DETAILED ACTION
In response to communications filed on 19 March 2025, this is the first Office Action of the merits. Claims 1-7 are amended. Claims 1-7 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-5 are recited as being directed to a “apparatus”. Claim 6 is recited as being directed to a “method” and claim 7 is being directed to a “computer-readable medium”.
Regarding claim 1,
Step 2A: Prong One:
Claim 1 recites limitations:
calculate a likelihood ratio indicating likelihood of a class to which the series data belong, by simultaneously inputting the plurality of elements and calculating a relation between the plurality of elements; and
classify the series data into at least one class of a plurality of classes serving as classification candidates, based on the likelihood ratio.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to calculate a likelihood ratio indicating likelihood of a class to which the series data belong, by simultaneously inputting the plurality of elements and calculating a relation between the plurality of elements. A human being can apply evaluation to classify the series data into at least one class of a plurality of classes serving as classification candidates, based on the likelihood ratio.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 1 further recites limitations:
An information processing apparatus comprising:
at least one memory that is configured to store instructions; and
at least one processor that is configured to execute the instructions to:
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 1 further recites limitations:
acquire a plurality of elements included in series data;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data being received and do not appear to integrate the abstract idea into a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 1 further recites limitations:
An information processing apparatus comprising:
at least one memory that is configured to store instructions; and
at least one processor that is configured to execute the instructions to:
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 1 further recites limitations:
acquire a plurality of elements included in series data;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Receiving or transmitting data over a network, e.g., using the Internet to gather data” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claims 6 and 7 incorporate substantively all the limitations of claim 1 in a method form (wherein claim limitations - An information processing method that is executed by at least one computer, the information processing method comprising: in Step 2A: Prong Two as these claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application. These claim limitations in Step 2B appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and computer readable form (wherein claim limitations - A non-transitory recording medium on which a computer program that allows at least one computer to execute an information processing method is recorded, the information processing method including: in Step 2A: Prong Two as these claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application. These claim limitations in Step 2B appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and are rejected under the same rationale.
Regarding claims 2-5,
Claim 2 further recites limitations:
to simultaneously input inconsecutive elements in the series data and calculate a relation between the inconsecutive elements.
Claim 3 further recites limitations:
to calculate the likelihood ratio by using a self-attention mechanism..
Claim 4 further recites limitations:
the likelihood ratio by integrating a plurality of outputs from the self-attention mechanism.
Claim 5 further recites limitations:
to integrate the plurality of outputs by dividing a sum of the plurality of outputs, by a maximum value of the plurality of outputs.
These claim limitations appear to be reciting a “Mental Process” including evaluation and observation.
A human mind can mentally evaluate to input inconsecutive elements and calculate a relation between the inconsecutive elements. A human being can apply evaluation to calculate the likelihood ratio by using a self-attention mechanism. A human mind can evaluate to determine the likelihood ratio by integrating a plurality of outputs from the self-attention mechanism. The human mind can mentally evaluate to integrate the plurality of outputs by dividing a sum of the plurality of outputs, by a maximum value of the plurality of outputs.
There are no other claim limitations that can be integrated into a practical application or that amount to significantly more.
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.
Claims 1, 3-4 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Ide (US 2013/0262013 A1, hereinafter “Ide”) in view of Li et al. (US 2021/0382944 A1, hereinafter “Li”).
Regarding claim 1, Ide teaches
An information processing apparatus comprising: (see Ide, [0010] “there is provided an information processing method of an information processing device that includes a sensor that measures predetermined data and a model storage unit that stores a model obtained by modeling time series data measured”; [0207] “the information processing device includes the time series data input unit 11”).
at least one memory that is configured to store instructions; and at least one processor that is configured to execute the instructions to: (see Ide, [0079] “the measurement system 1 of FIG. 1 can be realized as a computer by causing a CPU (Central Processing Unit) to execute data measurement in a plurality of divided programs in a parallel manner, and causing the memory (storage unit) to store measured data and the parameters of the learning model”; [0328] “A program for causing a computer of a device that includes a sensor that measures predetermined data and a model storage unit that stores a model obtained by modeling time series data measured in the past to execute processes”).
acquire a plurality of elements included in series data; (see Ide, [0088] “The time series data of FIG. 4 is data including three types of data pieces (data 1, data 2, and data 3) arranged in a time series manner, and times at which the data pieces are obtained”).
