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 .
Claims Status
Claims 1, 14 and 15 filed 01/09/2026 have been amended. Claims 16-18 are newly added claims. Claims 1-18 are pending. Claims 1-17 have been rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/07/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claims 18 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 01/09/2026 have been fully considered but they are not persuasive. Applicant’s representative asserts that the cited references fail to teach or suggest “the multiple nodes including one or more first nodes and one or more second nodes, each of the first nodes and the second nodes being a part of the multiple nodes, each of the first nodes corresponding to one of the extracted features, and each of the second nodes corresponding to one of the pieces of output data.” However, the Examiner’s respectfully disagrees as Haderbache et al. (U.S. Publication 2022/0067246) in paragraphs 0037-0038, 0044-0045 & 0105, shows the result data represents a physical amount of the object calculated based on the mesh data. The feature amount represents a feature of a positional relationship of the plurality of nodes. The simulation system generates the learned model by using the training data. The simulation system generates the feature amount representing a feature of a positional relationship of nodes from the mesh data through a topological data analysis. The training data includes a plurality of samples generated by performing coarse simulations on various object shapes. As it is Applicant's right to claim as broadly as possible their invention, it is also the Examiner's right to interpret the claim language as broadly as possible. It is the Examiner's position that the detailed functionality that allows for Applicant's invention to overcome the prior art used in the rejection, fails to differentiate in detail how these features are unique. It is clear that Applicant must be able to submit claim language to distinguish over the prior arts used in the above rejection sections that discloses distinctive features of Applicant's claimed invention. It is suggested that Applicant compare the original specification and claim language with the cited prior art used in the rejection section above or the remark section below to draw an amended claim set to further the prosecution.
Failure for Applicant to narrow the definition/scope of the claims and supply arguments commensurate in scope with the claims implies the Applicant's intent to broaden claimed invention.
Based on the rationale explained above, the Examiner disagrees with the prior arts being silent to the claimed embodiment.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 10, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Isezaki et al (U.S Publication 2022/0012262), hereinafter “Isezaki” in view of Haderbache et al. (U.S. Publication 2022/0067246), hereinafter “Haderbache”.
As to claims 1, 14 and 15, Isezaki discloses an information processing apparatus, method and a non-transitory computer readable recording medium comprising one or more hardware processors configured to: extract multiple features representing characteristics of pieces of second sampling data, the pieces of second sampling data including pieces of first sampling data and pieces of output data obtained by using the pieces of first sampling data as input (Isezaki, see [0048-0049], extracting multiple amplitude characteristics from the waveform data from the myoelectric signal storage section at predetermined sampling intervals. See [0088], data for each amplitude characteristic amount data transformed is displayed by projecting, wherein outputs the display data to the display device), the pieces of first sampling data being generated by using a multidimensional first probability distribution representing a distribution of each of pieces of input data related to an object to be analyzed (Isezaki, see [0031], data of each myoelectric signal output is acquired from the myoelectric signal measurement device, in order to have a multi-dimensional temporally sequential data. See [0091], a motion state visualization device is provided with the projective transform model to apply the self-organizing map. The motion state visualization device acquires the multi-dimensional input data of each of a positive example and a negative example and calculates an amplitude characteristic amount from each multi-dimensional input data), Isezaki is silent to the pieces of output data representing physical quantity of the object to be analyzed; and generate a network model representing a relationship among multiple nodes corresponding to the physical quantity of the object to be analyzed, the multiple nodes including one or more first nodes and one or more second nodes, each of the first nodes and the second nodes being a part of the multiple nodes, each of the first nodes corresponding to one of the extracted features, and each of the second nodes corresponding to one of the pieces of output data. However, Haderbache discloses the pieces of output data representing physical quantity of the object to be analyzed (Haderbache, see [0032], this simulation system performs a structural analysis simulation for analyzing physical characteristics of an object from a shape of the object. See [0037], the result data represents a physical amount of the object calculated based on the mesh data); and generate a network model representing a relationship among multiple nodes corresponding to the physical quantity of the object to be analyzed, the multiple nodes including one or more first nodes and one or more second nodes, each of the first nodes and the second nodes being a part of the multiple nodes, each of the first nodes corresponding to one of the extracted features, and each of the second nodes corresponding to one of the pieces of output data (Haderbache, see [0037-0038], the result data represents a physical amount of the object calculated based on the mesh data. The feature amount represents a feature of a positional relationship of the plurality of nodes. See [0044-0045], the simulation system generates the learned model by using the training data. The simulation system generates the feature amount representing a feature of a positional relationship of nodes from the mesh data through a topological data analysis. See [0105], the training data includes a plurality of samples generated by performing coarse simulations on various object shapes).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Isezaki in view of Haderbache in order to further modify the method for provided with a projective transform model including a plurality of nodes and a projection table from the teachings of Isezaki with the method of representing a shape of an object with a plurality of nodes from the teachings of Haderbache.
