DETAILED ACTION
This communication is in response to the Application filed on 12/06/2024. Claims 1-10 are pending and have been examined.
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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/06/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 3 is objected to because of the following informalities: in S3-2, “vectoring” should be “vectorizing”. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: “BiLSTM” and “CRF” need to be expanded from their short form. Appropriate correction is required.
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.
Claim 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites the limitation of “same structure”. There is no antecedent basis for “same structure”. Further, it is unclear what is meant by “the same structure” such as in reference to what since per claim 5, there are multiple layers and modules and it appears the Applicant is referencing to the entirety of claim 5’s models but using different data (see Figure 1) . Therefore, this should be explicitly claimed to resolve the lack of clarity.
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 is rejected under 35 U.S.C. 101 because the claims appear to be directed to a software embodiment and not to hardware embodiment, where a machine claim is directed towards a system, apparatus, or arrangement. The claim appears to be directed towards a software embodiment/data structure. The claim describes the elements of the system as comprising “layers” which are often related to a model. There is no structure claimed in the entire claim 1. Therefore, a “layer” is not a structural component. The claimed limitations are capable of being performed as software as described in the above paragraphs, alone since no hardware component is being claimed. Software, alone, are not physical components and thus are not statutory since software do not define any structural and functional interrelationships between the computer programs and other claimed elements of a computer, which permit the computer’s program functionality to be realized. Hence, the stated functions comprise software and is thus not directed to a hardware embodiment. Data structures not claimed as embodied in computer readable media are descriptive material per se and are not statutory because they are not capable of causing functional change in the computer. See e.g., Warmerdam, 33 F.3d at 1361, 31, USPQ2d at 1760 (claim to a data structure per se held nonstatutory). Such claimed data structures do not define any structural and functional interrelationships between data and other claimed aspects of the invention, which permit the data structure’s functionality to be realized. In contrast, a claimed computer readable medium encoded with a data structure defines structural and functional interrelationships between the data structure and the computer software and hardware components which permit the data structure’s functionality to be realized, and is thus statutory.
Claims 1-6 and 8-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 2, and 10 relate to a system (claim 1), method and system where claims 2 and 10 relate to a statutory category. The claims further recite per claim 1 “a word embedding layer, a class feature static fusion layer, and a binary dynamic model, wherein the word embedding layer is used for converting inputted texts into word vectors; the class feature static fusion layer is used for dividing the inputted texts into three classes of medicinal materials, prescriptions and diagnosis and treatment methods, and fusing the word vectors with the corresponding classes to obtain static fusion features; and the binary dynamic model is used for acquiring dynamic features according to the static fusion features, fusing the dynamic features with the static fusion features to obtain overall fusion features, and constructing a final predicted tag sequence according to the overall fusion features”, claim 2 recites “S1, acquiring text samples related to traditional Tibetan medicines, as training samples; S2, converting the training samples into word vectors recorded as (bs, seq_len, dim1), wherein bs is a batch size, seq_len is a sentence length, and dim1 is a word vector feature dimension; S3, classifying the training samples, and fusing a classifying result with the word vectors to obtain static fusion features; S4, constructing a binary dynamic model, and feeding the static fusion features into the binary dynamic model to obtain a final predicted tag sequence; S5, calculating a loss value of the binary dynamic model, and updating parameters to obtain an updated binary dynamic model; and S6, performing joint extraction of an entity relationship using the updated binary dynamic model” and claim 10 incorporates claim 2.
The limitation of claims 1 of “embedding layer…”, “class feature static fusion layer…”, “binary dynamic model...”, as drafted covers mental activities. More specifically, a human receiving Tibetan medicinal information and converting these text into numeric form based on mappings. Then, segmenting these texts into various classes by classifying each text as medicinal, prescription or diagnosis/treatment. Then combining the classification with dynamic features from new input text to predict a tag.
