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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN202311163712.0, filed on 9/8/2023.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated below. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
an obtaining module in claims 8
a construction module in claim 8
a determining module in claims 8
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter without significantly more. The claims as whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
Independent claims 1, 8 and 15 recite “calculating a distance between every two ones of texts, to obtain a text set, wherein the text set comprises every two ones of texts and the distance between every two ones of texts; constructing a text relation map based on the text set, wherein the text relation map comprises nodes and connection edges, each of the nodes corresponds to a corresponding one of the texts, at least two ones of the nodes are connected via the connection edge, and a weight of each of the connection edges corresponds to the distance between two of the texts corresponding to two of the nodes connected through the connected edge; and in response to the distance between two ones of the texts greater than a preset threshold value, determining a text similarity between the two ones of the texts according to a path between two of the nodes corresponding to the two ones of the texts, wherein the path comprises at least two of the connected edges. “The limitations of calculating, constructing, determining as drafted cover a mental process when a human hears another person speak and puts the sentences as nodes on a graph, and writes on vertices connecting the nodes a similarity value between 0 and 1 to indicate how similar the nodes are.
This judicial exception is not integrated into a practical application. In particular claim 9 recites additional element of processor, which is a form of generic computer equipment. In the as-filed Specifications ¶[0151] In an embodiment, the processor is configured to calculate the preset threshold value by the operations of: [0152] determining a text set for each of the N texts, where the text set for each of the N texts consists of the text pairs including each of the N texts; [0153] calculating the text screening threshold value of each of the N texts based on the text set
for each of the N texts; and [0154] calculating an average value of the text screening threshold values of respective ones of the texts as the preset threshold value.” Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an 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.
Similarly, dependent claims 2-7, 9-14 and 16-20, are also not patent eligible as they include additional steps that are directed towards calculating, constructing, determining and thus are also directed towards an abstract idea as they can be practically performed in the mind without being integrated into a practical application, or including any additional elements sufficient to amount to significantly more than the judicial exception. No additional limitations are recited in the dependent claims.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 8, 9, 10, 15, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Betthauser (US 20230401491 A11), and in further view of Hristova (US 20240134907 A1).
Regarding claims 1, and 8 Betthauser teaches (claim 1) A text processing method, comprising: (claim 8) A text processing apparatus, comprising: (claim 8) calculating a distance between every two ones of texts, to obtain a text set, wherein the text set comprises every two ones of texts and the distance between every two ones of texts (Betthauser ¶[0021] The computation graph 120 may include a representation of these pair-wise similarity values [weights] as relative distances [distance] between nodes of the computation graph 120. These nodes can represent individual tokens [text ] or other parts of an input processed by the candidate model 105. An example of a computation graph 120 is shown in FIGS. 4A, 4B, and 4C. The examples which follow describe how the computation graph 120 may be generated by the computation graph unit 115 from the self-attention values 110.);
constructing a text relation map based on the text set, wherein the text relation map comprises nodes and connection edges, each of the nodes corresponds to a corresponding one of the texts, at least two ones of the nodes are connected via the connection edge, and a weight of each of the connection edges corresponds to the distance between two of the texts corresponding to two of the nodes connected through the connected edge (Betthauser ¶[0021] The computation graph 120 may include a representation of these pair-wise similarity values [weights] as relative distances [distance] between nodes of the computation graph 120. These nodes can represent individual tokens [text ] or other parts of an input processed by the candidate model 105. An example of a computation graph 120 is shown in FIGS. 4A, 4B, and 4C. The examples which follow describe how the computation graph 120 may be generated by the computation graph unit 115 from the self-attention values 110.);
Betthauser does not explicitly disclose however Hristova teaches of and in response to the distance between two ones of the texts greater than a preset threshold value, determining a text similarity between the two ones of the texts according to a path between two of the nodes corresponding to the two ones of the texts, wherein the path comprises at least two of the connected edges (Hristova ¶[0087] For example, the similarity distance [distance] between content item 408 and content item 410 does not satisfy (e.g., is greater [threshold greater than] than) a threshold similarity distance in FIG. 4A, and thus the content items are grouped into distinct clusters. Stated another way, the similarity distance between content item 408 and content item 410 is 0.9, which is greater than the threshold distance of 0.7 described above with reference to FIG. 4A.)
