that can be readily modified or trained to adapt to a particular subject’s characteristics or response to an audio output”, paragraph 9, page 3).
Regarding claim 11, Van teaches the method performed by the at least one processor further comprising:
After generating, by the at least one processor, at least one sleeping aid audio according to the sleeping aid audio spectrum chain:
Obtaining, by the at least one processor, a sleep state of a user while a sleeping aid audio of the at least one sleeping aid audio is being played (“obtain a set of values of the one or more sleep parameters for each iterative modification to the at least one characteristics of the audio output”, paragraph 3, page 4);
Determining, by the at least one processor, a sleeping aid value of the sleeping aid audio according to the sleep state (“specific response of the subject to different audio outputs can be isolated and used to determine which characteristics best suit the individual and/or desired sleep state for that individual”, paragraph 3, page 4); and
Updating, by the at least one processor, according to the sleeping aid value of the sleeping aid audio, the plurality of sleeping aid audio spectrums (“system may iteratively adjust sound intensity, frequency or modify the type of audio output…adjustment is deemed to have an effect on the falling asleep process and the set of rules can then be updated with this knowledge”, paragraph 2, page 13), to obtain a plurality of sleeping aid audio spectrums updated (“updating or modifying the set of rules…algorithm forming a rule or retraining or modifying weights of a machine-learning algorithm”, paragraph 3, page 13).
Regarding claim 12, Van teaches wherein obtaining, by the at least one processor, the sleep state of the user while the sleeping aid audio of the at least one sleeping aid audio is being played comprises:
Obtaining a plurality of physiological parameters of the user collected by a wearable device (“components of system 1 may be grouped as part of a headband and/or other garment(s) to be worn by the subject 10”, paragraph 4, page 10), wherein the plurality of physiological parameters comprises at least one of: a number of times of turn-overs, a heart rate, a blood pressure, a respiratory rate or a head motion frequency (“sleep parameter monitor may monitor a heart rate, respiration rate, and/or body temperature”, paragraph 2, page 5); and
Determining the sleep state of the user according to the plurality of physiological parameters (“each of these parameters are responsive to a change in sleep state of the subject and can therefore be considered to be sleep parameters of the subject”, paragraph 2, page 5).
Regarding claim 13, Van teaches a non-transitory computer readable storage medium, having computer instructions stored thereon (“computer program comprising code means for implementing any described method when said program is run on a computer”, paragraph 6, page 5), wherein the computer instructions are configured to cause a computer to perform the method of claim 1.
Regarding claim 14, Van teaches wherein obtaining, by the at least one processor (processing system 5), the plurality of sleeping aid audio spectrums comprises:
Obtaining m reference audio spectrums (“inputs”, paragraph 5, page 3);
Processing, by the at least one processor, each reference audio spectrum with an identification model generated by training (“machine-learning algorithms”, paragraph 5, page 3) to determine a sleeping aid value of each reference audio spectrum; and
Determining, by the at least one processor, n reference audio spectrums (“outputs”, paragraph 5, page 3) from the m reference audio spectrums (“inputs…set of one or more rules may therefore comprise or consist of a model for generating the characteristics of the audio output”, paragraph 5, page 3) as the plurality of sleeping aid audio spectrums, wherein each of the n reference audio spectrums corresponds to a sleeping aid value greater than a first threshold, m is greater than n, and n is a natural number greater than 1 (“the second set of values can represent responses of the subject to different characteristics of the audio output…system to learn how a subject responds to particular characteristics…can thereby automatically learn and adapt to an individual subject”, paragraph 2, page 4; set of values indicating more than one input and more than one output).
Regarding claim 15, Van teaches wherein processing, by the at least one processor (processing system 5), the plurality of sleeping aid audio spectrums with the genetic algorithm, to obtain the sleeping aid audio spectrum chain comprises:
Processing, by the at least one processor, the plurality of sleeping aid audio spectrums with the genetic algorithm based on the sleeping aid values corresponding to the plurality of sleeping aid audio spectrums with the genetic algorithm based on the sleeping aid values corresponding to the plurality of sleeping aid audio spectrums, to obtain the sleeping aid audio spectrum chain (“the processing system may be adapted to: obtain a set of values of the one or more sleep parameters for each iterative modification to the at least one characteristics of the audio output; and modify the set of one or more rules based on the obtained set of values for each iterative modification”, paragraph 3, page 4).