Further, with respect to claim 2, the limitations of “acquiring…”, “converting…”, “classifying…”, “constructing…”, “calculating…”, and “performing…” as drafted covers mental activities. More specifically, a human receiving. More specifically, a human receiving Tibetan medicinal information for training. Then, converting these text into numeric form based on the mappings. Then classifying each training example and merging the classification with the word vector created. Then, determining a mapping model to create a classifier which uses the fused information so that when new text is provided determination of a tag can be made based on learning. Based on predicting the model accuracy based on computing a difference between the prediction and expected, updating the mappings of the model and performing extraction and relationship using the updated mappings.
This judicial exception is not integrated into a practical application. In particular, claims 1 and 2 recites no additional limitations. Claim 10 recites a general purpose processor and memory which is only being used as a tool on which the abstract idea is implemented. Paragraph [0047], of the specification as filed appears to provide a generic mention of “processor. Therefore, the Examiner construes these additional elements are general purpose elements. Thus, these additional elements do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception since the additional elements as recited above are written at a high level of generality which are well known, understood and conventional. The claims are not patent eligible.
With respect to claim 3, the claims relate to “wherein the step S3 comprises the following steps: S3-1, classifying the training samples into three classes of medicinal materials, prescriptions and the diagnosis and treatment methods, to obtain a classifying result as class features of the training samples; S3-2, vectoring each data in the training samples according to the class features of the training samples to obtain vectored sample data recorded as (bs2, seq_len2, dim2), wherein bs2 is a batch size of the vectored sample data, seq_len2 is a sentence length of the vectored sample data, and dim2 is a class feature dimension of the vectored sample data; and S3-3, fusing the vectored sample data with the word vectors to obtain static fusion features recorded as (bs3, seq_len3, dim1+dim2), wherein bs3 is a batch size of the static fusion features, seq_len3 is a sentence length of the static fusion features, and dim1+dim2 is a fusion feature dimension of the static fusion features.” This relates to a human using classifying the training examples into either medicinal material, prescription, and diagnosis/treatment method. Then, vectorizing by converting these training samples and classifications into numeric form. Further, once this is completed combining the numeric form to word vectors. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 4, the claim relates to “wherein the step S4 comprises the following steps: S4-1, feeding the static fusion features into a static feature learning module to obtain a predicted tag sequence; S4-2, feeding the predicted tag sequence and the static fusion features into a multi-feature dynamic fusion layer to obtain overall fusion features; and S4-3, feeding the overall fusion features into a dynamic feature learning module to obtain a final predicted tag sequence.” This relates to a human predicting the tag sequence and features collectively interpreted as fusion features based on the static fusion features from claim 3, then determining overall fusion features based on the predicted tag and the prior static fusion features, and finally determining the final predicted tag sequence based on the overall fusion features. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 5, the claim relates to “wherein the step S4-1 comprises the following steps: S4-1-1, feeding the static fusion features into a BiLSTM encoding layer to obtain encoded static fusion features; S4-1-2, processing the encoded static fusion features by using a Dropout function, to obtain processed static fusion features; S4-1-3, mapping dimensions of the processed static fusion features to tag dimensions by using a linear classifying layer, to obtain mapped static fusion features; S4-1-4, calculating a global optimal tag through a reward and punishment mechanism layer according to the mapped static fusion features; S4-1-5, inputting a global optimal path into a TagScorel layer, to acquire constraint tags; and S4-1-6, inputting the constraint tags into a CRF decoding layer, to obtain the predicted tag sequence.” The claim reads on a human converting static fusion features to obtain encoded features via model. Performing filtering on the encoded features followed by determining number of dimension of the processed static fusion features to tag dimensions to determine a match. Then performing a mathematical computation to determine the best tag, and determining an optimal path to then determine the predicted tag sequence. The claim comprises additional limitation of a BiLSTM layer but this layer is well known in the encoding art and is only being used as a tool for which the encoding is being operated on. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception as para 0064, 0070 provides a general inference to the model.
