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 . Pursuant to communications filed on 26 September 2024, this is a First Action Non-Final Rejection on the Merits. Claims 1-10 are currently pending in the instant application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 26 September 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the Examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a voice abnormality detector”, “a normal time model generator”, “a vehicle body detector” and an “abnormal condition determiner” as in claim 1; “a current model generator” in claim 2.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, an abnormal condition detection system, comprising:
a voice abnormality detector that comprises a normal time model generator for generating a model of normal time voice data of a person riding in a vehicle as a normal time model based on voice data including voice of the person, and detects an abnormality in the voice based on a current voice data of the person and the normal time model;
a vehicle body abnormality detector that detects an abnormality in a vehicle body of the vehicle based on acceleration data of the vehicle; and
an abnormal condition determiner that determines whether or not the vehicle is in an abnormal condition based on a detection result of the voice abnormality detector and a detection result of the vehicle body abnormality detector.
Step 1: Statutory Category – Yes.
The claim(s) recite a device (i.e. system), therefore, the claim(s) fall within one of the four statutory categories. MPEP 2106.03
Step 2A, Prong One evaluation: Judicial Exception – Yes.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under the broadest reasonable interpretation, the claim covers performance using mental processes.
The claim recites the limitation of “generating a model of normal time voice data of a person riding in a vehicle… and detects an abnormality in the voice based on a current voice data of the person and the normal time model” in the context of this claim is an abstract idea, wherein a human mentally generates/retains a model of normal time voice data of a person riding in a vehicle and further wherein a human mentally/audibly detects an abnormality in the voice based on a current voice data of the person and the normal time model. Humans have the ability to obtain, recognize and interpret data from multiple sources including other humans and machines, and therefore the Examiner submits that this action can be done within the human mind.
The claim additionally recites the limitation of “detects an abnormality in a vehicle body of the vehicle based on acceleration data of the vehicle”, in the context of this claim is an abstract idea, wherein a human mentally evaluates/determines an abnormality in a vehicle body based on received/collected acceleration data of the vehicle. Humans have the ability to obtain, recognize and interpret data from multiple sources including other humans and machines, and therefore the Examiner submits that this action can be done within the human mind.
The claim additionally recites the limitation of “determines whether or not the vehicle is in an abnormal condition based on a detection result of the voice abnormality detector and a detection result of the vehicle body abnormality detector”, in the context of this claim is an abstract idea, wherein a human mentally evaluates/determines if the vehicle is in an abnormal condition or not based on previously determined detection results by the voice abnormality detector and the vehicle body abnormality detector. Humans have the ability to obtain, recognize and interpret data from multiple sources including other humans and machines, and therefore the Examiner submits that this action can be done within the human mind.
Step 2A, Prong Two evaluation: Practical Application – No.
Claim 1, is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception.
Regarding the claimed limitation(s)/element(s) of “a voice abnormality detector”, “a vehicle body abnormality detector” and “an abnormal condition determiner”, the Examiner submits that these limitations are simply computing components that are recited at a high level of generality to which the abstract ideas are applied. These generic computing elements merely automate the abstract idea(s) presented above, without adding significantly more to distinguish themselves, such as by having unique structural components that incorporate features that cannot be done in the human mind. Regarding the claimed “a voice abnormality detector”, “a vehicle body abnormality detector” and “an abnormal condition determiner”, as it is stated in the claim and the specification, are generic computing element(s) that, as stated in paragraphs [0012] and [0033]-[0038] is/are a generic “processor”. Thus for the additional elements of claim 1 analyzed individually, and/or taken as a whole, there is insufficient reasoning as to why the additional elements turn the abstract ideas into practical applications, since the additional elements merely recite automating the abstract ideas. Accordingly the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is ineligible.
Step 2B, evaluation: Inventive Concept – No.
