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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process which the human mind can perform an observation, evaluation and judgement. This judicial exception is not integrated into a practical application because the claims are directed to mental processes without any significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a human can organize and perform the mental process. Below is the analysis.
Claim 1 recites, “A learning device comprising: a processor to execute a program; and a memory to store past congestion-area data indicating congestion-related information including a past congestion level of each of m areas (where m is an integer of two or more), past sensor data indicating values detected in the past by one or more sensors installed in n areas (where n is an integer of one or more and n<m) out of the m areas, and the program which, when executed by the processor, performs processes of, generating a first model by using the values indicated by the past sensor data as input data and using the congestion-related information indicated by the past congestion-area data as correct data, the first model being a learning model for predicting, from values detected by the one or more sensors, congestion-related information of a time point at which the one or more sensors perform detection; and generating a second model by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from the congestion-related information, the second time point being a time point after the first time point..”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “generating a second model by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from the congestion-related information, the second time point being a time point after the first time point”. Unlike the first model which the applicant describes the program, when executed by the processor, performs processes of, generating a first model by using the values indicated by the past sensor data as input data and using the congestion-related information indicated by the past congestion-area data as correct data, the first model being a learning model for predicting, from values detected by the one or more sensors, congestion-related information of a time point at which the one or more sensors perform detection, the applicant fails to elaborate how or what generates the second model, and further what does the predicting future congestion level or where the information is derived.
With the processor generated first model, the applicant clarifies the source of the data used for generating the model is derived from sensor data. However, the applicant fails to do the same for the second model. Though certain elements of the first model are tied to sensor data and processor(s) for future traffic predictive purposes, the second model fails to disclose the source of the data used for the traffic prediction. Even if one of ordinary skill in the artis to assume the data is sourced from more sensors, the means that generates the second model is not disclosed, infringing on the possibility of natural phenomena of the predictive step being done by the human mind. Thereby, simply acquiring/gathering information from sensor or computing devices, thereafter, further generating a formulaic model for predicting traffic within the human mind, based on the gathered information
Step 2A Prong Two: the claimed invention remains an abstract idea because The two “non-abstract” idea elements do not tie the abstract idea to anything substantially, i.e. the processor to execute a program, the memory to store past congestion-area data indicating congestion-related information, past sensor data detected in the past by one or more sensors installed in different areas, only define the generation of the first traffic model, but fail to tie the second generated traffic model to the prediction step. Failing to provide anything significantly more. Thereby, the invention is simply a gathering acquiring of information from sensors and computing devices, where generating a formulaic model for predicting traffic based on the gathered information may be done within the human mind
Claim 2 recites “wherein, the past congestion-area data indicates the congestion-related information of respective time points, and the past sensor data indicates the values of the respective time points.” The elements fail but fail to tie the second generated traffic model to the prediction step. They also fail to link or connect the first model and the second model to provide anything significantly more. Thereby do not cure the invention of natural phenomena that is gathering acquiring of information from sensors and computing devices and generating a formulaic model for predicting traffic based on the gathered information within the human mind
Claim 3 recites, “A learning device comprising: a processor to execute a program; and a memory to store past congestion-area data indicating congestion-related information including a past congestion level of each of m areas (where m is an integer of two or more), past sensor data indicating values detected in the past by one or more sensors installed in n areas (where n is an integer of one or more and n<m) out of the m areas, and the program which, when executed by the processor, performs processes of, generating a first model by using the congestion-related information indicated by the past congestion-area data as input data and using the values indicated by the past sensor data as correct data, the first model being a learning model for predicting, from the congestion-related information, values to be detected by the one or more sensors at a time point at which the congestion-related information is acquired; using the first model to predict, from the congestion-related information at a first time point indicated by the past congestion-area data, values to be detected by the one or more sensors at the first time point; and generating a second model by using the predicted values as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from values detected by the one or more sensors, the second time point being a time point after the first time point.”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “generating a second model by using the predicted values as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from values detected by the one or more sensors, the second time point being a time point after the first time point”. Unlike the first model which the applicant describes the program, when executed by the processor, performs processes of, generating a first model by using the values indicated by the past sensor data as input data and using the congestion-related information indicated by the past congestion-area data as correct data, the first model being a learning model for predicting, from values detected by the one or more sensors, congestion-related information of a time point at which the one or more sensors perform detection, the applicant fails to elaborate what generates the second model, and further what does the predicting future congestion level.