With respect to claim 6, the claim relates to “wherein the dynamic feature learning module and the static feature learning module in the step S4-3 have the same structure.” This related to a designed determining how to configure both modules. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 8, the claim relates to “wherein the step S4-2 comprises the following steps: S4-2-1, feeding the predicted tag sequence into a word segmentation information extractor, and constructing word segmentation information for an entity according to predicted tags, wherein the word segmentation information at a start position of the entity is labeled as 1, the word segmentation information at an end position of the entity is labeled as 3, the word segmentation information at a middle position of the entity is labeled as 2, and the word segmentation information of a non-entity is labeled as 0; S4-2-2, feeding the predicted tag sequence into a position information extractor, and constructing position information for the entity according to tags; S4-2-3, fusing the word segmentation information with the position information, to obtain dynamic fusion features; and S4-2-4, fusing the dynamic fusion features with the static fusion features to obtain the overall fusion features.” This relates to a human segmenting the predicted tag sequence by determining a start, middle and end and also determining the position of entity based on the predicted tag and combining the segmentation and position information to determine the dynamic features and combining with static features. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 9, the claim relates to “wherein the loss value of the binary dynamic model is calculated in the step S5 by using a loss value between the predicted tag sequence of the static feature learning module and the final predicted tag sequence of the dynamic feature learning module.” This relates to mathematical computation. No additional elements are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Miao (CN 115618005A).
As to claim 1, Miao teaches a joint extraction system for an entity relationship in the field of traditional Tibetan medicines, comprising a word embedding layer (see page 5, where embedding performed), a class feature static fusion layer (see page 4, last paragraph, knowledge graph model), and a binary dynamic model (see page 5,.s5, entity relation joint extraction model)
wherein the word embedding layer is used for converting inputted texts into word vectors (see page 5, S51, where sentence is embedded into vectors);
the class feature static fusion layer is used for dividing the inputted texts into three classes of medicinal materials, prescriptions and diagnosis and treatment methods, and fusing the word vectors with the corresponding classes to obtain static fusion features (see page 4, last paragraph, where different classes of medicinal materials, prescription, and treatment is provided within the graph model and page 5, S61, where a single vector based on triplet is determined); and
the binary dynamic model is used for acquiring dynamic features according to the static fusion features, fusing the dynamic features with the static fusion features to obtain overall fusion features, and constructing a final predicted tag sequence according to the overall fusion features (see page 5, S5. Where the entity relation joint extraction model adds small disturbance to vector representation of the original sample to obtain a confrontation sample and S64 where effective triplet determined).
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.
Claim(s) 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Miao in view of Liu (“Joint Model of Entity Recognition and Relation Extraction with Self-attention mechanism”) in view of Zhao (“Joint Entity Relation Extraction of Chinese Electronic Medical Record Based on Graph Convolutional Neural Network and Words for Relationship Discovery”).
As to claim 2, Miao teaches a joint extraction method for an entity relationship in the field of traditional Tibetan medicines, applied to the joint extraction system for an entity relationship in the field of traditional Tibetan medicine according to claim 1 (see claim 1 rejection), comprising the following steps:
S1, acquiring text samples related to traditional Tibetan medicines, as training samples (see bottom page 4, S1-S3, where graph model is divided into different classes and information is extracted from database, website, dictionary);
S2, converting the training samples into word vectors recorded as ( seq_len, dim1), seq_len is a sentence length, and dim1 is a word vector feature dimension (see page 5, S51, where input sentence is embedded according to word using word2vec or BERT and where dimension can be adjusted based on actual data quantity);
S3, classifying the training samples, and fusing a classifying result with the word vectors to obtain static fusion features (see page 4, last paragraph, where different classes of medicinal materials, prescription, and treatment is provided within the graph model and page 5, S61, where a single vector based on triplet is determined);
S4, constructing a binary dynamic model, and feeding the static fusion features into the binary dynamic model to obtain a final predicted tag sequence (see page 5, S5, where the entity relation joint extraction model is trained via countermeasure training and where the entity relation joint extraction model adds small disturbance to vector representation of the original sample to obtain a confrontation sample and S64 where effective triplet determined);
S5, calculating a loss value of the binary dynamic model (see page 5, S5, where cross entropy loss function is used),
However, Miao does not specifically teach wherein bs is a batch size, seq_len is a sentence length, and updating parameters to obtain an updated binary dynamic model; and S6, performing joint extraction of an entity relationship using the updated binary dynamic model.