Claim 1, is evaluated as to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
With regards to Step 2B of the 101 analysis, claim 1 does not recite any additional elements that amount to significantly more than the judicial exception for the same reasons as described above in Step 2A Prong Two. Specifically, the “a voice abnormality detector”, “a vehicle body abnormality detector” and “an abnormal condition determiner”, as defined in the specification, only recite applying generic computing elements to execute functions of the claim, and therefore do not recite significantly more than the judicial exception. Generally, applying an exception using generic computing element(s) or receiving and interpreting data cannot provide an inventive concept. Thus, since independent claim 1 is: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that independent claim 1 is directed towards non-statutory subject matter.
Regarding claims 2-4, these claims do not recite any further limitations that cause the claim(s) to be directed towards statutory subject matter. The claims merely recite an abstract idea. Each of the further limitations expound upon the abstract idea and do not recite additional elements that are not well understood, routine or conventional. Therefore, claims 2-4 are similarly rejected as being directed towards non-statutory subject matter.
Regarding claim 5, an abnormal condition detection method, comprising:
by a processor included in an abnormal condition detection system,
generating a model of normal time voice data of a person riding in a vehicle as a normal time model based on voice data including voice of the person, and detecting an abnormality in the voice based on a current voice data of the person and the normal time model;
detecting an abnormality in a vehicle body of the vehicle based on acceleration data of the vehicle; and
outputting, based on a detection result of the abnormality in the voice and a detection result of the abnormality in the vehicle body, a determination result as to whether or not the vehicle is in an abnormal condition.
Step 1: Statutory Category – Yes.
The claim(s) recite a method (i.e. process), therefore, the claim(s) fall within one of the four statutory categories. MPEP 2106.03
Step 2A, Prong One evaluation: Judicial Exception – Yes.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under the broadest reasonable interpretation, the claim covers performance using mental processes.
The claim recites the limitation of “generating a model of normal time voice data of a person riding in a vehicle… and detecting an abnormality in the voice based on a current voice data of the person and the normal time model” in the context of this claim is an abstract idea, wherein a human mentally generates/retains a model of normal time voice data of a person riding in a vehicle and further wherein a human mentally/audibly detects an abnormality in the voice based on a current voice data of the person and the normal time model. Humans have the ability to obtain, recognize and interpret data from multiple sources including other humans and machines, and therefore the Examiner submits that this action can be done within the human mind.
The claim additionally recites the limitation of “detecting an abnormality in a vehicle body of the vehicle based on acceleration data of the vehicle”, in the context of this claim is an abstract idea, wherein a human mentally evaluates/determines an abnormality in a vehicle body based on received/collected acceleration data of the vehicle. Humans have the ability to obtain, recognize and interpret data from multiple sources including other humans and machines, and therefore the Examiner submits that this action can be done within the human mind.
The claim additionally recites the limitation of “outputting, based on a detection result of the abnormality in the voice and a detection result of the abnormality in the vehicle body, a determination result as to whether or not the vehicle is in an abnormal condition”, in the context of this claim is an abstract idea, wherein a human mentally evaluates/determines if the vehicle is in an abnormal condition or not based on previously determined detection results of an abnormality in the voice and an abnormality in a vehicle body. Humans have the ability to obtain, recognize and interpret data from multiple sources including other humans and machines and subsequently provide the interpreted/determined information/results visually and/or audibly, and therefore the Examiner submits that this action can be done within the human mind.
Step 2A, Prong Two evaluation: Practical Application – No.
Claim 5, is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception.
Regarding the claimed limitation(s)/element(s) of “a processor”, the Examiner submits that this/these limitation(s) is/are simply a computing component(s) that is/are recited at a high level of generality to which the abstract ideas are applied. These generic computing element(s) merely automate the abstract idea(s) presented above, without adding significantly more to distinguish themselves, such as by having unique structural components that incorporate features that cannot be done in the human mind. Regarding the claimed “a processor”, as it is stated in the claim and the specification, is/are generic computing element(s) that, as stated in paragraphs [0012] and [0033]-[0038] is/are a generic “processor”. Thus for the additional elements of claim 5 analyzed individually, and/or taken as a whole, there is insufficient reasoning as to why the additional element(s) turn the abstract ideas into practical applications, since the additional element(s) merely recite automating the abstract ideas. Accordingly the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is ineligible.