One of ordinary skill in the art would have been motivated because it would allow to provide results of the simulation for predicting the physical amount that are automatically extracted (Haderbache – Paragraph 0046).
As to claim 2, Isezaki in view of Haderbache discloses everything disclosed in claim 1. Haderbache further discloses wherein the one or more hardware processors is configured to control the object to be analyzed by using the multiple features as control variables (Haderbache, see [0037], the storage apparatus can calculate physical amount (result data) for each of the plurality of nodes. A variable is assigned to each of the plurality of nodes, and based on a governing equation indicating a physical phenomenon, a coefficient matrix indicating a relationship among the plurality of variables is generated).
As to claim 3, Isezaki in view of Haderbache discloses everything disclosed in claim 1. Isezaki further discloses wherein the one or more hardware processors is configured to update the first probability distribution by using a monitoring data being the pieces of input data measured from the object to be analyzed (Isezaki, see [0058], updates the reference vectors of the nodes by using the amplitude characteristic amount data R.sub.1, R.sub.2, and R.sub.3).
As to claim 4, Isezaki in view of Haderbache discloses everything disclosed in claim 1. Isezaki further discloses wherein the one or more hardware processors is configured to modify the first probability distribution to optimize an index of the network model to be generated (Isezaki, see [0091], motion state visualization device acquires the multi-dimensional input data of each of a positive example and a negative example and calculates an amplitude characteristic amount from each multi-dimensional input data. The motion state visualization device updates coordinates in the projection table), and generate the pieces of first sampling data by using the modified first probability distribution (Isezaki, see [0088-0089], the motion state visualization section generates display data for simultaneously displaying a two-dimensional space coordinate value corresponding to each amplitude characteristic amount data transformed resulting in outputs. The projection table of the projective transform model provides updated to the coordinate value of each node after the transform).
As to claim 5, Isezaki in view of Haderbache discloses everything disclosed in claim 1, wherein the one or more hardware processors is configured to generate pieces of third sampling data on the basis of the multiple features (Isezaki, see [0044], three sequential myoelectric signals EMG1, EMG2, EMG3 are output for each of the positive and negative), and extract the multiple features to optimize an index indicating whether a multidimensional second probability distribution representing a distribution of the pieces of third sampling data is consistent with the first probability distribution (Isezaki, see [0048-0049], extracting multiple amplitude characteristics from the waveform data from the myoelectric signal storage section at predetermined sampling intervals. See [0088], data for each amplitude characteristic amount data transformed is displayed by projecting, wherein outputs the display data to the display device).
As to claim 10, Isezaki in view of Haderbache discloses everything disclosed in claim 9. Haderbache further discloses wherein the one or more hardware processors is configured to control the object to be analyzed such that the occurrence probability reaches a target value (Haderbache, see [0037], the storage apparatus can calculate physical amount (result data) for each of the plurality of nodes. A variable is assigned to each of the plurality of nodes, and based on a governing equation indicating a physical phenomenon, a coefficient matrix indicating a relationship among the plurality of variables is generated).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Isezaki et al (U.S Publication 2022/0012262), hereinafter “Isezaki” in view of Haderbache et al. (U.S. Publication 2022/0067246), hereinafter “Haderbache” and Yu et al. (U.S. Publication 2024/0346210), hereinafter “Yu”.
As to claim 6, Isezaki in view of Haderbache discloses everything disclosed in claim 1, but is silent to wherein the one or more hardware processors is configured to perform a quantum operation including an operation of decomposing a matrix based on a Hamiltonian generated from the pieces of first sampling data, and obtain the pieces of output data by performing an optimization process on results of the quantum operation. However, Yu discloses wherein the one or more hardware processors is configured to perform a quantum operation including an operation of decomposing a matrix based on a Hamiltonian generated from the pieces of first sampling data (Yu, see Yu, see [0061-0073], the quantum walk process is computed and simulated based on the graph-based adjacency matrix, wherein the spectrum decomposition of the Hamiltonian H is used. The quantum walk is sampled multiple times using a plurality of different scale factors), and obtain the pieces of output data by performing an optimization process on results of the quantum operation (Yu, see [0098], optimal modality combination can be obtained. See [0107], the multi-scale analysis method for time series based on quantum walk provided by the present disclosure, the time series are analyzed from the aspects of data generation, data screening, data modeling and prediction, and result evaluation, and a higher modeling or prediction accuracy may be obtained). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Isezaki in view of Haderbache and Yu in order to further modify the method for provided with a projective transform model including a plurality of nodes and a projection table from the teachings of Isezaki with the method of representing a shape of an object with a plurality of nodes from the teachings of Haderbache and the method for time series based on quantum walk from the teachings of Yu.
