DETAILED ACTION
This communication is in response to the Arguments and Remarks filed on 3/15/2024. Claims 1-15 are pending and have been examined. Hence, this Action has been made FINAL.
Any previous objection/rejection not mentioned in this Office Action has been withdrawn by the examiner.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 13, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
With respect to the 35 U.S.C. 101 rejections, the applicant asserts that the amended limitations cannot be practically performed by the human mind.
Examiner respectfully disagrees, the addition of a neural network merely introduces an additional component used for applying a mental process via a computing device. Furthermore, the limitations providing a description of the various neural network matrices represents a standard use neural network rather than further integrating the neural network into the system/method. Specifically, the limitation “to be a weight matrix that is learned” does not distinguish it from a mental process, but rather, provides an intended use for a piece of the neural network. Lastly, “transforming the token sequence and context information vector into a high-dimensional feature vector is capable of being performed by the human mind by simply combining the information in the vectors and including additional information that represents another dimension.
The applicant further asserts that these specific limitations relate to a practical application that improves neural network processing. The specification explains how the present claims "reduce[s] calculation cost in machine learning as compared with a common RNN" because out of the output weight matrix, the input weight matrix, and the connection weight matrix of a neural network, the output weight matrix needs to be learned and "it is not necessary to learn an input weight vector and the context weight matrix."
Examiner respectfully disagrees, even if this would be an improvement to the a common RNN based on the specification, this improvement is not implemented throughout the claim language. It is instead presented after the method is complete as an intended use for all three weight matrices. Furthermore, it is not clear where the output weight matrix comes from within the claim language giving it no specific form or structure. Thus, it cannot be considered the improvement to a technology.
With respect to the 35 U.S.C. 103 rejections, the applicant has amended the independent claims to overcome the current prior art. The second process in the output sequence generation is not present within the current prior art.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 8, and 15 recites the limitation "the output matrix" in the final limitation. There is insufficient antecedent basis for this limitation in the claim. Accordingly, claims 1-20 are rejected und 35 U.S.C. 112.
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 1-15 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8, and 15 recites An information processing apparatus comprising at least one [processor], the at least one processor carrying out: an acquisition process for acquiring a token sequence obtained from a medical sentence in an electronic medical record and a context information vector obtained from context information of the electronic medical record; and an output sequence generation process for generating an output sequence, using a [neural network], from the token sequence and the context information vector, the output sequence generation process comprising: a process for transforming the token sequence and the context information vector into a high-dimensional feature vector that has a higher dimension than a sum of a dimension of the token sequence and a dimension of the context information vector, and another process for transforming the high-dimensional feature vector into a lower- dimensional output vector using an output weight matrix obtained by pre-training, wherein the neural network comprises an input weight matrix, and a connection weight matrix, and the output weight matrix, and wherein the output weight matrix is a matrix among the output matrix, the input weight matrix, and the connection weight matrix, to be a weight matrix that is learned.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. A human mind is capable of looking at a medical document and tokenizing phrases. In this case, tokenizing could just mean grouping certain words together or assigning numbers to words. A human can also extract textual information from medical document by reading any additional information such as the authors name or occupation. This context could be converted to a vector by a human writing the information down in a format such as <author:John Smith>. Then a human could transform these into a higher dimension representation by combining the tokens, vectors, and a new piece of information. For example, <author:john smith:word token:assertion value> which would have more dimensions than the sum of the token and context vector. A human is then capable of converting this to lower dimension as an output format by, for example, converting it into a sentence that may only contain one dimension. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims list the additional component of a neural network. The neural network is merely being used to apply a mental process via a computing device. The neural network is described as an echo state network with a standard structure in paragraphs 43-44 of the specification. Claim 1 lists the addition component of a processor. The processor is detailed in paragraph 150 of the specification and is described as a generic computer component. Claim 15 lists the addition component of a non-transitory storage medium. The non-transitory storage medium is detailed in paragraph 153 of the specification and is described as a generic computer component. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
As to claims 8 and 15, apparatus claims 1 and 15 and method claim 8 are related as apparatus and the method of using same, with each claimed element's step corresponding to the claimed apparatus function. Accordingly claim 8 and 15 are similarly rejected under the same rationale as applied above with respect to apparatus claim 1.