Step 2B, evaluation: Inventive Concept – No.
Claim 5, is evaluated as to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
With regards to Step 2B of the 101 analysis, claim 5 does not recite any additional elements that amount to significantly more than the judicial exception for the same reasons as described above in Step 2A Prong Two. Specifically, the “processor”, as defined in the specification, only recite applying generic computing elements to execute functions of the claim, and therefore do not recite significantly more than the judicial exception. Generally, applying an exception using generic computing element(s) or receiving and interpreting data cannot provide an inventive concept. Thus, since independent claim 5 is: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that independent claim 5 is directed towards non-statutory subject matter.
Regarding claims 6-8, these claims do not recite any further limitations that cause the claim(s) to be directed towards statutory subject matter. The claims merely recite an abstract idea. Each of the further limitations expound upon the abstract idea and do not recite additional elements that are not well understood, routine or conventional. Therefore, claims 6-8 are similarly rejected as being directed towards non-statutory subject matter.
Regarding claim 9, an abnormal condition detection recording medium, recording a program for causing a processor included in an abnormal condition detection system perform the following processing:
generating a model of normal time voice data of a person riding in a vehicle as a normal time model based on voice data including voice of the person, and detecting an abnormality in the voice based on a current voice data of the person and the normal time model;
detecting an abnormality in a vehicle body of the vehicle based on acceleration data of the vehicle; and
outputting, based on a detection result of the abnormality in the voice and a detection result of the abnormality in the vehicle body, a determination result as to whether or not the vehicle is in an abnormal condition.
Step 1: Statutory Category – Yes.
The claim(s) recite a method (i.e. process), therefore, the claim(s) fall within one of the four statutory categories. MPEP 2106.03
Step 2A, Prong One evaluation: Judicial Exception – Yes.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under the broadest reasonable interpretation, the claim covers performance using mental processes.
The claim recites the limitation of “generating a model of normal time voice data of a person riding in a vehicle… and detecting an abnormality in the voice based on a current voice data of the person and the normal time model” in the context of this claim is an abstract idea, wherein a human mentally generates/retains a model of normal time voice data of a person riding in a vehicle and further wherein a human mentally/audibly detects an abnormality in the voice based on a current voice data of the person and the normal time model. Humans have the ability to obtain, recognize and interpret data from multiple sources including other humans and machines, and therefore the Examiner submits that this action can be done within the human mind.
The claim additionally recites the limitation of “detecting an abnormality in a vehicle body of the vehicle based on acceleration data of the vehicle”, in the context of this claim is an abstract idea, wherein a human mentally evaluates/determines an abnormality in a vehicle body based on received/collected acceleration data of the vehicle. Humans have the ability to obtain, recognize and interpret data from multiple sources including other humans and machines, and therefore the Examiner submits that this action can be done within the human mind.
The claim additionally recites the limitation of “outputting, based on a detection result of the abnormality in the voice and a detection result of the abnormality in the vehicle body, a determination result as to whether or not the vehicle is in an abnormal condition”, in the context of this claim is an abstract idea, wherein a human mentally evaluates/determines if the vehicle is in an abnormal condition or not based on previously determined detection results of an abnormality in the voice and an abnormality in a vehicle body. Humans have the ability to obtain, recognize and interpret data from multiple sources including other humans and machines and subsequently provide the interpreted/determined information/results visually and/or audibly, and therefore the Examiner submits that this action can be done within the human mind.
Step 2A, Prong Two evaluation: Practical Application – No.
Claim 9, is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception.