Though certain elements of the first model are tied to sensor data and processor(s) for future traffic predictive purposes, the second model fails to disclose the source of its generation. Even though the data used in the second model is sourced from more sensors, how and what generates the second model is not disclosed, infringing in the possibility of natural phenomena of the predictive step being done by the human mind; simply acquiring/gathering information from sensor or computing devices, and thereafter, generating a formulaic model for predicting traffic within the human mind, based on the gathered information.
Step 2A Prong Two: the claimed invention remains an abstract idea because The two “non-abstract” idea elements do not tie the abstract idea to anything substantially, i.e. the processor to execute a program, the memory to store past congestion-area data indicating congestion-related information, past sensor data detected in the past by one or more sensors installed in different areas, fail to tie the second generated traffic model to the prediction step. Thereby, the invention is simply a gathering acquiring of information from sensors and computing devices and generating a formulaic model for predicting traffic based on the gathered information within the human mind
Claim 4 recites “wherein, the past congestion-area data indicates the congestion-related information of respective time points, and the past sensor data indicates the values of the respective time points.” The elements fail but fail to tie the second generated traffic model to the prediction step. They also fail to link or connect the first model and the second model to provide anything significantly more. Thereby do not cure the invention of natural phenomena that is gathering acquiring of information from sensors and computing devices and generating a formulaic model for predicting traffic based on the gathered information within the human mind
Claim 5 recites, “A prediction device comprising: a processor to execute a program; and a memory to store the program which, when executed by the processor, performs processes of, acquiring values detected by one or more sensors installed in n areas (where n is an integer of one or more, and n<m) out of m areas (where m is an integer of two or more); using a first model to predict, from the acquired values, congestion-related information of a time point at which the one or more sensors perform detection, the first model being a learning model for predicting, from values to be detected by the one or more sensors, the congestion-related information of the time point at which the one or more sensors perform detection, the first model being generated by using values indicated by past sensor data indicating the values detected in the past by the one or more sensors installed in n areas (where n is an integer of one or more and n<m) out of the m areas as input data and using the congestion-related information indicated by past congestion-area data indicating the congestion-related information including a past congestion level of each of the m areas as correct data; and using a second model to predict, from the predicted congestion-related information, a future congestion level of any of the m areas, the second model being a learning model for predicting a future congestion level from the congestion-related information, the second model being generated by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second time point being a time point after the first time point.”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “using a first model to predict, from the acquired values, congestion-related information of a time point at which the one or more sensors perform detection, the first model being a learning model for predicting, from values to be detected by the one or more sensors, the congestion-related information of the time point at which the one or more sensors perform detection, the first model being generated by using values indicated by past sensor data indicating the values detected in the past by the one or more sensors installed in n areas (where n is an integer of one or more and n<m) out of the m areas as input data and using the congestion-related information indicated by past congestion-area data indicating the congestion-related information including a past congestion level of each of the m areas as correct data”. Here the claim language fails to establish what generates the first prediction model, and only establishes a processor to execute a program; and a memory to store the program which, when executed by the processor, performs processes of, acquiring values detected by one or more sensors installed in n areas (where n is an integer of one or more, and n<m) out of m areas (where m is an integer of two or more).