One of ordinary skill in the art would have been motivated because it would allow a multi-scale analysis for time series based on quantum walk (Yu – Paragraph 0001).
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Isezaki et al (U.S Publication 2022/0012262), hereinafter “Isezaki” in view of Haderbache et al. (U.S. Publication 2022/0067246), hereinafter “Haderbache” and Friedlander et al. (U.S. Publication 2020/0364597), hereinafter “Friedlander”.
As to claim 7, Isezaki in view of Haderbache discloses everything disclosed in claim 1, but is silent to wherein the one or more hardware processors is configured to obtain the pieces of output data by converting a real number optimization problem for obtaining a rate of change of the pieces of first sampling data into a binary variable optimization problem, and solving the binary variable optimization problem by a calculation using a quantum-inspired calculation or by a calculation using a quantum computer. However, Friedlander discloses wherein the one or more hardware processors is configured to obtain the pieces of output data by converting a real number optimization problem for obtaining a rate of change of the pieces of first sampling data into a binary variable optimization problem (Friedlander, see [0066-0068], value optimization can be provided by scaling parameters), and solving the binary variable optimization problem by a calculation using a quantum-inspired calculation or by a calculation using a quantum computer (Friedlander, see [0079], quadratic optimization problem is solved by the iterative optimization process can include determining current values of the set of scaling parameters). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Isezaki in view of Haderbache and Friedlander in order to further modify the method for provided with a projective transform model including a plurality of nodes and a projection table from the teachings of Isezaki with the method of representing a shape of an object with a plurality of nodes from the teachings of Haderbache and the method for optimatization of robust inference problem from the teachings of Friedlander.
One of ordinary skill in the art would have been motivated because it would allow to the probability distribution to determine the set of scaling parameters (Friedlander – Paragraph 0079-0081).
As to claim 8, Isezaki in view of Haderbache discloses everything disclosed in claim 1, but is silent to wherein the one or more hardware processors is configured to generate the pieces of first sampling data from the first probability distribution on the basis of a quantum-inspired calculation, a quantum computing calculation, a Lagrangian Monte Carlo calculation, a Hamiltonian Monte Carlo calculation, or a Markov chain Monte Carlo calculation. However, Friedlander discloses wherein the one or more hardware processors is configured to generate the pieces of first sampling data from the first probability distribution on the basis of a quantum-inspired calculation, a quantum computing calculation, a Lagrangian Monte Carlo calculation, a Hamiltonian Monte Carlo calculation, or a Markov chain Monte Carlo calculation (Friedlander, see [0114], the sampling device can comprise a quantum processor and a quantum device control system for obtaining the schedule of the set of scaling parameters and the data of the robust inference problem. The reconfigurable digital hardware is configured to implement a Markov Chain Monte Carlo algorithm). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Isezaki in view of Haderbache and Friedlander in order to further modify the method for provided with a projective transform model including a plurality of nodes and a projection table from the teachings of Isezaki with the method of representing a shape of an object with a plurality of nodes from the teachings of Haderbache and the method for optimatization of robust inference problem from the teachings of Friedlander.
One of ordinary skill in the art would have been motivated because it would allow to the probability distribution to determine the set of scaling parameters (Friedlander – Paragraph 0079-0081).
Claims 9, 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Isezaki et al (U.S Publication 2022/0012262), hereinafter “Isezaki” in view of Haderbache et al. (U.S. Publication 2022/0067246), hereinafter “Haderbache” and Hirohata et al. (U.S. Publication 2010/0274833), hereinafter “Hirohata”.
As to claim 9, Isezaki in view of Haderbache discloses everything disclosed in claim 1, but is silent to wherein the one or more hardware processors is configured to calculate an occurrence probability representing a ratio of the number of sampling data whose corresponding output data satisfying a specific condition to the number of the generated pieces of first sampling data. However, Hirohata discloses wherein the one or more hardware processors is configured to calculate an occurrence probability representing a ratio of the number of sampling data whose corresponding output data satisfying a specific condition to the number of the generated pieces of first sampling data (Hirohata, see [0032], if new data set of a predetermined quantity is acquired, by calculating new discretion information, new multidimensional probability distribution is calculated and transmitted to the server. A sampling set of the sampling data is unified with the new data set). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Isezaki in view of Haderbache and Hirohata in order to further modify the method for provided with a projective transform model including a plurality of nodes and a projection table from the teachings of Isezaki with the method of representing a shape of an object with a plurality of nodes from the teachings of Haderbache and the method for monitoring device acquires monitoring variables from an observation target from the teachings of Hirohata.