Regarding the claimed limitation(s)/element(s) of “a processor”, the Examiner submits that this/these limitation(s) is/are simply a computing component(s) that is/are recited at a high level of generality to which the abstract ideas are applied. These generic computing element(s) merely automate the abstract idea(s) presented above, without adding significantly more to distinguish themselves, such as by having unique structural components that incorporate features that cannot be done in the human mind. Regarding the claimed “a processor”, as it is stated in the claim and the specification, is/are generic computing element(s) that, as stated in paragraphs [0012] and [0033]-[0038] is/are a generic “processor”. Thus for the additional elements of claim 9 analyzed individually, and/or taken as a whole, there is insufficient reasoning as to why the additional element(s) turn the abstract ideas into practical applications, since the additional element(s) merely recite automating the abstract ideas. Accordingly the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is ineligible.
Step 2B, evaluation: Inventive Concept – No.
Claim 9, is evaluated as to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
With regards to Step 2B of the 101 analysis, claim 9 does not recite any additional elements that amount to significantly more than the judicial exception for the same reasons as described above in Step 2A Prong Two. Specifically, the “processor”, as defined in the specification, only recite applying generic computing elements to execute functions of the claim, and therefore do not recite significantly more than the judicial exception. Generally, applying an exception using generic computing element(s) or receiving and interpreting data cannot provide an inventive concept. Thus, since independent claim 9 is: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that independent claim 9 is directed towards non-statutory subject matter.
Regarding claim 10, these claims do not recite any further limitations that cause the claim(s) to be directed towards statutory subject matter. The claims merely recite an abstract idea. Each of the further limitations expound upon the abstract idea and do not recite additional elements that are not well understood, routine or conventional. Therefore, claim 10 is similarly rejected as being directed towards non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (US 2020/0094818 A1, hereinafter Li) in view of Gordon et al (US 2020/0020328 A1, hereinafter Gordon).
Regarding claim 1, Li teaches an abnormal condition detection system, comprising:
a voice abnormality detector (Figures 1-3, processor 2) that comprises (Figures 1-3 & 6-7; at least as in paragraphs 0026, 0031 and 0043-0049, specifically as in at least paragraph 0031, wherein “by the processor 2, it may be judged whether the sound information of the driver acquired by the sound detector 11 is abnormal state information, such as a screaming sound or the like” and further as in Figures 6 & 7, steps 101 & 102 and related text found in at least paragraph 0043, wherein “step 101, acquiring a body state information of the driver, in which step, the body state information may reflect a driving state of the driver, and the body state information may include a plurality of types of information, for example, brain wave information of the human body, eye closing information of the human body, sound information of the human body, facial expression information of the human body, heartbeat information of the human body, and the like; and step 102, judging whether the body state information is abnormal driving information”);
a vehicle body abnormality detector (Figures 2 & 4, processor 2) that detects an abnormality in a vehicle body of the vehicle based on acceleration data of the vehicle (Figures 2, 4 & 6-7; at least as in paragraphs 0029, 0035-0036 and 0046-0049, specifically as in at least paragraph 0029, wherein “the vehicle detector 4 is configured to detect vehicle state information of the vehicle; the input terminal of the processor 2 is connected with the vehicle detector 4, and the processor 2 is configured to judge whether the vehicle state information is vehicle abnormality information”, and further as in Figure 7, steps 121 & 122, and related text found in at least paragraph 0046 wherein “step 121, acquiring the vehicle state information of the vehicle; and step 122, judging whether the vehicle state information is the abnormal vehicle information” ); and
an abnormal condition determiner (Figures 1 & 2, processor 2) that determines whether or not the vehicle is in an abnormal condition based on a detection result of the voice abnormality detector and a detection result of the vehicle body abnormality detector (Figures 2, 6 & 7; at least as in paragraphs 0029 and 0043-0049, specifically as in Figure 7, step 122 and as further discussed in at least paragraph 0046, wherein “step 122, judging whether the vehicle state information is the abnormal vehicle information, and judging whether the body state information is the abnormal driving information, in which step, if the vehicle state information is the abnormal vehicle information, and/or the body state information is the abnormal driving information”). Li is silent specifically regarding wherein the system/method utilizes sound models to compare/evaluate, with the detected voice data of a user in a vehicle, an abnormality (i.e. event) associated with said voice data.