Furthermore, by stating, “using a second model to predict, from the predicted congestion-related information, a future congestion level of any of the m areas, the second model being a learning model for predicting a future congestion level from the congestion-related information, the second model being generated by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second time point being a time point after the first time point.”, the claim once again, fails to establish what generates the second prediction model. Though certain elements of the first and second models are tied to sensor data for future traffic predictive purposes, what generates the two models is not disclosed, enabling the possibility of natural phenomena infringement of the predictive step being done by the human mind. In essence, the invention acquires/gathers information from sensor or computing devices, thereafter, enabling the generation of a formulaic model for predicting traffic within the human mind, based on the gathered information
Step 2A Prong Two: the claimed invention remains an abstract idea because The two “non-abstract” idea elements do not tie the abstract idea to anything substantially, i.e. the processor to execute a program, the memory to store past congestion-area data indicating congestion-related information, past sensor data detected in the past by one or more sensors installed in different areas, fail to tie the second generated traffic model to the prediction step. They also fail to link or connect the first model and the second model to any processor or computing means to provide anything significantly more. Thereby, the invention is simply a gathering acquiring of information from sensors and computing devices and generating a formulaic model for predicting traffic based on the gathered information within the human mind.
Claim 6, recites, “A prediction device comprising: a processor to execute a program; and a memory to store the program which, when executed by the processor, performs processes of, acquiring values detected by one or more sensors installed in n areas (where n is an integer of one or more, and n<m) out of m areas (where m is an integer of two or more); and using a second model to predict a future congestion level of any of the m areas from the acquired values, the first model being a learning model for predicting, from congestion-related information, values to be detected by the one or more sensors at a time point at which the congestion-related information is acquired, the first model being generated by using, as input data, the congestion-related information indicated by past congestion-area data indicating the congestion-related information including a past congestion level of each of the m areas and by using, as correct data, values indicated by past sensor data indicating values detected by the one or more sensors in the past, the second model being a learning model for predicting a future congestion level from the values detected by the one or more sensors, the second model being generated by using, as input data, predicted values obtained by using a first model to predict the values to be detected by the one or more sensors at a first time point, from congestion-related information of the first time point indicated by the past congestion-area data and by using, as correct data, a congestion level in the congestion-related information of a second time point indicated by the past congestion-area data, the second time point being a time point after the first time point.”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “using a second model to predict a future congestion level of any of the m areas from the acquired values, the first model being a learning model for predicting, from congestion-related information, values to be detected by the one or more sensors at a time point at which the congestion-related information is acquired”. Here the claim language fails to establish what generates the second prediction model, and only establishes a processor to execute a program; and a memory to store the program which, when executed by the processor, performs processes of, acquiring values detected by one or more sensors installed in n areas (where n is an integer of one or more, and n<m) out of m areas (where m is an integer of two or more).
Furthermore, by stating, “using a first model to predict the values to be detected by the one or more sensors at a first time point, from congestion-related information of the first time point indicated by the past congestion-area data and by using, as correct data.”, the applicant once again, fails to establish what generates the first prediction model. Though certain elements of the first and second models are tied to sensor data for future traffic predictive purposes, both models fail to disclose the source of the data used for the traffic prediction as well as what generates the two models. This, enables the possibility of natural phenomena infringement of the predictive step being done by the human mind. In essence, the invention acquires/gathers information from sensor or computing devices, thereafter, generating a formulaic model for predicting traffic within the human mind, based on the gathered information
Step 2A Prong Two: the claimed invention remains an abstract idea because The two “non-abstract” idea elements do not tie the abstract idea to anything substantially, i.e. the processor to execute a program, the memory to store past congestion-area data indicating congestion-related information, past sensor data detected in the past by one or more sensors installed in different areas, fail to tie the second generated traffic model to the prediction step. They also fail to link or connect the first model and the second model using any processor or any computing means to provide anything significantly more. Thereby, the invention is simply a gathering acquiring of information from sensors and computing devices and generating a formulaic model for predicting traffic based on the gathered information within the human mind.