One of ordinary skill in the art would have been motivated because it would allow to calculates a positioning of the observation target using the sampling data (Hirohata – Abstract).
As to claim 11, Isezaki in view of Haderbache discloses everything disclosed in claim 1, but is silent to wherein the one or more hardware processors is configured to generate the pieces of first sampling data by using the first probability distribution. However, Hirohata discloses wherein the one or more hardware processors is configured to generate the pieces of first sampling data by using the first probability distribution (Hirohata, see [0031-0032], multidimensional distribution generation unit generates a multidimensional probability distribution, wherein the multidimensional probability distribution (individual multidimensional distribution) includes the optimized discretion information, and a probability density or a frequency of data belonging to each division element). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Isezaki in view of Haderbache and Hirohata in order to further modify the method for provided with a projective transform model including a plurality of nodes and a projection table from the teachings of Isezaki with the method of representing a shape of an object with a plurality of nodes from the teachings of Haderbache and the method for monitoring device acquires monitoring variables from an observation target from the teachings of Hirohata.
One of ordinary skill in the art would have been motivated because it would allow to calculates a positioning of the observation target using the sampling data (Hirohata – Abstract).
As to claim 12, Isezaki in view of Haderbache discloses everything disclosed in claim 1, but is silent to wherein the one or more hardware processors is configured to obtain the pieces of output data by receiving the pieces of first sampling data as input. However, Hirohata discloses wherein the one or more hardware processors is configured to obtain the pieces of output data by receiving the pieces of first sampling data as input (Hirohata, see [0023], a first server side generation unit is configured to generate first sampling data by sampling the individual multidimensional distribution collected). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Isezaki in view of Haderbache and Hirohata in order to further modify the method for provided with a projective transform model including a plurality of nodes and a projection table from the teachings of Isezaki with the method of representing a shape of an object with a plurality of nodes from the teachings of Haderbache and the method for monitoring device acquires monitoring variables from an observation target from the teachings of Hirohata.
One of ordinary skill in the art would have been motivated because it would allow to calculates a positioning of the observation target using the sampling data (Hirohata – Abstract).
As to claim 13, Isezaki in view of Haderbache discloses everything disclosed in claim 1. Isezaki discloses wherein the one or more hardware processors includes: a generation circuit to generate the pieces of first sampling data (Isezaki, see [0088], the motion state visualization section generates display data for simultaneously displaying a two-dimensional space coordinate value corresponding to each amplitude characteristic amount data transformed by projecting); an analysis circuit to obtain the pieces of output data (Isezaki, see [0031], data of each myoelectric signal output is acquired from the myoelectric signal measurement device, in order to have a multi-dimensional temporally sequential data. See [0091], the motion state visualization device acquires the multi-dimensional input data of each of a positive example and a negative example and calculates an amplitude characteristic amount from each multi-dimensional input data); and Haderbache further discloses a modeling circuit to generate the network model (Haderbache, see [0044-0045], the simulation system generates the learned model by using the training data).
As to claim 16, Isezaki in view of Haderbache discloses everything disclosed in claim 1. Haderbache further discloses wherein the one or more hardware processors are configured to control the object to be analyzed by using a part of the pieces of input data, which correlates with the first node, as control variables (Haderbache, see [0037], a variable is assigned to each of the plurality of nodes, and based on a governing equation indicating a physical phenomenon, a coefficient matrix indicating a relationship among the plurality of variables is generated. See [0043], a neural network can receive input data in a tensor form including the feature amounts. The input data can be a column vector in which the feature amounts are combined).
As to claim 17, Isezaki in view of Haderbache discloses everything disclosed in claim 1. Haderbache further discloses wherein the one or more hardware processors are configured to control the object to be analyzed by using one of a plurality of control methods depending on a number of the pieces of output data and a number of the pieces of input data (Haderbace, see [0045], the simulation system according to the first embodiment generates the feature amount representing a feature of a positional relationship of nodes from the mesh data through a topological data analysis. [0046], the accuracy of the analysis result is maintained. In addition, from the result of the simulation using the coarse mesh data, the feature amounts for predicting the physical amount are automatically extracted).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TANIA M PENA-SANTANA whose telephone number is (571)270-0627. The examiner can normally be reached Monday - Friday 8am to 4pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas R Taylor can be reached at 5712723889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TANIA M PENA-SANTANA/Examiner, Art Unit 2443
/CHRISTOPHER B ROBINSON/Primary Examiner, Art Unit 2443
Prosecution Insights