Gordon, in the same field of endeavor, teaches voice/audio analysis of a user by a smart speaker system that may be employed in a variety of environments (i.e. houses, vehicles, etc.). Gordon goes on to teach wherein “such analysis may comprise performing pattern analysis, feature extraction (e.g., amplitude, frequency, duration, etc.), and the like. The patterns and/or features may be used as a basis for comparing the audio sample, with sound models stored in the sound and event model repository 128 to thereby indicate a nature or type of the sound(s) in the audio sample and/or the nature or type of the sound sources generating the sound(s) in the audio sample. Pattern analysis may be applied to compare the audio sample waveform patterns to the stored sound models to determine a degree of matching of the captured audio sample to the stored sound models in the repository 128”. Gordon further teaches wherein said analysis includes “the operation starts with the identification of a sound from an audio sample captured from a monitored environment (step 610). Previously identified and processed sounds within a given time window are identified along with the currently identified sound (step 620). The collection of sounds, their types, their sources, sequence of sounds, and various other audio and content characteristics of the sounds are compared to event models defining criteria for different types of events (step 630)” and further “The configuration information and other knowledge base information that may be used to identify events and weighting the confidence scores associated with events occurring in the monitored environment may also be retrieved from corresponding stores (step 640). A weighted confidence score may be generated based on the degree of matching of the various factors of the sounds, configuration information, and knowledge base information (step 650). A ranked listing of the matched event models is generated and the confidence scores are compared to threshold requirements (step 660). A highest ranking event model with the threshold amount of confidence score is selected as a match (step 670). The matching event model may specify a default danger/risk level for the event and a suggested responsive action to be performed. This information may be utilized in steps 480 and 490 of FIG. 4 to determine danger/risk level and a corresponding responsive action to be performed” (Figures 1, 3, 5 & 6; at least paragraphs 0088-0089 and 0169-0172). Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Li, to include Gordon’s audio analysis techniques employing sound models to compare with voice data in a monitored environment to detect a corresponding event, since Gordon teaches wherein such a system/method provides improved cognitive analysis capabilities for cognitively determining the type of sound, the type of sound source whether an event is occurring or has occurred in the monitored environment based on the identified sounds over a specified period of time, and can identify and initiate responsive actions to such events, thereby providing a more dynamic and robust abnormality detection system and corresponding method.
Regarding claim 2, in view of the above combination of Li and Gordon, Gordon further teaches wherein the voice abnormality detector further comprises a current model generator for generating a model of the current voice data of the person as a current model based on the current voice data of the person, and determines that the voice is abnormal when it is determined that the current voice data of the person contains a scream based on a comparison result between the current model and the normal time model (Figures 1, 3, 5 & 6; at least paragraphs 0088-0089, 0093 and 0169-0172).
Regarding claim 3, in view of the above combination of Li and Gordon, Gordon further teaches wherein the voice abnormality detector determines that the voice is abnormal when it is determined that the current voice data of the person contains a scream based on a comparison result between a waveform histogram obtained from the normal time voice data and a waveform histogram obtained from the current voice data (Figures 1, 3, 5 & 6; at least paragraphs 0088-0089, 0093 and 0169-0172).
Regarding claim 4, in view of the above combination of Li and Gordon, Li further teaches wherein the abnormal condition determiner determines that the vehicle is in an abnormal condition in at least one of the following cases: a case where the detection result of the voice abnormality detector indicates that the voice is abnormal and a case where the detection result of the vehicle body abnormality detector indicates that the vehicle body is abnormal (Figures 2, 6 & 7; at least as in paragraphs 0029, 0031 and 0043-0049).