Liu does teach wherein seq_len is a sentence length (see page 59:9, sect. 4.4, 1st paragraph, where l is the length of the input w), and updating parameters to obtain an updated binary dynamic model (see page 10, sect 4.5, eqn 12 and last two paragraphs, where loss is described and where hyperparameters are determined to find optimal performance and see page 59:11, last paragraph, where parameters are optimized); and S6, performing joint extraction of an entity relationship using the updated binary dynamic model (see page 59:7, where output is described of the model of recognized entity label and set of tuples comprising the head tokens of the entity and types of relations between them).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed inventions to have modified the Tibetan medicine graph as taught by Miao with the updating of the model as taught by Liu in order to extract medical entities and relations simultaneously (see Liu sect 6, 1st para).
However, Miao in view of Liu do not specifically teach wherein bs is a batch size.
Zhao teaches wherein bs is a batch size (see page 6, Table 1).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed inventions to have modified the Tibetan medicine graph as taught by Miao in view of Liu with the inclusion of batch size as taught by Zhao in order to aide in selecting optimal parameters for the model and to thereby help in enhancing text semantic by constructing a word set for relation discovery which can reduce noise (See Zhao page 7, left column, 1st full paragraph).
As to claim 10, Miao in view of Liu in view of Zhao teach all of the limitations as in claim 2. Furthermore, Miao teaches a memory, being used for storing one or more programs (see ; and a processor, wherein when the one or more programs are executed by the processor, the method according to claim 2 is realized (the usage of a processor and memory is inherent in order to perform the functions noted in Figures 2+3 which require AI models).
Allowable Subject Matter
Claim 7 is 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.
Claims 3-6, 8-9 would be allowable if rewritten to overcome 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: None of the cited prior art alone or in combination teaches the combination of limitations as recited in claim 3.
More specifically with respect to claim 3, the closest prior art of record teaches Miao teaches the inclusion of medicinal materials (see bottom of page 4)Furthermore, Liu teaches classifying the training samples into three classes of medicinal materials, prescriptions and the diagnosis and treatment methods, to obtain a classifying result as class features of the training samples (see page 59:11, 1st paragraph, where 80% of the medicine instructions are used for training and classified as shown in Table 3 and 4, with respect to treatment, medicine, and cure/inhibition among others).
However, However, none of the prior art of record teach “S3-2, vectoring each data in the training samples according to the class features of the training samples to obtain vectored sample data recorded as (bs2, seq_len2, dim2), wherein bs2 is a batch size of the vectored sample data, seq_len2 is a sentence length of the vectored sample data, and dim2 is a class feature dimension of the vectored sample data; and S3-3, fusing the vectored sample data with the word vectors to obtain static fusion features recorded as (bs3, seq_len3, dim1+dim2), wherein bs3 is a batch size of the static fusion features, seq_len3 is a sentence length of the static fusion features, and dim1+dim2 is a fusion feature dimension of the static fusion features”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang (CN 111274397A) is cited to disclose extraction relationships between entities (see abstract). Feng (CN 113723103A) is cited to disclose locating entities using POS and NER tasks in Chinese medicine. Xu (CN 114036934A) is cited to disclose a joint extraction model for extracting entity relations between entities (see Figure 1).
Luo1st et al. (“Joint extraction method of entity relationship in Chinese Medicine based on Data Augmentation and Deep Learning”) is cited to disclose use of BILSTM in extracting entity relationship in Chinese medicine (see abstract, Figure 4). Wang et al. (Joint Extraction of Entities and Relations from Ancient Chinese Medical Literature) is cited to disclose joint model that uses NER and RE (see Figure 1) for medical entity extraction.
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/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
06/03/2026
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