Claims 2 and 9 recites wherein the acquisition process includes a token sequence generation process for generating the token sequence by transforming each word contained in the medical sentence into a token by embedding the each word in a first vector space defined in advance, and a context information vector generation process for generating the context information vector by extracting predetermined context information from the electronic medical record and embedding the predetermined context information in a second vector space defined in advance.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. A human is capable of writing all the tokens into a vector space by making some sort of matrix representation of all the tokens from the text. The embedding aspect of this could be converting each word token into a number using a system like ASCII or their own method. A vector space for the context information vectors could be made in the same way with the hand written matrix. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not add any addition components that were not present in the dependent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 3 and 10 recites wherein in the output sequence generation process, the input vector is transformed into the high-dimensional feature vector by multiplying the input vector by a predetermined connection weight matrix, the input vector including a product of a predetermined input weight matrix and the token and a product of a predetermined context weight matrix and the context information vector.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers a mathematical concept being performed by generic computer components. The steps in these claims are all mathematical equations involving matrix multiplication. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships or equations but being performed by generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not add any addition components that were not present in the dependent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 4 and 11 recites wherein in the output sequence generation process, the output sequence is further generated by multiplying the high-dimensional feature vector by the output weight matrix obtained by the pre-training.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers a mathematical concept being performed by generic computer components. This claim also contains the mathematical equations of matrix multiplication with the only added component of pre-training. As there is no further context given to the pre-training this is comparable to using a matrix that is part of a known formula as part of the matrix multiplication. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships or equations but being performed by generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not add any addition components that were not present in the dependent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 5 and 12 recites wherein the at least one processor further carries out a learning process for training, through machine learning, the output weight matrix with reference to training data including a plurality of sets of (i) the medical sentence and the context information and (ii) a positive or negative label regarding a given word contained in the medical sentence.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This step is pertaining to the design decision to use a certain type of data when training a type of model or neural network. The human mind is capable of making such design decisions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not add any addition components that were not present in the dependent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 6 and 13 recites wherein the context information is information indicative of a job title of a person who has written the medical sentence.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of interpreting context such as a job title. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not add any addition components that were not present in the dependent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 7 and 14 recites wherein the context information is a name of a field in which the medical sentence is recorded in the electronic medical record.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of interpreting context such as the medical field a medical record came from. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not add any addition components that were not present in the dependent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Allowable Subject Matter
Claims 1, 8, and 15 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, and the rejection(s) under 35 U.S.C. 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The closest prior art of record for claims 1-15 are US Patent Publication US 11809826 B2 (Ambati et al.) in view of “Towards Automated Detection of Contradictory Research Claims in Medical Literature Using Deep Learning Approach” (Yazi et al.).
Ambati et al. teaches An information processing apparatus comprising at least one processor, the at least one processor carrying out: (Col. 2, Lines 20-21)
Claim 15 specifically lists the alternative limitation of A non-transitory storage medium having a program stored therein, the program causing a computer to function as an information processing apparatus, the program causing the computer to carry out: (Col. 13, Lines 24-27).
an acquisition process for acquiring a token sequence obtained from a medical sentence in an electronic medical record and a context information vector obtained from context information of the electronic medical record; (Col. 5, Lines 5-15) (Col. 5, Lines 51-54).
and an output sequence generation process for generating an output sequence, using a neural network, from the token sequence and the context information vector, the output sequence generation process comprising: a process for transforming a token sequence and the context information vector into a high-dimensional feature vector that has a higher dimension than a sum of a dimension of the token sequence and a dimension of the context information vector. (Col. 8, Lines 9-26) (Col. 12, Lines 53-55)
Ambati et al. does not explicitly teach a context information vector obtained from context information of the electronic medical record; and another process for transforming the high-dimensional feature vector into a lower- dimensional output vector using an output weight matrix obtained by pre-training, wherein the neural network comprises an input weight matrix, and a connection weight matrix, and the output weight matrix, and wherein the output weight matrix is a matrix among the output matrix, the input weight matrix, and the connection weight matrix, to be a weight matrix that is learned.
However, Yazi et al. teaches a context information vector obtained from context information of the electronic medical record; (Section 3B, Paragraph 2)
Ambati et al. and Yazi et al. does not explicitly teach and another process for transforming the high-dimensional feature vector into a lower- dimensional output vector using an output weight matrix obtained by pre-training, wherein the neural network comprises an input weight matrix, and a connection weight matrix, and the output weight matrix, and wherein the output weight matrix is a matrix among the output matrix, the input weight matrix, and the connection weight matrix, to be a weight matrix that is learned.
However, none of the prior at, either alone or in combination, overcomes the limitations as presented in claims 1, 8, and 15 and the other dependent claims.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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 NICHOLAS DANIEL LOWEN whose telephone number is (571)272-5828. The examiner can normally be reached Mon-Fri 8:00am - 4:00pm.
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, Paras D Shah can be reached at (571) 270-1650. 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.
/NICHOLAS D LOWEN/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
06/24/2026
Prosecution Insights