Regarding claim 5, Li teaches an abnormal condition detection method, comprising: by a processor included in an abnormal condition detection system,
abnormality in the voice based on a current voice data of the person (Figures 1-3 & 6-7; at least as in paragraphs 0026, 0031 and 0043-0049, specifically as in at least paragraph 0031, wherein “by the processor 2, it may be judged whether the sound information of the driver acquired by the sound detector 11 is abnormal state information, such as a screaming sound or the like” and further as in Figures 6 & 7, steps 101 & 102 and related text found in at least paragraph 0043, wherein “step 101, acquiring a body state information of the driver, in which step, the body state information may reflect a driving state of the driver, and the body state information may include a plurality of types of information, for example, brain wave information of the human body, eye closing information of the human body, sound information of the human body, facial expression information of the human body, heartbeat information of the human body, and the like; and step 102, judging whether the body state information is abnormal driving information”);
detecting an abnormality in a vehicle body of the vehicle based on acceleration data of the vehicle (Figures 2, 4 & 6-7; at least as in paragraphs 0029, 0035-0036 and 0046-0049, specifically as in at least paragraph 0029, wherein “the vehicle detector 4 is configured to detect vehicle state information of the vehicle; the input terminal of the processor 2 is connected with the vehicle detector 4, and the processor 2 is configured to judge whether the vehicle state information is vehicle abnormality information”, and further as in Figure 7, steps 121 & 122, and related text found in at least paragraph 0046 wherein “step 121, acquiring the vehicle state information of the vehicle; and step 122, judging whether the vehicle state information is the abnormal vehicle information” ); and
outputting, based on a detection result of the abnormality in the voice and a detection result of the abnormality in the vehicle body, a determination result as to whether or not the vehicle is in an abnormal condition (Figures 2, 6 & 7; at least as in paragraphs 0029 and 0043-0049, specifically as in Figure 7, step 122 and as further discussed in at least paragraph 0046, wherein “step 122, judging whether the vehicle state information is the abnormal vehicle information, and judging whether the body state information is the abnormal driving information, in which step, if the vehicle state information is the abnormal vehicle information, and/or the body state information is the abnormal driving information”). Li is silent specifically regarding wherein the system/method utilizes sound models to compare/evaluate, with the detected voice data of a user in a vehicle, an abnormality (i.e. event) associated with said voice data.
Gordon, in the same field of endeavor, teaches voice/audio analysis of a user by a smart speaker system that may be employed in a variety of environments (i.e. houses, vehicles, etc.). Gordon goes on to teach wherein “such analysis may comprise performing pattern analysis, feature extraction (e.g., amplitude, frequency, duration, etc.), and the like. The patterns and/or features may be used as a basis for comparing the audio sample, with sound models stored in the sound and event model repository 128 to thereby indicate a nature or type of the sound(s) in the audio sample and/or the nature or type of the sound sources generating the sound(s) in the audio sample. Pattern analysis may be applied to compare the audio sample waveform patterns to the stored sound models to determine a degree of matching of the captured audio sample to the stored sound models in the repository 128”. Gordon further teaches wherein said analysis includes “the operation starts with the identification of a sound from an audio sample captured from a monitored environment (step 610). Previously identified and processed sounds within a given time window are identified along with the currently identified sound (step 620). The collection of sounds, their types, their sources, sequence of sounds, and various other audio and content characteristics of the sounds are compared to event models defining criteria for different types of events (step 630)” and further “The configuration information and other knowledge base information that may be used to identify events and weighting the confidence scores associated with events occurring in the monitored environment may also be retrieved from corresponding stores (step 640). A weighted confidence score may be generated based on the degree of matching of the various factors of the sounds, configuration information, and knowledge base information (step 650). A ranked listing of the matched event models is generated and the confidence scores are compared to threshold requirements (step 660). A highest ranking event model with the threshold amount of confidence score is selected as a match (step 670). The matching event model may specify a default danger/risk level for the event and a suggested responsive action to be performed. This information may be utilized in steps 480 and 490 of FIG. 4 to determine danger/risk level and a corresponding responsive action to be performed” (Figures 1, 3, 5 & 6; at least paragraphs 0088-0089 and 0169-0172). Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Li, to include Gordon’s audio analysis techniques employing sound models to compare with voice data in a monitored environment to detect a corresponding event, since Gordon teaches wherein such a system/method provides improved cognitive analysis capabilities for cognitively determining the type of sound, the type of sound source whether an event is occurring or has occurred in the monitored environment based on the identified sounds over a specified period of time, and can identify and initiate responsive actions to such events, thereby providing a more dynamic and robust abnormality detection system and corresponding method.