Claim 7, recites, “A learning prediction device comprising: a processor to execute a program; and a memory to store past congestion-area data indicating congestion-related information including a past congestion level of each of m areas (where m is an integer of two or more), past sensor data indicating values detected in the past by one or more sensors installed in n areas (where n is an integer of one or more and n<m) out of the m areas, and the program which, when executed by the processor, performs processes of, generating a first model by using the values indicated by the past sensor data as input data and using the congestion-related information indicated by the past congestion-area data as correct data, the first model being a learning model for predicting, from values detected by the one or more sensors, congestion-related information of a time point at which the one or more sensors perform detection; generating a second model by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from the congestion-related information, the second time point being a time point after the first time point; acquiring values detected by the one or more sensors; using the first model to predict, from the acquired values, the congestion-related information of a time point at which the one or more sensors perform detection; and using the second model to predict, from the predicted congestion-related information, a future congestion level of any of the m areas.”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “generating a second model by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from the congestion-related information, the second time point being a time point after the first time point”. Unlike the first model which the applicant describes the program, when executed by the processor, performs processes of, generating a first model by using the values indicated by the past sensor data as input data and using the congestion-related information indicated by the past congestion-area data as correct data, the first model being a learning model for predicting, from values detected by the one or more sensors, congestion-related information of a time point at which the one or more sensors perform detection, the applicant fails to elaborate how or what generates the second model, and further what does the predicting future congestion level or where the information is derived, i.e. no processor or computing device is tied to the generation of the second model.
Though certain elements of the first and second model are tied to sensor data and processor(s) for future traffic predictive purposes, how and what generates the second model is not disclosed, enabling the possibility of natural phenomena of the predictive step being done by the human mind; simply acquiring/gathering information from sensor or computing devices, thereafter, enabling the generation of a formulaic model for predicting traffic within the human mind, based on the gathered information
Step 2A Prong Two: the claimed invention remains an abstract idea because The two “non-abstract” idea elements do not tie the abstract idea to anything substantially, i.e. the processor to execute a program, the memory to store past congestion-area data indicating congestion-related information, past sensor data detected in the past by one or more sensors installed in different areas, only define the generation of the first traffic model, but fail to tie the second generated traffic model to the prediction step. Thereby, the invention is simply a gathering acquiring of information from sensors and computing devices and generating a formulaic model for predicting traffic based on the gathered information within the human mind.
Claim 8, recites, “A learning prediction device comprising: a processor to execute a program; and a memory to store past congestion-area data indicating congestion-related information including a past congestion level of each of m areas (where m is an integer of two or more), past sensor data indicating values detected in the past by one or more sensors installed in n areas (where n is an integer of one or more and n<m) out of the m areas, and the program which, when executed by the processor, performs processes of, generating a first model by using the congestion-related information indicated by the past congestion-area data as input data and using the values indicated by the past sensor data as correct data, the first model being a learning model for predicting, from the congestion-related information, values to be detected by the one or more sensors at a time point at which the congestion-related information is acquired; using the first model to predict, from the congestion-related information at a first time point indicated by the past congestion-area data, values to be detected by the one or more sensors at the first time point; generating a second model by using the predicted values as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from the values detected by the one or more sensors, the second time point being a time point after the first time point; acquiring the values detected by the one or more sensors; and using the second model to predict, from the acquired values, a future congestion level of any of the m areas.”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “generating a second model by using the predicted values as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from the values detected by the one or more sensors, the second time point being a time point after the first time point”. Unlike the first model which the applicant describes the program, when executed by the processor, performs processes of, generating a first model by using the values indicated by the past sensor data as input data and using the congestion-related information indicated by the past congestion-area data as correct data, the first model being a learning model for predicting, from values detected by the one or more sensors, congestion-related information of a time point at which the one or more sensors perform detection, the claim fails to elaborate how or what generates the second model, and further what does the predicting future congestion level or where the information is derived, i.e. no processor or computing device is tied to the generation of the second model.
