DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 12/10/2024 and 03/20/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 recites
“1. A method for training a text combination determining model, the method comprising:
obtaining at least one positive sample set and obtaining at least one negative sample set, a positive sample set of the at least one positive sample set including two texts that cannot be combined, and a negative sample set of the at least one negative sample set including two texts that can be combined; and
training the text combination determining model based on the at least one positive sample set and the at least one negative sample set.”
Claims 1, 10 and 19 recite substantially the same concept but do so in the context of a method, a system and a computer storage medium.
The limitations recited in the independent claims as drafted covers a mental process. More specifically, the underlying abstract idea revolved around what happen once a human takes a positive sample and a negative sample to determine whether two texts can be combine. Claim recites “training the text combination determining model” at high level of generality. There is no technical detail on how the text combination determining model is trained. See MPEP 2106.05(f).
The judicial exception is not integrated into a practical application. In particular, claims recite the additional limitations of “one or more processor”, “one or more storage devices” and “a computer storage medium”. The additional element(s) or combination of elements such as processor, device and/or computer storage medium in the claim(s) other than the abstract idea per se amount(s) to no more than (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device other than obtaining the positive sample set and the negative sample set and indicating that using the positive sample set and the negative sample set in training the text combination determining model. The mere recitation of processor, device and/or computer storage medium and/or the like is akin of adding the word “apply it” and/or “use it” with a computer in conjunction with the abstract idea. The paragraphs [0007-0009] disclose “[0007] According to a fifth aspect, an implementation of the present specification provides a computer storage medium. The computer storage medium stores a plurality of instructions, and the instructions are adapted to be loaded by a processor and to perform steps of the above methods, [0008] According to a sixth aspect, an implementation of the present specification provides a computer program product. The computer program product stores a plurality of instructions, and the instructions are adapted to be loaded by a processor and to perform steps of the above methods, [0009] According to a seventh aspect, an implementation of the present specification provides an electronic device. The electronic device can include a processor and a memory. The memory stores a computer program, and the computer program is adapted to be loaded by the processor and to perform the steps of the above methods.”
As filed in the specification, the computer is listed as a general-purpose computer and are mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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 the integration of the abstract idea into a practical application, the additional element of using a computer is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
The dependent claims further do not remedy the issues noted above. More specifically, Claims 2, 11, 20 recite a mental process of segmenting the text based on a symbol (e.g., punctuation marks). No additional limitations are presented. Claims 3, 12 recites a mental process of considering a character at a middle location of the text as a target character, determining whether the determined symbol on the left or right of the target character and segmenting the text. No additional limitations are presented. Claims 4 and 13 recites a mental process of segmenting the text based on the boundary. No additional limitations are presented. Claims 5 and 14 recites a mental process of converting text to vectors, performing full connection on the vectors and determining whether two text can be combined. No additional limitations are presented. Claims 6 and 15 recites a mental process of connecting vectors. No additional limitations are presented. Claims 7 and 16 recite a mental process of determining a probability that the at least two texts and determining whether two texts can be combined based on the determined probability. No additional limitations are presented. Claims 8 and 17 merely indicates a list of encoder names. There is no technical detail on how one or more these encoders perform. No additional limitations are presented. Claims 9 and 18 recites a mental process of determining whether two texts can be combines. No additional limitations are presented.
For at least the supra provided reasons, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
5. Claims 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 19-20 directed to “A/The computer storage medium…” However, the recitation of the medium in the specification is not exclusory with respect to non-statutory medium types (Specification [0007] According to a fifth aspect, an implementation of the present specification provides a computer storage medium. The computer storage medium stores a plurality of instructions, and the instructions are adapted to be loaded by a processor and to perform steps of the above methods, [0093] An implementation of the present specification further provides a computer storage medium. The computer storage medium can store a plurality of instructions, and the instructions are adapted to be loaded by a processor and to perform the text combination determining method in the implementations shown in FIG. 1 to FIG. 8. For a specific execution process, references can be made to the detailed descriptions in the implementations shown in FIG. 1 to FIG. 8. Details are omitted herein for simplicity, [0110] In this implementation of the present specification, at least one positive sample set and at least one negative sample set are properly constructed. A positive sample set in the at least one positive sample set includes texts that cannot be combined, and a negative sample set in the at least one negative sample set includes texts that can be combined. The text combination determining model can learn, in a self-supervised manner based on the at least one positive sample set and the at least one negative sample set, whether two texts have a relationship of whether the two texts can be combined, until the text combination determining model converges, to improve training efficiency of the text combination determining model. In addition, the text combination determining model is trained for a plurality of rounds based on at least one positive sample and at least one negative sample, so that the text combination determining model whose training is completed has relatively good interference resistance and robustness, and has relatively high accuracy of executing a task of determining whether to combine two texts, to obtain a combined text with complete semantics, and facilitate reading and understanding by a user. A person of ordinary skill in the art can understand that all or some of the procedures of the methods in the above implementations can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium. When the program is executed, the procedures in the method implementations can be included. The storage medium can be a magnetic disk, an optical disc, a read-only storage memory, a random storage memory, etc.) Additionally, variations of the term “storage” are not necessarily considered to limit a media claim to non-transitory embodiments because content may be considered to be stored on a signal during propagation and because many disclosures conflate storage media and signals.