Regarding claim 6, in view of the above combination of Li and Gordon, Gordon teaches the method further comprising: generating a model of the current voice data of the person as a current model based on the current voice data of the person, and determining that the voice is abnormal when it is determined that the current voice data of the person contains a scream based on a comparison result between the current model and the normal time model (Figures 1, 3, 5 & 6; at least paragraphs 0088-0089, 0093 and 0169-0172).
Regarding claim 7, in view of the above combination of Li and Gordon, Gordon teaches the method further comprising: determining that the voice is abnormal when it is determined that the current voice data of the person contains a scream based on a comparison result between a waveform histogram obtained from the normal time voice data and a waveform histogram obtained from the current voice data (Figures 1, 3, 5 & 6; at least paragraphs 0088-0089, 0093 and 0169-0172).
Regarding claim 8, in view of the above combination of Li and Gordon, Li teaches the method further comprising: determining that the vehicle is in an abnormal condition in at least one of the following cases: a case where the detection result of the voice abnormality detector indicates that the voice is abnormal and a case where the detection result of the vehicle body abnormality detector indicates that the vehicle body is abnormal (Figures 2, 6 & 7; at least as in paragraphs 0029, 0031 and 0043-0049).
Regarding claim 9, Li teaches an abnormal condition detection recording medium, recording a program for causing a processor included in an abnormal condition detection system perform the following processing:
(Figures 1-3 & 6-7; at least as in paragraphs 0026, 0031 and 0043-0049, specifically as in at least paragraph 0031, wherein “by the processor 2, it may be judged whether the sound information of the driver acquired by the sound detector 11 is abnormal state information, such as a screaming sound or the like” and further as in Figures 6 & 7, steps 101 & 102 and related text found in at least paragraph 0043, wherein “step 101, acquiring a body state information of the driver, in which step, the body state information may reflect a driving state of the driver, and the body state information may include a plurality of types of information, for example, brain wave information of the human body, eye closing information of the human body, sound information of the human body, facial expression information of the human body, heartbeat information of the human body, and the like; and step 102, judging whether the body state information is abnormal driving information”);
detecting an abnormality in a vehicle body of the vehicle based on acceleration data of the vehicle (Figures 2, 4 & 6-7; at least as in paragraphs 0029, 0035-0036 and 0046-0049, specifically as in at least paragraph 0029, wherein “the vehicle detector 4 is configured to detect vehicle state information of the vehicle; the input terminal of the processor 2 is connected with the vehicle detector 4, and the processor 2 is configured to judge whether the vehicle state information is vehicle abnormality information”, and further as in Figure 7, steps 121 & 122, and related text found in at least paragraph 0046 wherein “step 121, acquiring the vehicle state information of the vehicle; and step 122, judging whether the vehicle state information is the abnormal vehicle information” ); and
outputting, based on a detection result of the abnormality in the voice and a detection result of the abnormality in the vehicle body, a determination result as to whether or not the vehicle is in an abnormal condition (Figures 2, 6 & 7; at least as in paragraphs 0029 and 0043-0049, specifically as in Figure 7, step 122 and as further discussed in at least paragraph 0046, wherein “step 122, judging whether the vehicle state information is the abnormal vehicle information, and judging whether the body state information is the abnormal driving information, in which step, if the vehicle state information is the abnormal vehicle information, and/or the body state information is the abnormal driving information”). Li is silent specifically regarding wherein the system/method utilizes sound models to compare/evaluate, with the detected voice data of a user in a vehicle, an abnormality (i.e. event) associated with said voice data.
