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 .
Priority
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
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.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because it recites a computer program per se. MPEP 2106.03(I) states “a product claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category.” To narrow the claim to those embodiments that fall within a statutory category, the claim can be amended to “non-transitory computer-readable storage medium” described in [0040] of the specification.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-9 are directed to a bio-signal analysis method (process).
Claim 10 is directed to a computer program stored in a computer-readable storage medium (non-statutory, see above) and therefore directed to a non-statutory subject matter as set forth above. However, claim 10, if amended to claim those embodiments that fall within a statutory category (e.g., “non-transitory computer-readable storage medium”), claim 10 is further ineligible for the reasons provided below.
Claim 11 is directed to a computing device analyzing a bio-signal (machine).
Step 2A, Prong One
Regarding claims 1 and 10-11, the recited steps are directed to a mental process of performing concepts in a human mind or by a human using a pen and paper. See MPEP § 2106.04(a)(2)(Ill). The limitation(s) of “acquiring at least one lead-wise bio-signal from a plurality of leads; and deriving an analysis value…wherein the analysis value is derived by reflecting a correlation between the plurality of leads based on the input at least one lead-wise bio-signal regardless of a combination of lead- wise bio-signals which are enabled to be acquired from the plurality of leads” (claim 1); “an operation of acquiring at least one lead-wise bio-signal from a plurality of leads; and an operation of deriving an analysis value…wherein the analysis value is derived by reflecting a correlation between the plurality of leads based on the input at least one lead-wise bio-signal regardless of a combination of lead- wise bio-signals which are enabled to be acquired from the plurality of leads” (claim 10); and “acquiring at least one lead-wise bio-signal from a plurality of leads, wherein the processor derives an analysis value… and wherein the analysis value is derived by reflecting a correlation between the plurality of leads based on the input at least one lead-wise bio-signal regardless of a combination of lead- wise bio-signals which are enabled to be acquired from the plurality of leads” (claim 11) is/are a process that, as drafted, covers performance of the limitation by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard interpretation except for the recitations of “processor”, “computer program”, “memory”, and “network unit”. The recitations of “processor”, “computer program”, “memory”, and “network unit” is nothing more than parts of a generic computer (see [0036] of the specification). The limitation of “leads” is associated with bio-signal being provided but the actual acquisition of bio-signals bis being done by the “processor”, “computer program”, “memory”, and “network unit”, which are parts of a generic computer. That is, other than reciting that “processor”, “computer program”, “memory”, and “network unit” (nothing more than a generic computer) are performing these tasks, nothing in the claim precludes the steps from practically being performed in the human. For example, these limitations are nothing more than performing correlation analysis of electrocardiogram data for diagnosing cardiovascular/heart conditions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, claims 1 and 10-11 also recite “leads”, “at least one processor”, “a neural network model”, “a processor including least one core”, “a memory including program codes” and “a network unit”. The processor(s), memory and network unit are recited at a high-level of generality and amount to nothing more than parts of a generic computer. The neural network model generally could be applied to a piece of software rather than a physical structure. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “leads”, “at least one processor”, “a neural network model”, “a processor including least one core”, “a memory including program codes” and “a network unit” amount to nothing more than mere pre-solution activity of data gathering, which does not amount to an inventive concept. The additional elements recited above are well known in the field of medical leads and generic computer parts. Moreover, simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, is discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984. See MPEP § 2106.05(d).
Regarding dependent claims 2-9, the limitations of claim 1 further defines the limitations already indicated as being directed to the abstract idea.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4 and 9-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Anderson et al (U.S. 10,517,540). Anderson discloses (col. 8, line 5-col. 9, line 41) acquiring at least one lead-wise bio-signal from a plurality of leads (“The implantable components (102) may include one or more leads or sensors (106) that are configured to be placed at various brain or skull regions, an onboard processor (108), and a communication interface (110) for transmitting and receiving signals to and from the external components (104)”); and deriving an analysis value by inputting the acquired at least one lead-wise bio-signal into a neural network model (col. 18, line 65-col.19, line 41, especially “The neural activity data may be transmitted to an external processor and configured to train a convolutional neural network (CNN) to generate a 32×4 transformation matrix such that the 4 data channels of the output or reduced data channel vector contain a larger proportion of beta band neural signal data as compared to neural signal data in other frequency bands.”), wherein the analysis value is derived by reflecting a correlation between the plurality of leads based on the input at least one lead-wise bio-signal regardless of a combination of lead- wise bio-signals which are enabled to be acquired from the plurality of leads (col. 15, lines 5-67, especially “A transformation matrix TFM may be generated using acquired neural signal data, so that data channels of an input data channel vector corresponding to sensors with valid (and/or usable or relevant data) neural signal data are assigned a non-zero matrix value for one or more output data channels, and data channels corresponding to sensors with invalid (and/or unusable or irrelevant data) are assigned a zero matrix value. Non-zero matrix values may be selected and/or generated and/or identified using iterative methods that iterate through various combinations of matrix values until certain constraints and/or output data channel vector characteristics are met.”).