Though certain elements of the first and second model are tied to sensor data and processor(s) for future traffic predictive purposes, how and what generates the second model is not disclosed, enabling the possibility of natural phenomena of the predictive step being done by the human mind; simply acquiring/gathering information from sensor or computing devices, thereafter, enabling the generation of a formulaic model for predicting traffic within the human mind, based on the gathered information
Step 2A Prong Two: the claimed invention remains an abstract idea because The two “non-abstract” idea elements do not tie the abstract idea to anything substantially, i.e. the processor to execute a program, the memory to store past congestion-area data indicating congestion-related information, past sensor data detected in the past by one or more sensors installed in different areas, only define the generation of the first traffic model, but fail to tie the second generated traffic model to the prediction step. Thereby, the invention is simply a gathering acquiring of information from sensors and computing devices and generating a formulaic model for predicting traffic based on the gathered information within the human mind
Claim 9, recites, “A non-transitory computer-readable medium that stores therein a program that causes a computer to execute processes of: storing past congestion-area data indicating congestion-related information including a past congestion level of each of m areas (where m is an integer of two or more); storing past sensor data indicating values detected in the past by one or more sensors installed in n areas (where n is an integer of one or more and n<m) out of the m areas; generating a first model by using the values indicated by the past sensor data as input data and using the congestion-related information indicated by the past congestion-area data as correct data, the first model being a learning model for predicting, from values detected by the one or more sensors, congestion-related information of a time point at which the one or more sensors perform detection; and generating a second model by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from the congestion-related information, the second time point being a time point after the first time point..”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “generating a first model by using the values indicated by the past sensor data as input data and using the congestion-related information indicated by the past congestion-area data as correct data, the first model being a learning model for predicting, from values detected by the one or more sensors, congestion-related information of a time point at which the one or more sensors perform detection; and generating a second model by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from the congestion-related information, the second time point being a time point after the first time point”.
Though the claims, teach the first and second models, containing sensor value data and congestion related data, the claim fails to disclose what generates the two models, and further what does the predicting future congestion level or where the information is derived, i.e. no processor or computing device is tied to the generation of the first and second models.
Though certain elements of the first and second model are tied to sensor data for future traffic predictive purposes, how and what generates the first and second model is not disclosed, enabling the possibility of natural phenomena of the predictive step being done by the human mind; by simply acquiring/gathering information from sensor or computing devices, thereafter, further generating a formulaic model for predicting traffic within the human mind, based on the gathered information.
Step 2A Prong Two: the claimed invention remains an abstract idea because The two “non-abstract” idea elements do not tie the abstract idea to anything substantially, i.e. the processor to execute a program, the memory to store past congestion-area data indicating congestion-related information, past sensor data detected in the past by one or more sensors installed in different areas, only define the how the data is derived. What actually generates the first and second models is not addressed in the claims, nor the prediction step. Thereby, failing to add anything significantly more. The invention is simply a gathering acquiring of information from sensors and computing devices and generating a formulaic model for predicting traffic based on the gathered information within the human mind
Claim 10, recites, “A non-transitory computer-readable medium that stores therein a program that causes a computer to execute processes of: storing past congestion-area data indicating congestion-related information including a past congestion level of each of m areas (where m is an integer of two or more); storing past sensor data indicating values detected in the past by one or more sensors installed in n areas (where n is an integer of one or more and n<m) out of the m areas; generating a first model by using the congestion-related information indicated by the past congestion-area data as input data and using the values indicated by the past sensor data as correct data, the first model being a learning model for predicting, from the congestion-related information, values to be detected by the one or more sensors at a time point at which the congestion-related information is acquired; using the first model to predict, from the congestion-related information at a first time point indicated by the past congestion-area data, values to be detected by the one or more sensors at the first time point; and generating a second model by using the predicted values as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from values detected by the one or more sensors, the second time point being a time point after the first time point.”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “generating a first model by using the congestion-related information indicated by the past congestion-area data as input data and using the values indicated by the past sensor data as correct data, the first model being a learning model for predicting, from the congestion-related information, values to be detected by the one or more sensors at a time point at which the congestion-related information is acquired; using the first model to predict, from the congestion-related information at a first time point indicated by the past congestion-area data, values to be detected by the one or more sensors at the first time point; and generating a second model by using the predicted values as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from values detected by the one or more sensors, the second time point being a time point after the first time point.”
Though the claims, teach the first and second models, containing sensor value data and congestion related data, the claim fails to disclose what generates the two models, and further what does the predicting future congestion levels or where the information is derived, i.e. no processor or computing device is tied to the generation of the first and second models, as well as predicting future congestion levels step.