Thus, under the broadest reasonable interpretation, the claim(s) as a whole would include non-statutory mediums such as carrier waves. As per the USPTO notice signed by director David Kappos on 1/26/2010: “The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO.” See In re Zletz, 893 F.2d 319(Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. 101, Aug. 24, 2009; p. 2. The claims as a whole therefore include(s) signal-based mediums. A signal does not fall within one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter) because it is an ephemeral, transient signal and thus is non-statutory. Since the claims as a whole include these non-statutory instances, Claims 19-20 are directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
6. 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.
7. Claims 1, 9-10, 18-19 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by
Yuan et al. (US 2022/0215173 A1.)
With respect to Claim 1, Yuan et al. disclose
A method for training a text combination determining model, the method comprising:
obtaining at least one positive sample set and obtaining at least one negative sample set, a positive sample set of the at least one positive sample set including two texts that cannot be combined (Yuan et al. [0051] describes forming a sample for training data, the meaning of “give” with a sub-entity that is a person is not a logical combination Fig. 4B), and a negative sample set of the at least one negative sample set including two texts that can be combined (Yuan et al. [0050] describes forming a sample for training data, the meaning of “drove to” with a sub-entity that is a location is a logical combination, Fig. 4A); and
training the text combination determining model based on the at least one positive sample set and the at least one negative sample set (Yuan et al. [0017] describes forming a positive sample or a negative sample as training data for classifier, Fig. 2A element 212 In the training phase, construct a classifier that accepts the positive samples and the negative samples as input in the training phase.)
With respect to Claim 9, Yuan et al. disclose
The method according to claim 1, comprising:
obtaining two to-be-detected texts (Yuan et al. [0052] accepting input that includes a candidate sub-entity and a predicate); and
inputting the two to-be-detected texts into the text combination determining model, to obtain a determining result regarding whether the two to-be-detected texts can be combined (Yuan et al. [0052] determine an indicator 446, which indicates whether the combination of the inputted candidate sub-entity and predicate is logical according the classification result.)
With respect to Claim 10, Claim 10 recites the similar features as Claim 1, thus Claim 10 is rejected as the same ground as Claim 1.
With respect to Claim 18, Claim 18 recites the similar features as Claim 9, thus Claim 18 is rejected as the same ground as Claim 9.
With respect to Claim 19, Claims 19 recites the similar features as Claim 1, thus Claim 19 is rejected as the same ground as Claim 1.
Allowable Subject Matter
8. Claims 2-8, 11-17 and 20 are objected 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. The claims stand rejected under 101 Abstract idea, and for the application to pass to allowance this rejection need to be overcome. Any amendments to overcome the rejection that result in any change in scope require further search and/or consideration in order to determine it allowability.
The following is an examiner’s statement of reasons for allowable subject matter: the prior art(s) fail(s) to teach the following element(s) in combination with the other recited elements in the claim(s).
“wherein the obtaining the at least one negative sample set includes:
obtaining at least one to-be-segmented sample text; and separately segmenting a to-be-segmented sample text of the at least one to-be-segmented sample text based on a determined symbol in the to-be-segmented sample text, to obtain at least one negative sample set.” as recited in Claim 2.
Claims 11 and 20 recites similar features as Claim 2.
“wherein the text combination determining model includes a plurality of encoders, at least one fully connected layer, and a determining part;
the plurality of encoders are configured to encode a text, to obtain a plurality of feature vectors corresponding to the text;
the at least one fully connected layer is configured to perform full connection processing on a plurality of feature vectors corresponding to each of at least two texts, to obtain at least one connection result; and
the determining part is configured to determine, based on the at least one connection result, whether the at least two texts can be combined.” as recited in Claim 5.
Claims 14 recites the similar features as Claim 5.
The closest prior arts found as following.
Yuan et al. (US 2022/0215173 A1.) In this reference, Yuan et al. disclose forming a positive sample and negative sample for training the classifier (Yuan et al. [0050] FIG. 4A is an example 400 of forming a positive sample for a training phase in the process of FIGS. 2A-2B, in accordance with embodiments of the present invention. Example 400 includes a sentence 402 (i.e., “I drove to Central Park Tower in Thomas John Watson”). NER improvement system 104 (see FIG. 1) extracts a predicate 404 (i.e., “drove to”) from sentence 402 and uses a candidate sub-entity 406 (i.e., Thomas John Watson) that was provided by the multi-label classification shown in FIG. 3. NER improvement system 104 (see FIG. 1) determines that a combination of the predicate 404 and candidate sub-entity 406 form a positive sample 408 for training data because combining the meaning of “drove to” with a sub-entity that is a location is a logical combination. Positive sample 408 is a combination of “drove to” and the Location type of candidate sub-entity 406), [0051] FIG. 4B is an example 420 of forming a negative sample for a training phase in the process of FIGS. 2A-2B, in accordance with embodiments of the present invention. Example 420 includes a sentence 422 (i.e., “Look, Tomas give me a breakfast”). NER improvement system 104 (see FIG. 1) extracts a predicate 424 (i.e., “give”) from sentence 422 and uses a candidate sub-entity 406 (i.e., Thomas John Watson) in sentence 402. NER improvement system 104 (see FIG. 1) determines that a combination of the predicate 424 and candidate sub-entity 406 form a negative sample 426 for training data because combining the meaning of “give” with a sub-entity that is a person is not a logical combination. Negative sample 426 is a combination of “give” and the Location type of candidate sub-entity 406). Yuan et al. extracts a predicate “drove to” from a sentence and uses a candidate sub-entity “Thomas John Watson” that was provided by the multi-label classification NER to form a positive sample. The positive sample in Yuan et al. is a claimed negative sample. However, Yuan et al. does not teach and/or suggest the forming the negative sample by segmenting a sentence based on the determined symbol in the text as recited in Claim 2. Yuan et al. does not teach and/or suggest a model including a plurality of encoders, at least on fully connected layer and a determining layer to determining whether the two texts can be combined as recited in Claim 5.