Regarding claim 2, Anderson discloses (col. 18, line 65-col.19, line 41) extracting a feature for the acquired at least one lead-wise bio-signal by using the neural network model (“The CNN may also calculate matrix values such that the resultant TFM adheres to one or more of the constraints described above. In some variations, a CNN may be trained over a specified length of time (e.g. 120 minutes) using input neural signal data and the desired target features, such as a matrix specifying the desired power spectra of the output signals. The generated TFM may be used to reduce the 32 data channels down to 4 data channels that contain a higher proportion of neural signal data in the beta band. Optionally, if other types of neural signal data are of interest (e.g., neural signal data in other frequency bands, other brain regions, etc.), the external processor may use the CNN with a different set of constraints and training data to calculate a different TFM to tailor the information content of the reduced data channel vector to contain a greater proportion of the neural signal data of interest.”), encoding positional information of a lead in which the bio-signal is acquired to the extracted feature by using the neural network model (col. 16, lines 1-10), and deriving the analysis value to which the correlation between the plurality of leads is reflected based on the feature encoded with the positional information of the lead by using the neural network model (col. 18, line 65-col. 19, line 41, especially “if other types of neural signal data are of interest (e.g., neural signal data in other frequency bands, other brain regions, etc.), the external processor may use the CNN with a different set of constraints and training data to calculate a different TFM to tailor the information content of the reduced data channel vector to contain a greater proportion of the neural signal data of interest.”).
Regarding claim 3, Anderson discloses (col. 2, line 34-col. 3, line 36) performing a self-attention based computation for reflecting the correlation between the plurality of leads based on the feature encoded with the positional information of the lead by using the neural network model, and deriving the analysis value based on a result of the self-attention based computation by using the neural network model (“Re-referencing may comprise redefining the values of each vector element to an electric potential difference between its associated electrode and a different point, or combination of points. This re-referencing may be accomplished by setting appropriate weights on the TFM before multiplying it with the input signals.”).
Regarding claim 4, Anderson discloses (col. 2, line 34-col. 3, line 36) generating a matrix for representing the correlation between the plurality of leads based on the feature encoded with the positional information of the lead by using the neural network model, and deriving the result of the self-attention based computation based on the matrix by using the neural network model (“the array of RLMs may be configured for systolic matrix multiplication of the TFM and a neural data vector (e.g., a data channel vector), in combination with sequential channel sampling, which may help facilitate the rapid processing of data acquired by a high-signal-count implantable device”).
Regarding claim 9, Anderson discloses col. 8, line 5-col. 9, line 41) the neural network model is pre-trained by randomly masking the matrix for representing the correlation between the plurality of leads and generated based on lead-wise bio-signals acquired in all of the plurality of leads (“the local processor may comprise an array of individually addressable logic blocks (which may each include, for example, a multiplication circuit and an additional circuit), such as random-logic macros or RLMs, that may be configured to suppress and/or remove noise, neural signal information that is not relevant to a neural characteristic of interest, consolidate or otherwise aggregate neural signal data that may be redundant or correlated, and/or amplify or boost neural signal information pertaining to a neural characteristic of interest.”).