Though certain elements of the first and second model are tied to sensor data for future traffic predictive purposes, how and what generates the first and second model is not disclosed, enabling the possibility of natural phenomena of the predictive step being done by the human mind; by simply acquiring/gathering information from sensor or computing devices, thereafter, generating a formulaic model for predicting traffic within the human mind, based on the gathered information.
Step 2A Prong Two: the claimed invention remains an abstract idea because The two “non-abstract” idea elements do not tie the abstract idea to anything substantially, i.e. the processor to execute a program, the memory to store past congestion-area data indicating congestion-related information, past sensor data detected in the past by one or more sensors installed in different areas, only define the how the data is derived. What actually generates the first and second models is not addressed in the claims, nor the prediction step. Thereby, failing to add anything significantly more. The invention is simply a gathering acquiring of information from sensors and computing devices and generating a formulaic model for predicting traffic based on the gathered information within the human mind.
Claim 11, recites, “A non-transitory computer-readable medium that stores therein a program that causes a computer to execute processes of: acquiring values detected by one or more sensors installed in n areas (where n is an integer of one or more, and n<m) out of m areas (where m is an integer of two or more); using a first model to predict, from the acquired values, congestion-related information of a time point at which the one or more sensors perform detection, the first model being a learning model for predicting, from values to be detected by the one or more sensors, the congestion-related information of the time point at which the one or more sensors perform detection, the first model being generated by using values indicated by past sensor data indicating the values detected in the past by the one or more sensors installed in n areas (where n is an integer of one or more and n<m) out of the m areas as input data and using the congestion-related information indicated by past congestion-area data indicating the congestion-related information including a past congestion level of each of the m areas as correct data; and using a second model to predict, from the predicted congestion-related information, a future congestion level of any of the m areas, the second model being a learning model for predicting a future congestion level from the congestion-related information, the second model being generated by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second time point being a time point after the first time point.”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “using a first model to predict, from the acquired values, congestion-related information of a time point at which the one or more sensors perform detection, the first model being a learning model for predicting, from values to be detected by the one or more sensors, the congestion-related information of the time point at which the one or more sensors perform detection, the first model being generated by using values indicated by past sensor data indicating the values detected in the past by the one or more sensors installed in n areas (where n is an integer of one or more and n<m) out of the m areas as input data and using the congestion-related information indicated by past congestion-area data indicating the congestion-related information including a past congestion level of each of the m areas as correct data.” And, “using a second model to predict, from the predicted congestion-related information, a future congestion level of any of the m areas, the second model being a learning model for predicting a future congestion level from the congestion-related information, the second model being generated by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second time point being a time point after the first time point”.
Though the claims, teach the first and second models, containing sensor value data and congestion related data, the claim fails to disclose what generates the two models, and further what does the predicting future congestion levels or where the information is derived, i.e. no processor or computing device is tied to the generation of the first and second models, as well as predicting future congestion levels step.
Though certain elements of the first and second model are tied to sensor data for future traffic predictive purposes, how and what generates the first and second model is not disclosed, enabling the possibility of natural phenomena of the predictive step being done by the human mind; by simply acquiring/gathering information from sensor or computing devices, thereafter, generating a formulaic model for predicting traffic within the human mind, based on the gathered information.