Hristova is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Betthauser further in view of Hristova to allow for in response to the distance between two ones of the texts greater than a preset threshold value, determining a text similarity between the two ones of the texts according to a path between two of the nodes corresponding to the two ones of the texts, wherein the path comprises at least two of the connected edges. Motivation to do so would allow for construction of minimum spanning trees (Hristova [0004]).
With respect to clams 2, 10 and 16 Hristova further teaches wherein the constructing of the text relation map based on the text set comprises: determining whether every two ones of texts as associated texts or non-associated texts based on the distance between the two ones of texts and the preset threshold value (Hristova ¶[0087] For example, the similarity distance [distance] between content item 408 and content item 410 does not satisfy (e.g., is greater [threshold greater than] than) a threshold similarity distance in FIG. 4A, and thus the content items are grouped into distinct clusters. Stated another way, the similarity distance between content item 408 and content item 410 is 0.9, which is greater than the threshold distance of 0.7 described above with reference to FIG. 4A.);
and constructing the text relation map according to the associated texts and the non-associated texts, wherein in the text relation map, wherein the nodes corresponding to two of the texts determined as the associated texts are directionally connected to each other, and the nodes corresponding to two of the texts determined as the non-associated texts pair are not connected (Hristova ¶[0087] For example, the similarity distance [distance] between content item 408 and content item 410 does not satisfy (e.g., is greater [threshold greater than] than) a threshold similarity distance in FIG. 4A, and thus the content items are grouped into distinct clusters. Stated another way, the similarity distance between content item 408 and content item 410 is 0.9, which is greater than the threshold distance of 0.7 described above with reference to FIG. 4A.)
With respect to claims 5, 13 and 19 Hristova further teaches wherein the determining of the text similarity between the two texts in the texts according to the path between two of the nodes corresponding to the two ones of the texts in response to the distance between the two ones of the texts greater than the preset threshold value comprises: determining at least one path between two of the nodes corresponding to the two texts based on the text relation map (Hristova ¶[0087] For example, the similarity distance [distance] between content item 408 and content item 410 does not satisfy (e.g., is greater [threshold greater than] than) a threshold similarity distance in FIG. 4A, and thus the content items are grouped into distinct clusters. Stated another way, the similarity distance between content item 408 and content item 410 is 0.9, which is greater than the threshold distance of 0.7 described above with reference to FIG. 4A.);
calculating a sum of the weights of the connection edges comprised in each of the at least one path as a similarity of each of the at least one path, to obtain similarities of respective ones of the at least one path (Hristova ¶[0087] For example, the similarity distance [distance] between content item 408 and content item 410 does not satisfy (e.g., is greater [threshold greater than] than) a threshold similarity distance in FIG. 4A, and thus the content items are grouped into distinct clusters. Stated another way, the similarity distance between content item 408 and content item 410 is 0.9, which is greater than the threshold distance of 0.7 described above with reference to FIG. 4A.);
and determining the text similarity between the two texts based on the similarities of respective ones of the at least one path (Hristova ¶[0087] For example, the similarity distance [distance] between content item 408 and content item 410 does not satisfy (e.g., is greater [threshold greater than] than) a threshold similarity distance in FIG. 4A, and thus the content items are grouped into distinct clusters. Stated another way, the similarity distance between content item 408 and content item 410 is 0.9, which is greater than the threshold distance of 0.7 described above with reference to FIG. 4A.)
With respect to claim 9, Betthauser teaches a processor and a memory configured to store a computer-executable instruction that, when executed, makes the processor perform the text processing method of claim 1 (0059] The machine 700 may include processors 710, memory 730, and I/O components 750, which may be communicatively coupled via, for example, a bus 702. The bus 702 may include multiple buses coupling various elements of machine 700 via various bus technologies and protocols. In an example, the processors 710 (including, for example, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an ASIC, or a suitable combination thereof) may include one or more processors 712a to 712n that may execute the instructions 716 and process data. In some examples, one or more processors 710 may execute instructions provided or identified by one or more other processors 710. The term “processor” includes a multi-core processor including cores that may execute instructions contemporaneously. Although FIG. 7 shows multiple processors, the machine 700 may include a single processor with a single core, a single processor with multiple cores (for example, a multi-core processor), multiple processors each with a single core, multiple processors each with multiple cores, or any combination thereof. In some examples, the machine 700 may include multiple processors distributed among multiple machines.)