calculate a likelihood ratio indicating likelihood of a class to which the series data belong, (see Ide, [0214] “parameters of a learning model are decided using time series data prepared as learning data, and states are given to each measured data piece of the time series data using the learning model. Then, each measured data piece of the time series data is classified for each state, each frequency probability is obtained from the number of times of succeeding measurement and the number of times of failing measurement in the states, and the success ratio sProb_i is thereby obtained”) by simultaneously inputting the plurality of elements (see Ide, [0111] “The measured data pieces… from the time 1 to the time t-1… are input to the measurement information amount computation unit 12 from the data storage unit 15 by the time series data input unit 11. The measurement information amount computation unit 12 determines whether or not a data piece X, of the time t is to be acquired by operating the sensor 14 using the Hidden Markov Model”; [0089] “a method in which measurement is continuously performed at given time intervals”) and calculating a relation between the plurality of elements; and (see Ide, [0108] “showing the relationship between time series data obtained by the sensor 14 and the Hidden Markov Model as a learning model in which the data is learned”).
classify the series data into at least one class of a plurality of classes… (see Ide, [0214] “parameters of a learning model are decided using time series data prepared as learning data, and states are given to each measured data piece of the time series data using the learning model. Then, each measured data piece of the time series data is classified for each state, each frequency probability is obtained from the number of times of succeeding measurement and the number of times of failing measurement in the states, and the success ratio sProb_i is thereby obtained”) based on the likelihood ratio (see Ide, [0214] “parameters of a learning model are decided using time series data prepared as learning data, and states are given to each measured data piece of the time series data using the learning model. Then, each measured data piece of the time series data is classified for each state, each frequency probability is obtained from the number of times of succeeding measurement and the number of times of failing measurement in the states, and the success ratio sProb_i is thereby obtained”).
Ide does not explicitly teach serving as classification candidates.
However, Li discloses data structures and teaches
serving as classification candidates, (see Li, [0088] “A classification mechanism 1122 operates on the output of the transformation unit 1110 to generate a probability score for each candidate topic within a set of possible candidate topics. Each probability score reflects the probability that the web document expresses a particular candidate topic. The classification mechanism 1122 also determines whether each candidate topic has a score above an environment-specific threshold value”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of serving as classification candidates, self-attention mechanism and integrating outputs as being disclosed and taught by Li, in the system taught by Ide to yield the predictable results of efficiently process documents to derive topics (see Li, [0038] “This characteristic of the topic-detecting system 110 also contributes to its efficiency. That is, the topic-detecting system 110 is said to be efficient in this regard because it does not require specialized algorithms or model variations to account for different kinds of web documents”).
Claims 6 and 7 incorporate substantively all the limitations of claim 1 in a method (see Ide, (see Ide, [0010] “there is provided an information processing method of an information processing device that includes a sensor that measures predetermined data and a model storage unit that stores a model obtained by modeling time series data measured”; [0251] “a method for storing and using time series data of the past in the model storage unit 18”) and computer-readable medium form (see Ide, [0011] “there is provided a program for causing a computer device that includes a sensor that measures predetermined data and a model storage unit that stores a model obtained by modeling time series data measured in the past to execute processes of computing”; [0084] “In the computer configured as above, the CPU 51 causes programs stored in, for example, the storage unit 58 to be loaded on the RAM 53 so as to be executed via the input and output interface 55 and the bus 54”) and are rejected under the same rationale.
Regarding claim 3, the proposed combination of Ide and Li teaches
wherein the at least one processor is configured to execute the instructions to (see Ide, [0079] “the measurement system 1 of FIG. 1 can be realized as a computer by causing a CPU (Central Processing Unit) to execute data measurement in a plurality of divided programs in a parallel manner, and causing the memory (storage unit) to store measured data and the parameters of the learning model”; [0328] “A program for causing a computer of a device that includes a sensor that measures predetermined data and a model storage unit that stores a model obtained by modeling time series data measured in the past to execute processes”) calculate the likelihood ratio (see Ide, [0214] “parameters of a learning model are decided using time series data prepared as learning data, and states are given to each measured data piece of the time series data using the learning model. Then, each measured data piece of the time series data is classified for each state, each frequency probability is obtained from the number of times of succeeding measurement and the number of times of failing measurement in the states, and the success ratio sProb_i is thereby obtained”) by using a self-attention mechanism (see Li, [0084] “The representative transformation unit 1110 includes a series of layers, including a self-attention mechanism 1114”). The motivation for the proposed combination is maintained.