Regarding claim 16, Van teaches wherein processing, by the at least one processor, the plurality of sleeping aid audio spectrums with the genetic algorithm based on the sleeping aid values corresponding to the plurality of sleeping aid audio spectrums (paragraphs 7-9, page 3; the “claimed” inherent steps of genetic algorithm discloses genetic algorithm of iteratively going through sets of data/rules to produce optimal audio output), to obtain the sleeping aid audio spectrum chain comprises:
Selecting one or more target sleeping aid audio spectrums to be processed from the plurality of sleeping aid audio spectrums based on sleeping aid values corresponding to the plurality of sleeping aid audio spectrums (“determine the response of the one or more sleep parameters to the audio output using the second values of the one or more sleep parameters”, paragraph 7, page 3);
Processing the one or more target sleeping aid audio spectrums by performing at least one of a crossover operation and a mutation operation, to generate a plurality of first-child-generation sleeping aid audio spectrums (“modify the set of one or more rules based on the determined response of the one or more sleep parameters to the audio output…set of one or more rules can be modified to reflect how the subject responds to a particular audio output”, paragraph 7, page 3);
Selecting one or more target first-child-generation sleeping aid audio spectrums from the plurality of first-child-generation sleeping aid audio spectrums based on sleeping aid values corresponding to the plurality of first-child-generation sleeping aid audio spectrums (“enabling future audio output of the system to be tailored to a particular subject or user…second values are preferably associated with the same sleep parameters used to modify the audio output”, paragraph 7, page 3); and
Performing, repeatedly by the at least one processor until a number of operations performed reaches a preset value (“the set of rules are modified based on inputs that can be adjusted…for a desired end result”, paragraph 6, page 13), at least one of the crossover operations and the mutation operation based on the one or more target first-child-generation sleeping aid audio spectrums (“system preferably performs iterative adjustments to the characteristics of the audio output, and monitors the subject’s response…to the changes in the characteristics”, paragraph 2, page 10), and
Determining the sleeping aid audio spectrum chain according to the one or more target sleeping aid audio spectrums and generated respective child generations of target sleeping aid audio spectrums (“machine-learning algorithm to determine suitable characteristics for the audio output. A machine-learning algorithm thereby provides a rule (or set of rules) that can be readily modified or trained to adapt to a particular subject’s characteristics or response to an audio output”, paragraph 9, page 3).
Regarding claim 17, Van teaches the method performed by the computer further comprising:
After generating, by the at least one processor, at least one sleeping aid audio according to the sleeping aid audio spectrum chain:
Obtaining, by the at least one processor, a sleep state of a user while a sleeping aid audio of the at least one sleeping aid audio is being played (“obtain a set of values of the one or more sleep parameters for each iterative modification to the at least one characteristics of the audio output”, paragraph 3, page 4);
Determining, by the at least one processor, a sleeping aid value of the sleeping aid audio according to the sleep state (“specific response of the subject to different audio outputs can be isolated and used to determine which characteristics best suit the individual and/or desired sleep state for that individual”, paragraph 3, page 4); and
Removing, by the at least one processor, in response to the sleeping aid value of the sleeping aid audio being less than a second threshold (“obtaining second values for the one or more sleep parameters of the subject…second values therefore consist of values of the one or more sleep parameters obtained after the audio output is initially provided to the subject”, paragraph 4, page 11), a sleeping aid audio spectrum from which the sleeping aid audio is generated from the plurality of sleeping aid audio spectrums (“system may iteratively adjust sound intensity, frequency or modify the type of audio output…adjustment is deemed to have an effect on the falling asleep process and the set of rules can then be updated with this knowledge”, paragraph 2, page 13), to obtain a plurality of sleeping aid audio spectrums updated (“updating or modifying the set of rules…algorithm forming a rule or retraining or modifying weights of a machine-learning algorithm”, paragraph 3, page 13).
Regarding claim 18, Van teaches wherein obtaining, by the at least one processor, the sleep state of the user while the sleeping aid audio being played comprises:
Obtaining a plurality of physiological parameters of the user collected by a wearable device (“components of system 1 may be grouped as part of a headband and/or other garment(s) to be worn by the subject 10”, paragraph 4, page 10), wherein the plurality of physiological parameters comprises at least one of: a number of times of turn-overs, a heart rate, a blood pressure, a respiratory rate or a head motion frequency (“sleep parameter monitor may monitor a heart rate, respiration rate, and/or body temperature”, paragraph 2, page 5); and
Determining the sleep state of the user according to the plurality of physiological parameters (“each of these parameters are responsive to a change in sleep state of the subject and can therefore be considered to be sleep parameters of the subject”, paragraph 2, page 5).
Regarding claim 19, Van teaches a computer program product comprising a computer program, wherein during execution of the computer program by at least one processor, the method of claim 1 is performed (“computer program comprising code means for implementing any described method when said program is run on a computer”, paragraph 6, page 5).
Regarding claim 20, Van teaches the method performed by the at least one processor further comprising:
After generating, by the at least one processor, at least one sleeping aid audio according to the sleeping aid audio spectrum chain:
Obtaining, by the at least one processor, a sleep state of a user while a sleeping aid audio of the at least one sleeping aid audio is being played (“obtain a set of values of the one or more sleep parameters for each iterative modification to the at least one characteristics of the audio output”, paragraph 3, page 4);
Determining, by the at least one processor, a sleeping aid value of the sleeping aid audio according to the sleep state (“specific response of the subject to different audio outputs can be isolated and used to determine which characteristics best suit the individual and/or desired sleep state for that individual”, paragraph 3, page 4); and
Removing, by the at least one processor, in response to the sleeping aid value of the sleeping aid audio being less than a second threshold (“obtaining second values for the one or more sleep parameters of the subject…second values therefore consist of values of the one or more sleep parameters obtained after the audio output is initially provided to the subject”, paragraph 4, page 11), a sleeping aid audio spectrum from which the sleeping aid audio is generated from the plurality of sleeping aid audio spectrums (“system may iteratively adjust sound intensity, frequency or modify the type of audio output…adjustment is deemed to have an effect on the falling asleep process and the set of rules can then be updated with this knowledge”, paragraph 2, page 13), to obtain a plurality of sleeping aid audio spectrums updated (“updating or modifying the set of rules…algorithm forming a rule or retraining or modifying weights of a machine-learning algorithm”, paragraph 3, page 13).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LARA LINH TRAN whose telephone number is (571)272-3598. The examiner can normally be reached 7:30am-5:00pm M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexander Valvis can be reached at 5712724233. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/L.L.T./Examiner, Art Unit 3791 /ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791
Prosecution Insights