Gordon, in the same field of endeavor, teaches voice/audio analysis of a user by a smart speaker system that may be employed in a variety of environments (i.e. houses, vehicles, etc.). Gordon goes on to teach wherein “such analysis may comprise performing pattern analysis, feature extraction (e.g., amplitude, frequency, duration, etc.), and the like. The patterns and/or features may be used as a basis for comparing the audio sample, with sound models stored in the sound and event model repository 128 to thereby indicate a nature or type of the sound(s) in the audio sample and/or the nature or type of the sound sources generating the sound(s) in the audio sample. Pattern analysis may be applied to compare the audio sample waveform patterns to the stored sound models to determine a degree of matching of the captured audio sample to the stored sound models in the repository 128”. Gordon further teaches wherein said analysis includes “the operation starts with the identification of a sound from an audio sample captured from a monitored environment (step 610). Previously identified and processed sounds within a given time window are identified along with the currently identified sound (step 620). The collection of sounds, their types, their sources, sequence of sounds, and various other audio and content characteristics of the sounds are compared to event models defining criteria for different types of events (step 630)” and further “The configuration information and other knowledge base information that may be used to identify events and weighting the confidence scores associated with events occurring in the monitored environment may also be retrieved from corresponding stores (step 640). A weighted confidence score may be generated based on the degree of matching of the various factors of the sounds, configuration information, and knowledge base information (step 650). A ranked listing of the matched event models is generated and the confidence scores are compared to threshold requirements (step 660). A highest ranking event model with the threshold amount of confidence score is selected as a match (step 670). The matching event model may specify a default danger/risk level for the event and a suggested responsive action to be performed. This information may be utilized in steps 480 and 490 of FIG. 4 to determine danger/risk level and a corresponding responsive action to be performed” (Figures 1, 3, 5 & 6; at least paragraphs 0088-0089 and 0169-0172). Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Li, to include Gordon’s audio analysis techniques employing sound models to compare with voice data in a monitored environment to detect a corresponding event, since Gordon teaches wherein such a system/method provides improved cognitive analysis capabilities for cognitively determining the type of sound, the type of sound source whether an event is occurring or has occurred in the monitored environment based on the identified sounds over a specified period of time, and can identify and initiate responsive actions to such events, thereby providing a more dynamic and robust abnormality detection system and corresponding method.
Regarding claim 10, in view of the above combination of Li and Gordon, Gordon further teaches wherein recording the program for causing the processor included in the abnormal condition detection system perform the following further processing: generating a model of the current voice data of the person as a current model based on the current voice data of the person, and determining that the voice is abnormal when it is determined that the current voice data of the person contains a scream based on a comparison result between the current model and the normal time model (Figures 1, 3, 5 & 6; at least paragraphs 0088-0089, 0093 and 0169-0172).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892 – Notice of References Cited form. Examiner additionally notes the following references, in the same field of endeavor as the instant invention and also appears to read on several of the currently provided claim limitations above;
US 2023/0154484 A1, issued to Kobayashi, which is directed towards a collection system for a vehicle that includes a passenger sensor unit to detect one or more passengers and a determination acquisition unit configured to acquire abnormality data related to a vehicle, and further utilizes said collected information to determine a dangerous situation/event.
US 2020/0394427 A1, issued to Takahashi et al, which is directed towards a video recording control device, system, method and non-transitory medium, that is utilized to detect an event related to a vehicle based on visual and audio data received and provide a vehicle control response based on said detected event.
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/JONATHAN L SAMPLE/Primary Examiner, Art Unit 3657