Regarding claim 4, the proposed combination of Ide and Li teaches
wherein the at least one processor is configured to execute the instructions to (see Ide, [0079] “the measurement system 1 of FIG. 1 can be realized as a computer by causing a CPU (Central Processing Unit) to execute data measurement in a plurality of divided programs in a parallel manner, and causing the memory (storage unit) to store measured data and the parameters of the learning model”; [0328] “A program for causing a computer of a device that includes a sensor that measures predetermined data and a model storage unit that stores a model obtained by modeling time series data measured in the past to execute processes”) the likelihood ratio (see Ide, [0214] “parameters of a learning model are decided using time series data prepared as learning data, and states are given to each measured data piece of the time series data using the learning model. Then, each measured data piece of the time series data is classified for each state, each frequency probability is obtained from the number of times of succeeding measurement and the number of times of failing measurement in the states, and the success ratio sProb_i is thereby obtained”) by integrating a plurality of outputs from the self-attention mechanism (see Li, [0087] “adds the input to the self-attention mechanism 1114 (i.e., the position-modified input embeddings) to the output result of the self-attention mechanism 1114, and then performs layer-normalization on that sum”). The motivation for the proposed combination is maintained.
Claims 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ide in view of Li further in view of Moussa (US 2016/0321247 A1, hereinafter “Moussa”).
Regarding claim 2, the proposed combination of Ide and Li teaches
wherein the at least one processor is configured to execute the instructions to (see Ide, [0079] “the measurement system 1 of FIG. 1 can be realized as a computer by causing a CPU (Central Processing Unit) to execute data measurement in a plurality of divided programs in a parallel manner, and causing the memory (storage unit) to store measured data and the parameters of the learning model”; [0328] “A program for causing a computer of a device that includes a sensor that measures predetermined data and a model storage unit that stores a model obtained by modeling time series data measured in the past to execute processes”) simultaneously input… (see Ide, [0059] “the time series data accumulated in the data storage unit 15 is also supplied to the time series data input unit 11 at a predetermined timing”) in the series data (see Ide, [0059] “the time series data accumulated in the data storage unit 15 is also supplied to the time series data input unit 11 at a predetermined timing”).
The proposed combination of Ide and Li does not explicitly teach inconsecutive elements in the series data and calculate a relation between the inconsecutive elements.
However, Moussa discloses processing datasets and teaches
inconsecutive elements… and calculate a relation between the inconsecutive elements (see Moussa, [0049] “the random data available in the input dataset and databases to generate the different relationships” – random data has been interpreted as inconsecutive elements).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of relation between inconsecutive elements as being disclosed and taught by Moussa, in the system taught by the proposed combination of Ide and Li to yield the predictable results of efficiently processing datasets to determine relationships (see Moussa, [0029] “collecting different datasets and databases to provide an input dataset, receiving a person's name in a native language, identifying in the input dataset a relationship between the person's name and genders, identifying in the input dataset a relationship between the person's name in the native language and an alternative name in a second language”).
Claims 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ide in view of Li further in view of Chou et al. (US 2021/0144351 A1, hereinafter “Chou”).
Regarding claim 5, the proposed combination of Ide and Li teaches
wherein the at least one processor is configured to execute the instructions to (see Ide, [0079] “the measurement system 1 of FIG. 1 can be realized as a computer by causing a CPU (Central Processing Unit) to execute data measurement in a plurality of divided programs in a parallel manner, and causing the memory (storage unit) to store measured data and the parameters of the learning model”; [0328] “A program for causing a computer of a device that includes a sensor that measures predetermined data and a model storage unit that stores a model obtained by modeling time series data measured in the past to execute processes”) integrate the plurality of outputs… (see Li, [0093] “can integrate additional information regarding a web document under consideration”; [0087] “adds the input to the self-attention mechanism 1114 (i.e., the position-modified input embeddings) to the output result of the self-attention mechanism 1114) of the plurality of outputs,… (see Li, [0093] “can integrate additional information regarding a web document under consideration”; [0087] “adds the input to the self-attention mechanism 1114 (i.e., the position-modified input embeddings) to the output result of the self-attention mechanism 1114) of the plurality of outputs (see Li, [0093] “can integrate additional information regarding a web document under consideration”; [0087] “adds the input to the self-attention mechanism 1114 (i.e., the position-modified input embeddings) to the output result of the self-attention mechanism 1114).
The proposed combination of Ide and Li does not explicitly teach by dividing a sum of the plurality of outputs, by a maximum value of the plurality of outputs.
However, Chou discloses random data sets and teaches
by dividing a sum value by a maximum value (see Chou, [0032 ] “to obtain a sum value; and dividing the sum value by the maximum weighting value to obtain the output value”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of dividing sum by maximum value as being disclosed and taught by Chou, in the system taught by the proposed combination of Ide and Li to yield the predictable results of efficiently processing data values (see Chou, [0032] “to obtain a sum value; and dividing the sum value by the maximum weighting value to obtain the output value”).
Citation Of Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Publication 2015/0279129 A1 (Ishikawa et al.) teaches time series data and The likelihood ratios thus calculated are regarded as the probability corresponding to each of the failure causes and are converted to percentages that will be output to the classification.
Conclusion
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/VAISHALI SHAH/Primary Examiner, Art Unit 2156
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