Regarding claim 10, Anderson discloses (col. 8, line 5-col. 9, line 41) a computer program stored in a computer-readable storage medium (“The processor (108) may be configured to run and/or execute application processes and/or other modules, processes and/or functions associated with the system and/or a network associated therewith (not shown), and may comprise one or more memory elements.”), wherein the computer program executes the following operations for analyzing a bio-signal when the computer program is executed by one or more processors, the operations comprising: an operation of acquiring at least one lead-wise bio-signal from a plurality of leads (“The implantable components (102) may include one or more leads or sensors (106) that are configured to be placed at various brain or skull regions, an onboard processor (108), and a communication interface (110) for transmitting and receiving signals to and from the external components (104)”); and an operation of deriving an analysis value by inputting the acquired at least one lead-wise bio-signal into a neural network model (col. 18, line 65-col.19, line 41, especially “The neural activity data may be transmitted to an external processor and configured to train a convolutional neural network (CNN) to generate a 32×4 transformation matrix such that the 4 data channels of the output or reduced data channel vector contain a larger proportion of beta band neural signal data as compared to neural signal data in other frequency bands.”), wherein the analysis value is derived by reflecting a correlation between the plurality of leads based on the input at least one lead-wise bio-signal regardless of a combination of lead- wise bio-signals which are enabled to be acquired from the plurality of leads (col. 15, lines 5-67, especially “A transformation matrix TFM may be generated using acquired neural signal data, so that data channels of an input data channel vector corresponding to sensors with valid (and/or usable or relevant data) neural signal data are assigned a non-zero matrix value for one or more output data channels, and data channels corresponding to sensors with invalid (and/or unusable or irrelevant data) are assigned a zero matrix value. Non-zero matrix values may be selected and/or generated and/or identified using iterative methods that iterate through various combinations of matrix values until certain constraints and/or output data channel vector characteristics are met.”).
Regarding claim 11, Anderson discloses (col. 8, line 5-col. 9, line 41) a processor including at least one core (“The processor (108) may be configured to run and/or execute application processes and/or other modules, processes and/or functions associated with the system and/or a network associated therewith (not shown), and may comprise one or more memory elements.”); a memory including program codes executable in the processor (see above citation); and a network unit acquiring at least one lead-wise bio-signal from a plurality of leads (“The implantable components (102) may include one or more leads or sensors (106) that are configured to be placed at various brain or skull regions, an onboard processor (108), and a communication interface (110) for transmitting and receiving signals to and from the external components (104)”), wherein the processor derives an analysis value by inputting the acquired at least one lead-wise bio-signal into a neural network model (col. 18, line 65-col.19, line 41, especially “The neural activity data may be transmitted to an external processor and configured to train a convolutional neural network (CNN) to generate a 32×4 transformation matrix such that the 4 data channels of the output or reduced data channel vector contain a larger proportion of beta band neural signal data as compared to neural signal data in other frequency bands.”), and wherein the analysis value is derived by reflecting a correlation between the plurality of leads based on the input at least one lead-wise bio-signal regardless of a combination of lead- wise bio-signals which are enabled to be acquired from the plurality of leads (col. 15, lines 5-67, especially “A transformation matrix TFM may be generated using acquired neural signal data, so that data channels of an input data channel vector corresponding to sensors with valid (and/or usable or relevant data) neural signal data are assigned a non-zero matrix value for one or more output data channels, and data channels corresponding to sensors with invalid (and/or unusable or irrelevant data) are assigned a zero matrix value. Non-zero matrix values may be selected and/or generated and/or identified using iterative methods that iterate through various combinations of matrix values until certain constraints and/or output data channel vector characteristics are met.”).
Claim Rejections - 35 USC § 103
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.
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 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Anderson et al (U.S. 10,517,540) in view of Dalli et al (U.S. 2022/0198254). Anderson discloses the claimed invention except for generating a query vector, a key vector, and a value vector based on the feature encoded with the positional information of the lead by using the neural network model, and generating a multi-head matrix based on the query vector and the key vector by using the neural network model. Dalli, however, discloses (par. 0067) a machine learning model for use in biological data gathering (par. 0212) that includes generating a multi-head matrix based on query vector, value vector and key vector (par. 0255). Dalli and Anderson both disclose methods of analyzing biological data using neural networks and other machine learning algorithms. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Anderson’s lead system with Dalli’s multi-head matrix in order to more efficiently train the algorithm (par. 0004 of Dalli “Transformers require significantly less time to train than other architectures, such as LSTM architectures and CNN architectures, due to parallelization of its components, such as computing the queries, keys, and values simultaneously.”).
Regarding claim 6, Dalli discloses (par. 0085) deriving a weighted sum of the value vector based on the multi-head matrix by using the neural network model.
Regarding claim 7, Anderson discloses (col. 16, line 11-col. 17, line 29) masking a matrix value corresponding to a lead in which the bio-signal is not acquired in the multi-head matrix (“The external component may evaluate the TFM-transformed neural signal data to evaluate whether it contains neural signal data or information of interest, has noise levels or a signal-to-noise ratio within acceptable tolerance ranges, combines data channels with redundant signal data, and/or suppresses data channels with invalid or irrelevant signal data.”).
Conclusion
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/DEBORAH L MALAMUD/Primary Examiner, Art Unit 3792