Step 2A Prong Two: the claimed invention remains an abstract idea because The two “non-abstract” idea elements do not tie the abstract idea to anything substantially, i.e. the processor to execute a program, the memory to store past congestion-area data indicating congestion-related information, past sensor data detected in the past by one or more sensors installed in different areas, only define the how the data is derived. What actually generates the first and second models is not addressed in the claims, nor the prediction step. Thereby, failing to add anything significantly more. The invention is simply a gathering acquiring of information from sensors and computing devices and generating a formulaic model for predicting traffic based on the gathered information within the human mind
Claim 12, recites, “A non-transitory computer-readable medium that stores therein a program that causes a computer to execute processes of: acquiring values detected by one or more sensors installed in n areas (where n is an integer of one or more, and n<m) out of m areas (where m is an integer of two or more); and using a second model to predict a future congestion level of any of the m areas from the acquired values, the first model being a learning model for predicting, from congestion-related information, values to be detected by the one or more sensors at a time point at which the congestion-related information is acquired, the first model being generated by using, as input data, the congestion-related information indicated by past congestion-area data indicating the congestion-related information including a past congestion level of each of the m areas and by using, as correct data, values indicated by past sensor data indicating values detected by the one or more sensors in the past, the second model being a learning model for predicting a future congestion level from the values detected by the one or more sensors, the second model being generated by using, as input data, predicted values obtained by using a first model to predict the values to be detected by the one or more sensors at a first time point, from congestion-related information of the first time point indicated by the past congestion-area data and by using, as correct data, a congestion level in the congestion-related information of a second time point indicated by the past congestion-area data, the second time point being a time point after the first time point.”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “using a second model to predict a future congestion level of any of the m areas from the acquired values, the first model being a learning model for predicting, from congestion-related information, values to be detected by the one or more sensors at a time point at which the congestion-related information is acquired, the first model being generated by using, as input data, the congestion-related information indicated by past congestion-area data indicating the congestion-related information including a past congestion level of each of the m areas and by using, as correct data, values indicated by past sensor data indicating values detected by the one or more sensors in the past, the second model being a learning model for predicting a future congestion level from the values detected by the one or more sensors, the second model being generated by using, as input data, predicted values obtained by using a first model to predict the values to be detected by the one or more sensors at a first time point”.
Though the claims, teach the first and second models, containing sensor value data and congestion related data, the claim fails to disclose what generates the two models, and further what does the predicting future congestion levels or where the information is derived, i.e. no processor or computing device is tied to the generation of the first and second models, as well as predicting future congestion levels step.
Though certain elements of the first and second model are tied to sensor data for future traffic predictive purposes, how and what generates the first and second model is not disclosed, enabling the possibility of natural phenomena of the predictive step being done by the human mind; by simply acquiring/gathering information from sensor or computing devices, thereafter, generating a formulaic model for predicting traffic within the human mind, based on the gathered information.
Step 2A Prong Two: the claimed invention remains an abstract idea because The two “non-abstract” idea elements do not tie the abstract idea to anything substantially, i.e. A non-transitory computer-readable medium that stores therein a program that causes a computer to execute processes of: acquiring values detected by one or more sensors installed in n areas (where n is an integer of one or more, and n<m) out of m areas (where m is an integer of two or more), only define the how the data is derived. What actually generates the first and second models is not addressed in the claims, nor the prediction step. Thereby, failing to add anything significantly more. The invention is simply a gathering acquiring of information from sensors and computing devices and generating a formulaic model for predicting traffic based on the gathered information within the human mind.
Claim 13, recites, “A non-transitory computer-readable medium that stores therein a program that causes a computer to execute processes of: storing past congestion-area data indicating congestion-related information including a past congestion level of each of m areas (where m is an integer of two or more); storing past sensor data indicating values detected in the past by one or more sensors installed in n areas (where n is an integer of one or more and n<m) out of the m areas; generating a first model by using the values indicated by the past sensor data as input data and using the congestion-related information indicated by the past congestion-area data as correct data, the first model being a learning model for predicting, from values detected by the one or more sensors, congestion-related information of a time point at which the one or more sensors perform detection; generating a second model by using the congestion-related information of a first time point indicated by the past congestion-area data as input data and using the congestion level in the congestion-related information of a second time point indicated by the past congestion-area data as correct data, the second model being a learning model for predicting a future congestion level from the congestion-related information, the second time point being a time point after the first time point; acquiring the values detected by the one or more sensors; using the first model to predict, from the acquired values, the congestion-related information of a time point at which the one or more sensors perform detection; and using the second model to predict, from the predicted congestion-related information, a future congestion level of any of the m areas..”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “generating a first model by using the values indicated by the past sensor data as input data and using the congestion-related information indicated by the past congestion-area data as correct data, the first model being a learning model for predicting, from values detected by the one or more sensors, congestion-related information of a time point at which the one or more sensors perform detection; generating a second model by using the