Wang (US 2023/0039496 A1.) In this reference, Wang et al. disclose a method and a system for obtaining the semantic similarity between the to-be-answered question and the standard question (Wang et al. [0124] A bidirectional encoder representations from transformers model is used to obtain a text representation vector of the to-be-answered question, a text representation vector of the standard question and interactive information of the text representation vector of the to-be-answered question and the text representation vector of the standard question according to the to-be-answered question and the standard question, [0125] Global max pool is performed on the text representation vector of the to-be-answered question and the text representation vector of the standard question, respectively, and global average pool perform on the text representation vector of the to-be-answered question and the text representation vector of the standard question, respectively, [0126] The interactive information, a difference between a result of the global max pool of the text representation vector of the to-be-answered question and a result of the global max pool of the text representation vector of the standard question and a difference between a result of the global average pool of the text representation vector of the to-be-answered question and a result of the global average pool of the text representation vector of the standard question are input into a full connection layer to obtain the semantic similarity between the to-be-answered question and the standard question.) Wang disclose a bidirectional encoder to encode a text, to obtain a plurality of feature vectors corresponding to the text, a full connection layer. The full connection layer is used to obtain the semantic similarity between the to-be-answered question and the standard question. The full connection layer in Wang is not used to determine whether the two texts can be combined. Wang does not teach and/or suggest the forming the negative sample by segmenting a sentence based on the determined symbol in the text as recited in Claim 2.
c. Kirch et al. (US 2024/0111955 A1.) In this reference, Kirch et al. disclose a method for splitting the input text into multiple rows of sentence fragments based on the punctuation (Kirch et al. [0029] The input is split sentences as determined by the system through punctuation or other means. A sentence can be further split into multiple rows of sentence fragments based on sentence structure and punctuation. It is understood that the use of sentences, for the purpose of this disclosure, can further include and refer to sentence fragments, rather than full sentences, and no limitation is intended. Each sentence is inserted into a row in a two dimensional matrix. After receiving the input matrix, the Neural Capsule Entity Disambiguator 115 uses at least one layer, with each layer consisting of at least one set of filters, and the derived features to identify, classify, and disambiguate the named entities in the text. The output is a three dimensional matrix of dimensions M×N×defined maximum number of named entity classes, as per the preferred embodiment. The Neural Network Layer 120 performs post-processing on the three dimensional matrix. The three dimensional matrix 125 is converted to a final two dimensional output 130, where the dimensions are the defined maximum number of named entity classes x input string length. Alternatively, the three dimensional matrix 125 can be converted to a final two dimensional output 130, where the dimensions are the number of named entity classes identified in the input text by the model x input string length. Each value in the matrix will be a non-zero value if it is a named entity, where the value will be the entity's unique ID number, and its position in the matrix is based on the named entity's location in the string and the cluster to which it belongs. In embodiments where NED is performed without prior or simultaneous performance of NER, a vector can be used, where each value will be a non-zero value if it is a named entity, where each value is the entity's unique ID number, and its position in the vector is based on the named entity's location in the string.) Kirch et al. segments the input text into sentences based on the punctuation. However, Kirch et al. does not teach and/or suggest segmenting the input text into two sentences to obtain negative sample set. Kirch et al. does not teach a full connection layer in determining whether two texts can be combined.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See PTO-892.
a. Song et al. (US 2022/0165269 A1.) In this reference, Song et al. disclose a method and a system for determining whether two sentences can be combined into a complete sentence.
b. Liang et al. (US 2020/0065604 A1.) In this reference, Liang et al. disclose a method and a system for whether a two or more consecutive words or punctuation are to be combined in a single segment.
c. Reiter et al. (US 2016/0217133 A1.) In this reference, Reiter et al. disclose a method and a system for determining whether two or more phrase specifications can be combined together linguistically to produce a more complex sentence.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to THUYKHANH LE whose telephone number is (571)272-6429. The examiner can normally be reached Mon-Fri: 9am-5pm.
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, Andrew C. Flanders can be reached on 571-272-7516. 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.
/THUYKHANH LE/Primary Examiner, Art Unit 2655
Prosecution Insights