With respect to claim 15, Betthauser teaches A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium is configured to store a computer-executable instruction, and when executed by a processor, the computer-executable instructions implement the text processing method of claim 1 ([0059] The machine 700 may include processors 710, memory 730, and I/O components 750, which may be communicatively coupled via, for example, a bus 702. The bus 702 may include multiple buses coupling various elements of machine 700 via various bus technologies and protocols. In an example, the processors 710 (including, for example, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an ASIC, or a suitable combination thereof) may include one or more processors 712a to 712n that may execute the instructions 716 and process data. In some examples, one or more processors 710 may execute instructions provided or identified by one or more other processors 710. The term “processor” includes a multi-core processor including cores that may execute instructions contemporaneously. Although FIG. 7 shows multiple processors, the machine 700 may include a single processor with a single core, a single processor with multiple cores (for example, a multi-core processor), multiple processors each with a single core, multiple processors each with multiple cores, or any combination thereof. In some examples, the machine 700 may include multiple processors distributed among multiple machines.)
Claims 3, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Betthauser, Hristova in further view of Joko (US 20220179890 A1).
With respect to clams 3, 11 and 17 Betthauser teaches before determining the text similarity between the two ones of the texts according to the path between two of the nodes corresponding to the two ones of the texts, determining a sub text set for each of the texts, wherein the sub text set comprises text pairs each comprising the text (Betthauser ¶[0021] The computation graph 120 may include a representation of these pair-wise similarity values as relative distances between nodes of the computation graph 120. These nodes can represent individual tokens [sub text ] or other parts of an input processed by the candidate model 105. An example of a computation graph 120 is shown in FIGS. 4A, 4B, and 4C. The examples which follow describe how the computation graph 120 may be generated by the computation graph unit 115 from the self-attention values 110.);
None of Betthauser, Hristova explicitly disclose however Joko teaches calculating a text screening threshold value of each of the texts based on the sub text set for the text, to obtain the text screening threshold values of respective ones of the texts (Joko ¶[0012] An information processing apparatus according to an aspect of the invention includes a retrieval-target storage unit configured to store multiple retrieval target sentences including multiple retrieval target tokens, the retrieval target tokens each being a smallest unit having a meaning; a similarity-determination-information storage unit configured to store similarity determination information indicating whether combinations of the respective retrieval target tokens and respective retrieval tokens have high similarity or low similarity, the retrieval tokens each being a smallest unit having a meaning and being included in a retrieval sentence; and an inter-sentence-similarity calculation unit configured to calculate inter-token similarity [sub-text similarity] for the combinations indicated to have high similarity in the similarity determination information, and sets the inter-token similarity [screening threshold] to a predetermined value for the combinations indicated to have low similarity in the similarity determination information, to calculate inter-sentence similarity between the retrieval sentence and the respective retrieval target sentences);
and calculating an average value of the text screening threshold values of respective ones of the texts as the preset threshold value (Joko ¶[0080] In the calculation of the inter-sentence similarity by the general maximum alignment method, the token having the highest inter-token similarity to each retrieval query token x.sub.i included in a retrieval query x is selected from retrieval target tokens Y, included in a retrieval target sentence Y.sub.j. Then, the inter-sentence similarity is calculated by the average value obtained by averaging the inter-token similarities φ(x.sub.i,Y.sub.jk) calculated for the selected i=|x| retrieval target tokens.)
Joko is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Betthauser further in view of Joko to allow for calculating an average value of the text screening threshold values of respective ones of the texts as the preset threshold value. Motivation to do so would allow for measuring similarity with high accuracy (Joko [0004]).
Claims 6, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Betthauser, Hristova in further view of Otaki (US 20210200797 A1).
With respect to claims 6, 14 and 20 none of Betthauser and Hristova explicitly disclose however Otaki teaches wherein the determining of the text similarity between the two texts based on the similarities of respective ones of the at least one path comprises: arraying the at least one path in a descending order of sizes of the similarities of respective ones of the at least one path to obtain a path list (Otaki ¶[0097] The similarities computed at S1307 and S1308 are added to the intra-path similarities. Similarities of all the nodes, and all the intra-route path similarities are computed (S1310, S1311). Finally, route paths are presented in descending order of intra-path similarities (S1312). In the relevance analyzing device in the present embodiment also, a route search can be implemented precisely and fast.);
and determining the similarity of one of the at least one path at a first order of the path list as the text similarity between the two texts (Otaki ¶[0097] The similarities computed at S1307 and S1308 are added to the intra-path similarities. Similarities of all the nodes, and all the intra-route path similarities are computed (S1310, S1311). Finally, route paths are presented in descending order of intra-path similarities (S1312). In the relevance analyzing device in the present embodiment also, a route search can be implemented precisely and fast.)
Otaki is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Betthauser further in view of Otaki to allow for determining the similarity of one of the at least one path at a first order of the path list as the text similarity between the two texts. Motivation to do so would allow for interrelationship between two events, and presents an edge with a high similarity as a route on the network (Joko [0019]).
Allowable Subject Matter
Claims 4, 7, 12, 18, would be allowable pending overcoming 101 rejections set forth in this Office Action.
Claims 4, 12 and 18 recite and identifying, as a target text pair, a text pair in the text pair list with an actual similarity ordered in the first order relative to an actual similarity of another text pair in the text pair list after the text pair, and determining a distance of the target text pair as the text screening threshold value for the text. The closest teaching comes from Hailpern who teaches “¶ [0031] Once the text document has been filtered and streamlined to include meaningful words and sentences, the WDBC algorithm 400 proceeds to identify representative clusters and representative sentences within the clusters. First, a similarity matrix of sentences is computed by calculating the average of pairwise distances between words for any two given sentences (425). That is, the matrix contains sentence pairs in its rows and columns, and averages of pairwise distances as the matrix values. The pairwise distances can be calculated by, for example, using WordNet (which is a graph of words linked by weighted edges based on semantic similarity) to find the semantic distance between concepts.”
However, none of Hailpern and other cited prior art of record teach the limitation as stated in the applicant’s claim specifically as noted/underlined earlier, including all supporting limitations thereof was not found in the relevant prior art of records. Therefore claims 4, 12 and 18 would be allowable.
Claim 7 recites further comprising: after constructing the text relation map based on the text set, determining two of the texts with the distance therebetween less than or equal to the 31 distance threshold as a first associated text pair, two of the texts with the distance therebetween greater than the distance threshold and less than or equal to the preset threshold as a second associated text pair, and two of the texts with the distance therebetween greater than the preset threshold as a non-associated text pair; and determining a text similarity between two of the texts in the first associated text pair according to the distance between two of the texts in the first associated text pair; and determining a text similarity between two of the texts in the second associated text pair according to a path between two of the nodes corresponding to two of the texts in the second associated text pair in the text relation map, wherein the path corresponding to the second associated text pair comprises at least two connecting edges. . The closest teaching comes from cited art (Hristova ¶[0087] For example, the similarity distance between content item 408 and content item 410 does not satisfy (e.g., is greater than) a threshold similarity distance in FIG. 4A, and thus the content items are grouped into distinct clusters. Stated another way, the similarity distance between content item 408 and content item 410 is 0.9, which is greater than the threshold distance of 0.7 described above with reference to FIG. 4A.), and (Otaki ¶[0097] The similarities computed at S1307 and S1308 are added to the intra-path similarities. Similarities of all the nodes, and all the intra-route path similarities are computed (S1310, S1311). Finally, route paths are presented in descending order of intra-path similarities (S1312). In the relevance analyzing device in the present embodiment also, a route search can be implemented precisely and fast.)
However, none of Hristova, Otaki and other cited prior art of record teach the limitation as stated in the applicant’s claim specifically as noted/underlined earlier, including all supporting limitations thereof was not found in the relevant prior art of records. Therefore claim 7would be allowable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ATHAR N PASHA whose telephone number is (408)918-7675. The examiner can normally be reached on Monday-Thursday Alternate Fridays, 7:30-4:30 PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached on (571)272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ATHAR N PASHA/Examiner, Art Unit 2657
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